diff --git a/.gitignore b/.gitignore index 8a353f3..2c7597c 100644 --- a/.gitignore +++ b/.gitignore @@ -151,3 +151,6 @@ prodigy_testing/ # locked .~lock* + +# DS Store +*DS_Store diff --git a/README.md b/README.md index 57c3249..17a5e91 100644 --- a/README.md +++ b/README.md @@ -6,7 +6,7 @@ If you want to get involved head over to [our site](https://climatemind.org/#get ## Development -We're currently at work labeling thousands of sentences for our models! If you want to see some of our WIP model code just head over to `src/model_dev`. Otherwise check back later for dataset & model releases. +We're currently at work labeling thousands of sentences for our models! If you want to see some of our WIP model code just head over to `model_dev`. Otherwise check back later for dataset & model releases. ## Thanks diff --git a/annotation_screenshot/__MACOSX/screenshot_docker/._.DS_Store b/annotation_screenshot/__MACOSX/screenshot_docker/._.DS_Store deleted file mode 100644 index a5b28df..0000000 Binary files a/annotation_screenshot/__MACOSX/screenshot_docker/._.DS_Store and /dev/null differ diff --git a/annotation_screenshot/screenshot_docker/.DS_Store b/annotation_screenshot/screenshot_docker/.DS_Store deleted file mode 100644 index 5008ddf..0000000 Binary files a/annotation_screenshot/screenshot_docker/.DS_Store and /dev/null differ diff --git a/docs/.gitignore b/docs/.gitignore deleted file mode 100644 index 57510a2..0000000 --- a/docs/.gitignore +++ /dev/null @@ -1 +0,0 @@ -_site/ diff --git a/docs/Gemfile b/docs/Gemfile deleted file mode 100644 index f2509a4..0000000 --- a/docs/Gemfile +++ /dev/null @@ -1,9 +0,0 @@ -source "https://rubygems.org" - -gem 'github-pages', group: :jekyll_plugins - -# Added at 2019-11-25 10:11:40 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diff --git a/src/model_dev/TF_to_Torch_conversion.ipynb b/model_dev/TF_to_Torch_conversion.ipynb similarity index 100% rename from src/model_dev/TF_to_Torch_conversion.ipynb rename to model_dev/TF_to_Torch_conversion.ipynb diff --git a/model_dev/data/all_gold_standard.jsonl b/model_dev/data/all_gold_standard.jsonl new file mode 100644 index 0000000..cf5eb70 --- /dev/null +++ b/model_dev/data/all_gold_standard.jsonl @@ -0,0 +1,1393 @@ +{"Used?":"","Dataset name":"","ignore?":"","reason":"","relation count":"","complexity":"","original_md_text":"These risks are crop insurance, health care, wildfire suppression, hurricane-related disaster relief, and federal facility flood risk, all of which are anticipated to cost billions of dollars more by the end of the century due to the impacts of climate change.23","text":"These risks are crop insurance, health care, wildfire suppression, hurricane-related disaster relief, and federal facility flood risk, all of which are anticipated to cost 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"end": 172, "id": 28, "ws": false, "disabled": false}, {"text": "]", "start": 172, "end": 173, "id": 29, "ws": true, "disabled": false}, {"text": "IMPLIED_BASE", "start": 174, "end": 186, "id": 30, "ws": true, "disabled": false}, {"text": "IMPLIED_BASE", "start": 187, "end": 199, "id": 31, "ws": false, "disabled": false}], "_session_id": "main_3_per_cluster-Nikita", "_view_id": "relations", "relations": [{"head": 6, "child": 8, "head_span": {"start": 30, "end": 38, "token_start": 6, "token_end": 6, "label": "type_of"}, "child_span": {"start": 39, "end": 51, "token_start": 7, "token_end": 8, "label": "base"}, "color": "#c5bdf4", "label": "Concept_Member"}, {"head": 8, "child": 21, "head_span": {"start": 39, "end": 51, "token_start": 7, "token_end": 8, "label": "base"}, "child_span": {"start": 127, "end": 134, "token_start": 21, "token_end": 21, "label": "base"}, "color": "#ffd882", "label": "Contributes_To"}, {"head": 11, "child": 21, "head_span": {"start": 65, "end": 73, "token_start": 11, "token_end": 11, "label": "base"}, "child_span": {"start": 127, "end": 134, "token_start": 21, "token_end": 21, "label": "base"}, "color": "#ffd882", "label": "Contributes_To"}], "answer": "accept"} +{"Used?": "", "Dataset name": "", "ignore?": "", "reason": "", "relation count": "", "complexity": "", "original_md_text": "Hot pavement also transfers heat to the surrounding air, adding to the urban heat island effect.", "text": "Hot pavement also transfers heat to the surrounding air, adding to the urban heat island effect. IMPLIED_BASE IMPLIED_BASE", "characters": "96", "document_index": "656", "md_sentence_index": "520", "classification_origin": "causal_sentence_classification", "url": "https://www.cdc.gov/climateandhealth/pubs/extreme-heat-guidebook.pdf", "combo_id": "656_520", "post-training batch1": "656_520", "_input_hash": 2065174250, "_task_hash": -1540344737, "spans": [{"start": 0, "end": 3, "token_start": 0, "token_end": 0, "label": "change_direction"}, {"start": 4, "end": 12, "token_start": 1, "token_end": 1, "label": "base"}, {"start": 71, "end": 76, "token_start": 13, "token_end": 13, "label": "type_of"}, {"start": 77, "end": 88, "token_start": 14, "token_end": 15, "label": "base"}], "tokens": [{"text": "Hot", "start": 0, "end": 3, "id": 0, "ws": true, "disabled": false}, {"text": "pavement", "start": 4, "end": 12, "id": 1, "ws": true, "disabled": false}, {"text": "also", "start": 13, "end": 17, "id": 2, "ws": true, "disabled": false}, {"text": "transfers", "start": 18, "end": 27, "id": 3, "ws": true, "disabled": false}, {"text": "heat", "start": 28, "end": 32, "id": 4, "ws": true, "disabled": false}, {"text": "to", "start": 33, "end": 35, "id": 5, "ws": true, "disabled": false}, {"text": "the", "start": 36, "end": 39, "id": 6, "ws": true, "disabled": false}, {"text": "surrounding", "start": 40, "end": 51, "id": 7, "ws": true, "disabled": false}, {"text": "air", "start": 52, "end": 55, "id": 8, "ws": false, "disabled": false}, {"text": ",", "start": 55, "end": 56, "id": 9, "ws": true, "disabled": false}, {"text": "adding", "start": 57, "end": 63, "id": 10, "ws": true, "disabled": false}, {"text": "to", "start": 64, "end": 66, "id": 11, "ws": true, "disabled": false}, {"text": "the", "start": 67, "end": 70, "id": 12, "ws": true, "disabled": false}, {"text": "urban", "start": 71, "end": 76, "id": 13, "ws": true, "disabled": false}, {"text": "heat", "start": 77, "end": 81, "id": 14, "ws": true, "disabled": false}, {"text": "island", "start": 82, "end": 88, "id": 15, "ws": true, "disabled": false}, {"text": "effect", "start": 89, "end": 95, "id": 16, "ws": false, "disabled": false}, {"text": ".", "start": 95, "end": 96, "id": 17, "ws": true, "disabled": false}, {"text": "IMPLIED_BASE", "start": 97, "end": 109, "id": 18, "ws": true, "disabled": false}, {"text": "IMPLIED_BASE", "start": 110, "end": 122, "id": 19, "ws": false, "disabled": false}], "_session_id": "main_3_per_cluster-Nikita", "_view_id": "relations", "relations": [{"head": 0, "child": 1, "head_span": {"start": 0, "end": 3, "token_start": 0, "token_end": 0, "label": "change_direction"}, "child_span": {"start": 4, "end": 12, "token_start": 1, "token_end": 1, "label": "base"}, "color": "#c5bdf4", "label": "Concept_Member"}, {"head": 13, "child": 15, "head_span": {"start": 71, "end": 76, "token_start": 13, "token_end": 13, "label": "type_of"}, "child_span": {"start": 77, "end": 88, "token_start": 14, "token_end": 15, "label": "base"}, "color": "#c5bdf4", "label": "Concept_Member"}, {"head": 1, "child": 15, "head_span": {"start": 4, "end": 12, "token_start": 1, "token_end": 1, "label": "base"}, "child_span": {"start": 77, "end": 88, "token_start": 14, "token_end": 15, "label": "base"}, "color": "#ffd882", "label": "Contributes_To"}], "answer": "accept"} diff --git a/model_dev/entity_extraction/PURE/PURE-training.ipynb b/model_dev/entity_extraction/PURE/PURE-training.ipynb new file mode 100644 index 0000000..aac2fe8 --- /dev/null +++ b/model_dev/entity_extraction/PURE/PURE-training.ipynb @@ -0,0 +1,339 @@ +{ + "cells": [ + { + "cell_type": "code", + "source": [ + "!git clone https://github.com/princeton-nlp/PURE.git" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": "Cloning into 'PURE'...\nremote: Enumerating objects: 61, done.\u001b[K\nremote: Counting objects: 100% (25/25), done.\u001b[K\nremote: Compressing objects: 100% (15/15), done.\u001b[K\nremote: Total 61 (delta 16), reused 10 (delta 10), pack-reused 36\u001b[K\nReceiving objects: 100% (61/61), 1.59 MiB | 7.41 MiB/s, done.\nResolving deltas: 100% (22/22), done.\n" + } + ], + "execution_count": 4, + "metadata": {} + }, + { + "cell_type": "code", + "source": [ + "!mkdir PURE/data\n", + "!mkdir PURE/output_ent\n", + "!mkdir PURE/output_rel" + ], + "outputs": [], + "execution_count": 6, + "metadata": { + "jupyter": { + "source_hidden": false, + "outputs_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + } + }, + { + "cell_type": "code", + "source": [ + "!pip install -r PURE/requirements.txt" + ], + "outputs": [], + "execution_count": null, + "metadata": { + "jupyter": { + "source_hidden": false, + "outputs_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + } + }, + { + "cell_type": "code", + "source": [ + "# generates ent_pred_dev.json\n", + "!python /run_entity.py \\\n", + " --do_train --do_eval \\\n", + " --learning_rate=1e-5 \\\n", + " --task_learning_rate=5e-4 \\\n", + " --train_batch_size=16 \\\n", + " --context_window 100 \\\n", + " --task ace05 \\\n", + " --data_dir /content/PURE/data \\\n", + " --model bert-base-uncased \\\n", + " --output_dir /content/PURE/output_ent" + ], + "outputs": [], + "execution_count": null, + "metadata": { + "jupyter": { + "source_hidden": false, + "outputs_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + } + }, + { + "cell_type": "code", + "source": [ + "# generates ent_pred_test.json\n", + "!python PURE/run_entity.py \\\n", + " --do_train --do_eval --eval_test \\\n", + " --learning_rate=1e-5 \\\n", + " --task_learning_rate=5e-4 \\\n", + " --train_batch_size=16 \\\n", + " --context_window 100 \\\n", + " --task ace05 \\\n", + " --data_dir /content/PURE/data \\\n", + " --model bert-base-uncased \\\n", + " --output_dir /content/PURE/output_ent" + ], + "outputs": [], + "execution_count": null, + "metadata": { + "jupyter": { + "source_hidden": false, + "outputs_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + } + }, + { + "cell_type": "code", + "source": [ + "data_folder = \"PURE/data2\"\n", + "output_rel = \"PURE/output_rel_ent2\"" + ], + "outputs": [], + "execution_count": 1, + "metadata": { + "jupyter": { + "source_hidden": false, + "outputs_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + }, + "gather": { + "logged": 1657726275171 + } + } + }, + { + "cell_type": "code", + "source": [ + "!python PURE/run_relation.py \\\n", + " --task ace05 \\\n", + " --do_train --train_file {data_folder}/train.json \\\n", + " --do_eval --eval_test \\\n", + " --model bert-base-uncased \\\n", + " --do_lower_case \\\n", + " --train_batch_size 32 \\\n", + " --eval_batch_size 32 \\\n", + " --learning_rate 2e-5 \\\n", + " --num_train_epochs 8 \\\n", + " --context_window 300 \\\n", + " --max_seq_length 128 \\\n", + " --no_cuda \\\n", + " --entity_output_dir {data_folder} \\\n", + " --output_dir {output_rel}" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": "07/13/2022 15:31:43 - INFO - root - Generate relation data from PURE/data2/train.json\n07/13/2022 15:31:43 - INFO - root - #samples: 3214, max #sent.samples: 182\n07/13/2022 15:31:43 - INFO - root - Generate relation data from PURE/data2/ent_pred_dev.json\n07/13/2022 15:31:43 - INFO - root - #samples: 84, max #sent.samples: 30\n07/13/2022 15:31:43 - INFO - root - Generate relation data from PURE/data2/ent_pred_test.json\n07/13/2022 15:31:43 - INFO - root - #samples: 112, max #sent.samples: 20\n07/13/2022 15:31:43 - INFO - __main__ - ['PURE/run_relation.py', '--task', 'ace05', '--do_train', '--train_file', 'PURE/data2/train.json', '--do_eval', '--eval_test', '--model', 'bert-base-uncased', '--do_lower_case', '--train_batch_size', '32', '--eval_batch_size', '32', '--learning_rate', '2e-5', '--num_train_epochs', '8', '--context_window', '300', '--max_seq_length', '128', '--no_cuda', '--entity_output_dir', 'PURE/data2', '--output_dir', 'PURE/output_rel_ent2']\n07/13/2022 15:31:43 - INFO - __main__ - Namespace(add_new_tokens=False, bertadam=False, context_window=300, do_eval=True, do_lower_case=True, do_train=True, entity_output_dir='PURE/data2', entity_predictions_dev='ent_pred_dev.json', entity_predictions_test='ent_pred_test.json', eval_batch_size=32, eval_metric='f1', eval_per_epoch=10, eval_test=True, eval_with_gold=False, learning_rate=2e-05, max_seq_length=128, model='bert-base-uncased', negative_label='no_relation', no_cuda=True, num_train_epochs=8.0, output_dir='PURE/output_rel_ent2', prediction_file='predictions.json', seed=0, task='ace05', train_batch_size=32, train_file='PURE/data2/train.json', train_mode='random_sorted', warmup_proportion=0.1)\n07/13/2022 15:31:43 - INFO - __main__ - device: cpu, n_gpu: 0\n07/13/2022 15:31:43 - INFO - transformers.configuration_utils - loading configuration file https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-config.json from cache at /home/azureuser/.cache/torch/transformers/4dad0251492946e18ac39290fcfe91b89d370fee250efe9521476438fe8ca185.7156163d5fdc189c3016baca0775ffce230789d7fa2a42ef516483e4ca884517\n07/13/2022 15:31:43 - INFO - transformers.configuration_utils - Model config BertConfig {\n \"architectures\": [\n \"BertForMaskedLM\"\n ],\n \"attention_probs_dropout_prob\": 0.1,\n \"gradient_checkpointing\": false,\n \"hidden_act\": \"gelu\",\n \"hidden_dropout_prob\": 0.1,\n \"hidden_size\": 768,\n \"initializer_range\": 0.02,\n \"intermediate_size\": 3072,\n \"layer_norm_eps\": 1e-12,\n \"max_position_embeddings\": 512,\n \"model_type\": \"bert\",\n \"num_attention_heads\": 12,\n \"num_hidden_layers\": 12,\n \"pad_token_id\": 0,\n \"type_vocab_size\": 2,\n \"vocab_size\": 30522\n}\n\n07/13/2022 15:31:43 - INFO - transformers.tokenization_utils_base - loading file https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-vocab.txt from cache at /home/azureuser/.cache/torch/transformers/26bc1ad6c0ac742e9b52263248f6d0f00068293b33709fae12320c0e35ccfbbb.542ce4285a40d23a559526243235df47c5f75c197f04f37d1a0c124c32c9a084\n07/13/2022 15:31:43 - INFO - __main__ - Writing example 0 of 84\n07/13/2022 15:31:43 - INFO - __main__ - *** Example ***\n07/13/2022 15:31:43 - INFO - __main__ - guid: 250@0::(7,7)-(17,17)\n07/13/2022 15:31:43 - INFO - __main__ - tokens: [CLS] between 2007 and 2011 alone urban flooding [unused7] in [unused8] cook county illinois resulted in over 176 000 claims [unused9] or [unused10] flood losses at a cost of 660 ##m 54 ##5 ##m implied _ base implied _ base [SEP]\n07/13/2022 15:31:43 - INFO - __main__ - input_ids: 101 2090 2289 1998 2249 2894 3923 9451 8 1999 9 5660 2221 4307 4504 1999 2058 18561 2199 4447 10 2030 11 7186 6409 2012 1037 3465 1997 20982 2213 5139 2629 2213 13339 1035 2918 13339 1035 2918 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 15:31:43 - INFO - __main__ - input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 15:31:43 - INFO - __main__ - segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 15:31:43 - INFO - __main__ - label: Contributes_To (id = 1)\n07/13/2022 15:31:43 - INFO - __main__ - sub_idx, obj_idx: 8, 20\n07/13/2022 15:31:43 - INFO - __main__ - *** Example ***\n07/13/2022 15:31:43 - INFO - __main__ - guid: 250@0::(7,7)-(20,20)\n07/13/2022 15:31:43 - INFO - __main__ - tokens: [CLS] between 2007 and 2011 alone urban flooding [unused7] in [unused8] cook county illinois resulted in over 176 000 claims or flood losses [unused9] at [unused10] a cost of 660 ##m 54 ##5 ##m implied _ base implied _ base [SEP]\n07/13/2022 15:31:43 - INFO - __main__ - input_ids: 101 2090 2289 1998 2249 2894 3923 9451 8 1999 9 5660 2221 4307 4504 1999 2058 18561 2199 4447 2030 7186 6409 10 2012 11 1037 3465 1997 20982 2213 5139 2629 2213 13339 1035 2918 13339 1035 2918 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 15:31:43 - INFO - __main__ - input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 15:31:43 - INFO - __main__ - segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 15:31:43 - INFO - __main__ - label: Contributes_To (id = 1)\n07/13/2022 15:31:43 - INFO - __main__ - sub_idx, obj_idx: 8, 23\n07/13/2022 15:31:43 - INFO - __main__ - *** Example ***\n07/13/2022 15:31:43 - INFO - __main__ - guid: 250@0::(17,17)-(7,7)\n07/13/2022 15:31:43 - INFO - __main__ - tokens: [CLS] between 2007 and 2011 alone urban flooding [unused9] in [unused10] cook county illinois resulted in over 176 000 claims [unused7] or [unused8] flood losses at a cost of 660 ##m 54 ##5 ##m implied _ base implied _ base [SEP]\n07/13/2022 15:31:43 - INFO - __main__ - input_ids: 101 2090 2289 1998 2249 2894 3923 9451 10 1999 11 5660 2221 4307 4504 1999 2058 18561 2199 4447 8 2030 9 7186 6409 2012 1037 3465 1997 20982 2213 5139 2629 2213 13339 1035 2918 13339 1035 2918 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 15:31:43 - INFO - __main__ - input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 15:31:43 - INFO - __main__ - segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 15:31:43 - INFO - __main__ - label: no_relation (id = 0)\n07/13/2022 15:31:43 - INFO - __main__ - sub_idx, obj_idx: 20, 8\n07/13/2022 15:31:43 - INFO - __main__ - *** Example ***\n07/13/2022 15:31:43 - INFO - __main__ - guid: 250@0::(17,17)-(20,20)\n07/13/2022 15:31:43 - INFO - __main__ - tokens: [CLS] between 2007 and 2011 alone urban flooding in cook county illinois resulted in over 176 000 claims [unused7] or [unused8] flood losses [unused9] at [unused10] a cost of 660 ##m 54 ##5 ##m implied _ base implied _ base [SEP]\n07/13/2022 15:31:43 - INFO - __main__ - input_ids: 101 2090 2289 1998 2249 2894 3923 9451 1999 5660 2221 4307 4504 1999 2058 18561 2199 4447 8 2030 9 7186 6409 10 2012 11 1037 3465 1997 20982 2213 5139 2629 2213 13339 1035 2918 13339 1035 2918 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 15:31:43 - INFO - __main__ - input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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INFO - __main__ - input_ids: 101 2090 2289 1998 2249 2894 3923 9451 10 1999 11 5660 2221 4307 4504 1999 2058 18561 2199 4447 2030 7186 6409 8 2012 9 1037 3465 1997 20982 2213 5139 2629 2213 13339 1035 2918 13339 1035 2918 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 15:31:43 - INFO - __main__ - input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 15:31:43 - INFO - __main__ - segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 15:31:43 - INFO - __main__ - segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 15:31:43 - INFO - __main__ - label: Contributes_To (id = 1)\n07/13/2022 15:31:43 - INFO - __main__ - sub_idx, obj_idx: 5, 20\n07/13/2022 15:31:43 - INFO - __main__ - *** Example ***\n07/13/2022 15:31:43 - INFO - __main__ - guid: 217@0::(22,22)-(2,2)\n07/13/2022 15:31:43 - INFO - __main__ - tokens: [CLS] antarctic glaciers [unused9] have [unused10] been melting at an accelerating pace over the past four decades thanks to an influx of warm ocean water [unused7] a [unused8] startling new finding that researchers say could mean sea levels are poised to rise more quickly than predicted in coming decades implied _ base implied _ base [SEP]\n07/13/2022 15:31:43 - INFO - __main__ - input_ids: 101 10227 21193 10 2031 11 2042 13721 2012 2019 29494 6393 2058 1996 2627 2176 5109 4283 2000 2019 18050 1997 4010 4153 2300 8 1037 9 19828 2047 4531 2008 6950 2360 2071 2812 2712 3798 2024 22303 2000 4125 2062 2855 2084 10173 1999 2746 5109 13339 1035 2918 13339 1035 2918 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 15:31:43 - INFO - __main__ - input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 15:31:43 - INFO - __main__ - segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 15:31:43 - INFO - __main__ - label: Contributes_To (id = 1)\n07/13/2022 15:31:43 - INFO - __main__ - sub_idx, obj_idx: 25, 3\n07/13/2022 15:31:43 - INFO - __main__ - *** Example ***\n07/13/2022 15:31:43 - INFO - __main__ - guid: 217@0::(2,2)-(32,32)\n07/13/2022 15:31:43 - INFO - __main__ - tokens: [CLS] antarctic glaciers [unused7] have [unused8] been melting at an accelerating pace over the past four decades thanks to an influx of warm ocean water a startling new finding that researchers say could mean sea [unused9] levels [unused10] are poised to rise more quickly than predicted in coming decades implied _ base implied _ base [SEP]\n07/13/2022 15:31:43 - INFO - __main__ - input_ids: 101 10227 21193 8 2031 9 2042 13721 2012 2019 29494 6393 2058 1996 2627 2176 5109 4283 2000 2019 18050 1997 4010 4153 2300 1037 19828 2047 4531 2008 6950 2360 2071 2812 2712 10 3798 11 2024 22303 2000 4125 2062 2855 2084 10173 1999 2746 5109 13339 1035 2918 13339 1035 2918 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 15:31:43 - INFO - __main__ - input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 15:31:43 - INFO - __main__ - segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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intense they threaten to worse ##n inequality and housing instability implied _ base implied _ base [SEP]\n07/13/2022 15:31:43 - INFO - __main__ - input_ids: 101 1998 2004 6034 4633 2824 2066 1996 3409 2543 1998 7064 8 3814 9 2029 3908 3053 3263 2199 5014 10 1999 11 3146 2468 2119 2062 6976 1998 2062 6387 2027 15686 2000 4788 2078 16440 1998 3847 18549 13339 1035 2918 13339 1035 2918 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 15:31:43 - INFO - __main__ - input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 15:31:43 - INFO - __main__ - segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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36\n07/13/2022 15:31:43 - INFO - __main__ - *** Example ***\n07/13/2022 15:31:43 - INFO - __main__ - guid: 195@0::(11,11)-(34,35)\n07/13/2022 15:31:43 - INFO - __main__ - tokens: [CLS] and as extreme weather events like the camp fire and hurricane [unused7] maria [unused8] which destroyed nearly 200 000 homes in texas become both more frequent and more intense they threaten to worse ##n inequality and housing [unused9] instability implied _ base [unused10] implied _ base [SEP]\n07/13/2022 15:31:43 - INFO - __main__ - input_ids: 101 1998 2004 6034 4633 2824 2066 1996 3409 2543 1998 7064 8 3814 9 2029 3908 3053 3263 2199 5014 1999 3146 2468 2119 2062 6976 1998 2062 6387 2027 15686 2000 4788 2078 16440 1998 3847 10 18549 13339 1035 2918 11 13339 1035 2918 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 15:31:43 - INFO - __main__ - input_mask: 1 1 1 1 1 1 1 1 1 1 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INFO - __main__ - sub_idx, obj_idx: 5, 38\n07/13/2022 15:31:43 - INFO - __main__ - *** Example ***\n07/13/2022 15:31:43 - INFO - __main__ - guid: 195@0::(9,9)-(32,32)\n07/13/2022 15:31:43 - INFO - __main__ - tokens: [CLS] and as extreme weather events like the camp fire [unused7] and [unused8] hurricane maria which destroyed nearly 200 000 homes in texas become both more frequent and more intense they threaten to worse ##n inequality [unused9] and [unused10] housing instability implied _ base implied _ base [SEP]\n07/13/2022 15:31:43 - INFO - __main__ - input_ids: 101 1998 2004 6034 4633 2824 2066 1996 3409 2543 8 1998 9 7064 3814 2029 3908 3053 3263 2199 5014 1999 3146 2468 2119 2062 6976 1998 2062 6387 2027 15686 2000 4788 2078 16440 10 1998 11 3847 18549 13339 1035 2918 13339 1035 2918 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 15:31:43 - INFO - __main__ - input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 15:31:43 - INFO - __main__ - segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 15:31:43 - INFO - __main__ - label: Contributes_To (id = 1)\n07/13/2022 15:31:43 - INFO - __main__ - sub_idx, obj_idx: 10, 36\n07/13/2022 15:31:43 - INFO - __main__ - *** Example ***\n07/13/2022 15:31:43 - INFO - __main__ - guid: 195@0::(9,9)-(34,35)\n07/13/2022 15:31:43 - INFO - __main__ - tokens: [CLS] and as extreme weather events like the camp fire [unused7] and [unused8] hurricane maria which destroyed nearly 200 000 homes in texas become both more frequent and more intense they threaten to worse ##n inequality and housing [unused9] instability implied _ base [unused10] implied _ base [SEP]\n07/13/2022 15:31:43 - INFO - __main__ - input_ids: 101 1998 2004 6034 4633 2824 2066 1996 3409 2543 8 1998 9 7064 3814 2029 3908 3053 3263 2199 5014 1999 3146 2468 2119 2062 6976 1998 2062 6387 2027 15686 2000 4788 2078 16440 1998 3847 10 18549 13339 1035 2918 11 13339 1035 2918 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 15:31:43 - INFO - __main__ - input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 15:31:43 - INFO - __main__ - segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 15:31:43 - INFO - __main__ - label: Contributes_To (id = 1)\n07/13/2022 15:31:43 - INFO - __main__ - sub_idx, obj_idx: 10, 38\n07/13/2022 15:31:43 - INFO - __main__ - *** Example ***\n07/13/2022 15:31:43 - INFO - __main__ - guid: 175@0::(14,16)-(10,10)\n07/13/2022 15:31:43 - INFO - __main__ - tokens: [CLS] w ##ys ##ock ##i points to a naturally occurring shift in an air [unused9] pattern [unused10] called the north [unused7] atlantic os ##ci ##llation which [unused8] can play a role in air temperatures in the northeast implied _ base implied _ base [SEP]\n07/13/2022 15:31:43 - INFO - __main__ - input_ids: 101 1059 7274 7432 2072 2685 2000 1037 8100 10066 5670 1999 2019 2250 10 5418 11 2170 1996 2167 8 4448 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__main__ - sub_idx, obj_idx: 20, 14\n07/13/2022 15:31:43 - INFO - __main__ - *** Example ***\n07/13/2022 15:31:43 - INFO - __main__ - guid: 29@0::(10,10)-(7,7)\n07/13/2022 15:31:43 - INFO - __main__ - tokens: [CLS] wear heavy dark clothing which absorb ##s heat [unused9] from [unused10] the sun [unused7] implied _ base [unused8] implied _ base [SEP]\n07/13/2022 15:31:43 - INFO - __main__ - input_ids: 101 4929 3082 2601 5929 2029 16888 2015 3684 10 2013 11 1996 3103 8 13339 1035 2918 9 13339 1035 2918 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 15:31:43 - INFO - __main__ - input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 15:31:43 - INFO - __main__ - segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 15:31:43 - INFO - __main__ - label: Contributes_To (id = 1)\n07/13/2022 15:31:43 - INFO - __main__ - sub_idx, obj_idx: 14, 9\n07/13/2022 15:31:43 - INFO - __main__ - *** Example ***\n07/13/2022 15:31:43 - INFO - __main__ - guid: 142@0::(3,3)-(7,7)\n07/13/2022 15:31:43 - INFO - __main__ - tokens: [CLS] a sc ##or ##ching summer [unused7] led [unused8] to dry conditions [unused9] never [unused10] before experienced implied _ base implied _ base [SEP]\n07/13/2022 15:31:43 - INFO - __main__ - input_ids: 101 1037 8040 2953 8450 2621 8 2419 9 2000 4318 3785 10 2196 11 2077 5281 13339 1035 2918 13339 1035 2918 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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15:31:43 - INFO - __main__ - guid: 96@0::(12,12)-(2,2)\n07/13/2022 15:31:43 - INFO - __main__ - tokens: [CLS] insurance companies [unused9] could [unused10] withdraw from california in the wake of devastating wild ##fires [unused7] and [unused8] home values could plum ##met on coastline ##s and in flood ##pl ##ains implied _ base implied _ base [SEP]\n07/13/2022 15:31:43 - INFO - __main__ - input_ids: 101 5427 3316 10 2071 11 10632 2013 2662 1999 1996 5256 1997 14886 3748 26332 8 1998 9 2188 5300 2071 22088 11368 2006 15458 2015 1998 1999 7186 24759 28247 13339 1035 2918 13339 1035 2918 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 15:31:43 - INFO - __main__ - input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 15:31:43 - INFO - __main__ - segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 15:31:43 - INFO - __main__ - label: Contributes_To (id = 1)\n07/13/2022 15:31:43 - INFO - __main__ - sub_idx, obj_idx: 16, 3\n07/13/2022 15:31:43 - INFO - __main__ - Average #tokens: 47.29\n07/13/2022 15:31:43 - INFO - __main__ - Max #tokens: 69\n07/13/2022 15:31:43 - INFO - __main__ - 84 (100.00 %) examples can fit max_seq_length = 128\n07/13/2022 15:31:43 - INFO - __main__ - ***** Dev *****\n07/13/2022 15:31:43 - INFO - __main__ - Num examples = 84\n07/13/2022 15:31:43 - INFO - __main__ - Batch size = 32\n07/13/2022 15:31:44 - INFO - __main__ - Writing example 0 of 3214\n07/13/2022 15:31:44 - INFO - __main__ - *** Example ***\n07/13/2022 15:31:44 - INFO - __main__ - guid: 185@0::(16,16)-(10,10)\n07/13/2022 15:31:44 - INFO - __main__ - tokens: [CLS] known or potential el risk factors for di ##spar ##ities in asthma [unused9] or [unused10] asthma mor ##bid ##ity include cigarette smoking [unused7] and [unused8] environmental tobacco smoke et ##s prem ##at ##urity or low birth weight all ##er ##gen exposure indoor and outdoor air pollution diet obesity vitamin d ins ##uf ##fi ##ciency viral respiratory infections e g due to crowd ##ing psycho ##so ##cial stress and poor adherence with prescribed treatment implied _ base implied _ base [SEP]\n07/13/2022 15:31:44 - INFO - __main__ - input_ids: 101 2124 2030 4022 3449 3891 5876 2005 4487 27694 6447 1999 26180 10 2030 11 26180 22822 17062 3012 2421 9907 9422 8 1998 9 4483 9098 5610 3802 2015 26563 4017 25137 2030 2659 4182 3635 2035 2121 6914 7524 7169 1998 7254 2250 10796 8738 24552 17663 1040 16021 16093 8873 29125 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15:31:44 - INFO - __main__ - *** Example ***\n07/13/2022 15:31:44 - INFO - __main__ - guid: 185@0::(16,16)-(18,20)\n07/13/2022 15:31:44 - INFO - __main__ - tokens: [CLS] known or potential el risk factors for di ##spar ##ities in asthma or asthma mor ##bid ##ity include cigarette smoking [unused7] and [unused8] environmental [unused9] tobacco smoke et ##s [unused10] prem ##at ##urity or low birth weight all ##er ##gen exposure indoor and outdoor air pollution diet obesity vitamin d ins ##uf ##fi ##ciency viral respiratory infections e g due to crowd ##ing psycho ##so ##cial stress and poor adherence with prescribed treatment implied _ base implied _ base [SEP]\n07/13/2022 15:31:44 - INFO - __main__ - input_ids: 101 2124 2030 4022 3449 3891 5876 2005 4487 27694 6447 1999 26180 2030 26180 22822 17062 3012 2421 9907 9422 8 1998 9 4483 10 9098 5610 3802 2015 11 26563 4017 25137 2030 2659 4182 3635 2035 2121 6914 7524 7169 1998 7254 2250 10796 8738 24552 17663 1040 16021 16093 8873 29125 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15:31:44 - INFO - __main__ - *** Example ***\n07/13/2022 15:31:44 - INFO - __main__ - guid: 185@0::(16,16)-(22,22)\n07/13/2022 15:31:44 - INFO - __main__ - tokens: [CLS] known or potential el risk factors for di ##spar ##ities in asthma or asthma mor ##bid ##ity include cigarette smoking [unused7] and [unused8] environmental tobacco smoke et ##s prem ##at ##urity [unused9] or [unused10] low birth weight all ##er ##gen exposure indoor and outdoor air pollution diet obesity vitamin d ins ##uf ##fi ##ciency viral respiratory infections e g due to crowd ##ing psycho ##so ##cial stress and poor adherence with prescribed treatment implied _ base implied _ base [SEP]\n07/13/2022 15:31:44 - INFO - __main__ - input_ids: 101 2124 2030 4022 3449 3891 5876 2005 4487 27694 6447 1999 26180 2030 26180 22822 17062 3012 2421 9907 9422 8 1998 9 4483 9098 5610 3802 2015 26563 4017 25137 10 2030 11 2659 4182 3635 2035 2121 6914 7524 7169 1998 7254 2250 10796 8738 24552 17663 1040 16021 16093 8873 29125 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15:31:44 - INFO - __main__ - *** Example ***\n07/13/2022 15:31:44 - INFO - __main__ - guid: 185@0::(16,16)-(25,25)\n07/13/2022 15:31:44 - INFO - __main__ - tokens: [CLS] known or potential el risk factors for di ##spar ##ities in asthma or asthma mor ##bid ##ity include cigarette smoking [unused7] and [unused8] environmental tobacco smoke et ##s prem ##at ##urity or low birth [unused9] weight [unused10] all ##er ##gen exposure indoor and outdoor air pollution diet obesity vitamin d ins ##uf ##fi ##ciency viral respiratory infections e g due to crowd ##ing psycho ##so ##cial stress and poor adherence with prescribed treatment implied _ base implied _ base [SEP]\n07/13/2022 15:31:44 - INFO - __main__ - input_ids: 101 2124 2030 4022 3449 3891 5876 2005 4487 27694 6447 1999 26180 2030 26180 22822 17062 3012 2421 9907 9422 8 1998 9 4483 9098 5610 3802 2015 26563 4017 25137 2030 2659 4182 10 3635 11 2035 2121 6914 7524 7169 1998 7254 2250 10796 8738 24552 17663 1040 16021 16093 8873 29125 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15:31:44 - INFO - __main__ - *** Example ***\n07/13/2022 15:31:44 - INFO - __main__ - guid: 185@0::(16,16)-(27,27)\n07/13/2022 15:31:44 - INFO - __main__ - tokens: [CLS] known or potential el risk factors for di ##spar ##ities in asthma or asthma mor ##bid ##ity include cigarette smoking [unused7] and [unused8] environmental tobacco smoke et ##s prem ##at ##urity or low birth weight all ##er ##gen [unused9] exposure [unused10] indoor and outdoor air pollution diet obesity vitamin d ins ##uf ##fi ##ciency viral respiratory infections e g due to crowd ##ing psycho ##so ##cial stress and poor adherence with prescribed treatment implied _ base implied _ base [SEP]\n07/13/2022 15:31:44 - INFO - __main__ - input_ids: 101 2124 2030 4022 3449 3891 5876 2005 4487 27694 6447 1999 26180 2030 26180 22822 17062 3012 2421 9907 9422 8 1998 9 4483 9098 5610 3802 2015 26563 4017 25137 2030 2659 4182 3635 2035 2121 6914 10 7524 11 7169 1998 7254 2250 10796 8738 24552 17663 1040 16021 16093 8873 29125 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15:31:44 - INFO - __main__ - *** Example ***\n07/13/2022 15:31:44 - INFO - __main__ - guid: 185@0::(18,20)-(10,10)\n07/13/2022 15:31:44 - INFO - __main__ - tokens: [CLS] known or potential el risk factors for di ##spar ##ities in asthma [unused9] or [unused10] asthma mor ##bid ##ity include cigarette smoking and environmental [unused7] tobacco smoke et ##s [unused8] prem ##at ##urity or low birth weight all ##er ##gen exposure indoor and outdoor air pollution diet obesity vitamin d ins ##uf ##fi ##ciency viral respiratory infections e g due to crowd ##ing psycho ##so ##cial stress and poor adherence with prescribed treatment implied _ base implied _ base [SEP]\n07/13/2022 15:31:44 - INFO - __main__ - input_ids: 101 2124 2030 4022 3449 3891 5876 2005 4487 27694 6447 1999 26180 10 2030 11 26180 22822 17062 3012 2421 9907 9422 1998 4483 8 9098 5610 3802 2015 9 26563 4017 25137 2030 2659 4182 3635 2035 2121 6914 7524 7169 1998 7254 2250 10796 8738 24552 17663 1040 16021 16093 8873 29125 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15:31:44 - INFO - __main__ - *** Example ***\n07/13/2022 15:31:44 - INFO - __main__ - guid: 185@0::(22,22)-(10,10)\n07/13/2022 15:31:44 - INFO - __main__ - tokens: [CLS] known or potential el risk factors for di ##spar ##ities in asthma [unused9] or [unused10] asthma mor ##bid ##ity include cigarette smoking and environmental tobacco smoke et ##s prem ##at ##urity [unused7] or [unused8] low birth weight all ##er ##gen exposure indoor and outdoor air pollution diet obesity vitamin d ins ##uf ##fi ##ciency viral respiratory infections e g due to crowd ##ing psycho ##so ##cial stress and poor adherence with prescribed treatment implied _ base implied _ base [SEP]\n07/13/2022 15:31:44 - INFO - __main__ - input_ids: 101 2124 2030 4022 3449 3891 5876 2005 4487 27694 6447 1999 26180 10 2030 11 26180 22822 17062 3012 2421 9907 9422 1998 4483 9098 5610 3802 2015 26563 4017 25137 8 2030 9 2659 4182 3635 2035 2121 6914 7524 7169 1998 7254 2250 10796 8738 24552 17663 1040 16021 16093 8873 29125 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15:31:44 - INFO - __main__ - *** Example ***\n07/13/2022 15:31:44 - INFO - __main__ - guid: 185@0::(25,25)-(10,10)\n07/13/2022 15:31:44 - INFO - __main__ - tokens: [CLS] known or potential el risk factors for di ##spar ##ities in asthma [unused9] or [unused10] asthma mor ##bid ##ity include cigarette smoking and environmental tobacco smoke et ##s prem ##at ##urity or low birth [unused7] weight [unused8] all ##er ##gen exposure indoor and outdoor air pollution diet obesity vitamin d ins ##uf ##fi ##ciency viral respiratory infections e g due to crowd ##ing psycho ##so ##cial stress and poor adherence with prescribed treatment implied _ base implied _ base [SEP]\n07/13/2022 15:31:44 - INFO - __main__ - input_ids: 101 2124 2030 4022 3449 3891 5876 2005 4487 27694 6447 1999 26180 10 2030 11 26180 22822 17062 3012 2421 9907 9422 1998 4483 9098 5610 3802 2015 26563 4017 25137 2030 2659 4182 8 3635 9 2035 2121 6914 7524 7169 1998 7254 2250 10796 8738 24552 17663 1040 16021 16093 8873 29125 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15:31:44 - INFO - __main__ - *** Example ***\n07/13/2022 15:31:44 - INFO - __main__ - guid: 185@0::(27,27)-(10,10)\n07/13/2022 15:31:44 - INFO - __main__ - tokens: [CLS] known or potential el risk factors for di ##spar ##ities in asthma [unused9] or [unused10] asthma mor ##bid ##ity include cigarette smoking and environmental tobacco smoke et ##s prem ##at ##urity or low birth weight all ##er ##gen [unused7] exposure [unused8] indoor and outdoor air pollution diet obesity vitamin d ins ##uf ##fi ##ciency viral respiratory infections e g due to crowd ##ing psycho ##so ##cial stress and poor adherence with prescribed treatment implied _ base implied _ base [SEP]\n07/13/2022 15:31:44 - INFO - __main__ - input_ids: 101 2124 2030 4022 3449 3891 5876 2005 4487 27694 6447 1999 26180 10 2030 11 26180 22822 17062 3012 2421 9907 9422 1998 4483 9098 5610 3802 2015 26563 4017 25137 2030 2659 4182 3635 2035 2121 6914 8 7524 9 7169 1998 7254 2250 10796 8738 24552 17663 1040 16021 16093 8873 29125 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15:31:44 - INFO - __main__ - *** Example ***\n07/13/2022 15:31:44 - INFO - __main__ - guid: 185@0::(32,33)-(10,10)\n07/13/2022 15:31:44 - INFO - __main__ - tokens: [CLS] known or potential el risk factors for di ##spar ##ities in asthma [unused9] or [unused10] asthma mor ##bid ##ity include cigarette smoking and environmental tobacco smoke et ##s prem ##at ##urity or low birth weight all ##er ##gen exposure indoor and outdoor air [unused7] pollution diet [unused8] obesity vitamin d ins ##uf ##fi ##ciency viral respiratory infections e g due to crowd ##ing psycho ##so ##cial stress and poor adherence with prescribed treatment implied _ base implied _ base [SEP]\n07/13/2022 15:31:44 - INFO - __main__ - input_ids: 101 2124 2030 4022 3449 3891 5876 2005 4487 27694 6447 1999 26180 10 2030 11 26180 22822 17062 3012 2421 9907 9422 1998 4483 9098 5610 3802 2015 26563 4017 25137 2030 2659 4182 3635 2035 2121 6914 7524 7169 1998 7254 2250 8 10796 8738 9 24552 17663 1040 16021 16093 8873 29125 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15:31:44 - INFO - __main__ - *** Example ***\n07/13/2022 15:31:44 - INFO - __main__ - guid: 185@0::(34,34)-(10,10)\n07/13/2022 15:31:44 - INFO - __main__ - tokens: [CLS] known or potential el risk factors for di ##spar ##ities in asthma [unused9] or [unused10] asthma mor ##bid ##ity include cigarette smoking and environmental tobacco smoke et ##s prem ##at ##urity or low birth weight all ##er ##gen exposure indoor and outdoor air pollution diet [unused7] obesity [unused8] vitamin d ins ##uf ##fi ##ciency viral respiratory infections e g due to crowd ##ing psycho ##so ##cial stress and poor adherence with prescribed treatment implied _ base implied _ base [SEP]\n07/13/2022 15:31:44 - INFO - __main__ - input_ids: 101 2124 2030 4022 3449 3891 5876 2005 4487 27694 6447 1999 26180 10 2030 11 26180 22822 17062 3012 2421 9907 9422 1998 4483 9098 5610 3802 2015 26563 4017 25137 2030 2659 4182 3635 2035 2121 6914 7524 7169 1998 7254 2250 10796 8738 8 24552 9 17663 1040 16021 16093 8873 29125 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15:31:44 - INFO - __main__ - *** Example ***\n07/13/2022 15:31:44 - INFO - __main__ - guid: 185@0::(35,35)-(10,10)\n07/13/2022 15:31:44 - INFO - __main__ - tokens: [CLS] known or potential el risk factors for di ##spar ##ities in asthma [unused9] or [unused10] asthma mor ##bid ##ity include cigarette smoking and environmental tobacco smoke et ##s prem ##at ##urity or low birth weight all ##er ##gen exposure indoor and outdoor air pollution diet obesity [unused7] vitamin [unused8] d ins ##uf ##fi ##ciency viral respiratory infections e g due to crowd ##ing psycho ##so ##cial stress and poor adherence with prescribed treatment implied _ base implied _ base [SEP]\n07/13/2022 15:31:44 - INFO - __main__ - input_ids: 101 2124 2030 4022 3449 3891 5876 2005 4487 27694 6447 1999 26180 10 2030 11 26180 22822 17062 3012 2421 9907 9422 1998 4483 9098 5610 3802 2015 26563 4017 25137 2030 2659 4182 3635 2035 2121 6914 7524 7169 1998 7254 2250 10796 8738 24552 8 17663 9 1040 16021 16093 8873 29125 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15:31:44 - INFO - __main__ - *** Example ***\n07/13/2022 15:31:44 - INFO - __main__ - guid: 185@0::(36,38)-(10,10)\n07/13/2022 15:31:44 - INFO - __main__ - tokens: [CLS] known or potential el risk factors for di ##spar ##ities in asthma [unused9] or [unused10] asthma mor ##bid ##ity include cigarette smoking and environmental tobacco smoke et ##s prem ##at ##urity or low birth weight all ##er ##gen exposure indoor and outdoor air pollution diet obesity vitamin [unused7] d ins ##uf ##fi ##ciency viral [unused8] respiratory infections e g due to crowd ##ing psycho ##so ##cial stress and poor adherence with prescribed treatment implied _ base implied _ base [SEP]\n07/13/2022 15:31:44 - INFO - __main__ - input_ids: 101 2124 2030 4022 3449 3891 5876 2005 4487 27694 6447 1999 26180 10 2030 11 26180 22822 17062 3012 2421 9907 9422 1998 4483 9098 5610 3802 2015 26563 4017 25137 2030 2659 4182 3635 2035 2121 6914 7524 7169 1998 7254 2250 10796 8738 24552 17663 8 1040 16021 16093 8873 29125 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13\n07/13/2022 15:31:44 - INFO - __main__ - *** Example ***\n07/13/2022 15:31:44 - INFO - __main__ - guid: 185@0::(41,41)-(10,10)\n07/13/2022 15:31:44 - INFO - __main__ - tokens: [CLS] known or potential el risk factors for di ##spar ##ities in asthma [unused9] or [unused10] asthma mor ##bid ##ity include cigarette smoking and environmental tobacco smoke et ##s prem ##at ##urity or low birth weight all ##er ##gen exposure indoor and outdoor air pollution diet obesity vitamin d ins ##uf ##fi ##ciency viral respiratory infections [unused7] e [unused8] g due to crowd ##ing psycho ##so ##cial stress and poor adherence with prescribed treatment implied _ base implied _ base [SEP]\n07/13/2022 15:31:44 - INFO - __main__ - input_ids: 101 2124 2030 4022 3449 3891 5876 2005 4487 27694 6447 1999 26180 10 2030 11 26180 22822 17062 3012 2421 9907 9422 1998 4483 9098 5610 3802 2015 26563 4017 25137 2030 2659 4182 3635 2035 2121 6914 7524 7169 1998 7254 2250 10796 8738 24552 17663 1040 16021 16093 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13\n07/13/2022 15:31:44 - INFO - __main__ - *** Example ***\n07/13/2022 15:31:44 - INFO - __main__ - guid: 185@0::(46,46)-(41,41)\n07/13/2022 15:31:44 - INFO - __main__ - tokens: [CLS] known or potential el risk factors for di ##spar ##ities in asthma or asthma mor ##bid ##ity include cigarette smoking and environmental tobacco smoke et ##s prem ##at ##urity or low birth weight all ##er ##gen exposure indoor and outdoor air pollution diet obesity vitamin d ins ##uf ##fi ##ciency viral respiratory infections [unused9] e [unused10] g due to crowd ##ing [unused7] psycho ##so ##cial [unused8] stress and poor adherence with prescribed treatment implied _ base implied _ base [SEP]\n07/13/2022 15:31:44 - INFO - __main__ - input_ids: 101 2124 2030 4022 3449 3891 5876 2005 4487 27694 6447 1999 26180 2030 26180 22822 17062 3012 2421 9907 9422 1998 4483 9098 5610 3802 2015 26563 4017 25137 2030 2659 4182 3635 2035 2121 6914 7524 7169 1998 7254 2250 10796 8738 24552 17663 1040 16021 16093 8873 29125 13434 16464 15245 10 1041 11 1043 2349 2000 4306 2075 8 18224 6499 13247 9 6911 1998 3532 29235 2007 16250 3949 13339 1035 2918 13339 1035 2918 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 15:31:44 - INFO - __main__ - input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 15:31:44 - INFO - __main__ - segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 15:31:44 - INFO - __main__ - label: Contributes_To (id = 1)\n07/13/2022 15:31:44 - INFO - __main__ - sub_idx, obj_idx: 62, 54\n07/13/2022 15:31:44 - INFO - __main__ - *** Example ***\n07/13/2022 15:31:44 - INFO - __main__ - guid: 185@0::(48,48)-(10,10)\n07/13/2022 15:31:44 - INFO - __main__ - tokens: [CLS] known or potential el risk factors for di ##spar ##ities in asthma [unused9] or [unused10] asthma mor ##bid ##ity include cigarette smoking and environmental tobacco smoke et ##s prem ##at ##urity or low birth weight all ##er ##gen exposure indoor and outdoor air pollution diet obesity vitamin d ins ##uf ##fi ##ciency viral respiratory infections e g due to crowd ##ing psycho ##so ##cial stress [unused7] and [unused8] poor adherence with prescribed treatment implied _ base implied _ base [SEP]\n07/13/2022 15:31:44 - INFO - __main__ - input_ids: 101 2124 2030 4022 3449 3891 5876 2005 4487 27694 6447 1999 26180 10 2030 11 26180 22822 17062 3012 2421 9907 9422 1998 4483 9098 5610 3802 2015 26563 4017 25137 2030 2659 4182 3635 2035 2121 6914 7524 7169 1998 7254 2250 10796 8738 24552 17663 1040 16021 16093 8873 29125 13434 16464 15245 1041 1043 2349 2000 4306 2075 18224 6499 13247 6911 8 1998 9 3532 29235 2007 16250 3949 13339 1035 2918 13339 1035 2918 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 15:31:44 - INFO - __main__ - input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 15:31:44 - INFO - __main__ - segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 15:31:44 - INFO - __main__ - label: Contributes_To (id = 1)\n07/13/2022 15:31:44 - INFO - __main__ - sub_idx, obj_idx: 66, 13\n07/13/2022 15:31:44 - INFO - __main__ - *** Example ***\n07/13/2022 15:31:44 - INFO - __main__ - guid: 185@0::(51,54)-(10,10)\n07/13/2022 15:31:44 - INFO - __main__ - tokens: [CLS] known or potential el risk factors for di ##spar ##ities in asthma [unused9] or [unused10] asthma mor ##bid ##ity include cigarette smoking and environmental tobacco smoke et ##s prem ##at ##urity or low birth weight all ##er ##gen exposure indoor and outdoor air pollution diet obesity vitamin d ins ##uf ##fi ##ciency viral respiratory infections e g due to crowd ##ing psycho ##so ##cial stress and poor adherence [unused7] with prescribed treatment implied _ base [unused8] implied _ base [SEP]\n07/13/2022 15:31:44 - INFO - __main__ - input_ids: 101 2124 2030 4022 3449 3891 5876 2005 4487 27694 6447 1999 26180 10 2030 11 26180 22822 17062 3012 2421 9907 9422 1998 4483 9098 5610 3802 2015 26563 4017 25137 2030 2659 4182 3635 2035 2121 6914 7524 7169 1998 7254 2250 10796 8738 24552 17663 1040 16021 16093 8873 29125 13434 16464 15245 1041 1043 2349 2000 4306 2075 18224 6499 13247 6911 1998 3532 29235 8 2007 16250 3949 13339 1035 2918 9 13339 1035 2918 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 15:31:44 - INFO - __main__ - input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 15:31:44 - INFO - __main__ - segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 15:31:44 - INFO - __main__ - label: Contributes_To (id = 1)\n07/13/2022 15:31:44 - INFO - __main__ - sub_idx, obj_idx: 69, 13\n07/13/2022 15:31:44 - INFO - __main__ - *** Example ***\n07/13/2022 15:31:44 - INFO - __main__ - guid: 47@0::(32,33)-(4,4)\n07/13/2022 15:31:44 - INFO - __main__ - tokens: [CLS] individuals with mental health [unused9] and [unused10] stress related disorders such as pts ##d depression anxiety sleep difficulties and sometimes those who abuse drugs or alcohol can experience an ex ##ace ##rba ##tion of symptoms following a traumatic [unused7] event implied _ base [unused8] implied _ base [SEP]\n07/13/2022 15:31:44 - INFO - __main__ - input_ids: 101 3633 2007 5177 2740 10 1998 11 6911 3141 10840 2107 2004 19637 2094 6245 10089 3637 8190 1998 2823 2216 2040 6905 5850 2030 6544 2064 3325 2019 4654 10732 28483 3508 1997 8030 2206 1037 19686 8 2724 13339 1035 2918 9 13339 1035 2918 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 15:31:44 - INFO - __main__ - input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 15:31:44 - INFO - __main__ - segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 15:31:44 - INFO - __main__ - label: Contributes_To (id = 1)\n07/13/2022 15:31:44 - INFO - __main__ - sub_idx, obj_idx: 39, 5\n07/13/2022 15:31:44 - INFO - __main__ - *** Example ***\n07/13/2022 15:31:44 - INFO - __main__ - guid: 47@0::(32,33)-(8,8)\n07/13/2022 15:31:44 - INFO - __main__ - tokens: [CLS] individuals with mental health and stress related disorders [unused9] such [unused10] as pts ##d depression anxiety sleep difficulties and sometimes those who abuse drugs or alcohol can experience an ex ##ace ##rba ##tion of symptoms following a traumatic [unused7] event implied _ base [unused8] implied _ base [SEP]\n07/13/2022 15:31:44 - INFO - __main__ - input_ids: 101 3633 2007 5177 2740 1998 6911 3141 10840 10 2107 11 2004 19637 2094 6245 10089 3637 8190 1998 2823 2216 2040 6905 5850 2030 6544 2064 3325 2019 4654 10732 28483 3508 1997 8030 2206 1037 19686 8 2724 13339 1035 2918 9 13339 1035 2918 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 15:31:44 - INFO - __main__ - input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 15:31:44 - INFO - __main__ - segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 15:31:44 - INFO - __main__ - label: Contributes_To (id = 1)\n07/13/2022 15:31:44 - INFO - __main__ - sub_idx, obj_idx: 39, 9\n07/13/2022 15:31:44 - INFO - __main__ - *** Example ***\n07/13/2022 15:31:44 - INFO - __main__ - guid: 47@0::(32,33)-(11,11)\n07/13/2022 15:31:44 - INFO - __main__ - tokens: [CLS] individuals with mental health and stress related disorders such as pts ##d [unused9] depression [unused10] anxiety sleep difficulties and sometimes those who abuse drugs or alcohol can experience an ex ##ace ##rba ##tion of symptoms following a traumatic [unused7] event implied _ base [unused8] implied _ base [SEP]\n07/13/2022 15:31:44 - INFO - __main__ - input_ids: 101 3633 2007 5177 2740 1998 6911 3141 10840 2107 2004 19637 2094 10 6245 11 10089 3637 8190 1998 2823 2216 2040 6905 5850 2030 6544 2064 3325 2019 4654 10732 28483 3508 1997 8030 2206 1037 19686 8 2724 13339 1035 2918 9 13339 1035 2918 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 15:31:44 - INFO - __main__ - input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 15:31:44 - INFO - __main__ - segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 15:31:44 - INFO - __main__ - label: Contributes_To (id = 1)\n07/13/2022 15:31:44 - INFO - __main__ - sub_idx, obj_idx: 39, 13\n07/13/2022 15:31:51 - INFO - __main__ - Average #tokens: 57.95\n07/13/2022 15:31:51 - INFO - __main__ - Max #tokens: 151\n07/13/2022 15:31:51 - INFO - __main__ - 3048 (94.84 %) examples can fit max_seq_length = 128\n07/13/2022 15:31:51 - INFO - __main__ - ***** Training *****\n07/13/2022 15:31:51 - INFO - __main__ - Num examples = 3214\n07/13/2022 15:31:51 - INFO - __main__ - Batch size = 32\n07/13/2022 15:31:51 - INFO - __main__ - Num steps = 808\n07/13/2022 15:31:51 - INFO - transformers.configuration_utils - loading configuration file https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-config.json from cache at /home/azureuser/.cache/torch/transformers/4dad0251492946e18ac39290fcfe91b89d370fee250efe9521476438fe8ca185.7156163d5fdc189c3016baca0775ffce230789d7fa2a42ef516483e4ca884517\n07/13/2022 15:31:51 - INFO - transformers.configuration_utils - Model config BertConfig {\n \"architectures\": [\n \"BertForMaskedLM\"\n ],\n \"attention_probs_dropout_prob\": 0.1,\n \"gradient_checkpointing\": false,\n \"hidden_act\": \"gelu\",\n \"hidden_dropout_prob\": 0.1,\n \"hidden_size\": 768,\n \"initializer_range\": 0.02,\n \"intermediate_size\": 3072,\n \"layer_norm_eps\": 1e-12,\n \"max_position_embeddings\": 512,\n \"model_type\": \"bert\",\n \"num_attention_heads\": 12,\n \"num_hidden_layers\": 12,\n \"pad_token_id\": 0,\n \"type_vocab_size\": 2,\n \"vocab_size\": 30522\n}\n\n07/13/2022 15:31:52 - INFO - transformers.modeling_utils - loading weights file https://cdn.huggingface.co/bert-base-uncased-pytorch_model.bin from cache at /home/azureuser/.cache/torch/transformers/f2ee78bdd635b758cc0a12352586868bef80e47401abe4c4fcc3832421e7338b.36ca03ab34a1a5d5fa7bc3d03d55c4fa650fed07220e2eeebc06ce58d0e9a157\n07/13/2022 15:31:57 - WARNING - transformers.modeling_utils - Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForRelation: ['cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias']\n- This IS expected if you are initializing BertForRelation from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPretraining model).\n- This IS NOT expected if you are initializing BertForRelation from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n07/13/2022 15:31:57 - WARNING - transformers.modeling_utils - Some weights of BertForRelation were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['layer_norm.weight', 'layer_norm.bias', 'classifier.weight', 'classifier.bias']\nYou should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n07/13/2022 15:31:57 - INFO - __main__ - Start epoch #0 (lr = 2e-05)...\n07/13/2022 15:32:56 - INFO - __main__ - Epoch: 0, Step: 10 / 101, used_time = 58.40s, loss = 0.916461\n07/13/2022 15:33:00 - INFO - __main__ - ***** Eval results *****\n07/13/2022 15:33:00 - INFO - __main__ - accuracy = 0.5\n07/13/2022 15:33:00 - INFO - __main__ - eval_loss = 0.7977293531099955\n07/13/2022 15:33:00 - INFO - __main__ - f1 = 0.3636363636363637\n07/13/2022 15:33:00 - INFO - __main__ - n_correct = 12\n07/13/2022 15:33:00 - INFO - __main__ - n_gold = 26\n07/13/2022 15:33:00 - INFO - __main__ - n_pred = 40\n07/13/2022 15:33:00 - INFO - __main__ - precision = 0.3\n07/13/2022 15:33:00 - INFO - __main__ - recall = 0.46153846153846156\n07/13/2022 15:33:00 - INFO - __main__ - task_f1 = 0.3636363636363637\n07/13/2022 15:33:00 - INFO - __main__ - task_ngold = 26\n07/13/2022 15:33:00 - INFO - __main__ - task_recall = 0.46153846153846156\n07/13/2022 15:33:00 - INFO - __main__ - !!! Best dev f1 (lr=2e-05, epoch=0): 36.36\n07/13/2022 15:33:00 - INFO - __main__ - Saving model to PURE/output_rel_ent2\n07/13/2022 15:34:03 - INFO - __main__ - Epoch: 0, Step: 20 / 101, used_time = 125.89s, loss = 0.764144\n07/13/2022 15:34:07 - INFO - __main__ - ***** Eval results *****\n07/13/2022 15:34:07 - INFO - __main__ - accuracy = 0.7023809523809523\n07/13/2022 15:34:07 - INFO - __main__ - eval_loss = 0.7380537788073221\n07/13/2022 15:34:07 - INFO - __main__ - f1 = 0.1935483870967742\n07/13/2022 15:34:07 - INFO - __main__ - n_correct = 3\n07/13/2022 15:34:07 - INFO - __main__ - n_gold = 26\n07/13/2022 15:34:07 - INFO - __main__ - n_pred = 5\n07/13/2022 15:34:07 - INFO - __main__ - precision = 0.6\n07/13/2022 15:34:07 - INFO - __main__ - recall = 0.11538461538461539\n07/13/2022 15:34:07 - INFO - __main__ - task_f1 = 0.1935483870967742\n07/13/2022 15:34:07 - INFO - __main__ - task_ngold = 26\n07/13/2022 15:34:07 - INFO - __main__ - task_recall = 0.11538461538461539\n07/13/2022 15:35:05 - INFO - __main__ - Epoch: 0, Step: 30 / 101, used_time = 188.14s, loss = 0.723215\n07/13/2022 15:35:10 - INFO - __main__ - ***** Eval results *****\n07/13/2022 15:35:10 - INFO - __main__ - accuracy = 0.7142857142857143\n07/13/2022 15:35:10 - INFO - __main__ - eval_loss = 0.7671570579210917\n07/13/2022 15:35:10 - INFO - __main__ - f1 = 0.14285714285714288\n07/13/2022 15:35:10 - INFO - __main__ - n_correct = 2\n07/13/2022 15:35:10 - INFO - __main__ - n_gold = 26\n07/13/2022 15:35:10 - INFO - __main__ - n_pred = 2\n07/13/2022 15:35:10 - INFO - __main__ - precision = 1.0\n07/13/2022 15:35:10 - INFO - __main__ - recall = 0.07692307692307693\n07/13/2022 15:35:10 - INFO - __main__ - task_f1 = 0.14285714285714288\n07/13/2022 15:35:10 - INFO - __main__ - task_ngold = 26\n07/13/2022 15:35:10 - INFO - __main__ - task_recall = 0.07692307692307693\n07/13/2022 15:36:04 - INFO - __main__ - Epoch: 0, Step: 40 / 101, used_time = 246.59s, loss = 0.681228\n07/13/2022 15:36:08 - INFO - __main__ - ***** Eval results *****\n07/13/2022 15:36:08 - INFO - __main__ - accuracy = 0.6904761904761905\n07/13/2022 15:36:08 - INFO - __main__ - eval_loss = 0.6627793510754904\n07/13/2022 15:36:08 - INFO - __main__ - f1 = 0.1875\n07/13/2022 15:36:08 - INFO - __main__ - n_correct = 3\n07/13/2022 15:36:08 - INFO - __main__ - n_gold = 26\n07/13/2022 15:36:08 - INFO - __main__ - n_pred = 6\n07/13/2022 15:36:08 - INFO - __main__ - precision = 0.5\n07/13/2022 15:36:08 - INFO - __main__ - recall = 0.11538461538461539\n07/13/2022 15:36:08 - INFO - __main__ - task_f1 = 0.1875\n07/13/2022 15:36:08 - INFO - __main__ - task_ngold = 26\n07/13/2022 15:36:08 - INFO - __main__ - task_recall = 0.11538461538461539\n07/13/2022 15:37:06 - INFO - __main__ - Epoch: 0, Step: 50 / 101, used_time = 308.33s, loss = 0.660327\n07/13/2022 15:37:10 - INFO - __main__ - ***** Eval results *****\n07/13/2022 15:37:10 - INFO - __main__ - accuracy = 0.7023809523809523\n07/13/2022 15:37:10 - INFO - __main__ - eval_loss = 0.6259163022041321\n07/13/2022 15:37:10 - INFO - __main__ - f1 = 0.28571428571428575\n07/13/2022 15:37:10 - INFO - __main__ - n_correct = 5\n07/13/2022 15:37:10 - INFO - __main__ - n_gold = 26\n07/13/2022 15:37:10 - INFO - __main__ - n_pred = 9\n07/13/2022 15:37:10 - INFO - __main__ - precision = 0.5555555555555556\n07/13/2022 15:37:10 - INFO - __main__ - recall = 0.19230769230769232\n07/13/2022 15:37:10 - INFO - __main__ - task_f1 = 0.28571428571428575\n07/13/2022 15:37:10 - INFO - __main__ - task_ngold = 26\n07/13/2022 15:37:10 - INFO - __main__ - task_recall = 0.19230769230769232\n07/13/2022 15:38:07 - INFO - __main__ - Epoch: 0, Step: 60 / 101, used_time = 369.98s, loss = 0.637364\n07/13/2022 15:38:11 - INFO - __main__ - ***** Eval results *****\n07/13/2022 15:38:11 - INFO - __main__ - accuracy = 0.6785714285714286\n07/13/2022 15:38:11 - INFO - __main__ - eval_loss = 0.6087214152018229\n07/13/2022 15:38:11 - INFO - __main__ - f1 = 0.30769230769230776\n07/13/2022 15:38:11 - INFO - __main__ - n_correct = 6\n07/13/2022 15:38:11 - INFO - __main__ - n_gold = 26\n07/13/2022 15:38:11 - INFO - __main__ - n_pred = 13\n07/13/2022 15:38:11 - INFO - __main__ - precision = 0.46153846153846156\n07/13/2022 15:38:11 - INFO - __main__ - recall = 0.23076923076923078\n07/13/2022 15:38:11 - INFO - __main__ - task_f1 = 0.30769230769230776\n07/13/2022 15:38:11 - INFO - __main__ - task_ngold = 26\n07/13/2022 15:38:11 - INFO - __main__ - task_recall = 0.23076923076923078\n07/13/2022 15:39:08 - INFO - __main__ - Epoch: 0, Step: 70 / 101, used_time = 430.66s, loss = 0.618292\n07/13/2022 15:39:12 - INFO - __main__ - ***** Eval results *****\n07/13/2022 15:39:12 - INFO - __main__ - accuracy = 0.7261904761904762\n07/13/2022 15:39:12 - INFO - __main__ - eval_loss = 0.6088446577390035\n07/13/2022 15:39:12 - INFO - __main__ - f1 = 0.25806451612903225\n07/13/2022 15:39:12 - INFO - __main__ - n_correct = 4\n07/13/2022 15:39:12 - INFO - __main__ - n_gold = 26\n07/13/2022 15:39:12 - INFO - __main__ - n_pred = 5\n07/13/2022 15:39:12 - INFO - __main__ - precision = 0.8\n07/13/2022 15:39:12 - INFO - __main__ - recall = 0.15384615384615385\n07/13/2022 15:39:12 - INFO - __main__ - task_f1 = 0.25806451612903225\n07/13/2022 15:39:12 - INFO - __main__ - task_ngold = 26\n07/13/2022 15:39:12 - INFO - __main__ - task_recall = 0.15384615384615385\n07/13/2022 15:40:07 - INFO - __main__ - Epoch: 0, Step: 80 / 101, used_time = 489.35s, loss = 0.610175\n07/13/2022 15:40:11 - INFO - __main__ - ***** Eval results *****\n07/13/2022 15:40:11 - INFO - __main__ - accuracy = 0.7380952380952381\n07/13/2022 15:40:11 - INFO - __main__ - eval_loss = 0.5618480443954468\n07/13/2022 15:40:11 - INFO - __main__ - f1 = 0.5416666666666667\n07/13/2022 15:40:11 - INFO - __main__ - n_correct = 13\n07/13/2022 15:40:11 - INFO - __main__ - n_gold = 26\n07/13/2022 15:40:11 - INFO - __main__ - n_pred = 22\n07/13/2022 15:40:11 - INFO - __main__ - precision = 0.5909090909090909\n07/13/2022 15:40:11 - INFO - __main__ - recall = 0.5\n07/13/2022 15:40:11 - INFO - __main__ - task_f1 = 0.5416666666666667\n07/13/2022 15:40:11 - INFO - __main__ - task_ngold = 26\n07/13/2022 15:40:11 - INFO - __main__ - task_recall = 0.5\n07/13/2022 15:40:11 - INFO - __main__ - !!! Best dev f1 (lr=2e-05, epoch=0): 54.17\n07/13/2022 15:40:11 - INFO - __main__ - Saving model to PURE/output_rel_ent2\n07/13/2022 15:41:12 - INFO - __main__ - Epoch: 0, Step: 90 / 101, used_time = 554.74s, loss = 0.592589\n07/13/2022 15:41:16 - INFO - __main__ - ***** Eval results *****\n07/13/2022 15:41:16 - INFO - __main__ - accuracy = 0.7142857142857143\n07/13/2022 15:41:16 - INFO - __main__ - eval_loss = 0.5545852382977804\n07/13/2022 15:41:16 - INFO - __main__ - f1 = 0.19999999999999998\n07/13/2022 15:41:16 - INFO - __main__ - n_correct = 3\n07/13/2022 15:41:16 - INFO - __main__ - n_gold = 26\n07/13/2022 15:41:16 - INFO - __main__ - n_pred = 4\n07/13/2022 15:41:16 - INFO - __main__ - precision = 0.75\n07/13/2022 15:41:16 - INFO - __main__ - recall = 0.11538461538461539\n07/13/2022 15:41:16 - INFO - __main__ - task_f1 = 0.19999999999999998\n07/13/2022 15:41:16 - INFO - __main__ - task_ngold = 26\n07/13/2022 15:41:16 - INFO - __main__ - task_recall = 0.11538461538461539\n07/13/2022 15:42:11 - INFO - __main__ - Epoch: 0, Step: 100 / 101, used_time = 613.58s, loss = 0.586592\n07/13/2022 15:42:15 - INFO - __main__ - ***** Eval results *****\n07/13/2022 15:42:15 - INFO - __main__ - accuracy = 0.7619047619047619\n07/13/2022 15:42:15 - INFO - __main__ - eval_loss = 0.5351888736089071\n07/13/2022 15:42:15 - INFO - __main__ - f1 = 0.411764705882353\n07/13/2022 15:42:15 - INFO - __main__ - n_correct = 7\n07/13/2022 15:42:15 - INFO - __main__ - n_gold = 26\n07/13/2022 15:42:15 - INFO - __main__ - n_pred = 8\n07/13/2022 15:42:15 - INFO - __main__ - precision = 0.875\n07/13/2022 15:42:15 - INFO - __main__ - recall = 0.2692307692307692\n07/13/2022 15:42:15 - INFO - __main__ - task_f1 = 0.411764705882353\n07/13/2022 15:42:15 - INFO - __main__ - task_ngold = 26\n07/13/2022 15:42:15 - INFO - __main__ - task_recall = 0.2692307692307692\n07/13/2022 15:42:20 - INFO - __main__ - Start epoch #1 (lr = 2e-05)...\n07/13/2022 15:43:12 - INFO - __main__ - Epoch: 1, Step: 10 / 101, used_time = 675.02s, loss = 0.566108\n07/13/2022 15:43:16 - INFO - __main__ - ***** Eval results *****\n07/13/2022 15:43:16 - INFO - __main__ - accuracy = 0.7261904761904762\n07/13/2022 15:43:16 - INFO - __main__ - eval_loss = 0.5593800743420919\n07/13/2022 15:43:16 - INFO - __main__ - f1 = 0.5660377358490566\n07/13/2022 15:43:16 - INFO - __main__ - n_correct = 15\n07/13/2022 15:43:16 - INFO - __main__ - n_gold = 26\n07/13/2022 15:43:16 - INFO - __main__ - n_pred = 27\n07/13/2022 15:43:16 - INFO - __main__ - precision = 0.5555555555555556\n07/13/2022 15:43:16 - INFO - __main__ - recall = 0.5769230769230769\n07/13/2022 15:43:16 - INFO - __main__ - task_f1 = 0.5660377358490566\n07/13/2022 15:43:16 - INFO - __main__ - task_ngold = 26\n07/13/2022 15:43:16 - INFO - __main__ - task_recall = 0.5769230769230769\n07/13/2022 15:43:16 - INFO - __main__ - !!! Best dev f1 (lr=2e-05, epoch=1): 56.60\n07/13/2022 15:43:16 - INFO - __main__ - Saving model to PURE/output_rel_ent2\n07/13/2022 15:44:16 - INFO - __main__ - Epoch: 1, Step: 20 / 101, used_time = 739.18s, loss = 0.550429\n07/13/2022 15:44:20 - INFO - __main__ - ***** Eval results *****\n07/13/2022 15:44:20 - INFO - __main__ - accuracy = 0.7261904761904762\n07/13/2022 15:44:20 - INFO - __main__ - eval_loss = 0.5391584038734436\n07/13/2022 15:44:20 - INFO - __main__ - f1 = 0.25806451612903225\n07/13/2022 15:44:20 - INFO - __main__ - n_correct = 4\n07/13/2022 15:44:20 - INFO - __main__ - n_gold = 26\n07/13/2022 15:44:20 - INFO - __main__ - n_pred = 5\n07/13/2022 15:44:20 - INFO - __main__ - precision = 0.8\n07/13/2022 15:44:20 - INFO - __main__ - recall = 0.15384615384615385\n07/13/2022 15:44:20 - INFO - __main__ - task_f1 = 0.25806451612903225\n07/13/2022 15:44:20 - INFO - __main__ - task_ngold = 26\n07/13/2022 15:44:20 - INFO - __main__ - task_recall = 0.15384615384615385\n07/13/2022 15:45:15 - INFO - __main__ - Epoch: 1, Step: 30 / 101, used_time = 797.83s, loss = 0.537053\n07/13/2022 15:45:19 - INFO - __main__ - ***** Eval results *****\n07/13/2022 15:45:19 - INFO - __main__ - accuracy = 0.7976190476190477\n07/13/2022 15:45:19 - INFO - __main__ - eval_loss = 0.46017494797706604\n07/13/2022 15:45:19 - INFO - __main__ - f1 = 0.6046511627906976\n07/13/2022 15:45:19 - INFO - __main__ - n_correct = 13\n07/13/2022 15:45:19 - INFO - __main__ - n_gold = 26\n07/13/2022 15:45:19 - INFO - __main__ - n_pred = 17\n07/13/2022 15:45:19 - INFO - __main__ - precision = 0.7647058823529411\n07/13/2022 15:45:19 - INFO - __main__ - recall = 0.5\n07/13/2022 15:45:19 - INFO - __main__ - task_f1 = 0.6046511627906976\n07/13/2022 15:45:19 - INFO - __main__ - task_ngold = 26\n07/13/2022 15:45:19 - INFO - __main__ - task_recall = 0.5\n07/13/2022 15:45:19 - INFO - __main__ - !!! Best dev f1 (lr=2e-05, epoch=1): 60.47\n07/13/2022 15:45:19 - INFO - __main__ - Saving model to PURE/output_rel_ent2\n07/13/2022 15:46:19 - INFO - __main__ - Epoch: 1, Step: 40 / 101, used_time = 861.90s, loss = 0.524793\n07/13/2022 15:46:23 - INFO - __main__ - ***** Eval results *****\n07/13/2022 15:46:23 - INFO - __main__ - accuracy = 0.8214285714285714\n07/13/2022 15:46:23 - INFO - __main__ - eval_loss = 0.45315021276474\n07/13/2022 15:46:23 - INFO - __main__ - f1 = 0.6666666666666666\n07/13/2022 15:46:23 - INFO - __main__ - n_correct = 15\n07/13/2022 15:46:23 - INFO - __main__ - n_gold = 26\n07/13/2022 15:46:23 - INFO - __main__ - n_pred = 19\n07/13/2022 15:46:23 - INFO - __main__ - precision = 0.7894736842105263\n07/13/2022 15:46:23 - INFO - __main__ - recall = 0.5769230769230769\n07/13/2022 15:46:23 - INFO - __main__ - task_f1 = 0.6666666666666666\n07/13/2022 15:46:23 - INFO - __main__ - task_ngold = 26\n07/13/2022 15:46:23 - INFO - __main__ - task_recall = 0.5769230769230769\n07/13/2022 15:46:23 - INFO - __main__ - !!! Best dev f1 (lr=2e-05, epoch=1): 66.67\n07/13/2022 15:46:23 - INFO - __main__ - Saving model to PURE/output_rel_ent2\n07/13/2022 15:47:24 - INFO - __main__ - Epoch: 1, Step: 50 / 101, used_time = 926.55s, loss = 0.515468\n07/13/2022 15:47:28 - INFO - __main__ - ***** Eval results *****\n07/13/2022 15:47:28 - INFO - __main__ - accuracy = 0.7857142857142857\n07/13/2022 15:47:28 - INFO - __main__ - eval_loss = 0.4835545818010966\n07/13/2022 15:47:28 - INFO - __main__ - f1 = 0.5\n07/13/2022 15:47:28 - INFO - __main__ - n_correct = 9\n07/13/2022 15:47:28 - INFO - __main__ - n_gold = 26\n07/13/2022 15:47:28 - INFO - __main__ - n_pred = 10\n07/13/2022 15:47:28 - INFO - __main__ - precision = 0.9\n07/13/2022 15:47:28 - INFO - __main__ - recall = 0.34615384615384615\n07/13/2022 15:47:28 - INFO - __main__ - task_f1 = 0.5\n07/13/2022 15:47:28 - INFO - __main__ - task_ngold = 26\n07/13/2022 15:47:28 - INFO - __main__ - task_recall = 0.34615384615384615\n07/13/2022 15:48:25 - INFO - __main__ - Epoch: 1, Step: 60 / 101, used_time = 987.51s, loss = 0.511669\n07/13/2022 15:48:29 - INFO - __main__ - ***** Eval results *****\n07/13/2022 15:48:29 - INFO - __main__ - accuracy = 0.7857142857142857\n07/13/2022 15:48:29 - INFO - __main__ - eval_loss = 0.43567747871081036\n07/13/2022 15:48:29 - INFO - __main__ - f1 = 0.608695652173913\n07/13/2022 15:48:29 - INFO - __main__ - n_correct = 14\n07/13/2022 15:48:29 - INFO - __main__ - n_gold = 26\n07/13/2022 15:48:29 - INFO - __main__ - n_pred = 20\n07/13/2022 15:48:29 - INFO - __main__ - precision = 0.7\n07/13/2022 15:48:29 - INFO - __main__ - recall = 0.5384615384615384\n07/13/2022 15:48:29 - INFO - __main__ - task_f1 = 0.608695652173913\n07/13/2022 15:48:29 - INFO - __main__ - task_ngold = 26\n07/13/2022 15:48:29 - INFO - __main__ - task_recall = 0.5384615384615384\n07/13/2022 15:49:27 - INFO - __main__ - Epoch: 1, Step: 70 / 101, used_time = 1050.14s, loss = 0.505721\n07/13/2022 15:49:32 - INFO - __main__ - ***** Eval results *****\n07/13/2022 15:49:32 - INFO - __main__ - accuracy = 0.8452380952380952\n07/13/2022 15:49:32 - INFO - __main__ - eval_loss = 0.40103983879089355\n07/13/2022 15:49:32 - INFO - __main__ - f1 = 0.7346938775510203\n07/13/2022 15:49:32 - INFO - __main__ - n_correct = 18\n07/13/2022 15:49:32 - INFO - __main__ - n_gold = 26\n07/13/2022 15:49:32 - INFO - __main__ - n_pred = 23\n07/13/2022 15:49:32 - INFO - __main__ - precision = 0.782608695652174\n07/13/2022 15:49:32 - INFO - __main__ - recall = 0.6923076923076923\n07/13/2022 15:49:32 - INFO - __main__ - task_f1 = 0.7346938775510203\n07/13/2022 15:49:32 - INFO - __main__ - task_ngold = 26\n07/13/2022 15:49:32 - INFO - __main__ - task_recall = 0.6923076923076923\n07/13/2022 15:49:32 - INFO - __main__ - !!! Best dev f1 (lr=2e-05, epoch=1): 73.47\n07/13/2022 15:49:32 - INFO - __main__ - Saving model to PURE/output_rel_ent2\n07/13/2022 15:50:37 - INFO - __main__ - Epoch: 1, Step: 80 / 101, used_time = 1119.89s, loss = 0.503396\n07/13/2022 15:50:42 - INFO - __main__ - ***** Eval results *****\n07/13/2022 15:50:42 - INFO - __main__ - accuracy = 0.8214285714285714\n07/13/2022 15:50:42 - INFO - __main__ - eval_loss = 0.38227670391400653\n07/13/2022 15:50:42 - INFO - __main__ - f1 = 0.6511627906976744\n07/13/2022 15:50:42 - INFO - __main__ - n_correct = 14\n07/13/2022 15:50:42 - INFO - __main__ - n_gold = 26\n07/13/2022 15:50:42 - INFO - __main__ - n_pred = 17\n07/13/2022 15:50:42 - INFO - __main__ - precision = 0.8235294117647058\n07/13/2022 15:50:42 - INFO - __main__ - recall = 0.5384615384615384\n07/13/2022 15:50:42 - INFO - __main__ - task_f1 = 0.6511627906976744\n07/13/2022 15:50:42 - INFO - __main__ - task_ngold = 26\n07/13/2022 15:50:42 - INFO - __main__ - task_recall = 0.5384615384615384\n07/13/2022 15:51:40 - INFO - __main__ - Epoch: 1, Step: 90 / 101, used_time = 1182.36s, loss = 0.497180\n07/13/2022 15:51:44 - INFO - __main__ - ***** Eval results *****\n07/13/2022 15:51:44 - INFO - __main__ - accuracy = 0.8095238095238095\n07/13/2022 15:51:44 - INFO - __main__ - eval_loss = 0.37580422560373944\n07/13/2022 15:51:44 - INFO - __main__ - f1 = 0.6\n07/13/2022 15:51:44 - INFO - __main__ - n_correct = 12\n07/13/2022 15:51:44 - INFO - __main__ - n_gold = 26\n07/13/2022 15:51:44 - INFO - __main__ - n_pred = 14\n07/13/2022 15:51:44 - INFO - __main__ - precision = 0.8571428571428571\n07/13/2022 15:51:44 - INFO - __main__ - recall = 0.46153846153846156\n07/13/2022 15:51:44 - INFO - __main__ - task_f1 = 0.6\n07/13/2022 15:51:44 - INFO - __main__ - task_ngold = 26\n07/13/2022 15:51:44 - INFO - __main__ - task_recall = 0.46153846153846156\n07/13/2022 15:52:42 - INFO - __main__ - Epoch: 1, Step: 100 / 101, used_time = 1245.25s, loss = 0.488401\n07/13/2022 15:52:47 - INFO - __main__ - ***** Eval results *****\n07/13/2022 15:52:47 - INFO - __main__ - accuracy = 0.8809523809523809\n07/13/2022 15:52:47 - INFO - __main__ - eval_loss = 0.35938455661137897\n07/13/2022 15:52:47 - INFO - __main__ - f1 = 0.7916666666666666\n07/13/2022 15:52:47 - INFO - __main__ - n_correct = 19\n07/13/2022 15:52:47 - INFO - __main__ - n_gold = 26\n07/13/2022 15:52:47 - INFO - __main__ - n_pred = 22\n07/13/2022 15:52:47 - INFO - __main__ - precision = 0.8636363636363636\n07/13/2022 15:52:47 - INFO - __main__ - recall = 0.7307692307692307\n07/13/2022 15:52:47 - INFO - __main__ - task_f1 = 0.7916666666666666\n07/13/2022 15:52:47 - INFO - __main__ - task_ngold = 26\n07/13/2022 15:52:47 - INFO - __main__ - task_recall = 0.7307692307692307\n07/13/2022 15:52:47 - INFO - __main__ - !!! Best dev f1 (lr=2e-05, epoch=1): 79.17\n07/13/2022 15:52:47 - INFO - __main__ - Saving model to PURE/output_rel_ent2\n07/13/2022 15:52:59 - INFO - __main__ - Start epoch #2 (lr = 2e-05)...\n07/13/2022 15:53:56 - INFO - __main__ - Epoch: 2, Step: 10 / 101, used_time = 1318.40s, loss = 0.475209\n07/13/2022 15:54:00 - INFO - __main__ - ***** Eval results *****\n07/13/2022 15:54:00 - INFO - __main__ - accuracy = 0.7976190476190477\n07/13/2022 15:54:00 - INFO - __main__ - eval_loss = 0.3676786720752716\n07/13/2022 15:54:00 - INFO - __main__ - f1 = 0.5641025641025641\n07/13/2022 15:54:00 - INFO - __main__ - n_correct = 11\n07/13/2022 15:54:00 - INFO - __main__ - n_gold = 26\n07/13/2022 15:54:00 - INFO - __main__ - n_pred = 13\n07/13/2022 15:54:00 - INFO - __main__ - precision = 0.8461538461538461\n07/13/2022 15:54:00 - INFO - __main__ - recall = 0.4230769230769231\n07/13/2022 15:54:00 - INFO - __main__ - task_f1 = 0.5641025641025641\n07/13/2022 15:54:00 - INFO - __main__ - task_ngold = 26\n07/13/2022 15:54:00 - INFO - __main__ - task_recall = 0.4230769230769231\n07/13/2022 15:54:55 - INFO - __main__ - Epoch: 2, Step: 20 / 101, used_time = 1378.09s, loss = 0.465232\n07/13/2022 15:55:00 - INFO - __main__ - ***** Eval results *****\n07/13/2022 15:55:00 - INFO - __main__ - accuracy = 0.8809523809523809\n07/13/2022 15:55:00 - INFO - __main__ - eval_loss = 0.36075010895729065\n07/13/2022 15:55:00 - INFO - __main__ - f1 = 0.7916666666666666\n07/13/2022 15:55:00 - INFO - __main__ - n_correct = 19\n07/13/2022 15:55:00 - INFO - __main__ - n_gold = 26\n07/13/2022 15:55:00 - INFO - __main__ - n_pred = 22\n07/13/2022 15:55:00 - INFO - __main__ - precision = 0.8636363636363636\n07/13/2022 15:55:00 - INFO - __main__ - recall = 0.7307692307692307\n07/13/2022 15:55:00 - INFO - __main__ - task_f1 = 0.7916666666666666\n07/13/2022 15:55:00 - INFO - __main__ - task_ngold = 26\n07/13/2022 15:55:00 - INFO - __main__ - task_recall = 0.7307692307692307\n07/13/2022 15:55:56 - INFO - __main__ - Epoch: 2, Step: 30 / 101, used_time = 1439.20s, loss = 0.460452\n07/13/2022 15:56:01 - INFO - __main__ - ***** Eval results *****\n07/13/2022 15:56:01 - INFO - __main__ - accuracy = 0.8571428571428571\n07/13/2022 15:56:01 - INFO - __main__ - eval_loss = 0.36445561051368713\n07/13/2022 15:56:01 - INFO - __main__ - f1 = 0.7391304347826088\n07/13/2022 15:56:01 - INFO - __main__ - n_correct = 17\n07/13/2022 15:56:01 - INFO - __main__ - n_gold = 26\n07/13/2022 15:56:01 - INFO - __main__ - n_pred = 20\n07/13/2022 15:56:01 - INFO - __main__ - precision = 0.85\n07/13/2022 15:56:01 - INFO - __main__ - recall = 0.6538461538461539\n07/13/2022 15:56:01 - INFO - __main__ - task_f1 = 0.7391304347826088\n07/13/2022 15:56:01 - INFO - __main__ - task_ngold = 26\n07/13/2022 15:56:01 - INFO - __main__ - task_recall = 0.6538461538461539\n07/13/2022 15:56:58 - INFO - __main__ - Epoch: 2, Step: 40 / 101, used_time = 1500.64s, loss = 0.454155\n07/13/2022 15:57:02 - INFO - __main__ - ***** Eval results *****\n07/13/2022 15:57:02 - INFO - __main__ - accuracy = 0.8452380952380952\n07/13/2022 15:57:02 - INFO - __main__ - eval_loss = 0.37432655692100525\n07/13/2022 15:57:02 - INFO - __main__ - f1 = 0.7111111111111111\n07/13/2022 15:57:02 - INFO - __main__ - n_correct = 16\n07/13/2022 15:57:02 - INFO - __main__ - n_gold = 26\n07/13/2022 15:57:02 - INFO - __main__ - n_pred = 19\n07/13/2022 15:57:02 - INFO - __main__ - precision = 0.8421052631578947\n07/13/2022 15:57:02 - INFO - __main__ - recall = 0.6153846153846154\n07/13/2022 15:57:02 - INFO - __main__ - task_f1 = 0.7111111111111111\n07/13/2022 15:57:02 - INFO - __main__ - task_ngold = 26\n07/13/2022 15:57:02 - INFO - __main__ - task_recall = 0.6153846153846154\n07/13/2022 15:57:58 - INFO - __main__ - Epoch: 2, Step: 50 / 101, used_time = 1560.72s, loss = 0.446306\n07/13/2022 15:58:02 - INFO - __main__ - ***** Eval results *****\n07/13/2022 15:58:02 - INFO - __main__ - accuracy = 0.8571428571428571\n07/13/2022 15:58:02 - INFO - __main__ - eval_loss = 0.3516247570514679\n07/13/2022 15:58:02 - INFO - __main__ - f1 = 0.7500000000000001\n07/13/2022 15:58:02 - INFO - __main__ - n_correct = 18\n07/13/2022 15:58:02 - INFO - __main__ - n_gold = 26\n07/13/2022 15:58:02 - INFO - __main__ - n_pred = 22\n07/13/2022 15:58:02 - INFO - __main__ - precision = 0.8181818181818182\n07/13/2022 15:58:02 - INFO - __main__ - recall = 0.6923076923076923\n07/13/2022 15:58:02 - INFO - __main__ - task_f1 = 0.7500000000000001\n07/13/2022 15:58:02 - INFO - __main__ - task_ngold = 26\n07/13/2022 15:58:02 - INFO - __main__ - task_recall = 0.6923076923076923\n07/13/2022 15:59:01 - INFO - __main__ - Epoch: 2, Step: 60 / 101, used_time = 1624.26s, loss = 0.438611\n07/13/2022 15:59:06 - INFO - __main__ - ***** Eval results *****\n07/13/2022 15:59:06 - INFO - __main__ - accuracy = 0.8928571428571429\n07/13/2022 15:59:06 - INFO - __main__ - eval_loss = 0.34480734666188556\n07/13/2022 15:59:06 - INFO - __main__ - f1 = 0.8235294117647058\n07/13/2022 15:59:06 - INFO - __main__ - n_correct = 21\n07/13/2022 15:59:06 - INFO - __main__ - n_gold = 26\n07/13/2022 15:59:06 - INFO - __main__ - n_pred = 25\n07/13/2022 15:59:06 - INFO - __main__ - precision = 0.84\n07/13/2022 15:59:06 - INFO - __main__ - recall = 0.8076923076923077\n07/13/2022 15:59:06 - INFO - __main__ - task_f1 = 0.8235294117647058\n07/13/2022 15:59:06 - INFO - __main__ - task_ngold = 26\n07/13/2022 15:59:06 - INFO - __main__ - task_recall = 0.8076923076923077\n07/13/2022 15:59:06 - INFO - __main__ - !!! Best dev f1 (lr=2e-05, epoch=2): 82.35\n07/13/2022 15:59:06 - INFO - __main__ - Saving model to PURE/output_rel_ent2\n07/13/2022 16:00:16 - INFO - __main__ - Epoch: 2, Step: 70 / 101, used_time = 1698.42s, loss = 0.432088\n07/13/2022 16:00:20 - INFO - __main__ - ***** Eval results *****\n07/13/2022 16:00:20 - INFO - __main__ - accuracy = 0.8928571428571429\n07/13/2022 16:00:20 - INFO - __main__ - eval_loss = 0.3474515577157338\n07/13/2022 16:00:20 - INFO - __main__ - f1 = 0.8163265306122449\n07/13/2022 16:00:20 - INFO - __main__ - n_correct = 20\n07/13/2022 16:00:20 - INFO - __main__ - n_gold = 26\n07/13/2022 16:00:20 - INFO - __main__ - n_pred = 23\n07/13/2022 16:00:20 - INFO - __main__ - precision = 0.8695652173913043\n07/13/2022 16:00:20 - INFO - __main__ - recall = 0.7692307692307693\n07/13/2022 16:00:20 - INFO - __main__ - task_f1 = 0.8163265306122449\n07/13/2022 16:00:20 - INFO - __main__ - task_ngold = 26\n07/13/2022 16:00:20 - INFO - __main__ - task_recall = 0.7692307692307693\n07/13/2022 16:01:19 - INFO - __main__ - Epoch: 2, Step: 80 / 101, used_time = 1761.49s, loss = 0.426444\n07/13/2022 16:01:23 - INFO - __main__ - ***** Eval results *****\n07/13/2022 16:01:23 - INFO - __main__ - accuracy = 0.8571428571428571\n07/13/2022 16:01:23 - INFO - __main__ - eval_loss = 0.35541315873463947\n07/13/2022 16:01:23 - INFO - __main__ - f1 = 0.7500000000000001\n07/13/2022 16:01:23 - INFO - __main__ - n_correct = 18\n07/13/2022 16:01:23 - INFO - __main__ - n_gold = 26\n07/13/2022 16:01:23 - INFO - __main__ - n_pred = 22\n07/13/2022 16:01:23 - INFO - __main__ - precision = 0.8181818181818182\n07/13/2022 16:01:23 - INFO - __main__ - recall = 0.6923076923076923\n07/13/2022 16:01:23 - INFO - __main__ - task_f1 = 0.7500000000000001\n07/13/2022 16:01:23 - INFO - __main__ - task_ngold = 26\n07/13/2022 16:01:23 - INFO - __main__ - task_recall = 0.6923076923076923\n07/13/2022 16:02:21 - INFO - __main__ - Epoch: 2, Step: 90 / 101, used_time = 1823.84s, loss = 0.420946\n07/13/2022 16:02:25 - INFO - __main__ - ***** Eval results *****\n07/13/2022 16:02:25 - INFO - __main__ - accuracy = 0.8452380952380952\n07/13/2022 16:02:25 - INFO - __main__ - eval_loss = 0.3554019828637441\n07/13/2022 16:02:25 - INFO - __main__ - f1 = 0.7346938775510203\n07/13/2022 16:02:25 - INFO - __main__ - n_correct = 18\n07/13/2022 16:02:25 - INFO - __main__ - n_gold = 26\n07/13/2022 16:02:25 - INFO - __main__ - n_pred = 23\n07/13/2022 16:02:25 - INFO - __main__ - precision = 0.782608695652174\n07/13/2022 16:02:25 - INFO - __main__ - recall = 0.6923076923076923\n07/13/2022 16:02:25 - INFO - __main__ - task_f1 = 0.7346938775510203\n07/13/2022 16:02:25 - INFO - __main__ - task_ngold = 26\n07/13/2022 16:02:25 - INFO - __main__ - task_recall = 0.6923076923076923\n07/13/2022 16:03:23 - INFO - __main__ - Epoch: 2, Step: 100 / 101, used_time = 1885.47s, loss = 0.415423\n07/13/2022 16:03:27 - INFO - __main__ - ***** Eval results *****\n07/13/2022 16:03:27 - INFO - __main__ - accuracy = 0.8571428571428571\n07/13/2022 16:03:27 - INFO - __main__ - eval_loss = 0.345476895570755\n07/13/2022 16:03:27 - INFO - __main__ - f1 = 0.76\n07/13/2022 16:03:27 - INFO - __main__ - n_correct = 19\n07/13/2022 16:03:27 - INFO - __main__ - n_gold = 26\n07/13/2022 16:03:27 - INFO - __main__ - n_pred = 24\n07/13/2022 16:03:27 - INFO - __main__ - precision = 0.7916666666666666\n07/13/2022 16:03:27 - INFO - __main__ - recall = 0.7307692307692307\n07/13/2022 16:03:27 - INFO - __main__ - task_f1 = 0.76\n07/13/2022 16:03:27 - INFO - __main__ - task_ngold = 26\n07/13/2022 16:03:27 - INFO - __main__ - task_recall = 0.7307692307692307\n07/13/2022 16:03:33 - INFO - __main__ - Start epoch #3 (lr = 2e-05)...\n07/13/2022 16:04:31 - INFO - __main__ - Epoch: 3, Step: 10 / 101, used_time = 1953.33s, loss = 0.408949\n07/13/2022 16:04:35 - INFO - __main__ - ***** Eval results *****\n07/13/2022 16:04:35 - INFO - __main__ - accuracy = 0.8452380952380952\n07/13/2022 16:04:35 - INFO - __main__ - eval_loss = 0.34736572702725727\n07/13/2022 16:04:35 - INFO - __main__ - f1 = 0.7111111111111111\n07/13/2022 16:04:35 - INFO - __main__ - n_correct = 16\n07/13/2022 16:04:35 - INFO - __main__ - n_gold = 26\n07/13/2022 16:04:35 - INFO - __main__ - n_pred = 19\n07/13/2022 16:04:35 - INFO - __main__ - precision = 0.8421052631578947\n07/13/2022 16:04:35 - INFO - __main__ - recall = 0.6153846153846154\n07/13/2022 16:04:35 - INFO - __main__ - task_f1 = 0.7111111111111111\n07/13/2022 16:04:35 - INFO - __main__ - task_ngold = 26\n07/13/2022 16:04:35 - INFO - __main__ - task_recall = 0.6153846153846154\n07/13/2022 16:05:33 - INFO - __main__ - Epoch: 3, Step: 20 / 101, used_time = 2015.76s, loss = 0.404520\n07/13/2022 16:05:37 - INFO - __main__ - ***** Eval results *****\n07/13/2022 16:05:37 - INFO - __main__ - accuracy = 0.8571428571428571\n07/13/2022 16:05:37 - INFO - __main__ - eval_loss = 0.3307813803354899\n07/13/2022 16:05:37 - INFO - __main__ - f1 = 0.7391304347826088\n07/13/2022 16:05:37 - INFO - __main__ - n_correct = 17\n07/13/2022 16:05:37 - INFO - __main__ - n_gold = 26\n07/13/2022 16:05:37 - INFO - __main__ - n_pred = 20\n07/13/2022 16:05:37 - INFO - __main__ - precision = 0.85\n07/13/2022 16:05:37 - INFO - __main__ - recall = 0.6538461538461539\n07/13/2022 16:05:37 - INFO - __main__ - task_f1 = 0.7391304347826088\n07/13/2022 16:05:37 - INFO - __main__ - task_ngold = 26\n07/13/2022 16:05:37 - INFO - __main__ - task_recall = 0.6538461538461539\n07/13/2022 16:06:34 - INFO - __main__ - Epoch: 3, Step: 30 / 101, used_time = 2077.29s, loss = 0.399070\n07/13/2022 16:06:39 - INFO - __main__ - ***** Eval results *****\n07/13/2022 16:06:39 - INFO - __main__ - accuracy = 0.8452380952380952\n07/13/2022 16:06:39 - INFO - __main__ - eval_loss = 0.33748696247736615\n07/13/2022 16:06:39 - INFO - __main__ - f1 = 0.7719298245614036\n07/13/2022 16:06:39 - INFO - __main__ - n_correct = 22\n07/13/2022 16:06:39 - INFO - __main__ - n_gold = 26\n07/13/2022 16:06:39 - INFO - __main__ - n_pred = 31\n07/13/2022 16:06:39 - INFO - __main__ - precision = 0.7096774193548387\n07/13/2022 16:06:39 - INFO - __main__ - recall = 0.8461538461538461\n07/13/2022 16:06:39 - INFO - __main__ - task_f1 = 0.7719298245614036\n07/13/2022 16:06:39 - INFO - __main__ - task_ngold = 26\n07/13/2022 16:06:39 - INFO - __main__ - task_recall = 0.8461538461538461\n07/13/2022 16:07:39 - INFO - __main__ - Epoch: 3, Step: 40 / 101, used_time = 2141.59s, loss = 0.393816\n07/13/2022 16:07:43 - INFO - __main__ - ***** Eval results *****\n07/13/2022 16:07:43 - INFO - __main__ - accuracy = 0.8452380952380952\n07/13/2022 16:07:43 - INFO - __main__ - eval_loss = 0.34273749589920044\n07/13/2022 16:07:43 - INFO - __main__ - f1 = 0.6976744186046512\n07/13/2022 16:07:43 - INFO - __main__ - n_correct = 15\n07/13/2022 16:07:43 - INFO - __main__ - n_gold = 26\n07/13/2022 16:07:43 - INFO - __main__ - n_pred = 17\n07/13/2022 16:07:43 - INFO - __main__ - precision = 0.8823529411764706\n07/13/2022 16:07:43 - INFO - __main__ - recall = 0.5769230769230769\n07/13/2022 16:07:43 - INFO - __main__ - task_f1 = 0.6976744186046512\n07/13/2022 16:07:43 - INFO - __main__ - task_ngold = 26\n07/13/2022 16:07:43 - INFO - __main__ - task_recall = 0.5769230769230769\n07/13/2022 16:08:42 - INFO - __main__ - Epoch: 3, Step: 50 / 101, used_time = 2204.74s, loss = 0.388671\n07/13/2022 16:08:46 - INFO - __main__ - ***** Eval results *****\n07/13/2022 16:08:46 - INFO - __main__ - accuracy = 0.8214285714285714\n07/13/2022 16:08:46 - INFO - __main__ - eval_loss = 0.3272111217180888\n07/13/2022 16:08:46 - INFO - __main__ - f1 = 0.6808510638297872\n07/13/2022 16:08:46 - INFO - __main__ - n_correct = 16\n07/13/2022 16:08:46 - INFO - __main__ - n_gold = 26\n07/13/2022 16:08:46 - INFO - __main__ - n_pred = 21\n07/13/2022 16:08:46 - INFO - __main__ - precision = 0.7619047619047619\n07/13/2022 16:08:46 - INFO - __main__ - recall = 0.6153846153846154\n07/13/2022 16:08:46 - INFO - __main__ - task_f1 = 0.6808510638297872\n07/13/2022 16:08:46 - INFO - __main__ - task_ngold = 26\n07/13/2022 16:08:46 - INFO - __main__ - task_recall = 0.6153846153846154\n07/13/2022 16:09:45 - INFO - __main__ - Epoch: 3, Step: 60 / 101, used_time = 2267.83s, loss = 0.384833\n07/13/2022 16:09:50 - INFO - __main__ - ***** Eval results *****\n07/13/2022 16:09:50 - INFO - __main__ - accuracy = 0.8571428571428571\n07/13/2022 16:09:50 - INFO - __main__ - eval_loss = 0.32626421252886456\n07/13/2022 16:09:50 - INFO - __main__ - f1 = 0.7857142857142856\n07/13/2022 16:09:50 - INFO - __main__ - n_correct = 22\n07/13/2022 16:09:50 - INFO - __main__ - n_gold = 26\n07/13/2022 16:09:50 - INFO - __main__ - n_pred = 30\n07/13/2022 16:09:50 - INFO - __main__ - precision = 0.7333333333333333\n07/13/2022 16:09:50 - INFO - __main__ - recall = 0.8461538461538461\n07/13/2022 16:09:50 - INFO - __main__ - task_f1 = 0.7857142857142856\n07/13/2022 16:09:50 - INFO - __main__ - task_ngold = 26\n07/13/2022 16:09:50 - INFO - __main__ - task_recall = 0.8461538461538461\n07/13/2022 16:10:51 - INFO - __main__ - Epoch: 3, Step: 70 / 101, used_time = 2333.31s, loss = 0.380094\n07/13/2022 16:10:55 - INFO - __main__ - ***** Eval results *****\n07/13/2022 16:10:55 - INFO - __main__ - accuracy = 0.8452380952380952\n07/13/2022 16:10:55 - INFO - __main__ - eval_loss = 0.3322831988334656\n07/13/2022 16:10:55 - INFO - __main__ - f1 = 0.7111111111111111\n07/13/2022 16:10:55 - INFO - __main__ - n_correct = 16\n07/13/2022 16:10:55 - INFO - __main__ - n_gold = 26\n07/13/2022 16:10:55 - INFO - __main__ - n_pred = 19\n07/13/2022 16:10:55 - INFO - __main__ - precision = 0.8421052631578947\n07/13/2022 16:10:55 - INFO - __main__ - recall = 0.6153846153846154\n07/13/2022 16:10:55 - INFO - __main__ - task_f1 = 0.7111111111111111\n07/13/2022 16:10:55 - INFO - __main__ - task_ngold = 26\n07/13/2022 16:10:55 - INFO - __main__ - task_recall = 0.6153846153846154\n07/13/2022 16:11:55 - INFO - __main__ - Epoch: 3, Step: 80 / 101, used_time = 2398.04s, loss = 0.375911\n07/13/2022 16:12:00 - INFO - __main__ - ***** Eval results *****\n07/13/2022 16:12:00 - INFO - __main__ - accuracy = 0.8452380952380952\n07/13/2022 16:12:00 - INFO - __main__ - eval_loss = 0.33634376525878906\n07/13/2022 16:12:00 - INFO - __main__ - f1 = 0.6829268292682926\n07/13/2022 16:12:00 - INFO - __main__ - n_correct = 14\n07/13/2022 16:12:00 - INFO - __main__ - n_gold = 26\n07/13/2022 16:12:00 - INFO - __main__ - n_pred = 15\n07/13/2022 16:12:00 - INFO - __main__ - precision = 0.9333333333333333\n07/13/2022 16:12:00 - INFO - __main__ - recall = 0.5384615384615384\n07/13/2022 16:12:00 - INFO - __main__ - task_f1 = 0.6829268292682926\n07/13/2022 16:12:00 - INFO - __main__ - task_ngold = 26\n07/13/2022 16:12:00 - INFO - __main__ - task_recall = 0.5384615384615384\n07/13/2022 16:12:55 - INFO - __main__ - Epoch: 3, Step: 90 / 101, used_time = 2457.64s, loss = 0.371801\n07/13/2022 16:12:59 - INFO - __main__ - ***** Eval results *****\n07/13/2022 16:12:59 - INFO - __main__ - accuracy = 0.8452380952380952\n07/13/2022 16:12:59 - INFO - __main__ - eval_loss = 0.344566951195399\n07/13/2022 16:12:59 - INFO - __main__ - f1 = 0.6829268292682926\n07/13/2022 16:12:59 - INFO - __main__ - n_correct = 14\n07/13/2022 16:12:59 - INFO - __main__ - n_gold = 26\n07/13/2022 16:12:59 - INFO - __main__ - n_pred = 15\n07/13/2022 16:12:59 - INFO - __main__ - precision = 0.9333333333333333\n07/13/2022 16:12:59 - INFO - __main__ - recall = 0.5384615384615384\n07/13/2022 16:12:59 - INFO - __main__ - task_f1 = 0.6829268292682926\n07/13/2022 16:12:59 - INFO - __main__ - task_ngold = 26\n07/13/2022 16:12:59 - INFO - __main__ - task_recall = 0.5384615384615384\n07/13/2022 16:13:54 - INFO - __main__ - Epoch: 3, Step: 100 / 101, used_time = 2516.35s, loss = 0.367388\n07/13/2022 16:13:58 - INFO - __main__ - ***** Eval results *****\n07/13/2022 16:13:58 - INFO - __main__ - accuracy = 0.8452380952380952\n07/13/2022 16:13:58 - INFO - __main__ - eval_loss = 0.3378210961818695\n07/13/2022 16:13:58 - INFO - __main__ - f1 = 0.7450980392156863\n07/13/2022 16:13:58 - INFO - __main__ - n_correct = 19\n07/13/2022 16:13:58 - INFO - __main__ - n_gold = 26\n07/13/2022 16:13:58 - INFO - __main__ - n_pred = 25\n07/13/2022 16:13:58 - INFO - __main__ - precision = 0.76\n07/13/2022 16:13:58 - INFO - __main__ - recall = 0.7307692307692307\n07/13/2022 16:13:58 - INFO - __main__ - task_f1 = 0.7450980392156863\n07/13/2022 16:13:58 - INFO - __main__ - task_ngold = 26\n07/13/2022 16:13:58 - INFO - __main__ - task_recall = 0.7307692307692307\n07/13/2022 16:14:03 - INFO - __main__ - Start epoch #4 (lr = 2e-05)...\n07/13/2022 16:14:58 - INFO - __main__ - Epoch: 4, Step: 10 / 101, used_time = 2580.32s, loss = 0.362274\n07/13/2022 16:15:02 - INFO - __main__ - ***** Eval results *****\n07/13/2022 16:15:02 - INFO - __main__ - accuracy = 0.8571428571428571\n07/13/2022 16:15:02 - INFO - __main__ - eval_loss = 0.34812777241071063\n07/13/2022 16:15:02 - INFO - __main__ - f1 = 0.76\n07/13/2022 16:15:02 - INFO - __main__ - n_correct = 19\n07/13/2022 16:15:02 - INFO - __main__ - n_gold = 26\n07/13/2022 16:15:02 - INFO - __main__ - n_pred = 24\n07/13/2022 16:15:02 - INFO - __main__ - precision = 0.7916666666666666\n07/13/2022 16:15:02 - INFO - __main__ - recall = 0.7307692307692307\n07/13/2022 16:15:02 - INFO - __main__ - task_f1 = 0.76\n07/13/2022 16:15:02 - INFO - __main__ - task_ngold = 26\n07/13/2022 16:15:02 - INFO - __main__ - task_recall = 0.7307692307692307\n07/13/2022 16:15:56 - INFO - __main__ - Epoch: 4, Step: 20 / 101, used_time = 2638.97s, loss = 0.359449\n07/13/2022 16:16:00 - INFO - __main__ - ***** Eval results *****\n07/13/2022 16:16:00 - INFO - __main__ - accuracy = 0.8214285714285714\n07/13/2022 16:16:00 - INFO - __main__ - eval_loss = 0.3487345774968465\n07/13/2022 16:16:00 - INFO - __main__ - f1 = 0.6808510638297872\n07/13/2022 16:16:00 - INFO - __main__ - n_correct = 16\n07/13/2022 16:16:00 - INFO - __main__ - n_gold = 26\n07/13/2022 16:16:00 - INFO - __main__ - n_pred = 21\n07/13/2022 16:16:00 - INFO - __main__ - precision = 0.7619047619047619\n07/13/2022 16:16:00 - INFO - __main__ - recall = 0.6153846153846154\n07/13/2022 16:16:00 - INFO - __main__ - task_f1 = 0.6808510638297872\n07/13/2022 16:16:00 - INFO - __main__ - task_ngold = 26\n07/13/2022 16:16:00 - INFO - __main__ - task_recall = 0.6153846153846154\n07/13/2022 16:16:55 - INFO - __main__ - Epoch: 4, Step: 30 / 101, used_time = 2697.33s, loss = 0.354335\n07/13/2022 16:16:59 - INFO - __main__ - ***** Eval results *****\n07/13/2022 16:16:59 - INFO - __main__ - accuracy = 0.8214285714285714\n07/13/2022 16:16:59 - INFO - __main__ - eval_loss = 0.3541119396686554\n07/13/2022 16:16:59 - INFO - __main__ - f1 = 0.6666666666666666\n07/13/2022 16:16:59 - INFO - __main__ - n_correct = 15\n07/13/2022 16:16:59 - INFO - __main__ - n_gold = 26\n07/13/2022 16:16:59 - INFO - __main__ - n_pred = 19\n07/13/2022 16:16:59 - INFO - __main__ - precision = 0.7894736842105263\n07/13/2022 16:16:59 - INFO - __main__ - recall = 0.5769230769230769\n07/13/2022 16:16:59 - INFO - __main__ - task_f1 = 0.6666666666666666\n07/13/2022 16:16:59 - INFO - __main__ - task_ngold = 26\n07/13/2022 16:16:59 - INFO - __main__ - task_recall = 0.5769230769230769\n07/13/2022 16:17:54 - INFO - __main__ - Epoch: 4, Step: 40 / 101, used_time = 2756.71s, loss = 0.350524\n07/13/2022 16:17:58 - INFO - __main__ - ***** Eval results *****\n07/13/2022 16:17:58 - INFO - __main__ - accuracy = 0.8452380952380952\n07/13/2022 16:17:58 - INFO - __main__ - eval_loss = 0.33518826961517334\n07/13/2022 16:17:58 - INFO - __main__ - f1 = 0.7346938775510203\n07/13/2022 16:17:58 - INFO - __main__ - n_correct = 18\n07/13/2022 16:17:58 - INFO - __main__ - n_gold = 26\n07/13/2022 16:17:58 - INFO - __main__ - n_pred = 23\n07/13/2022 16:17:58 - INFO - __main__ - precision = 0.782608695652174\n07/13/2022 16:17:58 - INFO - __main__ - recall = 0.6923076923076923\n07/13/2022 16:17:58 - INFO - __main__ - task_f1 = 0.7346938775510203\n07/13/2022 16:17:58 - INFO - __main__ - task_ngold = 26\n07/13/2022 16:17:58 - INFO - __main__ - task_recall = 0.6923076923076923\n07/13/2022 16:18:54 - INFO - __main__ - Epoch: 4, Step: 50 / 101, used_time = 2816.61s, loss = 0.346765\n07/13/2022 16:18:58 - INFO - __main__ - ***** Eval results *****\n07/13/2022 16:18:58 - INFO - __main__ - accuracy = 0.8452380952380952\n07/13/2022 16:18:58 - INFO - __main__ - eval_loss = 0.3337098757425944\n07/13/2022 16:18:58 - INFO - __main__ - f1 = 0.7450980392156863\n07/13/2022 16:18:58 - INFO - __main__ - n_correct = 19\n07/13/2022 16:18:58 - INFO - __main__ - n_gold = 26\n07/13/2022 16:18:58 - INFO - __main__ - n_pred = 25\n07/13/2022 16:18:58 - INFO - __main__ - precision = 0.76\n07/13/2022 16:18:58 - INFO - __main__ - recall = 0.7307692307692307\n07/13/2022 16:18:58 - INFO - __main__ - task_f1 = 0.7450980392156863\n07/13/2022 16:18:58 - INFO - __main__ - task_ngold = 26\n07/13/2022 16:18:58 - INFO - __main__ - task_recall = 0.7307692307692307\n07/13/2022 16:19:54 - INFO - __main__ - Epoch: 4, Step: 60 / 101, used_time = 2876.33s, loss = 0.344071\n07/13/2022 16:19:58 - INFO - __main__ - ***** Eval results *****\n07/13/2022 16:19:58 - INFO - __main__ - accuracy = 0.8452380952380952\n07/13/2022 16:19:58 - INFO - __main__ - eval_loss = 0.3412306209405263\n07/13/2022 16:19:58 - INFO - __main__ - f1 = 0.7234042553191489\n07/13/2022 16:19:58 - INFO - __main__ - n_correct = 17\n07/13/2022 16:19:58 - INFO - __main__ - n_gold = 26\n07/13/2022 16:19:58 - INFO - __main__ - n_pred = 21\n07/13/2022 16:19:58 - INFO - __main__ - precision = 0.8095238095238095\n07/13/2022 16:19:58 - INFO - __main__ - recall = 0.6538461538461539\n07/13/2022 16:19:58 - INFO - __main__ - task_f1 = 0.7234042553191489\n07/13/2022 16:19:58 - INFO - __main__ - task_ngold = 26\n07/13/2022 16:19:58 - INFO - __main__ - task_recall = 0.6538461538461539\n07/13/2022 16:20:54 - INFO - __main__ - Epoch: 4, Step: 70 / 101, used_time = 2936.59s, loss = 0.339704\n07/13/2022 16:20:58 - INFO - __main__ - ***** Eval results *****\n07/13/2022 16:20:58 - INFO - __main__ - accuracy = 0.8452380952380952\n07/13/2022 16:20:58 - INFO - __main__ - eval_loss = 0.35316518942515057\n07/13/2022 16:20:58 - INFO - __main__ - f1 = 0.7111111111111111\n07/13/2022 16:20:58 - INFO - __main__ - n_correct = 16\n07/13/2022 16:20:58 - INFO - __main__ - n_gold = 26\n07/13/2022 16:20:58 - INFO - __main__ - n_pred = 19\n07/13/2022 16:20:58 - INFO - __main__ - precision = 0.8421052631578947\n07/13/2022 16:20:58 - INFO - __main__ - recall = 0.6153846153846154\n07/13/2022 16:20:58 - INFO - __main__ - task_f1 = 0.7111111111111111\n07/13/2022 16:20:58 - INFO - __main__ - task_ngold = 26\n07/13/2022 16:20:58 - INFO - __main__ - task_recall = 0.6153846153846154\n07/13/2022 16:21:55 - INFO - __main__ - Epoch: 4, Step: 80 / 101, used_time = 2997.79s, loss = 0.336599\n07/13/2022 16:21:59 - INFO - __main__ - ***** Eval results *****\n07/13/2022 16:21:59 - INFO - __main__ - accuracy = 0.8571428571428571\n07/13/2022 16:21:59 - INFO - __main__ - eval_loss = 0.3377317388852437\n07/13/2022 16:21:59 - INFO - __main__ - f1 = 0.7391304347826088\n07/13/2022 16:21:59 - INFO - __main__ - n_correct = 17\n07/13/2022 16:21:59 - INFO - __main__ - n_gold = 26\n07/13/2022 16:21:59 - INFO - __main__ - n_pred = 20\n07/13/2022 16:21:59 - INFO - __main__ - precision = 0.85\n07/13/2022 16:21:59 - INFO - __main__ - recall = 0.6538461538461539\n07/13/2022 16:21:59 - INFO - __main__ - task_f1 = 0.7391304347826088\n07/13/2022 16:21:59 - INFO - __main__ - task_ngold = 26\n07/13/2022 16:21:59 - INFO - __main__ - task_recall = 0.6538461538461539\n07/13/2022 16:22:56 - INFO - __main__ - Epoch: 4, Step: 90 / 101, used_time = 3058.85s, loss = 0.333435\n07/13/2022 16:23:00 - INFO - __main__ - ***** Eval results *****\n07/13/2022 16:23:00 - INFO - __main__ - accuracy = 0.8571428571428571\n07/13/2022 16:23:00 - INFO - __main__ - eval_loss = 0.32262523969014484\n07/13/2022 16:23:00 - INFO - __main__ - f1 = 0.7777777777777779\n07/13/2022 16:23:00 - INFO - __main__ - n_correct = 21\n07/13/2022 16:23:00 - INFO - __main__ - n_gold = 26\n07/13/2022 16:23:00 - INFO - __main__ - n_pred = 28\n07/13/2022 16:23:00 - INFO - __main__ - precision = 0.75\n07/13/2022 16:23:00 - INFO - __main__ - recall = 0.8076923076923077\n07/13/2022 16:23:00 - INFO - __main__ - task_f1 = 0.7777777777777779\n07/13/2022 16:23:00 - INFO - __main__ - task_ngold = 26\n07/13/2022 16:23:00 - INFO - __main__ - task_recall = 0.8076923076923077\n07/13/2022 16:23:59 - INFO - __main__ - Epoch: 4, Step: 100 / 101, used_time = 3122.23s, loss = 0.331395\n07/13/2022 16:24:04 - INFO - __main__ - ***** Eval results *****\n07/13/2022 16:24:04 - INFO - __main__ - accuracy = 0.8452380952380952\n07/13/2022 16:24:04 - INFO - __main__ - eval_loss = 0.3234907388687134\n07/13/2022 16:24:04 - INFO - __main__ - f1 = 0.7636363636363636\n07/13/2022 16:24:04 - INFO - __main__ - n_correct = 21\n07/13/2022 16:24:04 - INFO - __main__ - n_gold = 26\n07/13/2022 16:24:04 - INFO - __main__ - n_pred = 29\n07/13/2022 16:24:04 - INFO - __main__ - precision = 0.7241379310344828\n07/13/2022 16:24:04 - INFO - __main__ - recall = 0.8076923076923077\n07/13/2022 16:24:04 - INFO - __main__ - task_f1 = 0.7636363636363636\n07/13/2022 16:24:04 - INFO - __main__ - task_ngold = 26\n07/13/2022 16:24:04 - INFO - __main__ - task_recall = 0.8076923076923077\n07/13/2022 16:24:10 - INFO - __main__ - Start epoch #5 (lr = 2e-05)...\n07/13/2022 16:25:08 - INFO - __main__ - Epoch: 5, Step: 10 / 101, used_time = 3191.17s, loss = 0.327904\n07/13/2022 16:25:13 - INFO - __main__ - ***** Eval results *****\n07/13/2022 16:25:13 - INFO - __main__ - accuracy = 0.8690476190476191\n07/13/2022 16:25:13 - INFO - __main__ - eval_loss = 0.3296708067258199\n07/13/2022 16:25:13 - INFO - __main__ - f1 = 0.7755102040816326\n07/13/2022 16:25:13 - INFO - __main__ - n_correct = 19\n07/13/2022 16:25:13 - INFO - __main__ - n_gold = 26\n07/13/2022 16:25:13 - INFO - __main__ - n_pred = 23\n07/13/2022 16:25:13 - INFO - __main__ - precision = 0.8260869565217391\n07/13/2022 16:25:13 - INFO - __main__ - recall = 0.7307692307692307\n07/13/2022 16:25:13 - INFO - __main__ - task_f1 = 0.7755102040816326\n07/13/2022 16:25:13 - INFO - __main__ - task_ngold = 26\n07/13/2022 16:25:13 - INFO - __main__ - task_recall = 0.7307692307692307\n07/13/2022 16:26:11 - INFO - __main__ - Epoch: 5, Step: 20 / 101, used_time = 3254.26s, loss = 0.325002\n07/13/2022 16:26:16 - INFO - __main__ - ***** Eval results *****\n07/13/2022 16:26:16 - INFO - __main__ - accuracy = 0.8571428571428571\n07/13/2022 16:26:16 - INFO - __main__ - eval_loss = 0.3357766767342885\n07/13/2022 16:26:16 - INFO - __main__ - f1 = 0.7391304347826088\n07/13/2022 16:26:16 - INFO - __main__ - n_correct = 17\n07/13/2022 16:26:16 - INFO - __main__ - n_gold = 26\n07/13/2022 16:26:16 - INFO - __main__ - n_pred = 20\n07/13/2022 16:26:16 - INFO - __main__ - precision = 0.85\n07/13/2022 16:26:16 - INFO - __main__ - recall = 0.6538461538461539\n07/13/2022 16:26:16 - INFO - __main__ - task_f1 = 0.7391304347826088\n07/13/2022 16:26:16 - INFO - __main__ - task_ngold = 26\n07/13/2022 16:26:16 - INFO - __main__ - task_recall = 0.6538461538461539\n07/13/2022 16:27:12 - INFO - __main__ - Epoch: 5, Step: 30 / 101, used_time = 3315.02s, loss = 0.322472\n07/13/2022 16:27:16 - INFO - __main__ - ***** Eval results *****\n07/13/2022 16:27:16 - INFO - __main__ - accuracy = 0.8214285714285714\n07/13/2022 16:27:16 - INFO - __main__ - eval_loss = 0.3332035740216573\n07/13/2022 16:27:16 - INFO - __main__ - f1 = 0.7169811320754716\n07/13/2022 16:27:16 - INFO - __main__ - n_correct = 19\n07/13/2022 16:27:16 - INFO - __main__ - n_gold = 26\n07/13/2022 16:27:16 - INFO - __main__ - n_pred = 27\n07/13/2022 16:27:16 - INFO - __main__ - precision = 0.7037037037037037\n07/13/2022 16:27:16 - INFO - __main__ - recall = 0.7307692307692307\n07/13/2022 16:27:16 - INFO - __main__ - task_f1 = 0.7169811320754716\n07/13/2022 16:27:16 - INFO - __main__ - task_ngold = 26\n07/13/2022 16:27:16 - INFO - __main__ - task_recall = 0.7307692307692307\n07/13/2022 16:28:11 - INFO - __main__ - Epoch: 5, Step: 40 / 101, used_time = 3373.92s, loss = 0.320085\n07/13/2022 16:28:15 - INFO - __main__ - ***** Eval results *****\n07/13/2022 16:28:15 - INFO - __main__ - accuracy = 0.8452380952380952\n07/13/2022 16:28:15 - INFO - __main__ - eval_loss = 0.33793266614278156\n07/13/2022 16:28:15 - INFO - __main__ - f1 = 0.7346938775510203\n07/13/2022 16:28:15 - INFO - __main__ - n_correct = 18\n07/13/2022 16:28:15 - INFO - __main__ - n_gold = 26\n07/13/2022 16:28:15 - INFO - __main__ - n_pred = 23\n07/13/2022 16:28:15 - INFO - __main__ - precision = 0.782608695652174\n07/13/2022 16:28:15 - INFO - __main__ - recall = 0.6923076923076923\n07/13/2022 16:28:15 - INFO - __main__ - task_f1 = 0.7346938775510203\n07/13/2022 16:28:15 - INFO - __main__ - task_ngold = 26\n07/13/2022 16:28:15 - INFO - __main__ - task_recall = 0.6923076923076923\n07/13/2022 16:29:10 - INFO - __main__ - Epoch: 5, Step: 50 / 101, used_time = 3432.35s, loss = 0.318127\n07/13/2022 16:29:14 - INFO - __main__ - ***** Eval results *****\n07/13/2022 16:29:14 - INFO - __main__ - accuracy = 0.8333333333333334\n07/13/2022 16:29:14 - INFO - __main__ - eval_loss = 0.368054856856664\n07/13/2022 16:29:14 - INFO - __main__ - f1 = 0.6818181818181818\n07/13/2022 16:29:14 - INFO - __main__ - n_correct = 15\n07/13/2022 16:29:14 - INFO - __main__ - n_gold = 26\n07/13/2022 16:29:14 - INFO - __main__ - n_pred = 18\n07/13/2022 16:29:14 - INFO - __main__ - precision = 0.8333333333333334\n07/13/2022 16:29:14 - INFO - __main__ - recall = 0.5769230769230769\n07/13/2022 16:29:14 - INFO - __main__ - task_f1 = 0.6818181818181818\n07/13/2022 16:29:14 - INFO - __main__ - task_ngold = 26\n07/13/2022 16:29:14 - INFO - __main__ - task_recall = 0.5769230769230769\n07/13/2022 16:30:16 - INFO - __main__ - Epoch: 5, Step: 60 / 101, used_time = 3498.51s, loss = 0.315078\n07/13/2022 16:30:20 - INFO - __main__ - ***** Eval results *****\n07/13/2022 16:30:20 - INFO - __main__ - accuracy = 0.8452380952380952\n07/13/2022 16:30:20 - INFO - __main__ - eval_loss = 0.35303998986879986\n07/13/2022 16:30:20 - INFO - __main__ - f1 = 0.7111111111111111\n07/13/2022 16:30:20 - INFO - __main__ - n_correct = 16\n07/13/2022 16:30:20 - INFO - __main__ - n_gold = 26\n07/13/2022 16:30:20 - INFO - __main__ - n_pred = 19\n07/13/2022 16:30:20 - INFO - __main__ - precision = 0.8421052631578947\n07/13/2022 16:30:20 - INFO - __main__ - recall = 0.6153846153846154\n07/13/2022 16:30:20 - INFO - __main__ - task_f1 = 0.7111111111111111\n07/13/2022 16:30:20 - INFO - __main__ - task_ngold = 26\n07/13/2022 16:30:20 - INFO - __main__ - task_recall = 0.6153846153846154\n07/13/2022 16:31:16 - INFO - __main__ - Epoch: 5, Step: 70 / 101, used_time = 3558.67s, loss = 0.313621\n07/13/2022 16:31:20 - INFO - __main__ - ***** Eval results *****\n07/13/2022 16:31:20 - INFO - __main__ - accuracy = 0.8333333333333334\n07/13/2022 16:31:20 - INFO - __main__ - eval_loss = 0.3386992613474528\n07/13/2022 16:31:20 - INFO - __main__ - f1 = 0.7083333333333333\n07/13/2022 16:31:20 - INFO - __main__ - n_correct = 17\n07/13/2022 16:31:20 - INFO - __main__ - n_gold = 26\n07/13/2022 16:31:20 - INFO - __main__ - n_pred = 22\n07/13/2022 16:31:20 - INFO - __main__ - precision = 0.7727272727272727\n07/13/2022 16:31:20 - INFO - __main__ - recall = 0.6538461538461539\n07/13/2022 16:31:20 - INFO - __main__ - task_f1 = 0.7083333333333333\n07/13/2022 16:31:20 - INFO - __main__ - task_ngold = 26\n07/13/2022 16:31:20 - INFO - __main__ - task_recall = 0.6538461538461539\n07/13/2022 16:32:18 - INFO - __main__ - Epoch: 5, Step: 80 / 101, used_time = 3621.16s, loss = 0.310459\n07/13/2022 16:32:22 - INFO - __main__ - ***** Eval results *****\n07/13/2022 16:32:22 - INFO - __main__ - accuracy = 0.8452380952380952\n07/13/2022 16:32:22 - INFO - __main__ - eval_loss = 0.33789045612017315\n07/13/2022 16:32:22 - INFO - __main__ - f1 = 0.7346938775510203\n07/13/2022 16:32:22 - INFO - __main__ - n_correct = 18\n07/13/2022 16:32:22 - INFO - __main__ - n_gold = 26\n07/13/2022 16:32:22 - INFO - __main__ - n_pred = 23\n07/13/2022 16:32:22 - INFO - __main__ - precision = 0.782608695652174\n07/13/2022 16:32:22 - INFO - __main__ - recall = 0.6923076923076923\n07/13/2022 16:32:22 - INFO - __main__ - task_f1 = 0.7346938775510203\n07/13/2022 16:32:22 - INFO - __main__ - task_ngold = 26\n07/13/2022 16:32:22 - INFO - __main__ - task_recall = 0.6923076923076923\n07/13/2022 16:33:15 - INFO - __main__ - Epoch: 5, Step: 90 / 101, used_time = 3677.76s, loss = 0.307045\n07/13/2022 16:33:19 - INFO - __main__ - ***** Eval results *****\n07/13/2022 16:33:19 - INFO - __main__ - accuracy = 0.8333333333333334\n07/13/2022 16:33:19 - INFO - __main__ - eval_loss = 0.3380821943283081\n07/13/2022 16:33:19 - INFO - __main__ - f1 = 0.7083333333333333\n07/13/2022 16:33:19 - INFO - __main__ - n_correct = 17\n07/13/2022 16:33:19 - INFO - __main__ - n_gold = 26\n07/13/2022 16:33:19 - INFO - __main__ - n_pred = 22\n07/13/2022 16:33:19 - INFO - __main__ - precision = 0.7727272727272727\n07/13/2022 16:33:19 - INFO - __main__ - recall = 0.6538461538461539\n07/13/2022 16:33:19 - INFO - __main__ - task_f1 = 0.7083333333333333\n07/13/2022 16:33:19 - INFO - __main__ - task_ngold = 26\n07/13/2022 16:33:19 - INFO - __main__ - task_recall = 0.6538461538461539\n07/13/2022 16:34:18 - INFO - __main__ - Epoch: 5, Step: 100 / 101, used_time = 3740.87s, loss = 0.304456\n07/13/2022 16:34:23 - INFO - __main__ - ***** Eval results *****\n07/13/2022 16:34:23 - INFO - __main__ - accuracy = 0.8452380952380952\n07/13/2022 16:34:23 - INFO - __main__ - eval_loss = 0.34017383058865863\n07/13/2022 16:34:23 - INFO - __main__ - f1 = 0.7234042553191489\n07/13/2022 16:34:23 - INFO - __main__ - n_correct = 17\n07/13/2022 16:34:23 - INFO - __main__ - n_gold = 26\n07/13/2022 16:34:23 - INFO - __main__ - n_pred = 21\n07/13/2022 16:34:23 - INFO - __main__ - precision = 0.8095238095238095\n07/13/2022 16:34:23 - INFO - __main__ - recall = 0.6538461538461539\n07/13/2022 16:34:23 - INFO - __main__ - task_f1 = 0.7234042553191489\n07/13/2022 16:34:23 - INFO - __main__ - task_ngold = 26\n07/13/2022 16:34:23 - INFO - __main__ - task_recall = 0.6538461538461539\n07/13/2022 16:34:29 - INFO - __main__ - Start epoch #6 (lr = 2e-05)...\n07/13/2022 16:35:26 - INFO - __main__ - Epoch: 6, Step: 10 / 101, used_time = 3809.25s, loss = 0.302217\n07/13/2022 16:35:31 - INFO - __main__ - ***** Eval results *****\n07/13/2022 16:35:31 - INFO - __main__ - accuracy = 0.8333333333333334\n07/13/2022 16:35:31 - INFO - __main__ - eval_loss = 0.3337924877802531\n07/13/2022 16:35:31 - INFO - __main__ - f1 = 0.7307692307692306\n07/13/2022 16:35:31 - INFO - __main__ - n_correct = 19\n07/13/2022 16:35:31 - INFO - __main__ - n_gold = 26\n07/13/2022 16:35:31 - INFO - __main__ - n_pred = 26\n07/13/2022 16:35:31 - INFO - __main__ - precision = 0.7307692307692307\n07/13/2022 16:35:31 - INFO - __main__ - recall = 0.7307692307692307\n07/13/2022 16:35:31 - INFO - __main__ - task_f1 = 0.7307692307692306\n07/13/2022 16:35:31 - INFO - __main__ - task_ngold = 26\n07/13/2022 16:35:31 - INFO - __main__ - task_recall = 0.7307692307692307\n07/13/2022 16:36:29 - INFO - __main__ - Epoch: 6, Step: 20 / 101, used_time = 3871.62s, loss = 0.299706\n07/13/2022 16:36:33 - INFO - __main__ - ***** Eval results *****\n07/13/2022 16:36:33 - INFO - __main__ - accuracy = 0.8333333333333334\n07/13/2022 16:36:33 - INFO - __main__ - eval_loss = 0.33674557010332745\n07/13/2022 16:36:33 - INFO - __main__ - f1 = 0.7083333333333333\n07/13/2022 16:36:33 - INFO - __main__ - n_correct = 17\n07/13/2022 16:36:33 - INFO - __main__ - n_gold = 26\n07/13/2022 16:36:33 - INFO - __main__ - n_pred = 22\n07/13/2022 16:36:33 - INFO - __main__ - precision = 0.7727272727272727\n07/13/2022 16:36:33 - INFO - __main__ - recall = 0.6538461538461539\n07/13/2022 16:36:33 - INFO - __main__ - task_f1 = 0.7083333333333333\n07/13/2022 16:36:33 - INFO - __main__ - task_ngold = 26\n07/13/2022 16:36:33 - INFO - __main__ - task_recall = 0.6538461538461539\n07/13/2022 16:37:32 - INFO - __main__ - Epoch: 6, Step: 30 / 101, used_time = 3934.61s, loss = 0.297327\n07/13/2022 16:37:36 - INFO - __main__ - ***** Eval results *****\n07/13/2022 16:37:36 - INFO - __main__ - accuracy = 0.8333333333333334\n07/13/2022 16:37:36 - INFO - __main__ - eval_loss = 0.3350888291994731\n07/13/2022 16:37:36 - INFO - __main__ - f1 = 0.7083333333333333\n07/13/2022 16:37:36 - INFO - __main__ - n_correct = 17\n07/13/2022 16:37:36 - INFO - __main__ - n_gold = 26\n07/13/2022 16:37:36 - INFO - __main__ - n_pred = 22\n07/13/2022 16:37:36 - INFO - __main__ - precision = 0.7727272727272727\n07/13/2022 16:37:36 - INFO - __main__ - recall = 0.6538461538461539\n07/13/2022 16:37:36 - INFO - __main__ - task_f1 = 0.7083333333333333\n07/13/2022 16:37:36 - INFO - __main__ - task_ngold = 26\n07/13/2022 16:37:36 - INFO - __main__ - task_recall = 0.6538461538461539\n07/13/2022 16:38:35 - INFO - __main__ - Epoch: 6, Step: 40 / 101, used_time = 3997.51s, loss = 0.295234\n07/13/2022 16:38:39 - INFO - __main__ - ***** Eval results *****\n07/13/2022 16:38:39 - INFO - __main__ - accuracy = 0.8452380952380952\n07/13/2022 16:38:39 - INFO - __main__ - eval_loss = 0.33707395195961\n07/13/2022 16:38:39 - INFO - __main__ - f1 = 0.7450980392156863\n07/13/2022 16:38:39 - INFO - __main__ - n_correct = 19\n07/13/2022 16:38:39 - INFO - __main__ - n_gold = 26\n07/13/2022 16:38:39 - INFO - __main__ - n_pred = 25\n07/13/2022 16:38:39 - INFO - __main__ - precision = 0.76\n07/13/2022 16:38:39 - INFO - __main__ - recall = 0.7307692307692307\n07/13/2022 16:38:39 - INFO - __main__ - task_f1 = 0.7450980392156863\n07/13/2022 16:38:39 - INFO - __main__ - task_ngold = 26\n07/13/2022 16:38:39 - INFO - __main__ - task_recall = 0.7307692307692307\n07/13/2022 16:39:40 - INFO - __main__ - Epoch: 6, Step: 50 / 101, used_time = 4062.48s, loss = 0.293189\n07/13/2022 16:39:45 - INFO - __main__ - ***** Eval results *****\n07/13/2022 16:39:45 - INFO - __main__ - accuracy = 0.8333333333333334\n07/13/2022 16:39:45 - INFO - __main__ - eval_loss = 0.33970723549524945\n07/13/2022 16:39:45 - INFO - __main__ - f1 = 0.7083333333333333\n07/13/2022 16:39:45 - INFO - __main__ - n_correct = 17\n07/13/2022 16:39:45 - INFO - __main__ - n_gold = 26\n07/13/2022 16:39:45 - INFO - __main__ - n_pred = 22\n07/13/2022 16:39:45 - INFO - __main__ - precision = 0.7727272727272727\n07/13/2022 16:39:45 - INFO - __main__ - recall = 0.6538461538461539\n07/13/2022 16:39:45 - INFO - __main__ - task_f1 = 0.7083333333333333\n07/13/2022 16:39:45 - INFO - __main__ - task_ngold = 26\n07/13/2022 16:39:45 - INFO - __main__ - task_recall = 0.6538461538461539\n07/13/2022 16:40:48 - INFO - __main__ - Epoch: 6, Step: 60 / 101, used_time = 4130.56s, loss = 0.290946\n07/13/2022 16:40:52 - INFO - __main__ - ***** Eval results *****\n07/13/2022 16:40:52 - INFO - __main__ - accuracy = 0.8214285714285714\n07/13/2022 16:40:52 - INFO - __main__ - eval_loss = 0.3385854959487915\n07/13/2022 16:40:52 - INFO - __main__ - f1 = 0.693877551020408\n07/13/2022 16:40:52 - INFO - __main__ - n_correct = 17\n07/13/2022 16:40:52 - INFO - __main__ - n_gold = 26\n07/13/2022 16:40:52 - INFO - __main__ - n_pred = 23\n07/13/2022 16:40:52 - INFO - __main__ - precision = 0.7391304347826086\n07/13/2022 16:40:52 - INFO - __main__ - recall = 0.6538461538461539\n07/13/2022 16:40:52 - INFO - __main__ - task_f1 = 0.693877551020408\n07/13/2022 16:40:52 - INFO - __main__ - task_ngold = 26\n07/13/2022 16:40:52 - INFO - __main__ - task_recall = 0.6538461538461539\n07/13/2022 16:41:56 - INFO - __main__ - Epoch: 6, Step: 70 / 101, used_time = 4199.00s, loss = 0.288167\n07/13/2022 16:42:01 - INFO - __main__ - ***** Eval results *****\n07/13/2022 16:42:01 - INFO - __main__ - accuracy = 0.8452380952380952\n07/13/2022 16:42:01 - INFO - __main__ - eval_loss = 0.34024981657663983\n07/13/2022 16:42:01 - INFO - __main__ - f1 = 0.7234042553191489\n07/13/2022 16:42:01 - INFO - __main__ - n_correct = 17\n07/13/2022 16:42:01 - INFO - __main__ - n_gold = 26\n07/13/2022 16:42:01 - INFO - __main__ - n_pred = 21\n07/13/2022 16:42:01 - INFO - __main__ - precision = 0.8095238095238095\n07/13/2022 16:42:01 - INFO - __main__ - recall = 0.6538461538461539\n07/13/2022 16:42:01 - INFO - __main__ - task_f1 = 0.7234042553191489\n07/13/2022 16:42:01 - INFO - __main__ - task_ngold = 26\n07/13/2022 16:42:01 - INFO - __main__ - task_recall = 0.6538461538461539\n07/13/2022 16:43:03 - INFO - __main__ - Epoch: 6, Step: 80 / 101, used_time = 4266.23s, loss = 0.285729\n07/13/2022 16:43:08 - INFO - __main__ - ***** Eval results *****\n07/13/2022 16:43:08 - INFO - __main__ - accuracy = 0.8452380952380952\n07/13/2022 16:43:08 - INFO - __main__ - eval_loss = 0.33826547861099243\n07/13/2022 16:43:08 - INFO - __main__ - f1 = 0.7234042553191489\n07/13/2022 16:43:08 - INFO - __main__ - n_correct = 17\n07/13/2022 16:43:08 - INFO - __main__ - n_gold = 26\n07/13/2022 16:43:08 - INFO - __main__ - n_pred = 21\n07/13/2022 16:43:08 - INFO - __main__ - precision = 0.8095238095238095\n07/13/2022 16:43:08 - INFO - __main__ - recall = 0.6538461538461539\n07/13/2022 16:43:08 - INFO - __main__ - task_f1 = 0.7234042553191489\n07/13/2022 16:43:08 - INFO - __main__ - task_ngold = 26\n07/13/2022 16:43:08 - INFO - __main__ - task_recall = 0.6538461538461539\n07/13/2022 16:44:10 - INFO - __main__ - Epoch: 6, Step: 90 / 101, used_time = 4333.08s, loss = 0.283444\n07/13/2022 16:44:15 - INFO - __main__ - ***** Eval results *****\n07/13/2022 16:44:15 - INFO - __main__ - accuracy = 0.8452380952380952\n07/13/2022 16:44:15 - INFO - __main__ - eval_loss = 0.33657071987787884\n07/13/2022 16:44:15 - INFO - __main__ - f1 = 0.7234042553191489\n07/13/2022 16:44:15 - INFO - __main__ - n_correct = 17\n07/13/2022 16:44:15 - INFO - __main__ - n_gold = 26\n07/13/2022 16:44:15 - INFO - __main__ - n_pred = 21\n07/13/2022 16:44:15 - INFO - __main__ - precision = 0.8095238095238095\n07/13/2022 16:44:15 - INFO - __main__ - recall = 0.6538461538461539\n07/13/2022 16:44:15 - INFO - __main__ - task_f1 = 0.7234042553191489\n07/13/2022 16:44:15 - INFO - __main__ - task_ngold = 26\n07/13/2022 16:44:15 - INFO - __main__ - task_recall = 0.6538461538461539\n07/13/2022 16:45:16 - INFO - __main__ - Epoch: 6, Step: 100 / 101, used_time = 4399.17s, loss = 0.281296\n07/13/2022 16:45:21 - INFO - __main__ - ***** Eval results *****\n07/13/2022 16:45:21 - INFO - __main__ - accuracy = 0.8214285714285714\n07/13/2022 16:45:21 - INFO - __main__ - eval_loss = 0.3332536419232686\n07/13/2022 16:45:21 - INFO - __main__ - f1 = 0.693877551020408\n07/13/2022 16:45:21 - INFO - __main__ - n_correct = 17\n07/13/2022 16:45:21 - INFO - __main__ - n_gold = 26\n07/13/2022 16:45:21 - INFO - __main__ - n_pred = 23\n07/13/2022 16:45:21 - INFO - __main__ - precision = 0.7391304347826086\n07/13/2022 16:45:21 - INFO - __main__ - recall = 0.6538461538461539\n07/13/2022 16:45:21 - INFO - __main__ - task_f1 = 0.693877551020408\n07/13/2022 16:45:21 - INFO - __main__ - task_ngold = 26\n07/13/2022 16:45:21 - INFO - __main__ - task_recall = 0.6538461538461539\n07/13/2022 16:45:24 - INFO - __main__ - Start epoch #7 (lr = 2e-05)...\n07/13/2022 16:46:30 - INFO - __main__ - Epoch: 7, Step: 10 / 101, used_time = 4473.01s, loss = 0.278694\n07/13/2022 16:46:35 - INFO - __main__ - ***** Eval results *****\n07/13/2022 16:46:35 - INFO - __main__ - accuracy = 0.8214285714285714\n07/13/2022 16:46:35 - INFO - __main__ - eval_loss = 0.3332260549068451\n07/13/2022 16:46:35 - INFO - __main__ - f1 = 0.693877551020408\n07/13/2022 16:46:35 - INFO - __main__ - n_correct = 17\n07/13/2022 16:46:35 - INFO - __main__ - n_gold = 26\n07/13/2022 16:46:35 - INFO - __main__ - n_pred = 23\n07/13/2022 16:46:35 - INFO - __main__ - precision = 0.7391304347826086\n07/13/2022 16:46:35 - INFO - __main__ - recall = 0.6538461538461539\n07/13/2022 16:46:35 - INFO - __main__ - task_f1 = 0.693877551020408\n07/13/2022 16:46:35 - INFO - __main__ - task_ngold = 26\n07/13/2022 16:46:35 - INFO - __main__ - task_recall = 0.6538461538461539\n07/13/2022 16:47:38 - INFO - __main__ - Epoch: 7, Step: 20 / 101, used_time = 4540.61s, loss = 0.277178\n07/13/2022 16:47:43 - INFO - __main__ - ***** Eval results *****\n07/13/2022 16:47:43 - INFO - __main__ - accuracy = 0.8452380952380952\n07/13/2022 16:47:43 - INFO - __main__ - eval_loss = 0.3316306372483571\n07/13/2022 16:47:43 - INFO - __main__ - f1 = 0.7450980392156863\n07/13/2022 16:47:43 - INFO - __main__ - n_correct = 19\n07/13/2022 16:47:43 - INFO - __main__ - n_gold = 26\n07/13/2022 16:47:43 - INFO - __main__ - n_pred = 25\n07/13/2022 16:47:43 - INFO - __main__ - precision = 0.76\n07/13/2022 16:47:43 - INFO - __main__ - recall = 0.7307692307692307\n07/13/2022 16:47:43 - INFO - __main__ - task_f1 = 0.7450980392156863\n07/13/2022 16:47:43 - INFO - __main__ - task_ngold = 26\n07/13/2022 16:47:43 - INFO - __main__ - task_recall = 0.7307692307692307\n07/13/2022 16:48:39 - INFO - __main__ - Epoch: 7, Step: 30 / 101, used_time = 4601.49s, loss = 0.274809\n07/13/2022 16:48:43 - INFO - __main__ - ***** Eval results *****\n07/13/2022 16:48:43 - INFO - __main__ - accuracy = 0.8452380952380952\n07/13/2022 16:48:43 - INFO - __main__ - eval_loss = 0.33112101753552753\n07/13/2022 16:48:43 - INFO - __main__ - f1 = 0.7450980392156863\n07/13/2022 16:48:43 - INFO - __main__ - n_correct = 19\n07/13/2022 16:48:43 - INFO - __main__ - n_gold = 26\n07/13/2022 16:48:43 - INFO - __main__ - n_pred = 25\n07/13/2022 16:48:43 - INFO - __main__ - precision = 0.76\n07/13/2022 16:48:43 - INFO - __main__ - recall = 0.7307692307692307\n07/13/2022 16:48:43 - INFO - __main__ - task_f1 = 0.7450980392156863\n07/13/2022 16:48:43 - INFO - __main__ - task_ngold = 26\n07/13/2022 16:48:43 - INFO - __main__ - task_recall = 0.7307692307692307\n07/13/2022 16:49:38 - INFO - __main__ - Epoch: 7, Step: 40 / 101, used_time = 4660.86s, loss = 0.272865\n07/13/2022 16:49:42 - INFO - __main__ - ***** Eval results *****\n07/13/2022 16:49:42 - INFO - __main__ - accuracy = 0.8333333333333334\n07/13/2022 16:49:42 - INFO - __main__ - eval_loss = 0.33297746380170185\n07/13/2022 16:49:42 - INFO - __main__ - f1 = 0.7199999999999999\n07/13/2022 16:49:42 - INFO - __main__ - n_correct = 18\n07/13/2022 16:49:42 - INFO - __main__ - n_gold = 26\n07/13/2022 16:49:42 - INFO - __main__ - n_pred = 24\n07/13/2022 16:49:42 - INFO - __main__ - precision = 0.75\n07/13/2022 16:49:42 - INFO - __main__ - recall = 0.6923076923076923\n07/13/2022 16:49:42 - INFO - __main__ - task_f1 = 0.7199999999999999\n07/13/2022 16:49:42 - INFO - __main__ - task_ngold = 26\n07/13/2022 16:49:42 - INFO - __main__ - task_recall = 0.6923076923076923\n07/13/2022 16:50:37 - INFO - __main__ - Epoch: 7, Step: 50 / 101, used_time = 4720.23s, loss = 0.271114\n07/13/2022 16:50:41 - INFO - __main__ - ***** Eval results *****\n07/13/2022 16:50:41 - INFO - __main__ - accuracy = 0.8214285714285714\n07/13/2022 16:50:41 - INFO - __main__ - eval_loss = 0.33466725548108417\n07/13/2022 16:50:41 - INFO - __main__ - f1 = 0.693877551020408\n07/13/2022 16:50:41 - INFO - __main__ - n_correct = 17\n07/13/2022 16:50:41 - INFO - __main__ - n_gold = 26\n07/13/2022 16:50:41 - INFO - __main__ - n_pred = 23\n07/13/2022 16:50:41 - INFO - __main__ - precision = 0.7391304347826086\n07/13/2022 16:50:41 - INFO - __main__ - recall = 0.6538461538461539\n07/13/2022 16:50:41 - INFO - __main__ - task_f1 = 0.693877551020408\n07/13/2022 16:50:41 - INFO - __main__ - task_ngold = 26\n07/13/2022 16:50:41 - INFO - __main__ - task_recall = 0.6538461538461539\n07/13/2022 16:51:37 - INFO - __main__ - Epoch: 7, Step: 60 / 101, used_time = 4779.38s, loss = 0.269497\n07/13/2022 16:51:41 - INFO - __main__ - ***** Eval results *****\n07/13/2022 16:51:41 - INFO - __main__ - accuracy = 0.8333333333333334\n07/13/2022 16:51:41 - INFO - __main__ - eval_loss = 0.33334875106811523\n07/13/2022 16:51:41 - INFO - __main__ - f1 = 0.7199999999999999\n07/13/2022 16:51:41 - INFO - __main__ - n_correct = 18\n07/13/2022 16:51:41 - INFO - __main__ - n_gold = 26\n07/13/2022 16:51:41 - INFO - __main__ - n_pred = 24\n07/13/2022 16:51:41 - INFO - __main__ - precision = 0.75\n07/13/2022 16:51:41 - INFO - __main__ - recall = 0.6923076923076923\n07/13/2022 16:51:41 - INFO - __main__ - task_f1 = 0.7199999999999999\n07/13/2022 16:51:41 - INFO - __main__ - task_ngold = 26\n07/13/2022 16:51:41 - INFO - __main__ - task_recall = 0.6923076923076923\n07/13/2022 16:52:38 - INFO - __main__ - Epoch: 7, Step: 70 / 101, used_time = 4840.83s, loss = 0.267872\n07/13/2022 16:52:42 - INFO - __main__ - ***** Eval results *****\n07/13/2022 16:52:42 - INFO - __main__ - accuracy = 0.8333333333333334\n07/13/2022 16:52:42 - INFO - __main__ - eval_loss = 0.33420833945274353\n07/13/2022 16:52:42 - INFO - __main__ - f1 = 0.7083333333333333\n07/13/2022 16:52:42 - INFO - __main__ - n_correct = 17\n07/13/2022 16:52:42 - INFO - __main__ - n_gold = 26\n07/13/2022 16:52:42 - INFO - __main__ - n_pred = 22\n07/13/2022 16:52:42 - INFO - __main__ - precision = 0.7727272727272727\n07/13/2022 16:52:42 - INFO - __main__ - recall = 0.6538461538461539\n07/13/2022 16:52:42 - INFO - __main__ - task_f1 = 0.7083333333333333\n07/13/2022 16:52:42 - INFO - __main__ - task_ngold = 26\n07/13/2022 16:52:42 - INFO - __main__ - task_recall = 0.6538461538461539\n07/13/2022 16:53:37 - INFO - __main__ - Epoch: 7, Step: 80 / 101, used_time = 4899.69s, loss = 0.265969\n07/13/2022 16:53:41 - INFO - __main__ - ***** Eval results *****\n07/13/2022 16:53:41 - INFO - __main__ - accuracy = 0.8333333333333334\n07/13/2022 16:53:41 - INFO - __main__ - eval_loss = 0.3344587783018748\n07/13/2022 16:53:41 - INFO - __main__ - f1 = 0.7083333333333333\n07/13/2022 16:53:41 - INFO - __main__ - n_correct = 17\n07/13/2022 16:53:41 - INFO - __main__ - n_gold = 26\n07/13/2022 16:53:41 - INFO - __main__ - n_pred = 22\n07/13/2022 16:53:41 - INFO - __main__ - precision = 0.7727272727272727\n07/13/2022 16:53:41 - INFO - __main__ - recall = 0.6538461538461539\n07/13/2022 16:53:41 - INFO - __main__ - task_f1 = 0.7083333333333333\n07/13/2022 16:53:41 - INFO - __main__ - task_ngold = 26\n07/13/2022 16:53:41 - INFO - __main__ - task_recall = 0.6538461538461539\n07/13/2022 16:54:36 - INFO - __main__ - Epoch: 7, Step: 90 / 101, used_time = 4959.07s, loss = 0.264155\n07/13/2022 16:54:40 - INFO - __main__ - ***** Eval results *****\n07/13/2022 16:54:40 - INFO - __main__ - accuracy = 0.8214285714285714\n07/13/2022 16:54:40 - INFO - __main__ - eval_loss = 0.3338797887166341\n07/13/2022 16:54:40 - INFO - __main__ - f1 = 0.693877551020408\n07/13/2022 16:54:40 - INFO - __main__ - n_correct = 17\n07/13/2022 16:54:40 - INFO - __main__ - n_gold = 26\n07/13/2022 16:54:40 - INFO - __main__ - n_pred = 23\n07/13/2022 16:54:40 - INFO - __main__ - precision = 0.7391304347826086\n07/13/2022 16:54:40 - INFO - __main__ - recall = 0.6538461538461539\n07/13/2022 16:54:40 - INFO - __main__ - task_f1 = 0.693877551020408\n07/13/2022 16:54:40 - INFO - __main__ - task_ngold = 26\n07/13/2022 16:54:40 - INFO - __main__ - task_recall = 0.6538461538461539\n07/13/2022 16:55:36 - INFO - __main__ - Epoch: 7, Step: 100 / 101, used_time = 5018.31s, loss = 0.262187\n07/13/2022 16:55:40 - INFO - __main__ - ***** Eval results *****\n07/13/2022 16:55:40 - INFO - __main__ - accuracy = 0.8333333333333334\n07/13/2022 16:55:40 - INFO - __main__ - eval_loss = 0.3338385025660197\n07/13/2022 16:55:40 - INFO - __main__ - f1 = 0.7083333333333333\n07/13/2022 16:55:40 - INFO - __main__ - n_correct = 17\n07/13/2022 16:55:40 - INFO - __main__ - n_gold = 26\n07/13/2022 16:55:40 - INFO - __main__ - n_pred = 22\n07/13/2022 16:55:40 - INFO - __main__ - precision = 0.7727272727272727\n07/13/2022 16:55:40 - INFO - __main__ - recall = 0.6538461538461539\n07/13/2022 16:55:40 - INFO - __main__ - task_f1 = 0.7083333333333333\n07/13/2022 16:55:40 - INFO - __main__ - task_ngold = 26\n07/13/2022 16:55:40 - INFO - __main__ - task_recall = 0.6538461538461539\n07/13/2022 16:55:45 - INFO - __main__ - {'SUBJ_START': '[unused1]', 'SUBJ_END': '[unused2]', 'OBJ_START': '[unused3]', 'OBJ_END': '[unused4]', 'SUBJ=BASE': '[unused5]', 'OBJ=BASE': '[unused6]', 'SUBJ_START=BASE': '[unused7]', 'SUBJ_END=BASE': '[unused8]', 'OBJ_START=BASE': '[unused9]', 'OBJ_END=BASE': '[unused10]'}\n07/13/2022 16:55:45 - INFO - __main__ - Writing example 0 of 112\n07/13/2022 16:55:45 - INFO - __main__ - *** Example ***\n07/13/2022 16:55:45 - INFO - __main__ - guid: 59@0::(18,19)-(10,10)\n07/13/2022 16:55:45 - INFO - __main__ - tokens: [CLS] instead they are pushed around the ocean by wind streams [unused9] which [unused10] are caused by high and low pressure [unused7] systems in [unused8] the atmosphere implied _ base implied _ base [SEP]\n07/13/2022 16:55:45 - INFO - __main__ - input_ids: 101 2612 2027 2024 3724 2105 1996 4153 2011 3612 9199 10 2029 11 2024 3303 2011 2152 1998 2659 3778 8 3001 1999 9 1996 7224 13339 1035 2918 13339 1035 2918 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 16:55:45 - INFO - __main__ - input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 16:55:45 - INFO - __main__ - segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 16:55:45 - INFO - __main__ - label: Contributes_To (id = 1)\n07/13/2022 16:55:45 - INFO - __main__ - sub_idx, obj_idx: 21, 11\n07/13/2022 16:55:45 - INFO - __main__ - *** Example ***\n07/13/2022 16:55:45 - INFO - __main__ - guid: 59@0::(10,10)-(18,19)\n07/13/2022 16:55:45 - INFO - __main__ - tokens: [CLS] instead they are pushed around the ocean by wind streams [unused7] which [unused8] are caused by high and low pressure [unused9] systems in [unused10] the atmosphere implied _ base implied _ base [SEP]\n07/13/2022 16:55:45 - INFO - __main__ - input_ids: 101 2612 2027 2024 3724 2105 1996 4153 2011 3612 9199 8 2029 9 2024 3303 2011 2152 1998 2659 3778 10 3001 1999 11 1996 7224 13339 1035 2918 13339 1035 2918 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 16:55:45 - INFO - __main__ - input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 16:55:45 - INFO - __main__ - segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 16:55:45 - INFO - __main__ - label: no_relation (id = 0)\n07/13/2022 16:55:45 - INFO - __main__ - sub_idx, obj_idx: 11, 21\n07/13/2022 16:55:45 - INFO - __main__ - *** Example ***\n07/13/2022 16:55:45 - INFO - __main__ - guid: 258@0::(19,20)-(26,26)\n07/13/2022 16:55:45 - INFO - __main__ - tokens: [CLS] assessing the risk from both wind and storm surge the author of a 2017 paper concludes in combination climate [unused7] change and [unused8] coastal development will cause hurricane [unused9] damage [unused10] to increase faster than the u s economy is expected to grow implied _ base implied _ base [SEP]\n07/13/2022 16:55:45 - INFO - __main__ - input_ids: 101 20077 1996 3891 2013 2119 3612 1998 4040 12058 1996 3166 1997 1037 2418 3259 14730 1999 5257 4785 8 2689 1998 9 5780 2458 2097 3426 7064 10 4053 11 2000 3623 5514 2084 1996 1057 1055 4610 2003 3517 2000 4982 13339 1035 2918 13339 1035 2918 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 16:55:45 - INFO - __main__ - input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 16:55:45 - INFO - __main__ - segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 16:55:45 - INFO - __main__ - label: Contributes_To (id = 1)\n07/13/2022 16:55:45 - INFO - __main__ - sub_idx, obj_idx: 20, 29\n07/13/2022 16:55:45 - INFO - __main__ - *** Example ***\n07/13/2022 16:55:45 - INFO - __main__ - guid: 258@0::(19,20)-(23,23)\n07/13/2022 16:55:45 - INFO - __main__ - tokens: [CLS] assessing the risk from both wind and storm surge the author of a 2017 paper concludes in combination climate [unused7] change and [unused8] coastal development [unused9] will [unused10] cause hurricane damage to increase faster than the u s economy is expected to grow implied _ base implied _ base [SEP]\n07/13/2022 16:55:45 - INFO - __main__ - input_ids: 101 20077 1996 3891 2013 2119 3612 1998 4040 12058 1996 3166 1997 1037 2418 3259 14730 1999 5257 4785 8 2689 1998 9 5780 2458 10 2097 11 3426 7064 4053 2000 3623 5514 2084 1996 1057 1055 4610 2003 3517 2000 4982 13339 1035 2918 13339 1035 2918 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 16:55:45 - INFO - __main__ - input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 16:55:45 - INFO - __main__ - segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 16:55:45 - INFO - __main__ - label: no_relation (id = 0)\n07/13/2022 16:55:45 - INFO - __main__ - sub_idx, obj_idx: 20, 26\n07/13/2022 16:55:45 - INFO - __main__ - *** Example ***\n07/13/2022 16:55:45 - INFO - __main__ - guid: 258@0::(26,26)-(19,20)\n07/13/2022 16:55:45 - INFO - __main__ - tokens: [CLS] assessing the risk from both wind and storm surge the author of a 2017 paper concludes in combination climate [unused9] change and [unused10] coastal development will cause hurricane [unused7] damage [unused8] to increase faster than the u s economy is expected to grow implied _ base implied _ base [SEP]\n07/13/2022 16:55:45 - INFO - __main__ - input_ids: 101 20077 1996 3891 2013 2119 3612 1998 4040 12058 1996 3166 1997 1037 2418 3259 14730 1999 5257 4785 10 2689 1998 11 5780 2458 2097 3426 7064 8 4053 9 2000 3623 5514 2084 1996 1057 1055 4610 2003 3517 2000 4982 13339 1035 2918 13339 1035 2918 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 16:55:45 - INFO - __main__ - input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 16:55:45 - INFO - __main__ - segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 16:55:45 - INFO - __main__ - label: no_relation (id = 0)\n07/13/2022 16:55:45 - INFO - __main__ - sub_idx, obj_idx: 29, 20\n07/13/2022 16:55:45 - INFO - __main__ - *** Example ***\n07/13/2022 16:55:45 - INFO - __main__ - guid: 258@0::(23,23)-(26,26)\n07/13/2022 16:55:45 - INFO - __main__ - tokens: [CLS] assessing the risk from both wind and storm surge the author of a 2017 paper concludes in combination climate change and coastal development [unused7] will [unused8] cause hurricane [unused9] damage [unused10] to increase faster than the u s economy is expected to grow implied _ base implied _ base [SEP]\n07/13/2022 16:55:45 - INFO - __main__ - input_ids: 101 20077 1996 3891 2013 2119 3612 1998 4040 12058 1996 3166 1997 1037 2418 3259 14730 1999 5257 4785 2689 1998 5780 2458 8 2097 9 3426 7064 10 4053 11 2000 3623 5514 2084 1996 1057 1055 4610 2003 3517 2000 4982 13339 1035 2918 13339 1035 2918 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 16:55:45 - INFO - __main__ - input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 16:55:45 - INFO - __main__ - segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 16:55:45 - INFO - __main__ - label: Contributes_To (id = 1)\n07/13/2022 16:55:45 - INFO - __main__ - sub_idx, obj_idx: 24, 29\n07/13/2022 16:55:45 - INFO - __main__ - *** Example ***\n07/13/2022 16:55:45 - INFO - __main__ - guid: 263@0::(9,9)-(13,14)\n07/13/2022 16:55:45 - INFO - __main__ - tokens: [CLS] one of the most common environmental all ##er ##gens is rag ##weed [unused7] which [unused8] can cause hay [unused9] fever and [unused10] trigger asthma attacks especially in children and the elderly 2 implied _ base implied _ base [SEP]\n07/13/2022 16:55:45 - INFO - __main__ - input_ids: 101 2028 1997 1996 2087 2691 4483 2035 2121 21230 2003 17768 18041 8 2029 9 2064 3426 10974 10 9016 1998 11 9495 26180 4491 2926 1999 2336 1998 1996 9750 1016 13339 1035 2918 13339 1035 2918 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 16:55:45 - INFO - __main__ - input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 16:55:45 - INFO - __main__ - segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 16:55:45 - INFO - __main__ - label: Contributes_To (id = 1)\n07/13/2022 16:55:45 - INFO - __main__ - sub_idx, obj_idx: 13, 19\n07/13/2022 16:55:45 - INFO - __main__ - *** Example ***\n07/13/2022 16:55:45 - INFO - __main__ - guid: 263@0::(9,9)-(17,17)\n07/13/2022 16:55:45 - INFO - __main__ - tokens: [CLS] one of the most common environmental all ##er ##gens is rag ##weed [unused7] which [unused8] can cause hay fever and trigger asthma [unused9] attacks [unused10] especially in children and the elderly 2 implied _ base implied _ base [SEP]\n07/13/2022 16:55:45 - INFO - __main__ - input_ids: 101 2028 1997 1996 2087 2691 4483 2035 2121 21230 2003 17768 18041 8 2029 9 2064 3426 10974 9016 1998 9495 26180 10 4491 11 2926 1999 2336 1998 1996 9750 1016 13339 1035 2918 13339 1035 2918 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 16:55:45 - INFO - __main__ - input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 16:55:45 - INFO - __main__ - segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 16:55:45 - INFO - __main__ - label: Contributes_To (id = 1)\n07/13/2022 16:55:45 - INFO - __main__ - sub_idx, obj_idx: 13, 23\n07/13/2022 16:55:45 - INFO - __main__ - *** Example ***\n07/13/2022 16:55:45 - INFO - __main__ - guid: 123@0::(14,14)-(6,6)\n07/13/2022 16:55:45 - INFO - __main__ - tokens: [CLS] together increased stall ##ing and increased rain [unused9] during [unused10] stalls imply increased coastal rainfall from tc ##s [unused7] other [unused8] factors equal implied _ base implied _ base [SEP]\n07/13/2022 16:55:45 - INFO - __main__ - input_ids: 101 2362 3445 13498 2075 1998 3445 4542 10 2076 11 19753 19515 3445 5780 10101 2013 22975 2015 8 2060 9 5876 5020 13339 1035 2918 13339 1035 2918 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 16:55:45 - INFO - __main__ - input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 16:55:45 - INFO - __main__ - segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 16:55:45 - INFO - __main__ - label: Contributes_To (id = 1)\n07/13/2022 16:55:45 - INFO - __main__ - sub_idx, obj_idx: 19, 8\n07/13/2022 16:55:45 - INFO - __main__ - *** Example ***\n07/13/2022 16:55:45 - INFO - __main__ - guid: 116@0::(3,4)-(12,12)\n07/13/2022 16:55:45 - INFO - __main__ - tokens: [CLS] that rapid climate [unused7] change will [unused8] and already is negatively affecting the livelihood ##s [unused9] and [unused10] aggressive tendencies of individuals is obvious but it is inform ##ative to consider how entire populations respond to these effects implied _ base implied _ base [SEP]\n07/13/2022 16:55:45 - INFO - __main__ - input_ids: 101 2008 5915 4785 8 2689 2097 9 1998 2525 2003 19762 12473 1996 24585 2015 10 1998 11 9376 20074 1997 3633 2003 5793 2021 2009 2003 12367 8082 2000 5136 2129 2972 7080 6869 2000 2122 3896 13339 1035 2918 13339 1035 2918 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 16:55:45 - INFO - __main__ - input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 16:55:45 - INFO - __main__ - segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 16:55:45 - INFO - __main__ - label: Contributes_To (id = 1)\n07/13/2022 16:55:45 - INFO - __main__ - sub_idx, obj_idx: 4, 16\n07/13/2022 16:55:45 - INFO - __main__ - *** Example ***\n07/13/2022 16:55:45 - INFO - __main__ - guid: 116@0::(3,4)-(14,15)\n07/13/2022 16:55:45 - INFO - __main__ - tokens: [CLS] that rapid climate [unused7] change will [unused8] and already is negatively affecting the livelihood ##s and aggressive [unused9] tendencies of [unused10] individuals is obvious but it is inform ##ative to consider how entire populations respond to these effects implied _ base implied _ base [SEP]\n07/13/2022 16:55:45 - INFO - __main__ - input_ids: 101 2008 5915 4785 8 2689 2097 9 1998 2525 2003 19762 12473 1996 24585 2015 1998 9376 10 20074 1997 11 3633 2003 5793 2021 2009 2003 12367 8082 2000 5136 2129 2972 7080 6869 2000 2122 3896 13339 1035 2918 13339 1035 2918 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 16:55:45 - INFO - __main__ - input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 16:55:45 - INFO - __main__ - segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 16:55:45 - INFO - __main__ - label: Contributes_To (id = 1)\n07/13/2022 16:55:45 - INFO - __main__ - sub_idx, obj_idx: 4, 18\n07/13/2022 16:55:45 - INFO - __main__ - *** Example ***\n07/13/2022 16:55:45 - INFO - __main__ - guid: 220@0::(5,6)-(7,7)\n07/13/2022 16:55:45 - INFO - __main__ - tokens: [CLS] studies estimate that with climate [unused7] change water [unused8] [unused9] availability [unused10] per capita could decrease by as much as 88 percent in places and crop yields in coastal regions may drop by a third implied _ base implied _ base [SEP]\n07/13/2022 16:55:45 - INFO - __main__ - input_ids: 101 2913 10197 2008 2007 4785 8 2689 2300 9 10 11343 11 2566 8353 2071 9885 2011 2004 2172 2004 6070 3867 1999 3182 1998 10416 16189 1999 5780 4655 2089 4530 2011 1037 2353 13339 1035 2918 13339 1035 2918 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 16:55:45 - INFO - __main__ - input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 16:55:45 - INFO - __main__ - segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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[unused7] which [unused8] lowers the risk from disease [unused9] but [unused10] we also travel more which increases the spread of diseases implied _ base implied _ base [SEP]\n07/13/2022 16:55:45 - INFO - __main__ - input_ids: 101 2058 1996 2627 2301 2057 2031 2468 2488 2012 4200 8 2029 9 24950 1996 3891 2013 4295 10 2021 11 2057 2036 3604 2062 2029 7457 1996 3659 1997 7870 13339 1035 2918 13339 1035 2918 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 16:55:45 - INFO - __main__ - input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 16:55:45 - INFO - __main__ - segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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***\n07/13/2022 16:55:45 - INFO - __main__ - guid: 129@0::(3,3)-(27,27)\n07/13/2022 16:55:45 - INFO - __main__ - tokens: [CLS] the greenhouse gas [unused7] emissions [unused8] that drive warming now substantially exceed the highest concentrations recorded in ice cores during the past 800 000 years the ip ##cc said implied _ base [unused9] implied _ base [unused10] [SEP]\n07/13/2022 16:55:45 - INFO - __main__ - input_ids: 101 1996 16635 3806 8 11768 9 2008 3298 12959 2085 12381 13467 1996 3284 14061 2680 1999 3256 25562 2076 1996 2627 5385 2199 2086 1996 12997 9468 2056 13339 1035 2918 10 13339 1035 2918 11 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 16:55:45 - INFO - __main__ - input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 16:55:45 - INFO - __main__ - segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 16:55:45 - INFO - __main__ - label: Contributes_To (id = 1)\n07/13/2022 16:55:45 - INFO - __main__ - sub_idx, obj_idx: 4, 33\n07/13/2022 16:55:45 - INFO - __main__ - *** Example ***\n07/13/2022 16:55:45 - INFO - __main__ - guid: 280@0::(2,2)-(34,34)\n07/13/2022 16:55:45 - INFO - __main__ - tokens: [CLS] air pollution [unused7] dust [unused8] clouds the stability of the upper atmosphere and the relative warmth of the atlantic ocean compared to the indian and pacific oceans can all influence the intensity and frequency of hurricanes [unused9] implied _ base [unused10] implied _ base [SEP]\n07/13/2022 16:55:45 - INFO - __main__ - input_ids: 101 2250 10796 8 6497 9 8044 1996 9211 1997 1996 3356 7224 1998 1996 5816 8251 1997 1996 4448 4153 4102 2000 1996 2796 1998 3534 17401 2064 2035 3747 1996 8015 1998 6075 1997 17035 10 13339 1035 2918 11 13339 1035 2918 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 16:55:45 - INFO - __main__ - input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 16:55:45 - INFO - __main__ - segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 16:55:45 - INFO - __main__ - label: Contributes_To (id = 1)\n07/13/2022 16:55:45 - INFO - __main__ - sub_idx, obj_idx: 3, 37\n07/13/2022 16:55:45 - INFO - __main__ - *** Example ***\n07/13/2022 16:55:45 - INFO - __main__ - guid: 280@0::(4,4)-(34,34)\n07/13/2022 16:55:45 - INFO - __main__ - tokens: [CLS] air pollution dust clouds [unused7] the [unused8] stability of the upper atmosphere and the relative warmth of the atlantic ocean compared to the indian and pacific oceans can all influence the intensity and frequency of hurricanes [unused9] implied _ base [unused10] implied _ base [SEP]\n07/13/2022 16:55:45 - INFO - __main__ - input_ids: 101 2250 10796 6497 8044 8 1996 9 9211 1997 1996 3356 7224 1998 1996 5816 8251 1997 1996 4448 4153 4102 2000 1996 2796 1998 3534 17401 2064 2035 3747 1996 8015 1998 6075 1997 17035 10 13339 1035 2918 11 13339 1035 2918 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 16:55:45 - INFO - __main__ - input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 16:55:45 - INFO - __main__ - segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 16:55:45 - INFO - __main__ - label: Contributes_To (id = 1)\n07/13/2022 16:55:45 - INFO - __main__ - sub_idx, obj_idx: 5, 37\n07/13/2022 16:55:45 - INFO - __main__ - *** Example ***\n07/13/2022 16:55:45 - INFO - __main__ - guid: 280@0::(10,10)-(34,34)\n07/13/2022 16:55:45 - INFO - __main__ - tokens: [CLS] air pollution dust clouds the stability of the upper atmosphere [unused7] and [unused8] the relative warmth of the atlantic ocean compared to the indian and pacific oceans can all influence the intensity and frequency of hurricanes [unused9] implied _ base [unused10] implied _ base [SEP]\n07/13/2022 16:55:45 - INFO - __main__ - input_ids: 101 2250 10796 6497 8044 1996 9211 1997 1996 3356 7224 8 1998 9 1996 5816 8251 1997 1996 4448 4153 4102 2000 1996 2796 1998 3534 17401 2064 2035 3747 1996 8015 1998 6075 1997 17035 10 13339 1035 2918 11 13339 1035 2918 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 16:55:45 - INFO - __main__ - input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 16:55:45 - INFO - __main__ - segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 16:55:45 - INFO - __main__ - label: Contributes_To (id = 1)\n07/13/2022 16:55:45 - INFO - __main__ - sub_idx, obj_idx: 11, 37\n07/13/2022 16:55:45 - INFO - __main__ - *** Example ***\n07/13/2022 16:55:45 - INFO - __main__ - guid: 280@0::(18,18)-(34,34)\n07/13/2022 16:55:45 - INFO - __main__ - tokens: [CLS] air pollution dust clouds the stability of the upper atmosphere and the relative warmth of the atlantic ocean [unused7] compared [unused8] to the indian and pacific oceans can all influence the intensity and frequency of hurricanes [unused9] implied _ base [unused10] implied _ base [SEP]\n07/13/2022 16:55:45 - INFO - __main__ - input_ids: 101 2250 10796 6497 8044 1996 9211 1997 1996 3356 7224 1998 1996 5816 8251 1997 1996 4448 4153 8 4102 9 2000 1996 2796 1998 3534 17401 2064 2035 3747 1996 8015 1998 6075 1997 17035 10 13339 1035 2918 11 13339 1035 2918 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 16:55:45 - INFO - __main__ - input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 16:55:45 - INFO - __main__ - segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n07/13/2022 16:55:45 - INFO - __main__ - label: Contributes_To (id = 1)\n07/13/2022 16:55:45 - INFO - __main__ - sub_idx, obj_idx: 19, 37\n07/13/2022 16:55:45 - INFO - __main__ - Average #tokens: 45.77\n07/13/2022 16:55:45 - INFO - __main__ - Max #tokens: 65\n07/13/2022 16:55:45 - INFO - __main__ - 112 (100.00 %) examples can fit max_seq_length = 128\n07/13/2022 16:55:45 - INFO - __main__ - {'SUBJ_START': '[unused1]', 'SUBJ_END': '[unused2]', 'OBJ_START': '[unused3]', 'OBJ_END': '[unused4]', 'SUBJ=BASE': '[unused5]', 'OBJ=BASE': '[unused6]', 'SUBJ_START=BASE': '[unused7]', 'SUBJ_END=BASE': '[unused8]', 'OBJ_START=BASE': '[unused9]', 'OBJ_END=BASE': '[unused10]'}\n07/13/2022 16:55:45 - INFO - __main__ - ***** Test *****\n07/13/2022 16:55:45 - INFO - __main__ - Num examples = 112\n07/13/2022 16:55:45 - INFO - __main__ - Batch size = 32\n07/13/2022 16:55:45 - INFO - transformers.configuration_utils - loading configuration file PURE/output_rel_ent2/config.json\n07/13/2022 16:55:45 - INFO - transformers.configuration_utils - Model config BertConfig {\n \"architectures\": [\n \"BertForMaskedLM\"\n ],\n \"attention_probs_dropout_prob\": 0.1,\n \"gradient_checkpointing\": false,\n \"hidden_act\": \"gelu\",\n \"hidden_dropout_prob\": 0.1,\n \"hidden_size\": 768,\n \"initializer_range\": 0.02,\n \"intermediate_size\": 3072,\n \"layer_norm_eps\": 1e-12,\n \"max_position_embeddings\": 512,\n \"model_type\": \"bert\",\n \"num_attention_heads\": 12,\n \"num_hidden_layers\": 12,\n \"pad_token_id\": 0,\n \"type_vocab_size\": 2,\n \"vocab_size\": 30522\n}\n\n07/13/2022 16:55:45 - INFO - transformers.modeling_utils - loading weights file PURE/output_rel_ent2/pytorch_model.bin\n07/13/2022 16:55:56 - INFO - transformers.modeling_utils - All model checkpoint weights were used when initializing BertForRelation.\n\n07/13/2022 16:55:56 - INFO - transformers.modeling_utils - All the weights of BertForRelation were initialized from the model checkpoint at PURE/output_rel_ent2.\nIf your task is similar to the task the model of the ckeckpoint was trained on, you can already use BertForRelation for predictions without further training.\n07/13/2022 16:56:01 - INFO - __main__ - ***** Eval results *****\n07/13/2022 16:56:01 - INFO - __main__ - accuracy = 0.8660714285714286\n07/13/2022 16:56:01 - INFO - __main__ - eval_loss = 0.2834046147763729\n07/13/2022 16:56:01 - INFO - __main__ - f1 = 0.7647058823529411\n07/13/2022 16:56:01 - INFO - __main__ - n_correct = 26\n07/13/2022 16:56:01 - INFO - __main__ - n_gold = 31\n07/13/2022 16:56:01 - INFO - __main__ - n_pred = 37\n07/13/2022 16:56:01 - INFO - __main__ - precision = 0.7027027027027027\n07/13/2022 16:56:01 - INFO - __main__ - recall = 0.8387096774193549\n07/13/2022 16:56:01 - INFO - __main__ - task_f1 = 0.7761194029850748\n07/13/2022 16:56:01 - INFO - __main__ - task_ngold = 30\n07/13/2022 16:56:01 - INFO - __main__ - task_recall = 0.8666666666666667\n07/13/2022 16:56:01 - INFO - __main__ - *** Evaluation Results ***\n07/13/2022 16:56:01 - INFO - __main__ - accuracy = 0.8660714285714286\n07/13/2022 16:56:01 - INFO - __main__ - eval_loss = 0.2834046147763729\n07/13/2022 16:56:01 - INFO - __main__ - f1 = 0.7647058823529411\n07/13/2022 16:56:01 - INFO - __main__ - n_correct = 26\n07/13/2022 16:56:01 - INFO - __main__ - n_gold = 31\n07/13/2022 16:56:01 - INFO - __main__ - n_pred = 37\n07/13/2022 16:56:01 - INFO - __main__ - precision = 0.7027027027027027\n07/13/2022 16:56:01 - INFO - __main__ - recall = 0.8387096774193549\n07/13/2022 16:56:01 - INFO - __main__ - task_f1 = 0.7761194029850748\n07/13/2022 16:56:01 - INFO - __main__ - task_ngold = 30\n07/13/2022 16:56:01 - INFO - __main__ - task_recall = 0.8666666666666667\n07/13/2022 16:56:01 - INFO - __main__ - Output predictions to PURE/output_rel_ent2/predictions.json..\n" + } + ], + "execution_count": 2, + "metadata": { + "jupyter": { + "source_hidden": false, + "outputs_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + }, + "gather": { + "logged": 1657731363744 + } + } + }, + { + "cell_type": "code", + "source": [ + "#!python PURE/run_eval.py --prediction_file PURE/output_rel/predictions.json\n", + "!python PURE/run_eval.py --prediction_file PURE/output_rel_ent2/predictions.json" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": "Evaluation result PURE/output_rel_ent2/predictions.json\r\nNER - P: 1.000000, R: 1.000000, F1: 1.000000\r\nREL - P: 0.702703, R: 0.838710, F1: 0.764706\r\nREL (strict) - P: 0.702703, R: 0.838710, F1: 0.764706\r\n" + } + ], + "execution_count": 12, + "metadata": { + "jupyter": { + "source_hidden": false, + "outputs_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + }, + "gather": { + "logged": 1657732336869 + } + } + }, + { + "cell_type": "code", + "source": [ + "!zip -r output_rel_azure_07_13.zip {output_rel}" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": " adding: PURE/output_rel_ent2/ (stored 0%)\n adding: PURE/output_rel_ent2/.amlignore (deflated 32%)\n adding: PURE/output_rel_ent2/.amlignore.amltmp (deflated 32%)\n adding: PURE/output_rel_ent2/config.json (deflated 45%)\n adding: PURE/output_rel_ent2/label_list.json (stored 0%)\n adding: PURE/output_rel_ent2/predictions.json (deflated 78%)\n adding: PURE/output_rel_ent2/pytorch_model.bin (deflated 7%)\n adding: PURE/output_rel_ent2/special_tokens.json (deflated 67%)\n adding: PURE/output_rel_ent2/train.log (deflated 90%)\n adding: PURE/output_rel_ent2/vocab.txt (deflated 53%)\n" + } + ], + "execution_count": 11, + "metadata": { + "jupyter": { + "source_hidden": false, + "outputs_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + } + }, + { + "cell_type": "code", + "source": [ + "import json\n", + "\n", + "file1 = open('PURE/output_rel_ent2/predictions.json', 'r')\n", + "lines = file1.readlines()\n", + "true_pos = 0\n", + "false_pos = 0\n", + "false_neg = 0\n", + "tot = 0\n", + "\n", + "def calc_true_pos(predicted, actual):\n", + " count = 0\n", + " for p in predicted:\n", + " if p in actual:\n", + " count += 1\n", + " return count\n", + "\n", + "for l in lines:\n", + " d = json.loads(l)\n", + " predicted = d[\"predicted_relations\"][0]\n", + " actual = d[\"relations\"][0]\n", + "\n", + " #print(\" \".join(d[\"sentences\"][0]))\n", + " #print(predicted)\n", + " #print(actual)\n", + " #print(\"====\")\n", + " true_pos += calc_true_pos(predicted, actual)\n", + " #false_pos += calc_false_pos(predicted, actual)\n", + " #false_neg += calc_false_neg(predicted, actual)\n", + " tot += len(actual)\n", + "\n", + "print(true_pos)\n", + "print(tot)" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": "26\n31\n" + } + ], + "execution_count": 6, + "metadata": { + "jupyter": { + "source_hidden": false, + "outputs_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + }, + "gather": { + "logged": 1657731584986 + } + } + }, + { + "cell_type": "code", + "source": [], + "outputs": [], + "execution_count": null, + "metadata": { + "jupyter": { + "source_hidden": false, + "outputs_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + } + } + ], + "metadata": { + "kernelspec": { + "name": "python38-azureml", + "language": "python", + "display_name": "Python 3.8 - AzureML" + }, + "language_info": { + "name": "python", + "version": "3.8.5", + "mimetype": "text/x-python", + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "pygments_lexer": "ipython3", + "nbconvert_exporter": "python", + "file_extension": ".py" + }, + "kernel_info": { + "name": "python38-azureml" + }, + "nteract": { + "version": "nteract-front-end@1.0.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/model_dev/entity_extraction/PURE/PURE_data_setup.py b/model_dev/entity_extraction/PURE/PURE_data_setup.py new file mode 100644 index 0000000..47a36a9 --- /dev/null +++ b/model_dev/entity_extraction/PURE/PURE_data_setup.py @@ -0,0 +1,110 @@ +import srsly +import json +from sklearn.model_selection import train_test_split +import os + +file_name_answers = "gold_standard" +file_path_answers = "/Users/ash/Desktop/PURE/PURE/new_data/"+file_name_answers+".jsonl" +data = srsly.read_jsonl(file_path_answers) +final_sent = {} +final_sent_arr = [] +final_sent_left_to_right = [] +final_sent_right_to_left = [] +rel_labels = ["Contributes_To"] +ents = ["base", "aspect_changing", "change_direction", "type_of"] +tmp_index = 0 +total_span_count = 0 +total_ent_count = 0 + +def find_token(tokens, index, is_start): + for t in tokens: + if is_start: + if t["start"] == index: + return tokens.index(t) + 1 + else: + if t["end"] == index: + return tokens.index(t) + 1 + +for entry in data: + text = entry["text"] + if text: + sentences = [] + if entry['answer'] == "accept": + if entry['_session_id'] in ["main_3_per_cluster-Kameron", "main_batchF-KameronRodrigues"]: + ner = [] + rel = [] + token_sent = [] + for t in entry["tokens"]: + token_sent.append(t["text"]) + sentences.append(token_sent) + left_to_right = 0 + right_to_left = 0 + + for span in entry['spans']: + if ("label" in span) and ("start" in span) and ("end" in span): + if span["label"] in ents: + # word = text[span["start"]:span["end"]] + total_span_count += 1 + start_index = find_token(entry["tokens"], span["start"], True) + end_index = find_token(entry["tokens"], span["end"], False) + ner.append([start_index, end_index, span["label"]]) + + for relation in entry['relations']: + if ("label" in relation) and ("head_span" in relation) and ("child_span" in relation): + if relation["label"] in rel_labels: + tmp = [] + head_span_start = relation["head_span"]["start"] + head_span_end = relation["head_span"]["end"] + child_span_start = relation["child_span"]["start"] + child_span_end = relation["child_span"]["end"] + head_word = text[head_span_start:head_span_end] + child_word = text[child_span_start:child_span_end] + + if head_span_start > child_span_start: + right_to_left += 1 + else: + left_to_right += 1 + + # ner.append([relation["head_span"]["token_start"] + 1, relation["head_span"]["token_end"] + 1, "base"]) + # ner.append([relation["child_span"]["token_start"] + 1, relation["child_span"]["token_end"] + 1, "base"]) + + rel.append([relation["head_span"]["token_start"] + 1, relation["head_span"]["token_end"] + 1, + relation["child_span"]["token_start"] + 1, relation["child_span"]["token_end"] + 1, + "Contributes_To"]) + + final_sent['doc_key'] = str(tmp_index) + final_sent['sentences'] = sentences + final_sent['ner'] = [ner] + # final_sent['predicted_ner'] = [ner] + final_sent['relations'] = [rel] + total_ent_count += len(ner) + # final_sent['right_to_left'] = right_to_left + # final_sent['left_to_right'] = left_to_right + + assert total_ent_count == total_span_count + + if final_sent: + final_sent_arr.append(final_sent) + final_sent = {} + tmp_index += 1 + +train, test = train_test_split(final_sent_arr, test_size=0.1) +total_sent = len(final_sent_arr) + +data_folder = "data" +os.makedirs(data_folder, exist_ok=True) + +with open('./{}/train.json'.format(data_folder), 'w') as file: + for item in train: + # del item["right_to_left"] + # del item["left_to_right"] + file.write("%s\n" % json.dumps(item)) + +with open('./{}/dev.json'.format(data_folder), 'w') as file: + for item in test: + # del item["right_to_left"] + # del item["left_to_right"] + file.write("%s\n" % json.dumps(item)) + +print("Total sentences: {}".format(total_sent)) +print("Done!") diff --git a/model_dev/entity_extraction/PURE/PURE_ent_data_setup.py b/model_dev/entity_extraction/PURE/PURE_ent_data_setup.py new file mode 100644 index 0000000..3a68649 --- /dev/null +++ b/model_dev/entity_extraction/PURE/PURE_ent_data_setup.py @@ -0,0 +1,79 @@ +import srsly +import json +from sklearn.model_selection import train_test_split +import os +import shutil + +file_name_answers = "checkin_answers" +file_path_answers = "/Users/ash/Desktop/PURE/PURE/"+file_name_answers+".jsonl" +data = srsly.read_jsonl(file_path_answers) +final_sent = {} +final_sent_arr = [] +tmp_index = 0 +ents = ["base", "aspect_changing", "change_direction", "type_of"] +total_span_count = 0 +total_ent_count = 0 + +def find_token(tokens, index, is_start): + for t in tokens: + if is_start: + if t["start"] == index: + return tokens.index(t) + 1 + else: + if t["end"] == index: + return tokens.index(t) + 1 + +for entry in data: + text = entry["text"] + if text: + sentences = [] + if entry['answer'] == "accept": + if entry['_session_id'] == "main_3_per_cluster-Kameron": + ner = [] + token_sent = [] + for t in entry["tokens"]: + token_sent.append(t["text"]) + sentences.append(token_sent) + for span in entry['spans']: + if ("label" in span) and ("start" in span) and ("end" in span): + if span["label"] in ents: + #word = text[span["start"]:span["end"]] + total_span_count += 1 + start_index = find_token(entry["tokens"], span["start"], True) + end_index = find_token(entry["tokens"], span["end"], False) + ner.append([start_index, end_index, span["label"]]) + total_ent_count += len(ner) + + final_sent['doc_key'] = str(tmp_index) + final_sent['sentences'] = sentences + final_sent['ner'] = [ner] + + assert total_ent_count == total_span_count + + if final_sent: + final_sent_arr.append(final_sent) + final_sent = {} + tmp_index += 1 + + +train, test = train_test_split(final_sent_arr, test_size=0.1) +#val, test = train_test_split(test, test_size=0.5) +data_folder = "data_ent1" + +#shutil.rmtree(data_folder) +os.makedirs(data_folder, exist_ok=True) + +#with open('./{}/dev.json'.format(data_folder), 'w') as file: +# for item in val: +# file.write("%s\n" % json.dumps(item)) + +with open('./{}/train.json'.format(data_folder), 'w') as file: + for item in train: + file.write("%s\n" % json.dumps(item)) + +with open('./{}/dev.json'.format(data_folder), 'w') as file: + for item in test: + file.write("%s\n" % json.dumps(item)) + +print("Done!") + diff --git a/model_dev/entity_extraction/PURE/PURE_relation_extraction_eval.py b/model_dev/entity_extraction/PURE/PURE_relation_extraction_eval.py new file mode 100644 index 0000000..eda315d --- /dev/null +++ b/model_dev/entity_extraction/PURE/PURE_relation_extraction_eval.py @@ -0,0 +1,99 @@ +import srsly +import json + +base_path = "/Users/ash/Desktop/PURE/PURE/" +actual_answers_file = "main_3_per_cluster_download.cba617d8-a055-4622-97a3-c194a148cbed" +#actual_file_path = base_path+actual_answers_file+".json" +#actual_file_path = "/Users/ash/Desktop/PURE/PURE/main_3_per_cluster_download.cba617d8-a055-4622-97a3-c194a148cbed.jsonl" +actual_file_path = "/Users/ash/Desktop/PURE/PURE/data1/predictions (1).json" +rel_labels = ["Contributes_To"] + +def generate_spans(user=None): + session = srsly.read_jsonl(actual_file_path) + rel = {} + for entry in session: + text = entry["text"] + if entry["_session_id"]: + if text: + if entry['answer'] == "accept": + if entry['_session_id'] == user: + for relation in entry['relations']: + tmp = [] + if ("label" in relation) and ("head_span" in relation) and ("child_span" in relation): + if relation["label"] in rel_labels: + tmp.append([relation["head_span"]["token_start"], relation["head_span"]["token_end"], + relation["child_span"]["token_start"], relation["child_span"]["token_end"]]) + if text not in rel: + rel[text] = tmp + else: + old_rel = rel[text] + rel[text] = old_rel + tmp + return rel + +#Precision P = Number of correctly extracted entity relations / Total number of extracted entity relations +def calc_precision(other_rel, gold_rel): + precision = 0 + fp = 0 + tp = 0 + + for r in other_rel: + if r in gold_rel: + o = other_rel[r] + g = gold_rel[r] + + for x in o: + if x in g: + tp += 1 + else: + fp += 1 + + precision = (tp / (tp + fp)) + + return precision + +#Recall R = Number of correctly extracted entity relations / Actual number of extracted entity relations +def calc_recall(other_rel, gold_rel): + recall = 0 + fn = 0 + tp = 0 + count = 0 + + for r in other_rel: + if r in gold_rel: + count += 1 + o = other_rel[r] + g = gold_rel[r] + + for x in o: + if x in g: + tp += 1 + + for x in g: + if x not in o: + fn += 1 + + recall = (tp / (tp + fn)) + print(count) + return recall + +def calc_f1(precison, recall): + + f1 = 2 * ((precison * recall) / (precison + recall)) + + return f1 + +if __name__ == "__main__": + other_rel_Sampath = generate_spans("main_3_per_cluster-Sampath") + other_rel_Nikita = generate_spans("main_3_per_cluster-Nikita") + gold_rel = generate_spans("main_3_per_cluster-Kameron") + + p_sampath = calc_precision(other_rel_Sampath, gold_rel) + r_sampath = calc_recall(other_rel_Sampath, gold_rel) + f1_sampath = calc_f1(p_sampath, r_sampath) + print(f1_sampath) + + p_nikita = calc_precision(other_rel_Nikita, gold_rel) + r_nikita = calc_recall(other_rel_Nikita, gold_rel) + f1_nikita = calc_f1(p_nikita, r_nikita) + print(f1_nikita) + diff --git a/model_dev/entity_extraction/PURE/PURE_to_nerevaluate.py b/model_dev/entity_extraction/PURE/PURE_to_nerevaluate.py new file mode 100644 index 0000000..52b62a5 --- /dev/null +++ b/model_dev/entity_extraction/PURE/PURE_to_nerevaluate.py @@ -0,0 +1,47 @@ +import srsly +import json +from nervaluate import Evaluator + +base_path = "/Users/ash/Desktop/PURE/PURE/data_ent/" +actual_answers_file = "dev" +actual_file_path = base_path+actual_answers_file+".json" +actual_data = srsly.read_jsonl(actual_file_path) + +predicted_answers_file = "ent_pred_dev" +predicted_file_path = base_path+predicted_answers_file+".json" +predicted_data = srsly.read_jsonl(predicted_file_path) + +def create_nerevaluate_file(ans_file, ner_ent): + ans = [] + + for entry in ans_file: + for n in entry[ner_ent]: + tmp = [] + for x in n: + ans_dict = {} + ans_dict["start"] = x[0] + ans_dict["end"] = x[1] + ans_dict["label"] = x[2] + tmp.append(ans_dict) + ans.append(tmp) + + return ans + +actual_answers = create_nerevaluate_file(actual_data, "ner") +predicted_answers = create_nerevaluate_file(predicted_data, "predicted_ner") + +assert len(predicted_answers) == len(actual_answers) + +entities = ["base", "aspect_changing", "change_direction", "type_of"] +evaluator = Evaluator(actual_answers, predicted_answers, tags=entities) + +results, results_per_tag = evaluator.evaluate() +#print(results) +#print() +#print(results_per_tag) + +with open(base_path + "results_PURE.json", "w") as f: + json.dump(results, f, indent=4) +with open(base_path + "results_per_tag_PURE.json", "w") as f: + json.dump(results_per_tag, f, indent=4) + diff --git a/model_dev/entity_extraction/sample_checkin_answers.jsonl b/model_dev/entity_extraction/sample_checkin_answers.jsonl new file mode 100644 index 0000000..74bc184 --- /dev/null +++ b/model_dev/entity_extraction/sample_checkin_answers.jsonl @@ -0,0 +1,1122 @@ 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noqa: F401\r\n File \"/anaconda/envs/azureml_py38/lib/python3.8/site-packages/spacy/pipeline/__init__.py\", line 1, in \r\n from .attributeruler import AttributeRuler\r\n File \"/anaconda/envs/azureml_py38/lib/python3.8/site-packages/spacy/pipeline/attributeruler.py\", line 6, in \r\n from .pipe import Pipe\r\n File \"spacy/pipeline/pipe.pyx\", line 8, in init spacy.pipeline.pipe\r\n File \"/anaconda/envs/azureml_py38/lib/python3.8/site-packages/spacy/training/__init__.py\", line 11, in \r\n from .callbacks import create_copy_from_base_model # noqa: F401\r\n File \"/anaconda/envs/azureml_py38/lib/python3.8/site-packages/spacy/training/callbacks.py\", line 3, in \r\n from ..language import Language\r\n File \"/anaconda/envs/azureml_py38/lib/python3.8/site-packages/spacy/language.py\", line 23, in \r\n from .training.initialize import init_vocab, init_tok2vec\r\n File \"/anaconda/envs/azureml_py38/lib/python3.8/site-packages/spacy/training/initialize.py\", line 14, in \r\n from .pretrain import get_tok2vec_ref\r\n File \"/anaconda/envs/azureml_py38/lib/python3.8/site-packages/spacy/training/pretrain.py\", line 16, in \r\n from ..schemas import ConfigSchemaPretrain\r\n File \"/anaconda/envs/azureml_py38/lib/python3.8/site-packages/spacy/schemas.py\", line 200, in \r\n class TokenPatternOperator(str, Enum):\r\n File \"/anaconda/envs/azureml_py38/lib/python3.8/enum.py\", line 127, in __prepare__\r\n enum_dict = _EnumDict()\r\n File \"/anaconda/envs/azureml_py38/lib/python3.8/enum.py\", line 58, in __init__\r\n def __init__(self):\r\nKeyboardInterrupt\r\n" + } + ], + "execution_count": 44, + "metadata": { + "gather": { + "logged": 1664394649746 + } + }, + "id": "8e75dad9" + }, + { + "cell_type": "code", + "source": [ + "from __future__ import unicode_literals, print_function\n", + "import pickle\n", + "import plac\n", + "import random\n", + "from pathlib import Path\n", + "import spacy\n", + "from spacy.util import minibatch, compounding\n", + "from spacy.training import Example\n", + "import srsly\n", + "import pickle\n", + "import os\n", + "import csv\n", + "import numpy as np\n", + "import time\n", + "import json\n", + "from nervaluate import Evaluator" + ], + "outputs": [], + "execution_count": 1, + "metadata": { + "gather": { + "logged": 1664390567502 + } + }, + "id": "56eb7e8d" + }, + { + "cell_type": "code", + "source": [ + "spacy.__version__" + ], + "outputs": [ + { + "output_type": "execute_result", + "execution_count": 2, + "data": { + "text/plain": "'3.1.3'" + }, + "metadata": {} + } + ], + "execution_count": 2, + "metadata": { + "jupyter": { + "source_hidden": false, + "outputs_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + }, + "gather": { + "logged": 1664390567578 + } + }, + "id": "4f4879c1-665b-4414-bffb-bb0328f3d39e" + }, + { + "cell_type": "code", + "source": [ + "output_dir = \"data\"\n", + "result_dir = \"result\"" + ], + "outputs": [], + "execution_count": 21, + "metadata": { + "jupyter": { + "source_hidden": false, + "outputs_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + }, + "gather": { + "logged": 1664393915519 + } + }, + "id": "ea9e9f52-8b8d-4f06-bbe1-9ef57330e33e" + }, + { + "cell_type": "code", + "source": [ + "LABEL = [\"base\", \"aspect_changing\", \"change_direction\", \"type_of\"]\n", + "\n", + "with open (output_dir + \"/training_data.txt\", 'rb') as file:\n", + " TRAIN_DATA = pickle.load(file)\n", + "\n", + "with open (output_dir + \"/testing_data.txt\", 'rb') as file:\n", + " TEST_DATA = pickle.load(file)" + ], + "outputs": [], + "execution_count": 14, + "metadata": { + "gather": { + "logged": 1664390867667 + } + }, + "id": "191260bc" + }, + { + "cell_type": "code", + "source": [ + "model = \"en_core_web_md\"\n", + "new_model_name = \"custom_entity_extraction\"\n", + "n_iter = 200\n", + "batch_size = 50" + ], + "outputs": [], + "execution_count": 15, + "metadata": { + "gather": { + "logged": 1664390875215 + } + }, + "id": "2de44e61" + }, + { + "cell_type": "code", + "source": [ + "print(len(TRAIN_DATA))\n", + "print(len(TEST_DATA))" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": "449\n50\n" + } + ], + "execution_count": 16, + "metadata": { + "gather": { + "logged": 1664390876002 + } + }, + "id": "5a7b9f6c" + }, + { + "cell_type": "code", + "source": [ + "TEST_DATA" + ], + "outputs": [ + { + "output_type": "execute_result", + "execution_count": 25, + "data": { + "text/plain": "[('His research team compared known health impacts from air pollution against future climate scenarios to derive its projections. IMPLIED_BASE IMPLIED_BASE',\n {'entities': [(33, 39, 'base'),\n (40, 47, 'aspect_changing'),\n (53, 56, 'type_of'),\n (57, 66, 'base')]}),\n ('Among its impacts, the heatwave caused crop failures across Europe, fanned wildfires from Manchester in the UK to Yosemite National Park in the US and exposed more than 34,000 people to power outages in Los Angeles as the grid experienced an unprecedented demand for air conditioning. IMPLIED_BASE IMPLIED_BASE',\n {'entities': [(23, 31, 'base'),\n (39, 43, 'base'),\n (44, 52, 'aspect_changing'),\n (68, 74, 'change_direction'),\n (75, 84, 'base'),\n (186, 191, 'base'),\n (192, 199, 'aspect_changing'),\n (222, 226, 'base'),\n (256, 262, 'aspect_changing'),\n (267, 283, 'base')]}),\n ('Regenerative farming, rotational and mob grazing, reducing tillage, agroforestry, and soil improvements are all means to help mitigate the effects of extreme weather, and the resulting food shocks. IMPLIED_BASE IMPLIED_BASE',\n {'entities': [(0, 12, 'type_of'),\n (13, 20, 'base'),\n (22, 32, 'type_of'),\n (37, 40, 'type_of'),\n (41, 48, 'base'),\n (50, 58, 'change_direction'),\n (86, 90, 'base'),\n (91, 103, 'aspect_changing'),\n (150, 157, 'type_of'),\n (158, 165, 'base'),\n (185, 189, 'base'),\n (190, 196, 'aspect_changing'),\n (59, 66, 'base'),\n (68, 80, 'base')]}),\n ('Back in December 2018, a study of the 2017/18 heatwave in the Tasman Sea concluded the “overall intensity of the 2017/18 Tasman [heatwave] was virtually impossible without anthropogenic forcing”. IMPLIED_BASE IMPLIED_BASE',\n {'entities': [(46, 54, 'base'),\n (69, 72, 'type_of'),\n (96, 105, 'aspect_changing'),\n (129, 137, 'base'),\n (172, 193, 'base')]}),\n ('The risk of heat stroke, which is potentially fatal, increases because older adults may be less mobile, and thus less able to reach cooler locations in a heat wave. IMPLIED_BASE IMPLIED_BASE',\n {'entities': [(4, 8, 'aspect_changing'),\n (12, 23, 'base'),\n (53, 62, 'change_direction'),\n (71, 76, 'type_of'),\n (77, 83, 'base'),\n (91, 102, 'change_direction'),\n (113, 117, 'change_direction'),\n (118, 148, 'aspect_changing'),\n (154, 163, 'base'),\n (165, 177, 'base')]}),\n ('A record cold spell in Florida in 2010 induced genetic adaptation in the surviving Burmese pythons that made them more cold tolerant. IMPLIED_BASE IMPLIED_BASE',\n {'entities': [(2, 8, 'type_of'),\n (9, 19, 'base'),\n (47, 54, 'base'),\n (55, 65, 'aspect_changing'),\n (114, 118, 'change_direction'),\n (119, 132, 'aspect_changing'),\n (134, 146, 'base')]}),\n ('Tackling the social determinants of health is critical to address health inequities, which arise because people with the least social and economic power tend to have the worst health, live in unhealthier environments and have worse access to healthcare. IMPLIED_BASE IMPLIED_BASE',\n {'entities': [(66, 72, 'base'),\n (73, 83, 'aspect_changing'),\n (105, 111, 'base'),\n (112, 126, 'change_direction'),\n (127, 152, 'aspect_changing'),\n (176, 182, 'base')]}),\n ('The 26-year-old Maui native can tick off the dangers climate change poses to the ocean he loves: “Bleaching of reefs; the damage of the shoreline from stronger storms; the changing of wind patterns so that a particular wave might not be as good anymore; the shifting of currents, which could destroy a particular break along a coastline. IMPLIED_BASE IMPLIED_BASE',\n {'entities': [(53, 67, 'base'),\n (81, 86, 'base'),\n (98, 107, 'change_direction'),\n (111, 116, 'base'),\n (122, 128, 'aspect_changing'),\n (136, 145, 'base'),\n (151, 159, 'change_direction'),\n (160, 166, 'base'),\n (172, 180, 'change_direction'),\n (184, 188, 'base'),\n (189, 197, 'aspect_changing'),\n (219, 223, 'base'),\n (230, 239, 'change_direction'),\n (240, 244, 'type_of'),\n (258, 266, 'change_direction'),\n (270, 278, 'base'),\n (292, 299, 'change_direction'),\n (313, 318, 'aspect_changing'),\n (338, 350, 'base')]}),\n ('Migration toward cities that already face capacity constraints — due to environmental conditions, resource scarcity, and unemployment — “creates for further mayhem [and] civil unrest, leading to larger state-on-state conflict,” Goodman says. IMPLIED_BASE IMPLIED_BASE',\n {'entities': [(0, 9, 'base'),\n (10, 62, 'type_of'),\n (72, 85, 'base'),\n (86, 96, 'aspect_changing'),\n (98, 106, 'base'),\n (107, 115, 'aspect_changing'),\n (121, 133, 'base'),\n (157, 163, 'base'),\n (170, 175, 'type_of'),\n (176, 182, 'base'),\n (195, 201, 'change_direction'),\n (202, 216, 'type_of'),\n (217, 225, 'base')]}),\n ('\"This research shows that climate change has made temperatures better for spore germination and growth, and made crop canopies wetter, raising the risk of Black Sigatoka infection in many banana-growing areas of Latin America. IMPLIED_BASE IMPLIED_BASE',\n {'entities': [(26, 40, 'base'),\n (50, 62, 'aspect_changing'),\n (63, 69, 'change_direction'),\n (74, 79, 'base'),\n (80, 91, 'aspect_changing'),\n (96, 102, 'aspect_changing'),\n (113, 117, 'base'),\n (118, 126, 'aspect_changing'),\n (127, 133, 'change_direction'),\n (135, 142, 'change_direction'),\n (147, 151, 'aspect_changing'),\n (155, 169, 'type_of'),\n (170, 179, 'base'),\n (227, 239, 'base')]}),\n ('Climate change is linked to rising sea levels and the increase in rainfall associated with cyclones, thus causing more devastation in coastal but also inland areas. IMPLIED_BASE IMPLIED_BASE',\n {'entities': [(0, 14, 'base'),\n (28, 34, 'change_direction'),\n (35, 38, 'base'),\n (39, 45, 'aspect_changing'),\n (54, 62, 'change_direction'),\n (66, 74, 'aspect_changing'),\n (91, 99, 'base'),\n (114, 118, 'change_direction'),\n (119, 130, 'aspect_changing'),\n (165, 177, 'base')]}),\n (\"Climate change is exacerbating extreme weather events, and the world's most vulnerable children will bear the brunt of these disasters. IMPLIED_BASE IMPLIED_BASE\",\n {'entities': [(0, 14, 'base'),\n (18, 30, 'change_direction'),\n (31, 38, 'type_of'),\n (39, 46, 'base'),\n (47, 53, 'aspect_changing')]}),\n ('Climate change can make a sizable dent on economic growth by disrupting supply chains and demand for products, and creating harsh working conditions, among other issues. IMPLIED_BASE IMPLIED_BASE',\n {'entities': [(0, 14, 'base'),\n (34, 38, 'change_direction'),\n (42, 50, 'base'),\n (51, 57, 'aspect_changing'),\n (61, 71, 'change_direction'),\n (72, 85, 'base'),\n (90, 96, 'aspect_changing'),\n (101, 109, 'base'),\n (124, 129, 'type_of'),\n (130, 148, 'base')]}),\n ('In recent years, disease and other disturbances have caused forests to die, emitting carbon dioxide instead as they rot. IMPLIED_BASE IMPLIED_BASE',\n {'entities': [(17, 24, 'base'),\n (60, 67, 'base'),\n (71, 74, 'change_direction'),\n (76, 84, 'change_direction'),\n (85, 99, 'base'),\n (116, 119, 'change_direction'),\n (121, 133, 'base')]}),\n ('For the most part, disasters like floods and cyclones result in temporary displacement, though that could mean months at a time, and almost always within national borders. IMPLIED_BASE IMPLIED_BASE',\n {'entities': [(19, 28, 'base'),\n (34, 40, 'base'),\n (45, 53, 'base'),\n (64, 86, 'aspect_changing'),\n (172, 184, 'base')]}),\n ('In some areas like Indonesia, Randerson and his colleagues have found that the radiocarbon age of carbon emissions from peat fires is about 800 years, which is then added to the greenhouse gases in that atmosphere that drive global warming. IMPLIED_BASE IMPLIED_BASE',\n {'entities': [(98, 104, 'base'),\n (105, 114, 'aspect_changing'),\n (120, 124, 'type_of'),\n (125, 130, 'base'),\n (165, 170, 'change_direction'),\n (178, 188, 'type_of'),\n (225, 239, 'base')]}),\n ('A warming world also has another consequence that may be contributing to fire conditions persisting over multiple days where they otherwise might not have in the past: higher nighttime temperatures. IMPLIED_BASE IMPLIED_BASE',\n {'entities': [(2, 9, 'change_direction'),\n (10, 15, 'base'),\n (73, 77, 'base'),\n (78, 88, 'aspect_changing'),\n (89, 99, 'change_direction'),\n (168, 174, 'change_direction'),\n (175, 197, 'aspect_changing'),\n (199, 211, 'base')]}),\n ('As governments and nonprofits cope with the aftermath of Dorian, and the increased damage of similar hurricanes in the near future, experts warn that they need to be creating plans for decades into the future, when climate change is expected to make storms even more intense and unpredictable than they are today. IMPLIED_BASE IMPLIED_BASE',\n {'entities': [(215, 229, 'base'),\n (250, 256, 'base'),\n (262, 292, 'change_direction')]}),\n ('Although the data are sparse, we do in fact find a significant positive trend in coastal annual-mean rainfall 1948–2017 from TCs that stall, and we verify that this is due to increased stalling frequency. IMPLIED_BASE IMPLIED_BASE',\n {'entities': [(63, 71, 'change_direction'),\n (72, 100, 'aspect_changing'),\n (101, 109, 'base'),\n (125, 128, 'base'),\n (129, 139, 'type_of'),\n (175, 184, 'change_direction'),\n (185, 203, 'aspect_changing'),\n (205, 217, 'base')]}),\n ('In many regions, floods and water quality problems are likely to be worse because of climate change. IMPLIED_BASE IMPLIED_BASE',\n {'entities': [(17, 23, 'base'),\n (28, 33, 'base'),\n (34, 50, 'aspect_changing'),\n (68, 73, 'change_direction'),\n (85, 99, 'base')]}),\n ('The projected emergence of unprecedented high-temperature extremes in the tropics will consequently lead to significantly larger impacts on agriculture and ecosystems. IMPLIED_BASE IMPLIED_BASE',\n {'entities': [(14, 23, 'change_direction'),\n (41, 66, 'aspect_changing'),\n (122, 128, 'change_direction'),\n (129, 136, 'aspect_changing'),\n (140, 151, 'base'),\n (156, 166, 'base'),\n (168, 180, 'base')]}),\n ('Coastlines at northern latitudes, like those in New England, will face higher flood levels primarily because of sea level rise. IMPLIED_BASE IMPLIED_BASE',\n {'entities': [(71, 77, 'change_direction'),\n (78, 83, 'base'),\n (84, 90, 'aspect_changing'),\n (112, 115, 'base'),\n (116, 121, 'aspect_changing'),\n (122, 126, 'change_direction')]}),\n ('Some of the negative health effects of climate change are already upon us, but it’s not all doom and gloom. IMPLIED_BASE IMPLIED_BASE',\n {'entities': [(12, 20, 'change_direction'),\n (21, 27, 'base'),\n (28, 35, 'aspect_changing'),\n (39, 53, 'base')]}),\n ('Climate change will affect the fundamental determinants of human health including “water, air, food quality and quantity, ecosystems, agriculture, livelihoods and infrastructure” [2] (p. 393). IMPLIED_BASE IMPLIED_BASE',\n {'entities': [(0, 14, 'base'),\n (31, 55, 'aspect_changing'),\n (59, 64, 'type_of'),\n (65, 71, 'base'),\n (83, 88, 'base'),\n (90, 93, 'base'),\n (95, 99, 'base'),\n (100, 107, 'aspect_changing'),\n (112, 120, 'aspect_changing'),\n (122, 132, 'base'),\n (134, 145, 'base'),\n (147, 158, 'base'),\n (163, 177, 'base')]}),\n ('California fires took a half-dozen lives and were so severe as to cause a national state of emergency; the 2018 Mendocino Complex Fire was the largest in California history. IMPLIED_BASE IMPLIED_BASE',\n {'entities': [(11, 16, 'base'),\n (17, 21, 'change_direction'),\n (35, 40, 'base'),\n (53, 59, 'type_of'),\n (74, 82, 'type_of'),\n (83, 101, 'base')]}),\n ('Improved human health: Many studies have found connections between exposure to nature and better mental and physical health. IMPLIED_BASE IMPLIED_BASE',\n {'entities': [(67, 75, 'aspect_changing'),\n (79, 85, 'base'),\n (90, 96, 'change_direction'),\n (97, 103, 'type_of'),\n (108, 116, 'type_of'),\n (117, 123, 'base')]}),\n ('Heat reduction: Trees provide shade for homes, office buildings, parks and roadways, cooling surface temperatures. IMPLIED_BASE IMPLIED_BASE',\n {'entities': [(16, 21, 'base'),\n (30, 35, 'base'),\n (85, 92, 'change_direction'),\n (93, 113, 'aspect_changing'),\n (115, 127, 'base')]}),\n ('As global temperatures rise, climate change’s impacts on mental health are becoming increasingly evident. IMPLIED_BASE IMPLIED_BASE',\n {'entities': [(3, 22, 'aspect_changing'),\n (23, 27, 'change_direction'),\n (29, 43, 'base'),\n (46, 53, 'aspect_changing'),\n (57, 63, 'type_of'),\n (64, 70, 'base'),\n (106, 118, 'base')]}),\n ('Philip Alston, UN special rapporteur on extreme poverty and human rights, said the impacts of global heating are likely to undermine not only basic rights to life, water, food, and housing for hundreds of millions of people, but also democracy and the rule of law. IMPLIED_BASE IMPLIED_BASE',\n {'entities': [(83, 90, 'aspect_changing'),\n (94, 108, 'base'),\n (123, 132, 'change_direction'),\n (142, 154, 'base'),\n (158, 162, 'type_of'),\n (164, 169, 'type_of'),\n (171, 175, 'type_of'),\n (181, 188, 'type_of'),\n (234, 243, 'base'),\n (252, 263, 'base')]}),\n ('Air pollution has improved dramatically over the past four decades because of federal rules. IMPLIED_BASE IMPLIED_BASE',\n {'entities': [(0, 3, 'type_of'),\n (4, 13, 'base'),\n (18, 26, 'change_direction'),\n (78, 85, 'type_of'),\n (86, 91, 'base')]}),\n ('\"If we look at deaths avoided per 100,000 people,\" Lo added, \"Miami and Detroit would have the highest numbers of heat-related deaths avoided among the 15 cities that we studied.\" IMPLIED_BASE IMPLIED_BASE',\n {'entities': [(42, 48, 'base'), (114, 118, 'base'), (127, 133, 'base')]}),\n ('Storms such as Hurricanes Andrew, Irma, and Katrina exemplify how major weather events magnified by global warming can have long-lasting effects on the economy. IMPLIED_BASE IMPLIED_BASE',\n {'entities': [(0, 6, 'base'),\n (72, 79, 'base'),\n (80, 86, 'aspect_changing'),\n (87, 96, 'change_direction'),\n (100, 114, 'base'),\n (137, 144, 'aspect_changing'),\n (152, 159, 'base')]}),\n ('Noting that carbon stays in the atmosphere for centuries, at least, and that a United Nations panel found in 2007 that climate change is “likely to adversely affect hundreds of millions of people through increased coastal flooding, reductions in water supplies, increased malnutrition and increased health impacts” in the next 100 years, Professor Nolt did a lot of division and multiplication and arrived at a stark conclusion: IMPLIED_BASE IMPLIED_BASE',\n {'entities': [(119, 133, 'base'),\n (148, 164, 'change_direction'),\n (189, 195, 'base'),\n (204, 213, 'change_direction'),\n (214, 221, 'type_of'),\n (222, 230, 'base'),\n (232, 242, 'change_direction'),\n (246, 251, 'base'),\n (252, 260, 'aspect_changing'),\n (262, 271, 'change_direction'),\n (272, 284, 'base'),\n (289, 298, 'change_direction'),\n (299, 305, 'base'),\n (306, 313, 'aspect_changing')]}),\n ('Another change in hurricanes that’s clearly linked to climate change is a tendency to intensify more rapidly. IMPLIED_BASE IMPLIED_BASE',\n {'entities': [(18, 28, 'base'),\n (54, 68, 'base'),\n (74, 108, 'aspect_changing')]}),\n ('The authors blame the poor air quality on car emissions, the burning of fossil fuels and the impacts of climate change, such as wildfires. IMPLIED_BASE IMPLIED_BASE',\n {'entities': [(22, 26, 'change_direction'),\n (27, 30, 'base'),\n (31, 38, 'aspect_changing'),\n (42, 45, 'base'),\n (46, 55, 'aspect_changing'),\n (61, 68, 'change_direction'),\n (72, 84, 'base'),\n (104, 118, 'base'),\n (128, 137, 'base')]}),\n ('Intense storms are more frequent as climates shift and the mangroves protect the coast from eroding, according to scientists. IMPLIED_BASE IMPLIED_BASE',\n {'entities': [(0, 7, 'type_of'),\n (8, 14, 'base'),\n (19, 32, 'change_direction'),\n (36, 44, 'base'),\n (45, 50, 'change_direction'),\n (59, 68, 'base'),\n (81, 86, 'base'),\n (92, 99, 'change_direction')]}),\n ('DiPerna says hotels are extremely vulnerable to the rise in extreme weather, perhaps more than any other client-facing industry. IMPLIED_BASE IMPLIED_BASE',\n {'entities': [(13, 19, 'base'),\n (52, 56, 'change_direction'),\n (60, 67, 'type_of'),\n (68, 75, 'base'),\n (105, 118, 'type_of'),\n (119, 127, 'base')]}),\n ('Crop shortages will likely result in higher prices for consumers and since corn and soy are basically in every part of the American diet, that could be a real problem. IMPLIED_BASE IMPLIED_BASE',\n {'entities': [(0, 4, 'base'),\n (5, 14, 'aspect_changing'),\n (37, 43, 'change_direction'),\n (44, 50, 'aspect_changing'),\n (75, 79, 'base'),\n (84, 87, 'base'),\n (123, 131, 'type_of'),\n (132, 136, 'base'),\n (168, 180, 'base')]}),\n ('The natural ocean cycle of El Niño is a key factor in pushing up temperatures in some parts of the ocean and the effect of global warming on the phenomenon remains uncertain, but the gradual overall heating of the oceans means heatwaves are worse when they strike. IMPLIED_BASE IMPLIED_BASE',\n {'entities': [(12, 23, 'base'),\n (27, 35, 'type_of'),\n (55, 65, 'change_direction'),\n (66, 78, 'aspect_changing'),\n (100, 105, 'base'),\n (184, 207, 'change_direction'),\n (215, 221, 'base'),\n (228, 237, 'base'),\n (242, 247, 'type_of')]}),\n ('In 2016, the annually-averaged ocean surface temperature for the world was 1.35 degrees Fahrenheit (0.75 degrees Celsius) higher than the 20th century average, continuing a steady climb in ocean surface temperatures over the past three decades.43,44 Record ocean temperatures have contributed to a decline in sea ice levels. IMPLIED_BASE IMPLIED_BASE',\n {'entities': [(257, 262, 'base'),\n (263, 275, 'aspect_changing'),\n (298, 305, 'change_direction'),\n (309, 312, 'type_of'),\n (313, 316, 'base'),\n (317, 323, 'aspect_changing')]}),\n ('There are studies linking heat to lowered cognitive function, an increased heart rate, higher testosterone production and even a perception of time passing more slowly than normal. IMPLIED_BASE IMPLIED_BASE',\n {'entities': [(26, 30, 'base'),\n (34, 41, 'change_direction'),\n (42, 51, 'base'),\n (52, 60, 'aspect_changing'),\n (65, 74, 'change_direction'),\n (75, 80, 'base'),\n (81, 85, 'aspect_changing'),\n (87, 93, 'change_direction'),\n (94, 106, 'base'),\n (107, 117, 'aspect_changing'),\n (129, 139, 'base'),\n (143, 147, 'type_of'),\n (156, 179, 'change_direction')]}),\n ('Economic DeprivationClimate change is projected to have a detrimental impact on economies worldwide, including reduced crop yields, less grazing land, and the loss of homes and jobs due to wildfires and flooding (IPCC, 2007). IMPLIED_BASE IMPLIED_BASE',\n {'entities': [(9, 34, 'base'),\n (58, 69, 'aspect_changing'),\n (70, 76, 'aspect_changing'),\n (80, 89, 'base'),\n (111, 118, 'change_direction'),\n (119, 123, 'base'),\n (124, 130, 'aspect_changing'),\n (132, 136, 'change_direction'),\n (137, 144, 'type_of'),\n (145, 149, 'base'),\n (159, 163, 'change_direction'),\n (167, 172, 'base'),\n (177, 181, 'base'),\n (189, 198, 'base'),\n (203, 211, 'base')]}),\n ('Although there are many such indirect pathways to increased violence, we outline four that clearly link rapid climate change to the development of violence-prone adults: food insecurity, economic deprivation, susceptibility to terrorism, and preferential ingroup treatment. IMPLIED_BASE IMPLIED_BASE',\n {'entities': [(50, 59, 'change_direction'),\n (60, 68, 'base'),\n (104, 109, 'type_of'),\n (110, 124, 'base'),\n (132, 143, 'change_direction'),\n (147, 161, 'type_of'),\n (162, 168, 'base'),\n (170, 185, 'base'),\n (187, 195, 'type_of'),\n (196, 207, 'base'),\n (209, 223, 'aspect_changing'),\n (227, 236, 'base'),\n (242, 272, 'base')]}),\n ('But climate change and extreme weather events, such as recent flooding that cut road access to many remote communities for several weeks, are making it ever-more difficult. IMPLIED_BASE IMPLIED_BASE',\n {'entities': [(4, 18, 'base'),\n (23, 30, 'type_of'),\n (31, 38, 'base'),\n (39, 45, 'aspect_changing'),\n (62, 70, 'base'),\n (76, 79, 'change_direction'),\n (80, 84, 'base'),\n (85, 91, 'aspect_changing'),\n (152, 171, 'change_direction'),\n (173, 185, 'base')]}),\n ('Some saw parents killed or lost in the flooding that followed the storms. IMPLIED_BASE IMPLIED_BASE',\n {'entities': [(9, 16, 'base'),\n (17, 23, 'change_direction'),\n (27, 31, 'change_direction'),\n (39, 47, 'base'),\n (66, 72, 'base')]}),\n ('A warming climate could also mean increased extinction risk for a large fraction of species, the spread of diseases, an undermining of food security, and reduced renewable surface water and groundwater resources. IMPLIED_BASE IMPLIED_BASE',\n {'entities': [(2, 9, 'change_direction'),\n (10, 17, 'base'),\n (34, 43, 'change_direction'),\n (44, 59, 'aspect_changing'),\n (64, 91, 'base'),\n (97, 103, 'change_direction'),\n (107, 115, 'base'),\n (120, 131, 'change_direction'),\n (135, 148, 'base'),\n (154, 161, 'change_direction'),\n (162, 171, 'type_of'),\n (172, 179, 'type_of'),\n (180, 185, 'base'),\n (190, 201, 'base'),\n (202, 211, 'aspect_changing')]}),\n ('Even droughts can make breathing harder. IMPLIED_BASE IMPLIED_BASE',\n {'entities': [(5, 13, 'base'),\n (23, 32, 'base'),\n (33, 39, 'change_direction')]}),\n ('Wildfires spew particulates, too, along with other pollutants. IMPLIED_BASE IMPLIED_BASE',\n {'entities': [(0, 9, 'base'), (15, 27, 'base'), (51, 61, 'base')]}),\n ('But Mongolia’s nomads are also being pushed off the land, as mining accelerates desertification of grasslands, through the mines’ heavy groundwater use and destruction of vegetation. IMPLIED_BASE IMPLIED_BASE',\n {'entities': [(61, 67, 'base'),\n (68, 79, 'change_direction'),\n (80, 95, 'aspect_changing'),\n (99, 109, 'base'),\n (130, 135, 'change_direction'),\n (136, 147, 'base'),\n (148, 151, 'change_direction'),\n (156, 167, 'aspect_changing'),\n (171, 181, 'base')]}),\n ('A new report from the Congressional Budget Office estimates annual losses from damage linked to hurricanes and flooding at $54 billion under current conditions (which are changing rapidly). IMPLIED_BASE IMPLIED_BASE',\n {'entities': [(60, 73, 'aspect_changing'),\n (79, 85, 'aspect_changing'),\n (96, 106, 'base'),\n (111, 119, 'base'),\n (190, 202, 'base'),\n (203, 215, 'base')]})]" + }, + "metadata": {} + } + ], + "execution_count": 25, + "metadata": { + "gather": { + "logged": 1664394043049 + } + }, + "id": "add7e3f5" + }, + { + "cell_type": "code", + "source": [ + "print(\"starting time: \" + time.strftime('%m/%d/%Y %H:%M:%S', time.localtime(time.time())))\n", + "if model is not None:\n", + " nlp = spacy.load(model)\n", + " print(\"Loaded model '%s'\" % model)\n", + "else:\n", + " nlp = spacy.blank('en')\n", + " print(\"Created blank 'en' model\")\n", + " \n", + "if 'ner' not in nlp.pipe_names:\n", + " ner = nlp.create_pipe('ner')\n", + " nlp.add_pipe(ner)\n", + "else:\n", + " ner = nlp.get_pipe('ner')\n", + "\n", + "for i in LABEL:\n", + " # Adding new entity\n", + " ner.add_label(i)\n", + "\n", + "if model is None:\n", + " optimizer = nlp.begin_training()\n", + "else:\n", + " #optimizer = nlp.entity.create_optimizer()\n", + " optimizer = nlp.create_optimizer()\n", + " \n", + "# Get names of other pipes to disable them during training to train only NER\n", + "other_pipes = [pipe for pipe in nlp.pipe_names if pipe != 'ner']\n", + "with nlp.disable_pipes(*other_pipes): # only train NER\n", + " for itn in range(n_iter):\n", + " random.shuffle(TRAIN_DATA)\n", + " losses = {}\n", + " batches = minibatch(TRAIN_DATA, size=compounding(4., 32., 1.001))\n", + " #for batch in batches:\n", + " # texts, annotations = zip(*batch)\n", + " # nlp.update(texts, annotations, sgd=optimizer, drop=0.2, losses=losses)\n", + " #print('Losses', losses)\n", + " for batch in spacy.util.minibatch(TRAIN_DATA, size=batch_size):\n", + " for text, annotations in batch:\n", + " doc = nlp.make_doc(text)\n", + " example = Example.from_dict(doc, annotations)\n", + " nlp.update([example], drop=0.35, sgd=optimizer, losses=losses)\n", + " print(str(itn) + \" loss: \", losses)\n", + " \n", + "# Save model \n", + "if output_dir is not None:\n", + " output_dir = Path(output_dir)\n", + " if not output_dir.exists():\n", + " output_dir.mkdir()\n", + " nlp.meta['name'] = new_model_name # rename model\n", + " nlp.to_disk(output_dir)\n", + " print(\"Saved model to\", output_dir)\n", + "\n", + "print(\"ending time: \" + time.strftime('%m/%d/%Y %H:%M:%S', time.localtime(time.time())))" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": "starting time: 09/28/2022 18:48:32\nLoaded model 'en_core_web_md'\n0 loss: {'ner': 4627.446830126743}\n1 loss: {'ner': 3614.1820618331544}\n2 loss: {'ner': 3152.8385711346823}\n3 loss: {'ner': 2836.829549912623}\n4 loss: {'ner': 2551.450287978364}\n5 loss: {'ner': 2360.723427600743}\n6 loss: {'ner': 2183.280440711687}\n7 loss: {'ner': 2103.070454425247}\n8 loss: {'ner': 1955.910360846576}\n9 loss: {'ner': 1968.3071469821134}\n10 loss: {'ner': 1816.3685661763102}\n11 loss: {'ner': 1744.216094576551}\n12 loss: {'ner': 1637.8667575899863}\n13 loss: {'ner': 1564.1229956903442}\n14 loss: {'ner': 1471.4620383356441}\n15 loss: {'ner': 1367.3956451999088}\n16 loss: {'ner': 1377.172620356496}\n17 loss: {'ner': 1297.544757553306}\n18 loss: {'ner': 1284.4496535209314}\n19 loss: {'ner': 1204.3186212644034}\n20 loss: {'ner': 1138.6926340063276}\n21 loss: {'ner': 1235.3325205859392}\n22 loss: {'ner': 1089.465987110618}\n23 loss: {'ner': 1131.4532132360669}\n24 loss: {'ner': 1071.0334421993246}\n25 loss: {'ner': 1049.6042716003878}\n26 loss: {'ner': 1017.0971025486042}\n27 loss: {'ner': 931.5932576794468}\n28 loss: {'ner': 924.4366085693342}\n29 loss: {'ner': 886.6799800545364}\n30 loss: {'ner': 889.530344080408}\n31 loss: {'ner': 817.7273675748677}\n32 loss: {'ner': 842.557590897665}\n33 loss: {'ner': 815.95325780789}\n34 loss: {'ner': 787.3847793653091}\n35 loss: {'ner': 776.334201297934}\n36 loss: {'ner': 771.6018036529664}\n37 loss: {'ner': 788.7768826002424}\n38 loss: {'ner': 716.5837454737837}\n39 loss: {'ner': 712.3726916336867}\n40 loss: {'ner': 693.0674051201519}\n41 loss: {'ner': 646.7892829151754}\n42 loss: {'ner': 676.0029188346005}\n43 loss: {'ner': 689.5475661005879}\n44 loss: {'ner': 693.8607682621014}\n45 loss: {'ner': 678.9627961055184}\n46 loss: {'ner': 696.4089518039324}\n47 loss: {'ner': 614.2609061818669}\n48 loss: {'ner': 603.9849875582212}\n49 loss: {'ner': 637.6786851458523}\n50 loss: {'ner': 634.8899759671217}\n51 loss: {'ner': 520.6705640892098}\n52 loss: {'ner': 583.4211539943703}\n53 loss: {'ner': 590.1733797246995}\n54 loss: {'ner': 567.1455799020044}\n55 loss: {'ner': 565.0507200211777}\n56 loss: {'ner': 575.8923159230889}\n57 loss: {'ner': 563.8583917031618}\n58 loss: {'ner': 545.0745039238806}\n59 loss: {'ner': 511.6298399295031}\n60 loss: {'ner': 527.9644694638876}\n61 loss: {'ner': 475.80184858101603}\n62 loss: {'ner': 529.316947606795}\n63 loss: {'ner': 502.1024680119334}\n64 loss: {'ner': 519.7979021901526}\n65 loss: {'ner': 491.58109333539215}\n66 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243.30821272993327}\n145 loss: {'ner': 259.7504959395019}\n146 loss: {'ner': 230.08687476153074}\n147 loss: {'ner': 226.88264986739125}\n148 loss: {'ner': 270.78037372000813}\n149 loss: {'ner': 284.0446218158415}\n150 loss: {'ner': 254.46343188595733}\n151 loss: {'ner': 243.10216508651033}\n152 loss: {'ner': 280.9748124543675}\n153 loss: {'ner': 286.23046847656735}\n154 loss: {'ner': 267.1888578503644}\n155 loss: {'ner': 268.05159169787737}\n156 loss: {'ner': 234.79845114785397}\n157 loss: {'ner': 289.6967007220469}\n158 loss: {'ner': 269.9239246904635}\n159 loss: {'ner': 264.55331660777927}\n160 loss: {'ner': 291.59587634154377}\n161 loss: {'ner': 246.20283233566263}\n162 loss: {'ner': 284.95653567414627}\n163 loss: {'ner': 240.6781748763379}\n164 loss: {'ner': 241.27129546723611}\n165 loss: {'ner': 237.78058412953217}\n166 loss: {'ner': 245.98373893606365}\n167 loss: {'ner': 220.81708261945585}\n168 loss: {'ner': 218.01139796313268}\n169 loss: {'ner': 246.94403073138818}\n170 loss: {'ner': 242.7901327242892}\n171 loss: {'ner': 269.8946341040147}\n172 loss: {'ner': 282.27152310344036}\n173 loss: {'ner': 258.17269829689883}\n174 loss: {'ner': 256.5938560831316}\n175 loss: {'ner': 233.0437624370013}\n176 loss: {'ner': 211.5189623507946}\n177 loss: {'ner': 253.07924509286}\n178 loss: {'ner': 275.00602464875567}\n179 loss: {'ner': 231.0286253833267}\n180 loss: {'ner': 232.8498481260817}\n181 loss: {'ner': 226.4740504460195}\n182 loss: {'ner': 253.85516085566667}\n183 loss: {'ner': 202.12165404374366}\n184 loss: {'ner': 208.4310410951603}\n185 loss: {'ner': 271.0896026243226}\n186 loss: {'ner': 208.83265557602428}\n187 loss: {'ner': 207.9098880021925}\n188 loss: {'ner': 200.66665557618833}\n189 loss: {'ner': 272.73987494572094}\n190 loss: {'ner': 278.8354613887889}\n191 loss: {'ner': 216.5242893905811}\n192 loss: {'ner': 224.42093539132733}\n193 loss: {'ner': 226.6322979864577}\n194 loss: {'ner': 241.16559448161652}\n195 loss: {'ner': 230.43593073077756}\n196 loss: {'ner': 193.14211659579766}\n197 loss: {'ner': 221.58755686291906}\n198 loss: {'ner': 237.70286581068717}\n199 loss: {'ner': 207.2084063749481}\nSaved model to data\nending time: 09/28/2022 19:38:34\n" + } + ], + "execution_count": 20, + "metadata": { + "gather": { + "logged": 1664393914837 + } + }, + "id": "fdb0e821" + }, + { + "cell_type": "code", + "source": [ + "# Test the saved model\n", + "print(\"Loading from\", output_dir)\n", + "nlp_test = spacy.load(output_dir)" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": "Loading from data\n" + } + ], + "execution_count": 22, + "metadata": { + "gather": { + "logged": 1664393935759 + } + }, + "id": "48674ab2" + }, + { + "cell_type": "code", + "source": [ + "result = os.path.join(output_dir, \"spacy_predictions.txt\")\n", + "\n", + "final_res = []\n", + "PRED_DATA = []\n", + "for sent in TEST_DATA:\n", + " doc2 = nlp_test(sent[0])\n", + " PRED_DATA.append(doc2.to_json()[\"ents\"])\n" + ], + "outputs": [], + 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{'start': 83, 'end': 88, 'label': 'type_of'},\n {'start': 90, 'end': 93, 'label': 'type_of'},\n {'start': 95, 'end': 99, 'label': 'base'},\n {'start': 100, 'end': 107, 'label': 'aspect_changing'},\n {'start': 112, 'end': 120, 'label': 'aspect_changing'},\n {'start': 122, 'end': 132, 'label': 'base'},\n {'start': 134, 'end': 145, 'label': 'base'},\n {'start': 163, 'end': 177, 'label': 'base'}],\n [{'start': 11, 'end': 16, 'label': 'base'},\n {'start': 35, 'end': 40, 'label': 'base'},\n {'start': 74, 'end': 91, 'label': 'aspect_changing'},\n {'start': 92, 'end': 101, 'label': 'type_of'},\n {'start': 165, 'end': 172, 'label': 'base'}],\n [{'start': 9, 'end': 14, 'label': 'type_of'},\n {'start': 15, 'end': 21, 'label': 'base'},\n {'start': 67, 'end': 75, 'label': 'aspect_changing'},\n {'start': 90, 'end': 96, 'label': 'change_direction'},\n {'start': 97, 'end': 103, 'label': 'type_of'},\n {'start': 108, 'end': 116, 'label': 'type_of'},\n {'start': 117, 'end': 123, 'label': 'base'}],\n [{'start': 0, 'end': 4, 'label': 'base'},\n {'start': 5, 'end': 14, 'label': 'change_direction'},\n {'start': 16, 'end': 21, 'label': 'base'},\n {'start': 30, 'end': 35, 'label': 'base'},\n {'start': 40, 'end': 45, 'label': 'type_of'},\n {'start': 47, 'end': 53, 'label': 'type_of'},\n {'start': 54, 'end': 63, 'label': 'base'},\n {'start': 65, 'end': 70, 'label': 'base'},\n {'start': 75, 'end': 83, 'label': 'base'},\n {'start': 93, 'end': 100, 'label': 'type_of'},\n {'start': 101, 'end': 113, 'label': 'aspect_changing'},\n {'start': 115, 'end': 127, 'label': 'base'}],\n [{'start': 3, 'end': 22, 'label': 'aspect_changing'},\n {'start': 23, 'end': 27, 'label': 'change_direction'},\n {'start': 29, 'end': 43, 'label': 'base'},\n {'start': 46, 'end': 53, 'label': 'aspect_changing'},\n {'start': 57, 'end': 63, 'label': 'type_of'},\n {'start': 64, 'end': 70, 'label': 'base'},\n {'start': 75, 'end': 104, 'label': 'change_direction'},\n {'start': 106, 'end': 118, 'label': 'base'}],\n [{'start': 48, 'end': 55, 'label': 'base'},\n {'start': 60, 'end': 65, 'label': 'type_of'},\n {'start': 66, 'end': 72, 'label': 'base'},\n {'start': 83, 'end': 90, 'label': 'aspect_changing'},\n {'start': 94, 'end': 108, 'label': 'base'},\n {'start': 158, 'end': 162, 'label': 'base'},\n {'start': 164, 'end': 169, 'label': 'base'},\n {'start': 171, 'end': 175, 'label': 'base'},\n {'start': 181, 'end': 188, 'label': 'base'},\n {'start': 217, 'end': 223, 'label': 'base'},\n {'start': 234, 'end': 243, 'label': 'base'},\n {'start': 252, 'end': 256, 'label': 'type_of'},\n {'start': 265, 'end': 277, 'label': 'base'}],\n [{'start': 0, 'end': 3, 'label': 'type_of'},\n {'start': 4, 'end': 13, 'label': 'base'},\n {'start': 78, 'end': 85, 'label': 'type_of'},\n {'start': 86, 'end': 91, 'label': 'base'}],\n [{'start': 15, 'end': 21, 'label': 'base'},\n {'start': 103, 'end': 110, 'label': 'aspect_changing'},\n {'start': 114, 'end': 126, 'label': 'type_of'},\n {'start': 127, 'end': 133, 'label': 'base'},\n {'start': 180, 'end': 192, 'label': 'base'}],\n [{'start': 0, 'end': 6, 'label': 'base'},\n {'start': 15, 'end': 25, 'label': 'base'},\n {'start': 66, 'end': 71, 'label': 'type_of'},\n {'start': 72, 'end': 79, 'label': 'base'},\n {'start': 80, 'end': 86, 'label': 'aspect_changing'},\n {'start': 87, 'end': 96, 'label': 'change_direction'},\n {'start': 100, 'end': 114, 'label': 'base'},\n {'start': 124, 'end': 136, 'label': 'aspect_changing'},\n {'start': 152, 'end': 159, 'label': 'base'}],\n [{'start': 12, 'end': 24, 'label': 'base'},\n {'start': 119, 'end': 133, 'label': 'base'},\n {'start': 148, 'end': 164, 'label': 'change_direction'},\n {'start': 189, 'end': 195, 'label': 'base'},\n {'start': 204, 'end': 213, 'label': 'change_direction'},\n {'start': 214, 'end': 221, 'label': 'type_of'},\n {'start': 222, 'end': 230, 'label': 'base'},\n {'start': 246, 'end': 260, 'label': 'base'},\n {'start': 262, 'end': 271, 'label': 'change_direction'},\n {'start': 272, 'end': 284, 'label': 'base'},\n {'start': 289, 'end': 298, 'label': 'change_direction'},\n {'start': 299, 'end': 305, 'label': 'base'},\n {'start': 306, 'end': 313, 'label': 'aspect_changing'},\n {'start': 366, 'end': 374, 'label': 'base'},\n {'start': 379, 'end': 393, 'label': 'base'},\n {'start': 429, 'end': 441, 'label': 'base'}],\n [{'start': 8, 'end': 14, 'label': 'aspect_changing'},\n {'start': 18, 'end': 28, 'label': 'base'},\n {'start': 54, 'end': 68, 'label': 'base'},\n {'start': 86, 'end': 95, 'label': 'change_direction'},\n {'start': 110, 'end': 122, 'label': 'base'}],\n [{'start': 22, 'end': 26, 'label': 'change_direction'},\n {'start': 27, 'end': 30, 'label': 'base'},\n {'start': 31, 'end': 38, 'label': 'aspect_changing'},\n {'start': 42, 'end': 45, 'label': 'base'},\n {'start': 46, 'end': 55, 'label': 'aspect_changing'},\n {'start': 61, 'end': 68, 'label': 'change_direction'},\n {'start': 72, 'end': 78, 'label': 'type_of'},\n {'start': 79, 'end': 84, 'label': 'base'},\n {'start': 104, 'end': 118, 'label': 'base'},\n {'start': 128, 'end': 137, 'label': 'base'},\n {'start': 139, 'end': 151, 'label': 'base'}],\n [{'start': 0, 'end': 7, 'label': 'type_of'},\n {'start': 8, 'end': 14, 'label': 'base'},\n {'start': 19, 'end': 32, 'label': 'change_direction'},\n {'start': 59, 'end': 76, 'label': 'base'},\n {'start': 92, 'end': 99, 'label': 'change_direction'}],\n [{'start': 13, 'end': 19, 'label': 'base'},\n {'start': 52, 'end': 56, 'label': 'change_direction'},\n {'start': 60, 'end': 67, 'label': 'type_of'},\n {'start': 68, 'end': 75, 'label': 'base'},\n {'start': 112, 'end': 118, 'label': 'change_direction'},\n {'start': 119, 'end': 127, 'label': 'base'},\n {'start': 129, 'end': 141, 'label': 'base'}],\n [{'start': 0, 'end': 4, 'label': 'base'},\n {'start': 5, 'end': 14, 'label': 'aspect_changing'},\n {'start': 37, 'end': 43, 'label': 'change_direction'},\n {'start': 44, 'end': 50, 'label': 'aspect_changing'},\n {'start': 75, 'end': 79, 'label': 'type_of'},\n {'start': 84, 'end': 87, 'label': 'base'},\n {'start': 132, 'end': 136, 'label': 'base'}],\n [{'start': 4, 'end': 23, 'label': 'aspect_changing'},\n {'start': 27, 'end': 35, 'label': 'base'},\n {'start': 66, 'end': 78, 'label': 'aspect_changing'},\n {'start': 124, 'end': 138, 'label': 'base'},\n {'start': 200, 'end': 207, 'label': 'change_direction'},\n {'start': 215, 'end': 221, 'label': 'base'},\n {'start': 228, 'end': 237, 'label': 'base'},\n {'start': 242, 'end': 247, 'label': 'change_direction'}],\n [{'start': 37, 'end': 56, 'label': 'aspect_changing'},\n {'start': 75, 'end': 87, 'label': 'change_direction'},\n {'start': 195, 'end': 215, 'label': 'aspect_changing'},\n {'start': 257, 'end': 275, 'label': 'aspect_changing'},\n {'start': 298, 'end': 305, 'label': 'change_direction'},\n {'start': 309, 'end': 312, 'label': 'type_of'},\n {'start': 313, 'end': 316, 'label': 'base'},\n {'start': 317, 'end': 323, 'label': 'aspect_changing'}],\n [{'start': 26, 'end': 30, 'label': 'base'},\n {'start': 34, 'end': 41, 'label': 'type_of'},\n {'start': 42, 'end': 51, 'label': 'base'},\n {'start': 52, 'end': 60, 'label': 'aspect_changing'},\n {'start': 65, 'end': 74, 'label': 'change_direction'},\n {'start': 75, 'end': 80, 'label': 'base'},\n {'start': 81, 'end': 85, 'label': 'aspect_changing'},\n {'start': 87, 'end': 93, 'label': 'change_direction'},\n {'start': 94, 'end': 106, 'label': 'type_of'},\n {'start': 107, 'end': 117, 'label': 'aspect_changing'},\n {'start': 129, 'end': 139, 'label': 'aspect_changing'},\n {'start': 156, 'end': 179, 'label': 'change_direction'}],\n [{'start': 0, 'end': 8, 'label': 'type_of'},\n {'start': 9, 'end': 34, 'label': 'base'},\n {'start': 58, 'end': 69, 'label': 'change_direction'},\n {'start': 80, 'end': 89, 'label': 'base'},\n {'start': 111, 'end': 118, 'label': 'change_direction'},\n {'start': 119, 'end': 123, 'label': 'base'},\n {'start': 124, 'end': 130, 'label': 'aspect_changing'},\n {'start': 132, 'end': 136, 'label': 'change_direction'},\n {'start': 137, 'end': 149, 'label': 'base'},\n {'start': 159, 'end': 163, 'label': 'aspect_changing'},\n {'start': 167, 'end': 172, 'label': 'base'},\n {'start': 177, 'end': 181, 'label': 'base'},\n {'start': 189, 'end': 198, 'label': 'base'},\n {'start': 203, 'end': 211, 'label': 'base'}],\n [{'start': 50, 'end': 59, 'label': 'change_direction'},\n {'start': 60, 'end': 68, 'label': 'base'},\n {'start': 104, 'end': 109, 'label': 'type_of'},\n {'start': 110, 'end': 124, 'label': 'base'},\n {'start': 132, 'end': 143, 'label': 'aspect_changing'},\n {'start': 147, 'end': 161, 'label': 'type_of'},\n {'start': 162, 'end': 168, 'label': 'base'},\n {'start': 170, 'end': 185, 'label': 'base'},\n {'start': 187, 'end': 195, 'label': 'type_of'},\n {'start': 196, 'end': 207, 'label': 'aspect_changing'},\n {'start': 209, 'end': 223, 'label': 'change_direction'},\n {'start': 227, 'end': 236, 'label': 'base'}],\n [{'start': 4, 'end': 18, 'label': 'base'},\n {'start': 23, 'end': 30, 'label': 'type_of'},\n {'start': 31, 'end': 38, 'label': 'base'},\n {'start': 39, 'end': 45, 'label': 'aspect_changing'},\n {'start': 55, 'end': 61, 'label': 'type_of'},\n {'start': 62, 'end': 70, 'label': 'base'},\n {'start': 76, 'end': 79, 'label': 'change_direction'},\n {'start': 80, 'end': 84, 'label': 'base'},\n {'start': 85, 'end': 91, 'label': 'aspect_changing'},\n {'start': 152, 'end': 171, 'label': 'change_direction'},\n {'start': 173, 'end': 185, 'label': 'base'}],\n [{'start': 17, 'end': 23, 'label': 'change_direction'},\n {'start': 27, 'end': 31, 'label': 'change_direction'},\n {'start': 39, 'end': 47, 'label': 'base'},\n {'start': 66, 'end': 72, 'label': 'base'}],\n [{'start': 2, 'end': 9, 'label': 'type_of'},\n {'start': 10, 'end': 17, 'label': 'base'},\n {'start': 34, 'end': 43, 'label': 'change_direction'},\n {'start': 44, 'end': 54, 'label': 'base'},\n {'start': 55, 'end': 59, 'label': 'aspect_changing'},\n {'start': 72, 'end': 80, 'label': 'aspect_changing'},\n {'start': 84, 'end': 91, 'label': 'base'},\n {'start': 97, 'end': 103, 'label': 'aspect_changing'},\n {'start': 107, 'end': 115, 'label': 'base'},\n {'start': 120, 'end': 131, 'label': 'change_direction'},\n {'start': 135, 'end': 139, 'label': 'type_of'},\n {'start': 140, 'end': 148, 'label': 'base'},\n {'start': 154, 'end': 179, 'label': 'change_direction'},\n {'start': 180, 'end': 185, 'label': 'base'},\n {'start': 190, 'end': 211, 'label': 'aspect_changing'},\n {'start': 213, 'end': 225, 'label': 'base'}],\n [{'start': 5, 'end': 13, 'label': 'base'},\n {'start': 23, 'end': 39, 'label': 'change_direction'}],\n [{'start': 0, 'end': 9, 'label': 'type_of'},\n {'start': 10, 'end': 14, 'label': 'type_of'},\n {'start': 15, 'end': 27, 'label': 'base'},\n {'start': 51, 'end': 61, 'label': 'base'}],\n [{'start': 61, 'end': 79, 'label': 'type_of'},\n {'start': 80, 'end': 95, 'label': 'aspect_changing'},\n {'start': 99, 'end': 109, 'label': 'base'},\n {'start': 123, 'end': 128, 'label': 'type_of'},\n {'start': 130, 'end': 135, 'label': 'type_of'},\n {'start': 136, 'end': 147, 'label': 'base'},\n {'start': 148, 'end': 151, 'label': 'aspect_changing'},\n {'start': 156, 'end': 167, 'label': 'aspect_changing'},\n {'start': 171, 'end': 181, 'label': 'base'}],\n [{'start': 67, 'end': 73, 'label': 'aspect_changing'},\n {'start': 79, 'end': 85, 'label': 'aspect_changing'},\n {'start': 96, 'end': 106, 'label': 'base'},\n {'start': 111, 'end': 119, 'label': 'base'},\n {'start': 171, 'end': 179, 'label': 'change_direction'},\n {'start': 190, 'end': 202, 'label': 'base'}]]" + }, + "metadata": {} + } + ], + "execution_count": 42, + "metadata": { + "jupyter": { + "source_hidden": false, + "outputs_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + }, + "gather": { + "logged": 1664394580674 + } + }, + "id": "0d8dae21-588d-4e10-9d5b-085603aebb0c" + }, + { + "cell_type": "code", + "source": [ + "def create_nerevaluate_file(data):\n", + " ans = []\n", + " for s in data:\n", + " tmp = []\n", + " for e in s[1][\"entities\"]:\n", + " ans_dict = {}\n", + " ans_dict[\"start\"] = e[0]\n", + " ans_dict[\"end\"] = e[1]\n", + " ans_dict[\"label\"] = e[2]\n", + " tmp.append(ans_dict)\n", + " ans.append(tmp)\n", + "\n", + " return ans\n", + "\n", + "actual_answers = create_nerevaluate_file(TEST_DATA)\n", + "\n", + "assert len(PRED_DATA) == len(actual_answers)\n", + "\n", + "evaluator = Evaluator(actual_answers, PRED_DATA, tags=LABEL)\n", + "\n", + "results, results_per_tag = evaluator.evaluate()\n", + "#print(results)\n", + "#print()\n", + "#print(results_per_tag)\n", + "\n", + "with open(result_dir + \"/results_SPACY.json\", \"w\") as f:\n", + " json.dump(results, f, indent=4)\n", + "with open(result_dir + \"/results_per_tag_SPACY.json\", \"w\") as f:\n", + " json.dump(results_per_tag, f, indent=4)" + ], + "outputs": [], + "execution_count": 48, + "metadata": { + "jupyter": { + "source_hidden": false, + "outputs_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + }, + "gather": { + "logged": 1664394720516 + } + }, + "id": "81941c15-0d24-408f-bd9a-5421c1616d7a" + }, + { + "cell_type": "code", + "source": [], + "outputs": [], + "execution_count": null, + "metadata": { + "jupyter": { + "source_hidden": false, + "outputs_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "id": "4dd31198-4a75-4ad9-a722-88c496fb2b3d" + } + ], + "metadata": { + "kernelspec": { + "name": "python3", + "language": "python", + "display_name": "Python 3" + }, + "language_info": { + "name": "python", + "version": "3.8.5", + "mimetype": "text/x-python", + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "pygments_lexer": "ipython3", + "nbconvert_exporter": "python", + "file_extension": ".py" + }, + "kernel_info": { + "name": "python3" + }, + "nteract": { + "version": "nteract-front-end@1.0.0" + }, + "microsoft": { + "host": { + "AzureML": { + "notebookHasBeenCompleted": true + } + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/src/utils/model/data_setup/spacy_data_setup.py b/model_dev/entity_extraction/spacy/spacy_data_setup.py similarity index 100% rename from src/utils/model/data_setup/spacy_data_setup.py rename to model_dev/entity_extraction/spacy/spacy_data_setup.py diff --git a/model_dev/entity_extraction/to_delete/analysis_change_dir.ipynb b/model_dev/entity_extraction/to_delete/analysis_change_dir.ipynb new file mode 100644 index 0000000..154bcea --- /dev/null +++ b/model_dev/entity_extraction/to_delete/analysis_change_dir.ipynb @@ -0,0 +1,2617 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "a32779bc", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[nltk_data] Downloading package punkt to /Users/ash/nltk_data...\n", + "[nltk_data] Package punkt is already up-to-date!\n", + "[nltk_data] Downloading package averaged_perceptron_tagger to\n", + "[nltk_data] /Users/ash/nltk_data...\n", + "[nltk_data] Package averaged_perceptron_tagger is already up-to-\n", + "[nltk_data] date!\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "from sklearn.preprocessing import LabelEncoder\n", + "import pickle\n", + "from pathlib import Path\n", + "import time, os, csv, srsly, collections\n", + "import spacy\n", + "#!python -m spacy download en_core_web_sm\n", + "nlp = spacy.load('en_core_web_sm')\n", + "import re\n", + "import nltk\n", + "nltk.download('punkt')\n", + "nltk.download('averaged_perceptron_tagger')\n", + "from nltk.tokenize import sent_tokenize" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "4fbc2145", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "created /Users/ash/Desktop/custom_entity_extractor_spacy_change_dir\n" + ] + } + ], + "source": [ + "# creating a folder to hold the data files, model results, output etc.\n", + "model_dir_name = \"custom_entity_extractor_spacy_change_dir\"\n", + "model_dir_parent = \"/Users/ash/Desktop\"\n", + "output_dir = os.path.join(model_dir_parent, model_dir_name)\n", + "os.makedirs(output_dir, exist_ok=True)\n", + "print(\"created\", output_dir)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "9b0dfa5e", + "metadata": {}, + "outputs": [], + "source": [ + "label = [\"change_direction\", \"base\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "c9a57bd9", + "metadata": {}, + "outputs": [], + "source": [ + "def prep_data(labels):\n", + " file_name_answers = \"main_3_per_cluster_download.66081fcf-3ef5-48ea-97e0-49298d29b477\"\n", + " file_path_answers = \"/Users/ash/Desktop/\" + file_name_answers + \".jsonl\"\n", + " data = srsly.read_jsonl(file_path_answers)\n", + " final_sent = []\n", + "\n", + " for entry in data:\n", + " if \"text\" in entry:\n", + " text = entry[\"text\"]\n", + " label_arr = []\n", + " label_tup = ()\n", + " if entry['answer'] == \"accept\":\n", + " if entry['_session_id'] == \"main_3_per_cluster-Kameron\":\n", + " for relation in entry['spans']:\n", + " if (\"label\" in relation) and (\"start\" in relation) and (\"end\" in relation):\n", + " child_span_start = relation[\"start\"]\n", + " child_span_end = relation[\"end\"]\n", + " word = text[child_span_start:child_span_end]\n", + " if relation[\"label\"] in labels:\n", + " tmp_tuple = (child_span_start, child_span_end, relation[\"label\"])\n", + " label_arr.append(tmp_tuple)\n", + " if len(label_arr) > 0:\n", + " label_tup = (text, {\"entities\": label_arr})\n", + " final_sent.append(label_tup)\n", + " else:\n", + " pass\n", + " #print(text)\n", + " return final_sent\n", + " \n", + "final_sent = prep_data(label)\n", + "training_data_index = int(len(final_sent)*0.7)\n", + "\n", + "with open(os.path.join(output_dir, 'training_data.txt'), 'wb') as file:\n", + " pickle.dump(final_sent[:training_data_index], file)\n", + " \n", + "with open(os.path.join(output_dir, 'testing_data.txt'), 'wb') as file:\n", + " pickle.dump(final_sent[training_data_index:], file)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "51cf0028", + "metadata": {}, + "outputs": [], + "source": [ + "with open (os.path.join(output_dir, 'training_data.txt'), 'rb') as file:\n", + " TRAIN_DATA = pickle.load(file)\n", + "\n", + "with open (os.path.join(output_dir, 'testing_data.txt'), 'rb') as file:\n", + " TEST_DATA = pickle.load(file)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "d132cd57", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[('These risks are crop insurance, health care, wildfire suppression, hurricane-related disaster relief, and federal facility flood risk, all of which are anticipated to cost billions of dollars more by the end of the century due to the impacts of climate change.23 IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(21, 30, 'base'),\n", + " (32, 43, 'base'),\n", + " (45, 65, 'base'),\n", + " (85, 100, 'base'),\n", + " (123, 128, 'base'),\n", + " (192, 196, 'change_direction'),\n", + " (245, 262, 'base')]}),\n", + " ('The IPCC reported that Alexandria’s beaches would be submerged even with a 0.5-metre sea-level rise, while 8 million people would be displaced by flooding in Alexandria and the Nile Delta if no protective measures are taken. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(36, 43, 'base'),\n", + " (85, 88, 'base'),\n", + " (95, 99, 'change_direction'),\n", + " (117, 123, 'base'),\n", + " (133, 142, 'change_direction'),\n", + " (146, 154, 'base')]}),\n", + " ('(Later, a severe mid-summer Arctic heat wave contributed to historic melting of the Greenland ice sheet, with 12.5 billion tons of ice melting into the ocean on a single day — the “biggest single-day volume loss on record,” according to the Washington Post). IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(35, 44, 'base'), (94, 103, 'base'), (131, 134, 'base')]}),\n", + " ('One is warming that leads to a rain-on-snow event, which can cause flooding and flushing of nutrients out of the system, and another is a winter heat wave, such as one that hit New England in February 2017 with 70-degree temperatures. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(7, 14, 'change_direction'),\n", + " (31, 35, 'base'),\n", + " (67, 75, 'base'),\n", + " (80, 91, 'change_direction'),\n", + " (92, 101, 'base'),\n", + " (145, 154, 'base'),\n", + " (235, 247, 'base')]}),\n", + " ('The increases in PTSD and depression found in pregnant women exposed to Hurricane Katrina were likely due to the severity of the event and the intensity of the disaster experience rather than a general exposure to the event.42, 156The many consequences of natural disasters, such as destruc- tion of homes, and of gradual climate change impacts, such as rising temperatures, incidence of vector-borne illness, water- borne illness, and even compromised food,160 can all contrib- ute to the emotional stress that women have while pregnant, nursing, or responsible for young children. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(4, 13, 'change_direction'),\n", + " (17, 21, 'base'),\n", + " (26, 36, 'base'),\n", + " (72, 81, 'base'),\n", + " (256, 273, 'base'),\n", + " (300, 305, 'base'),\n", + " (322, 336, 'base'),\n", + " (354, 360, 'change_direction'),\n", + " (401, 408, 'base'),\n", + " (423, 430, 'base'),\n", + " (453, 461, 'base'),\n", + " (500, 506, 'base'),\n", + " (583, 595, 'base')]}),\n", + " ('Under pressure from shareholders and regulators, companies are increasingly disclosing the specific financial impacts they could face as the planet warms, such as extreme weather that could disrupt their supply chains or stricter climate regulations that could hurt the value of coal, oil and gas investments. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(20, 32, 'base'),\n", + " (37, 47, 'base'),\n", + " (49, 58, 'base'),\n", + " (76, 86, 'change_direction'),\n", + " (141, 147, 'base'),\n", + " (148, 153, 'change_direction'),\n", + " (171, 178, 'base'),\n", + " (190, 197, 'change_direction'),\n", + " (204, 217, 'base'),\n", + " (221, 229, 'change_direction'),\n", + " (238, 249, 'base'),\n", + " (261, 265, 'change_direction'),\n", + " (297, 308, 'base'),\n", + " (310, 322, 'base')]}),\n", + " ('Diffenbaugh and his co-authors wrote that global climate models were zeroing in on \"robust increases in the occurrence of severe thunderstorm environments over the eastern United States in response to further global warming,\" and suggested \"a possible increase in the number of days supportive of tornadic storms.\" IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(91, 118, 'change_direction'),\n", + " (201, 208, 'change_direction'),\n", + " (209, 223, 'base'),\n", + " (252, 260, 'change_direction'),\n", + " (306, 312, 'base'),\n", + " (129, 141, 'base')]}),\n", + " ('But, it could also be argued that since a warmer world may experience a reduction in the equator to pole temperature contrast, the number of tornadoes and severe weather events would decrease,\" he said. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(42, 48, 'change_direction'),\n", + " (49, 54, 'base'),\n", + " (55, 58, 'change_direction'),\n", + " (72, 81, 'change_direction'),\n", + " (141, 150, 'base'),\n", + " (162, 169, 'base'),\n", + " (183, 191, 'change_direction'),\n", + " (203, 215, 'base')]}),\n", + " ('(Nitrous Oxide)—A clear non-flammable gas that has 300 times the global warming power of CO2; the majority of the N2O emissions are man-made n NOX (Nitrogen Dioxide and Nitric Acid)—Gases from the combustion of fossil fuels; react in the atmosphere to form O3, PM2 .5, and PM10 n Pb (Lead)—Elemental particulate coming primarily from leaded fuel n PM2.5—Particles with a diameter of 2 .5 micrometers or less coming from dust, smoke and soot, or formed from chemical reactions in the atmosphere n PM10—Particles with a diameter of 10 micrometers or less (includes PM2 .5) IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(38, 41, 'base'),\n", + " (65, 79, 'base'),\n", + " (89, 92, 'base'),\n", + " (114, 117, 'base'),\n", + " (176, 187, 'base'),\n", + " (218, 223, 'base'),\n", + " (257, 259, 'base'),\n", + " (261, 267, 'base'),\n", + " (273, 277, 'base'),\n", + " (300, 311, 'base'),\n", + " (341, 345, 'base'),\n", + " (420, 424, 'base'),\n", + " (426, 431, 'base'),\n", + " (436, 440, 'base')]}),\n", + " ('Similarly, shrub tundra is expanding into colder regions as a result of warming and this process will continue…There will be, as a result, a decrease in vegetation albedo – especially in spring – which will amplify the warming locally. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(17, 23, 'base'),\n", + " (27, 56, 'change_direction'),\n", + " (72, 79, 'base'),\n", + " (141, 149, 'change_direction'),\n", + " (164, 170, 'base'),\n", + " (207, 214, 'change_direction'),\n", + " (219, 226, 'base')]}),\n", + " ('The researchers predicted only modest carbon loss from tropical soils, but were surprised to find carbon emission increases of 55% over two years when those soils were heated by four degrees Celsius. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(64, 69, 'base'),\n", + " (98, 104, 'base'),\n", + " (114, 123, 'change_direction'),\n", + " (157, 162, 'base'),\n", + " (168, 174, 'change_direction')]}),\n", + " ('In many of these cases, an environmental disaster was a flashpoint which ultimately culminated in interpersonal violence due to lost infrastructure, fear and uncertainty, perceived scarcity or competition, or massive relocation—all factors that weaken social control and counteract the mechanisms societies use to resolve disputes peacefully (Nardulli et al., 2015). IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(41, 49, 'base'),\n", + " (112, 120, 'base'),\n", + " (128, 132, 'change_direction'),\n", + " (133, 147, 'base'),\n", + " (149, 153, 'base'),\n", + " (158, 169, 'base'),\n", + " (181, 189, 'base'),\n", + " (193, 204, 'base'),\n", + " (217, 227, 'base'),\n", + " (245, 251, 'change_direction'),\n", + " (252, 266, 'base'),\n", + " (271, 281, 'change_direction'),\n", + " (311, 341, 'base')]}),\n", + " ('The researchers estimated, based on these findings, that a 1oC increase in average temperature — a fairly conservative estimate of climate change in the following decades — will likely yield a 6% increase in violent crime rates, as many as 25,000 more serious and deadly assaults per year in the United States alone. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(63, 71, 'change_direction'),\n", + " (131, 145, 'base'),\n", + " (196, 204, 'change_direction'),\n", + " (216, 221, 'base'),\n", + " (247, 251, 'change_direction'),\n", + " (271, 279, 'base'),\n", + " (317, 329, 'base')]}),\n", + " ('This makes the fight against single-use plastic pollution more compelling and holistic, realizing that good choices in renewable energy and climate friendly decisions may also help reduce single-use plastic production and pollution, and vice versa. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(15, 20, 'base'),\n", + " (58, 86, 'change_direction'),\n", + " (129, 135, 'base'),\n", + " (157, 166, 'base'),\n", + " (176, 187, 'change_direction'),\n", + " (199, 206, 'base')]}),\n", + " ('A comprehensive meta-analysis of such studies predicted that a 1 standard deviation increase in global temperatures or extreme rainfall could increase the frequency of interpersonal violence by 4% and intergroup conflict by 14% (Hsiang et al., 2013). IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(84, 92, 'change_direction'),\n", + " (127, 135, 'base'),\n", + " (142, 150, 'change_direction'),\n", + " (182, 190, 'base'),\n", + " (212, 220, 'base'),\n", + " (251, 263, 'base')]}),\n", + " ('As an illustrative example of increased military costs due to climate change: receding Arctic ice cover requires deployment of new naval vessels to address growing tensions between Canada, the United States, Russia, and China over newly-exposed natural resources and shipping lanes (Department of Defense, 2015). IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(30, 39, 'change_direction'),\n", + " (40, 48, 'base'),\n", + " (62, 76, 'base'),\n", + " (78, 86, 'change_direction'),\n", + " (94, 97, 'base'),\n", + " (113, 123, 'change_direction'),\n", + " (131, 144, 'base'),\n", + " (156, 163, 'change_direction'),\n", + " (164, 172, 'base'),\n", + " (231, 244, 'change_direction'),\n", + " (245, 262, 'base'),\n", + " (267, 281, 'base')]}),\n", + " ('Such actions include reducing greenhouse gas emissions, which reduces the speed and magnitude of climate change, and the use of better population control (given that most of the increase in population in the next decade is expected to take place in developing countries, with huge increases in greenhouse gas emissions as a result). IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(21, 29, 'change_direction'),\n", + " (41, 44, 'base'),\n", + " (62, 69, 'change_direction'),\n", + " (97, 111, 'base'),\n", + " (128, 134, 'change_direction'),\n", + " (135, 153, 'base'),\n", + " (178, 186, 'change_direction'),\n", + " (190, 200, 'base'),\n", + " (281, 290, 'change_direction'),\n", + " (305, 308, 'base')]}),\n", + " ('\"Hundreds to thousands of heat-related deaths could be avoided per U.S. city per year during extremely hot years if the U.S. and other nations increase climate action,\" said study lead author Eunice Lo, a researcher at the University of Bristol in the United Kingdom. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(26, 30, 'base'),\n", + " (39, 45, 'base'),\n", + " (143, 151, 'change_direction'),\n", + " (152, 166, 'base')]}),\n", + " ('For the research, Lo and her collaborators focused on so-called “1-in-30 events,” severe heat waves that strike every few decades and which pose a major threat to children, older adults, outdoor workers and people living in poverty. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(89, 99, 'base'), (233, 245, 'base')]}),\n", + " ('“Each year, the data suggests with increasing certainty that fracking is causing irrevocable damage to public health, local economies, the environment, and to global sustainability,” said Physicians for Social Responsibility’s Kathleen Nolan, one of the study’s authors, in a statement. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(61, 69, 'base'),\n", + " (110, 116, 'base'),\n", + " (124, 133, 'base'),\n", + " (139, 150, 'base'),\n", + " (159, 180, 'base')]}),\n", + " ('He said to consider, for example, the situation in Puerto Rico, where many people have died of COVID-19 for the simple reason that they have not yet recovered, in terms of their public health infrastructure, from the devastating impacts of Hurricane Maria three years ago. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(75, 81, 'base'),\n", + " (87, 91, 'change_direction'),\n", + " (95, 103, 'base'),\n", + " (192, 206, 'base'),\n", + " (240, 249, 'base')]}),\n", + " ('Similarly, rapid permafrost thaw can also be triggered – and enhanced – by disturbances such as fire, abrupt drying events, soil subsidence and erosion resulting from ice-rich permafrost thaw (known as “thermokarst”). IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(17, 27, 'base'),\n", + " (45, 54, 'change_direction'),\n", + " (61, 69, 'change_direction'),\n", + " (96, 100, 'base'),\n", + " (102, 115, 'change_direction'),\n", + " (124, 128, 'base'),\n", + " (176, 186, 'base'),\n", + " (203, 214, 'base'),\n", + " (218, 230, 'base')]}),\n", + " ('The increasing intensity, duration, and frequency of heat waves due to human-caused climate change puts historically underserved populations in a heightened state of precarity, as studies observe that vulnerable communities—especially those within urban areas in the United States—are disproportionately exposed to extreme heat. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(4, 14, 'change_direction'),\n", + " (53, 63, 'base'),\n", + " (84, 98, 'base'),\n", + " (146, 156, 'change_direction'),\n", + " (166, 175, 'base'),\n", + " (323, 327, 'base')]}),\n", + " ('Given the large potential changes in precipitation and temperature regimes projected for the coming decades—with locations throughout the inhabited world expected to warm by 2 to 4 SDs by 2050—amplified rates of human conflict could represent a large and critical social impact of anthropogenic climate change in both low- IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(37, 50, 'base'),\n", + " (188, 202, 'change_direction'),\n", + " (218, 226, 'base'),\n", + " (295, 309, 'base')]}),\n", + " ('This year, both long-term trends and seasonal variability converged, causing extreme heat and dryness, as well as some unusual ignition events, like a dry lightning storm in Northern California that sparked more than 300 fires. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(46, 57, 'base'),\n", + " (85, 89, 'base'),\n", + " (165, 170, 'base'),\n", + " (199, 206, 'change_direction'),\n", + " (221, 226, 'base'),\n", + " (228, 240, 'base')]}),\n", + " ('Waiting to act will only increase the likelihood of a climate shock destabilizing the financial system and severely affecting the broader economy. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(0, 14, 'base'),\n", + " (25, 33, 'change_direction'),\n", + " (54, 67, 'base'),\n", + " (68, 81, 'change_direction'),\n", + " (86, 102, 'base'),\n", + " (138, 145, 'base')]}),\n", + " ('- U.S. EPA is allowing the sale of dirtier \"winter fuel\" gasoline during COVID-19, worsening air quality in the midst of a respiratory virus pandemic, which will lead to extra deaths. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(14, 22, 'change_direction'),\n", + " (57, 65, 'base'),\n", + " (83, 92, 'change_direction'),\n", + " (93, 96, 'base'),\n", + " (141, 149, 'base'),\n", + " (170, 175, 'change_direction'),\n", + " (176, 182, 'base')]}),\n", + " ('Increasingly, they found, studies connect fracking operations to air pollution, contaminated or depleted drinking water, and earthquakes. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(42, 50, 'base'),\n", + " (69, 78, 'base'),\n", + " (125, 136, 'base'),\n", + " (80, 92, 'change_direction'),\n", + " (96, 104, 'change_direction'),\n", + " (114, 119, 'base')]}),\n", + " ('And some prescription and over-the-counter medications for conditions ranging from allergies to high blood pressure and depression increase heat sensitivity. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(131, 139, 'change_direction'),\n", + " (43, 54, 'base'),\n", + " (158, 170, 'base')]}),\n", + " ('Wear heavy, dark clothing, which absorbs heat from the sun. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(17, 25, 'base'),\n", + " (33, 40, 'change_direction'),\n", + " (41, 45, 'base'),\n", + " (55, 58, 'base')]}),\n", + " ('at Stony Brook University, and published in Science Advances, found that Hurricane Florence produced more extreme rainfall and was spatially larger due to human-induced climate change. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(73, 82, 'base'),\n", + " (101, 105, 'change_direction'),\n", + " (114, 122, 'base'),\n", + " (131, 147, 'change_direction'),\n", + " (169, 183, 'base')]}),\n", + " ('Climate change will make those threats even worse, as floods, drought, storms and other types of extreme weather threaten to disrupt, and over time shrink, the global food supply. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(0, 14, 'base'),\n", + " (44, 49, 'change_direction'),\n", + " (54, 60, 'base'),\n", + " (62, 69, 'base'),\n", + " (71, 77, 'base'),\n", + " (97, 112, 'base'),\n", + " (125, 132, 'change_direction'),\n", + " (148, 154, 'change_direction'),\n", + " (167, 171, 'base'),\n", + " (180, 192, 'base')]}),\n", + " ('Flash flooding between January 22 and February 3, caused by tropical depression Amang, led to an additional 106,000 new displacements in the Davao. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(6, 14, 'base'),\n", + " (60, 79, 'base'),\n", + " (97, 107, 'change_direction'),\n", + " (148, 160, 'base')]}),\n", + " ('An infection can also reduce milk production, making dairy farming less efficient. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(3, 12, 'base'),\n", + " (22, 28, 'change_direction'),\n", + " (29, 33, 'base'),\n", + " (59, 66, 'base'),\n", + " (67, 71, 'change_direction')]}),\n", + " (\"The effort examined how future hurricanes and storm surges may be amplified by climate change and the risks these events could pose to resources relied upon by the Air Force's 45th Space Wing.85 IMPLIED_BASE IMPLIED_BASE\",\n", + " {'entities': [(31, 41, 'base'),\n", + " (46, 58, 'base'),\n", + " (66, 75, 'change_direction'),\n", + " (79, 93, 'base')]}),\n", + " ('People often develop shock and may lose limbs or kidneys, resulting in needing dialysis. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(35, 39, 'change_direction'),\n", + " (49, 56, 'base'),\n", + " (71, 78, 'change_direction'),\n", + " (79, 87, 'base')]}),\n", + " ('And once international scientists had eliminated the effect of temperature averages across the whole growing season, they still found that heatwaves, drought and torrential downfall accounted for 18% to 43% of losses. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(139, 148, 'base'),\n", + " (150, 157, 'base'),\n", + " (173, 181, 'base'),\n", + " (218, 230, 'base')]}),\n", + " ('Ranft, U., T. Schikowski, D. Sugiri, J. Krutmann, and U. Kramer, 2009: Long-term exposure to traffic-related particulate matter impairs cognitive function in the elderly. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(109, 127, 'base'),\n", + " (136, 154, 'base'),\n", + " (128, 135, 'change_direction')]}),\n", + " ('Failing crops and hence hunger are increasing. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(0, 7, 'change_direction'),\n", + " (8, 13, 'base'),\n", + " (24, 30, 'base')]}),\n", + " ('Following exposure to Hurricane Katrina, veterans with preexisting mental illness had a 6.8 times greater risk for developing any additional mental illness, compared to those veterans without a preex- isting mental illness.48 IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(22, 31, 'base'),\n", + " (98, 105, 'change_direction'),\n", + " (141, 155, 'base')]}),\n", + " ('Projecting further still is difficult, but if emissions continue unabated at their current high rates, those numbers will surely rise to the point where climate change might kill more people annually than covid-19 currently does before the century’s end. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(56, 73, 'change_direction'),\n", + " (153, 167, 'base'),\n", + " (174, 178, 'change_direction'),\n", + " (184, 190, 'base'),\n", + " (255, 267, 'base')]}),\n", + " ('The threat on global health are highlighted by many previous studies such as one report from Australia about an increased incidence in childhood pneumonia associated with sharp temperature drops from 1 day to the next [10], or the outbreak of Hantavirus, which occurred in Panama in 2000, linked to the increase in rodent population attributed to a substantial increase in rainfall [11]. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(21, 27, 'base'),\n", + " (112, 121, 'change_direction'),\n", + " (145, 154, 'base'),\n", + " (189, 217, 'change_direction'),\n", + " (243, 253, 'base'),\n", + " (303, 311, 'change_direction'),\n", + " (315, 321, 'base'),\n", + " (361, 369, 'change_direction'),\n", + " (373, 381, 'base'),\n", + " (388, 400, 'base')]}),\n", + " ('Combined with accumulating evidence on the potentially disruptive effects of climate change on human enterprise, such as through possible declines in global food production (3) and significant sea level rise (4), such findings have encouraged claims that climate change will worsen instability in already volatile regions (5–7). IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(77, 91, 'base'),\n", + " (138, 146, 'change_direction'),\n", + " (157, 161, 'base'),\n", + " (193, 196, 'base'),\n", + " (203, 207, 'change_direction'),\n", + " (255, 269, 'base'),\n", + " (275, 281, 'change_direction'),\n", + " (329, 341, 'base')]}),\n", + " (\"Heavy snow on top of that led to slushy conditions that aren't conducive to safe fishing. IMPLIED_BASE IMPLIED_BASE\",\n", + " {'entities': [(6, 10, 'base'),\n", + " (40, 50, 'base'),\n", + " (56, 75, 'change_direction'),\n", + " (81, 88, 'base')]}),\n", + " ('More explosive and rapidly spreading fires leave communities with little notice or chance to evacuate. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(0, 4, 'change_direction'),\n", + " (37, 42, 'base'),\n", + " (49, 60, 'base'),\n", + " (61, 72, 'change_direction')]}),\n", + " ('Turbulence is a threat to safety and economic security, but it’s only part of the harm caused by climate change. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(0, 10, 'base'),\n", + " (16, 22, 'change_direction'),\n", + " (26, 32, 'base'),\n", + " (46, 54, 'base'),\n", + " (97, 111, 'base')]}),\n", + " ('For many, especially children under 5, malaria triggers organ failure, coma and death. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(39, 46, 'base'),\n", + " (56, 69, 'base'),\n", + " (71, 75, 'base'),\n", + " (80, 85, 'base')]}),\n", + " ('Individuals with mental health and stress-related disorders, such as PTSD, depression, anxiety, sleep difficulties, and sometimes those who abuse drugs or alcohol, can experience an exacerbation of symptoms following a traumatic event. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(24, 30, 'base'),\n", + " (50, 59, 'base'),\n", + " (69, 73, 'base'),\n", + " (75, 85, 'base'),\n", + " (87, 94, 'base'),\n", + " (96, 114, 'base'),\n", + " (140, 145, 'base'),\n", + " (182, 194, 'change_direction'),\n", + " (219, 234, 'base')]}),\n", + " ('The analysis provides one of the clearest pictures to date of the companies behind the vast emissions of natural gas that have resulted from America’s shale oil boom, fueled by the use of hydraulic fracturing, or fracking, to unlock fossil fuels from shale rock. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(105, 116, 'base'),\n", + " (157, 160, 'base'),\n", + " (161, 165, 'change_direction'),\n", + " (188, 208, 'base'),\n", + " (213, 221, 'base'),\n", + " (226, 232, 'change_direction'),\n", + " (240, 245, 'base')]}),\n", + " ('For example, apart from contributing to global warming, rising carbon dioxide levels increase the amount of pollen that plants produce as a consequence of higher rates of photosynthesis. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(40, 54, 'base'),\n", + " (56, 62, 'change_direction'),\n", + " (63, 77, 'base'),\n", + " (85, 93, 'change_direction'),\n", + " (108, 114, 'base'),\n", + " (120, 126, 'base'),\n", + " (155, 161, 'change_direction'),\n", + " (171, 185, 'base')]}),\n", + " ('And longer heat waves, of course, make the biosphere warmer still, starting the cycle again. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(4, 10, 'change_direction'),\n", + " (11, 21, 'base'),\n", + " (43, 52, 'base'),\n", + " (53, 59, 'change_direction')]}),\n", + " ('(i.e., IgG subclass deficiency, IgA deficiency or common variable immunodeficiency), are associated with recurrent respiratory and gastrointestinal infections of varying severity [14]. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(7, 30, 'base'),\n", + " (32, 46, 'base'),\n", + " (66, 82, 'base'),\n", + " (148, 158, 'base')]}),\n", + " ('Once the temperature shifts, the metabolism of the plant has to adjust to the change and this leads to fewer products and a bitter flavor. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(21, 27, 'change_direction'),\n", + " (51, 56, 'base'),\n", + " (103, 108, 'change_direction'),\n", + " (124, 130, 'change_direction'),\n", + " (139, 151, 'base'),\n", + " (152, 164, 'base')]}),\n", + " ('One high-profile incident involved a 62-year-old man who died from V. vulnificus in 2018 – one of 58 cases reported by the Virginia health department that year. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(49, 52, 'base'),\n", + " (57, 61, 'change_direction'),\n", + " (67, 80, 'base'),\n", + " (132, 149, 'base')]}),\n", + " ('Though many of these studies describe the mental health impacts of specific historical events, they demonstrate thetypes of mental health issues that will continue to arise as climate change leads to increases in the frequency, severity, and duration of extreme climate- and weather-related events such as floods, hurricanes, droughts, and wildfires.17, 18, 19, 20, 21, 22, 23, 24, 25, 29, 41, 50, 51, 68, 70 Strong support is found in a number of recent studies for the potential for climate change-related psychological effects, including grief/bereavement, increased substance use or misuse, and thoughts of suicide.19, 35, 36, 37, 38, 60Research on individual resilience and recovery shows that a majority of individuals psychologically affected by a traumatic event will recover over time. 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IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(44, 52, 'base'),\n", + " (130, 139, 'change_direction'),\n", + " (158, 170, 'base')]}),\n", + " ('\"These lightning strikes, this unprecedented heat dome, these world record temperatures, unprecedented in human history – we’re experiencing more and more of that because of climate change, and as a consequence, these fires are more ferocious, and they’re moving at much more rapid speeds,\" Newsom said. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(7, 24, 'base'),\n", + " (45, 54, 'base'),\n", + " (141, 154, 'change_direction'),\n", + " (174, 188, 'base'),\n", + " (218, 223, 'base'),\n", + " (228, 242, 'change_direction'),\n", + " (271, 281, 'change_direction'),\n", + " (304, 316, 'base'),\n", + " (317, 329, 'base')]}),\n", + " ('Finally, the third cause would be the direct impact of deforestation – fewer trees mean less evapotranspiration and less moisture entering the atmosphere. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(55, 68, 'base'),\n", + " (71, 76, 'change_direction'),\n", + " (77, 82, 'base'),\n", + " (88, 92, 'change_direction'),\n", + " (93, 111, 'base'),\n", + " (116, 120, 'change_direction'),\n", + " (143, 153, 'base')]}),\n", + " ('Climate change is likewise having mental health impacts at the community level. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(0, 14, 'base'), (41, 47, 'base')]}),\n", + " ('Instead, they are pushed around the ocean by wind streams, which are caused by high and low pressure systems in the atmosphere. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(50, 57, 'base'), (92, 108, 'base')]}),\n", + " ('Worker productivity will likely suffer as increases in heat and humidity push the limits of human tolerance, making it difficult for outdoor workers to stay cool. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(0, 6, 'base'),\n", + " (32, 38, 'change_direction'),\n", + " (42, 51, 'change_direction'),\n", + " (92, 107, 'base'),\n", + " (119, 128, 'change_direction'),\n", + " (141, 148, 'base'),\n", + " (163, 175, 'base')]}),\n", + " ('Nine months after the election, Priya Shukla, a Ph.D. student at the University of California-Davis who studies how climate change affects shellfish aquaculture and coastal food security, was in the Bodega Marine Laboratory, examining data showing rising ocean acidity caused by greenhouse gas emissions. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(116, 130, 'base'),\n", + " (149, 160, 'base'),\n", + " (173, 186, 'base'),\n", + " (248, 254, 'change_direction'),\n", + " (255, 260, 'base'),\n", + " (290, 293, 'base')]}),\n", + " ('“With all the economic stress going on, farmers and ranchers are at higher risk for suicide,” said David Brown, a behavioral health state specialist at Iowa State University who helped to start a mental health program this year to inform the farming community about risk signs. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(23, 29, 'base'),\n", + " (68, 74, 'change_direction'),\n", + " (84, 91, 'base')]}),\n", + " ('Although struggles over water resources are not a new occurrence, climate change will only intensify and increase the frequency of such issues—similar to migration. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(66, 80, 'base'),\n", + " (91, 100, 'change_direction'),\n", + " (105, 113, 'change_direction'),\n", + " (154, 163, 'base')]}),\n", + " ('Flooding is a complex phenomenon with many causes, including land development and ground conditions. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(0, 8, 'base'), (66, 77, 'base'), (82, 88, 'base')]}),\n", + " ('Instead, rising temperatures weaken roadways, cause metal fatigue in bridges, and make the metal pipes that move water expand, crack, and leak. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(9, 15, 'change_direction'),\n", + " (29, 35, 'change_direction'),\n", + " (36, 44, 'base'),\n", + " (52, 57, 'base'),\n", + " (97, 102, 'base'),\n", + " (119, 125, 'change_direction'),\n", + " (127, 132, 'change_direction'),\n", + " (138, 142, 'change_direction'),\n", + " (144, 156, 'base')]}),\n", + " ('A report last year by the Intergovernmental Panel on Climate Change of the United Nations started that \"without increased and urgent mitigation ambition in the coming years, leading to a sharp decline in greenhouse gas emissions by 2030, global warming will surpass 1.5°C in the following decades, leading to irreversible loss of the most fragile ecosystems, and crisis after crisis for the most vulnerable people and societies.\" IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(112, 121, 'change_direction'),\n", + " (133, 143, 'base'),\n", + " (193, 200, 'change_direction'),\n", + " (215, 218, 'base'),\n", + " (238, 252, 'base'),\n", + " (322, 326, 'change_direction'),\n", + " (347, 357, 'base'),\n", + " (363, 369, 'base'),\n", + " (376, 382, 'base')]}),\n", + " ('Increased atmospheric moisture enhances the likelihood of extreme rainfall events of all types.17,18 Close to the center of a TC, the increases in rain rate can reach 10% per degree C of warming in some model projections,19 exceeding the 7% dictated by the Clausius-Clapeyron relationship. 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IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(18, 24, 'change_direction'),\n", + " (25, 29, 'base'),\n", + " (60, 71, 'base'),\n", + " (95, 102, 'base'),\n", + " (115, 123, 'base')]}),\n", + " ('Flooding in Iran displaced half a million people in the spring. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(0, 8, 'base'),\n", + " (17, 26, 'change_direction'),\n", + " (42, 48, 'base')]}),\n", + " ('Recreational opportunities are compromised by extreme weather, and children may suffer delayed cognitive development. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(0, 26, 'base'),\n", + " (31, 42, 'change_direction'),\n", + " (46, 61, 'base'),\n", + " (87, 94, 'change_direction'),\n", + " (105, 116, 'base')]}),\n", + " ('Prevalence of heatstroke and extreme weather will have redefined global labour and production beyond recognition,” as an editorial accompanying the report puts it. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(14, 24, 'base'),\n", + " (29, 44, 'base'),\n", + " (55, 64, 'change_direction'),\n", + " (72, 78, 'base'),\n", + " (83, 93, 'base')]}),\n", + " ('In urban areas, heatwaves are exacerbated by vehicles, industrial processes and the presence of heat-retaining concrete and asphalt. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(16, 25, 'base'),\n", + " (30, 41, 'change_direction'),\n", + " (45, 53, 'base'),\n", + " (55, 75, 'base'),\n", + " (111, 119, 'base'),\n", + " (124, 131, 'base')]}),\n", + " ('Many infectious diseases are sensitive to small shifts in climatic conditions, including vector-borne infections spread by mosquitoes and ticks. 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IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(110, 119, 'base'), (137, 142, 'base')]}),\n", + " ('The same refrigerants and gases that damage the ozone layer contribute to climate change. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(9, 21, 'base'),\n", + " (26, 31, 'base'),\n", + " (37, 43, 'change_direction'),\n", + " (48, 59, 'base'),\n", + " (74, 88, 'base')]}),\n", + " ('Fletcher also has concerns about more hot, windless days leading to serious health issues or even death, as other parts of the world have experienced. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(33, 37, 'change_direction'),\n", + " (52, 56, 'base'),\n", + " (76, 82, 'base'),\n", + " (98, 103, 'base')]}),\n", + " ('Internal corrosion induces vomit of blood, the color of coffee grounds, giving rise to the Spanish name for yellow fever, vómito negro (black vomit), which is sometimes followed by death. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(9, 18, 'base'),\n", + " (27, 32, 'base'),\n", + " (108, 120, 'base'),\n", + " (181, 186, 'base')]}),\n", + " ('Diseases spread by mosquitoes and ticks increase the chance of neurological problems. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(0, 8, 'base'),\n", + " (19, 29, 'base'),\n", + " (34, 39, 'base'),\n", + " (40, 48, 'change_direction'),\n", + " (63, 84, 'base')]}),\n", + " ('“It’s really incredible how much the U.S. has managed to clean up the air from other [pollution] sources, like power plants and industry and cars,” Mickley said. 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IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(16, 26, 'change_direction'),\n", + " (33, 38, 'base'),\n", + " (72, 78, 'change_direction'),\n", + " (98, 102, 'change_direction'),\n", + " (103, 114, 'base'),\n", + " (136, 148, 'base')]}),\n", + " ('But aviation accounts for only about 2.5 percent of global emissions, an amount that looks downright puny in the shadow cast by heavy industry. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(4, 12, 'base'), (134, 142, 'base'), (144, 156, 'base')]}),\n", + " ('A recent study (Boggs Bookwalter et al., 2020) finds that people suffering from PTSD may be at increased risk of developing autoimmune diseases. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(65, 79, 'change_direction'),\n", + " (80, 84, 'base'),\n", + " (95, 104, 'change_direction'),\n", + " (135, 143, 'base')]}),\n", + " (\"That's nearly double the number of people (3.8 million) who were displaced by violence in the same period. IMPLIED_BASE IMPLIED_BASE\",\n", + " {'entities': [(35, 41, 'base'), (78, 86, 'base')]}),\n", + " ('In 2018 the total burn areareached 676,312 hectares (up from 505,294 in 2017), and 85 people lost their lives in unspeakable suffering during the Camp Fire alone. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(18, 22, 'base'),\n", + " (86, 92, 'base'),\n", + " (93, 97, 'change_direction'),\n", + " (125, 134, 'base'),\n", + " (151, 155, 'base')]}),\n", + " ('In order to stop the accumulation of heat, we would have to eliminate not just carbon dioxide emissions, but all greenhouse gases, such as methane and nitrous oxide. 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IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(56, 70, 'base'),\n", + " (76, 93, 'change_direction'),\n", + " (118, 121, 'base')]}),\n", + " ('Any added heat, particularly in summer months, puts workers at greater risk for dehydration, heat stroke, and other heat-related illnesses. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(4, 9, 'change_direction'),\n", + " (10, 14, 'base'),\n", + " (63, 70, 'change_direction'),\n", + " (80, 91, 'base'),\n", + " (93, 104, 'base'),\n", + " (129, 138, 'base')]}),\n", + " ('“Particulate matter is extremely harmful and it leads to a large number of premature deaths,” said Richard L. Revesz, an expert in environmental law at New York University. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(1, 19, 'base'), (85, 91, 'base')]}),\n", + " ('We know that the warmer air gets, the more moisture it can hold – and then turn into flooding rains in a storm like this. 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IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(35, 41, 'base'),\n", + " (89, 104, 'base'),\n", + " (123, 135, 'base'),\n", + " (147, 166, 'base')]}),\n", + " ('In the decades that followed, the men who experienced maternal malnourishment during pregnancy were 2.5 times more likely than men who had not to develop antisocial personality disorder, a condition characterized by frequent violence and antisocial behavior (Neugebauer et al., 1999). IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(42, 53, 'change_direction'),\n", + " (63, 77, 'base'),\n", + " (110, 121, 'change_direction'),\n", + " (146, 153, 'change_direction'),\n", + " (154, 185, 'base'),\n", + " (225, 233, 'base'),\n", + " (238, 257, 'base')]}),\n", + " ('According to Dr Mindy Richlen, a research specialist at Woods Hole Oceanographic Institution who studies harmful algal blooms, ciguatera poisoning comes from eating fish contaminated with ciguatoxins, which come from a tropical dinoflagellate (tiny single-cell organism) that lives on microalgae growing, mostly, on dead coral. 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IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(11, 25, 'base'),\n", + " (48, 58, 'change_direction'),\n", + " (73, 84, 'base'),\n", + " (89, 110, 'base')]}),\n", + " ('For all the criticism, Mr. Johnson has maintained that the most significant result of the power shut-off strategy has been that it prevented the utility’s equipment from causing a wildfire. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(155, 164, 'base'), (180, 188, 'base')]}),\n", + " ('One consequence of global warming is that it intensifies natural disasters such as fires and floods, but insurers have struggled to anticipate the spiraling costs. 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IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(24, 38, 'base'),\n", + " (107, 126, 'base'),\n", + " (128, 138, 'base'),\n", + " (144, 152, 'base')]}),\n", + " ('Although the political process has a long history of misinformation and popular misperceptions, misinformation on social media has caused widespread alarm in recent years IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(96, 110, 'base'), (149, 154, 'base')]}),\n", + " ('Most of the evening he was on the defensive, as the Corps has been throughout the extraordinary spring deluge that has flooded rivers from Oklahoma to Louisiana, overtopping levees and forcing the federal agency into the position of choosing which communities will be inundated with water its dams can no longer hold. 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IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(77, 90, 'base'), (104, 116, 'base'), (138, 152, 'base')]}),\n", + " ('She said U.S. public health officials are not prepared for the kinds of heat waves seen in Europe in 2003 that killed over 70,000 people. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(72, 82, 'base'),\n", + " (111, 117, 'change_direction'),\n", + " (130, 136, 'base')]}),\n", + " ('Active hillslope erosional features will occupy 3% of abrupt thaw terrain by 2300 but emit one-third of abrupt thaw carbon losses. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(17, 35, 'base'),\n", + " (86, 90, 'change_direction'),\n", + " (116, 122, 'base')]}),\n", + " ('The warmer ocean temperatures are “absolutely responsible for the hyperactive season,” said James P. Kossin, a climate scientist with the National Oceanic and Atmospheric Administration. 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IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(15, 22, 'base'),\n", + " (44, 48, 'base'),\n", + " (53, 62, 'base'),\n", + " (76, 85, 'change_direction'),\n", + " (101, 106, 'base'),\n", + " (129, 142, 'change_direction'),\n", + " (147, 159, 'change_direction'),\n", + " (166, 184, 'change_direction'),\n", + " (185, 195, 'base'),\n", + " (200, 219, 'base')]}),\n", + " ('Besides all the damage to homes, businesses, and municipal infrastructure, increasingly frequent flooding events in the Midwest would have a huge impact on the nation’s ability to produce food. 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IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(27, 33, 'base'),\n", + " (49, 54, 'change_direction'),\n", + " (63, 71, 'change_direction'),\n", + " (94, 101, 'base'),\n", + " (103, 109, 'base'),\n", + " (119, 123, 'base'),\n", + " (128, 135, 'base')]})]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "TRAIN_DATA" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "c4f12b0e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[('Around the wine-growing world, smart producers have contemplated and experimented with adaptations, not only to hotter summers, but also to warmer winters, droughts and the sort of unexpected, sometimes violent events that stem from climate change: freak hailstorms, spring frosts, flooding and forest fires, just to name a few. 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IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(108, 115, 'base'), (134, 151, 'base')]}),\n", + " (\"While a more moderate emissions trajectory will still produce more intense, frequent and longer lasting droughts in most of the world's mid-latitude regions than current conditions, the shift will be less than if carbon emissions remain near the top of forecasts. IMPLIED_BASE IMPLIED_BASE\",\n", + " {'entities': [(8, 21, 'change_direction'),\n", + " (62, 103, 'change_direction'),\n", + " (104, 112, 'base'),\n", + " (213, 219, 'base'),\n", + " (264, 276, 'base')]}),\n", + " ('Malnutrition, low birth weight, nonexclusive breast-feeding, lack of measles vaccination, outdoor and indoor air pollution and crowding, mother’s education, parental smoking, vitamin A and/or zinc deficiencies are thought to influence children susceptibility to infections in developing countries. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(0, 12, 'base'),\n", + " (14, 17, 'change_direction'),\n", + " (18, 23, 'base'),\n", + " (45, 59, 'base'),\n", + " (61, 68, 'change_direction'),\n", + " (77, 88, 'base'),\n", + " (109, 122, 'base'),\n", + " (127, 135, 'base'),\n", + " (146, 155, 'base'),\n", + " (166, 173, 'base'),\n", + " (262, 272, 'base'),\n", + " (298, 310, 'base')]}),\n", + " ('When the rain hit your face, it was like being hit by a stone,” Fatuma, whose house was severely damaged by Cyclone Kenneth in April, told Oxfam. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(78, 83, 'base'),\n", + " (97, 104, 'change_direction'),\n", + " (108, 115, 'base')]}),\n", + " ('Wildfires, sparked by high winds and extremely dry conditions, are threatening Northern California wine country. 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IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(0, 10, 'base'),\n", + " (24, 30, 'change_direction'),\n", + " (34, 48, 'base'),\n", + " (53, 59, 'change_direction'),\n", + " (63, 71, 'base'),\n", + " (73, 89, 'base'),\n", + " (214, 226, 'base')]}),\n", + " ('It worked — the high number of people who evacuated ahead of the storm lead to a significantly lower death toll of 89. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(31, 37, 'base'),\n", + " (42, 51, 'change_direction'),\n", + " (95, 100, 'change_direction'),\n", + " (119, 131, 'base')]}),\n", + " ('Nitroglycerin becomes so sensitive when it absorbs moisture that even a slight shake can set it off. 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IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(3, 10, 'base'),\n", + " (15, 32, 'change_direction'),\n", + " (105, 116, 'base'),\n", + " (67, 90, 'change_direction'),\n", + " (121, 126, 'base')]}),\n", + " ('That means we will likely see a meter of sea level rise by 2100, from a combination of warmer water, which expands, and melting ice sheets in Greenland and Antarctica. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(41, 44, 'base'),\n", + " (51, 55, 'change_direction'),\n", + " (87, 93, 'change_direction'),\n", + " (94, 99, 'base'),\n", + " (107, 114, 'change_direction'),\n", + " (120, 127, 'change_direction'),\n", + " (128, 138, 'base'),\n", + " (168, 180, 'base')]}),\n", + " ('Before we go, we will buy enough offsets to capture the annual methane emanations of a dozen cows — that’s several times what is needed to balance out the carbon effects of our flights. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(22, 25, 'change_direction'),\n", + " (33, 40, 'base'),\n", + " (44, 51, 'change_direction'),\n", + " (63, 70, 'base'),\n", + " (93, 97, 'base'),\n", + " (155, 161, 'base'),\n", + " (186, 198, 'base'),\n", + " (199, 211, 'base')]}),\n", + " ('These changes vary by region and time of year, but there is evidence that anthropogenic warming causes a general weakening of summertime tropical circulation1,2,3,4,5,6,7,8. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(74, 95, 'base'),\n", + " (113, 125, 'change_direction'),\n", + " (174, 186, 'base')]}),\n", + " ('Warmer temperatures are already causing major damage to the world’s forests. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(0, 6, 'change_direction'),\n", + " (68, 75, 'base'),\n", + " (77, 89, 'base')]}),\n", + " ('In the 1940s, companies began offshore oil drilling projects and dredging near the island, activities which contributed to the erosion of the island and its island road.[11] IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(39, 42, 'base'),\n", + " (43, 51, 'change_direction'),\n", + " (65, 73, 'change_direction'),\n", + " (142, 148, 'base'),\n", + " (164, 172, 'base')]}),\n", + " ('Hot pavement also transfers heat to the surrounding air, adding to the urban heat island effect. IMPLIED_BASE IMPLIED_BASE',\n", + " {'entities': [(4, 12, 'base'),\n", + " (18, 35, 'change_direction'),\n", + " (52, 55, 'base'),\n", + " (71, 95, 'base')]})]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "TEST_DATA" + ] + }, + { + "cell_type": "markdown", + "id": "be3711e7", + "metadata": {}, + "source": [ + "# Finding POS Tags patterns" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "e5980ccc", + "metadata": {}, + "outputs": [], + "source": [ + "def get_ent_from_sent(x, label):\n", + " ent = []\n", + " sent = x[0].replace('IMPLIED_BASE', '')\n", + "\n", + " for y in x[1]['entities']:\n", + " if y[2] == label:\n", + " start_index_base = y[0]\n", + " end_index_base = y[1]\n", + " word_base = sent[start_index_base:end_index_base]\n", + " ent.append(word_base)\n", + " return ent" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "388b23e5", + "metadata": {}, + "outputs": [], + "source": [ + "def get_insights(data, label):\n", + " pos_patterns = []\n", + " freq = {}\n", + " count = 0\n", + " for x in data:\n", + " words = []\n", + " sent = x[0].replace('IMPLIED_BASE', '')\n", + " for e in x[1]['entities']:\n", + " if e[2] == label:\n", + " start_index = e[0]\n", + " end_index = e[1]\n", + " word = sent[start_index:end_index]\n", + " words.append(word)\n", + " doc = nlp(sent)\n", + " \n", + " for t in doc:\n", + " for w in words:\n", + " if len(w.split(\" \")) > 0:\n", + " for w1 in w.split(\" \"):\n", + " if t.text == w1 and t.pos_ in [\"VERB\", \"ADV\", \"ADJ\"]:\n", + " #print(t.text, t.pos_, t.dep_, t.head.text, t.head.pos_, [child for child in t.children])\n", + " if [t.pos_, t.dep_, t.head.pos_] not in pos_patterns:\n", + " pos_patterns.append([t.pos_, t.dep_, t.head.pos_])\n", + " if (t.pos_, t.dep_, t.head.pos_) in freq:\n", + " freq[(t.pos_, t.dep_, t.head.pos_)] += 1\n", + " else:\n", + " freq[(t.pos_, t.dep_, t.head.pos_)] = 1\n", + " else:\n", + " if t.text == w and t.pos_ in [\"VERB\", \"ADV\", \"ADJ\"]:\n", + " #print(t.text, t.pos_, t.dep_, t.head.text, t.head.pos_, [child for child in t.children])\n", + " if [t.pos_, t.dep_, t.head.pos_] not in pos_patterns:\n", + " pos_patterns.append([t.pos_, t.dep_, t.head.pos_])\n", + " if (t.pos_, t.dep_, t.head.pos_) in freq:\n", + " freq[(t.pos_, t.dep_, t.head.pos_)] += 1\n", + " else:\n", + " freq[(t.pos_, t.dep_, t.head.pos_)] = 1\n", + " \n", + " # deleting keys where value is <=1\n", + " freq = {k: v for k, v in freq.items() if v > 1}\n", + " \n", + " # sorting the dict\n", + " freq = {k: v for k, v in sorted(freq.items(), key=lambda v: v[1], reverse=True)}\n", + " return pos_patterns, freq" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "c8ceec1c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{('ADJ', 'amod', 'NOUN'): 62, ('VERB', 'ROOT', 'VERB'): 40, ('VERB', 'amod', 'NOUN'): 30, ('ADV', 'advmod', 'ADJ'): 26, ('VERB', 'ccomp', 'VERB'): 20, ('VERB', 'advcl', 'VERB'): 18, ('VERB', 'relcl', 'NOUN'): 18, ('VERB', 'conj', 'VERB'): 18, ('VERB', 'xcomp', 'VERB'): 12, ('ADJ', 'acomp', 'AUX'): 11, ('VERB', 'acl', 'NOUN'): 9, ('VERB', 'pcomp', 'ADP'): 8, ('ADV', 'advmod', 'VERB'): 7, ('ADJ', 'conj', 'ADJ'): 5, ('ADJ', 'ccomp', 'VERB'): 5, ('ADV', 'dobj', 'VERB'): 4, ('ADJ', 'conj', 'ADV'): 4, ('ADV', 'amod', 'NOUN'): 4, ('VERB', 'relcl', 'PROPN'): 3, ('VERB', 'ccomp', 'ADJ'): 3, ('ADV', 'advmod', 'ADV'): 3, ('ADV', 'ccomp', 'VERB'): 2, ('VERB', 'conj', 'NOUN'): 2, ('VERB', 'ccomp', 'AUX'): 2, ('VERB', 'pobj', 'ADP'): 2, ('ADJ', 'acomp', 'VERB'): 2}\n", + "------\n", + "{('VERB', 'ROOT', 'VERB'): 22, ('ADJ', 'amod', 'NOUN'): 17, ('VERB', 'ccomp', 'VERB'): 10, ('VERB', 'amod', 'NOUN'): 9, ('VERB', 'conj', 'VERB'): 8, ('ADV', 'advmod', 'ADJ'): 8, ('VERB', 'relcl', 'NOUN'): 8, ('VERB', 'xcomp', 'VERB'): 7, ('VERB', 'advcl', 'VERB'): 7, ('ADJ', 'acomp', 'VERB'): 6, ('ADV', 'advmod', 'VERB'): 3, ('ADJ', 'conj', 'ADJ'): 3, ('VERB', 'pcomp', 'ADP'): 3, ('VERB', 'acl', 'NOUN'): 3, ('ADV', 'advmod', 'ADV'): 2, ('ADJ', 'ccomp', 'VERB'): 2}\n", + "\n" + ] + } + ], + "source": [ + "insight_label = \"change_direction\"\n", + "print(get_insights(TRAIN_DATA, insight_label)[1])\n", + "print(\"------\")\n", + "print(get_insights(TEST_DATA, insight_label)[1])\n", + "print()\n", + "\n", + "pos_p = get_insights(TEST_DATA, insight_label)[0]\n", + "#for x in get_insights(TEST_DATA)[0]:\n", + "# if x not in get_insights(TRAIN_DATA)[0]:\n", + "# print(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "87a16693", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[['ADJ', 'amod', 'NOUN'],\n", + " ['VERB', 'conj', 'VERB'],\n", + " ['VERB', 'ROOT', 'VERB'],\n", + " ['VERB', 'amod', 'NOUN'],\n", + " ['VERB', 'xcomp', 'VERB'],\n", + " ['ADV', 'advmod', 'ADV'],\n", + " ['ADV', 'advmod', 'VERB'],\n", + " ['VERB', 'ccomp', 'VERB'],\n", + " ['ADV', 'advmod', 'ADJ'],\n", + " ['ADJ', 'conj', 'ADJ'],\n", + " ['ADV', 'conj', 'ADJ'],\n", + " ['VERB', 'conj', 'ADJ'],\n", + " ['VERB', 'relcl', 'PROPN'],\n", + " ['VERB', 'acl', 'PROPN'],\n", + " ['VERB', 'pcomp', 'ADP'],\n", + " ['VERB', 'relcl', 'NOUN'],\n", + " ['ADJ', 'acomp', 'VERB'],\n", + " ['VERB', 'acl', 'NOUN'],\n", + " ['VERB', 'advcl', 'VERB'],\n", + " ['ADJ', 'amod', 'ADV'],\n", + " ['ADV', 'neg', 'ADV'],\n", + " ['ADV', 'conj', 'NOUN'],\n", + " ['VERB', 'ccomp', 'NOUN'],\n", + " ['VERB', 'csubj', 'AUX'],\n", + " ['ADJ', 'ccomp', 'VERB'],\n", + " ['VERB', 'compound', 'NOUN'],\n", + " ['VERB', 'amod', 'SPACE'],\n", + " ['ADJ', 'dobj', 'VERB'],\n", + " ['ADV', 'advmod', 'AUX']]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pos_p" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "e31ca990", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'Around the wine-growing world, smart producers have contemplated and experimented with adaptations, not only to hotter summers, but also to warmer winters, droughts and the sort of unexpected, sometimes violent events that stem from climate change: freak hailstorms, spring frosts, flooding and forest fires, just to name a few. ': ['growing', 'smart', 'contemplated', 'experimented', 'hotter', 'warmer', 'unexpected', 'sometimes', 'violent', 'stem', 'just', 'name', 'few'], '“Extreme heat, powerful storms and floods, year-round wildfires, droughts, and other climate-related events have already caused thousands of deaths and displaced tens of thousands of people in the U.S. from their homes, with significant personal loss and mental health impacts especially for first responders and children,” the letter warns. ': ['Extreme', 'powerful', 'other', 'related', 'already', 'caused', 'displaced', 'significant', 'personal', 'mental', 'first', 'warns'], 'Historical warming has likely increased flood magnitude for mixed‐regime watersheds ': ['Historical', 'likely', 'increased'], 'Antarctic glaciers have been melting at an accelerating pace over the past four decades thanks to an influx of warm ocean water — a startling new finding that researchers say could mean sea levels are poised to rise more quickly than predicted in coming decades. ': ['Antarctic', 'melting', 'accelerating', 'past', 'warm', 'startling', 'new', 'say', 'mean', 'poised', 'rise', 'more', 'quickly', 'predicted', 'coming'], 'For Suzanne Drake, a historical-society volunteer who cobbled together enough money during the recession to buy “the ugliest house on the prettiest street in town,” talk of managed retreat has kicked up an anger she didn’t know she had. ': ['historical', 'cobbled', 'together', 'enough', 'buy', 'ugliest', 'managed', 'kicked', 'know', 'had'], 'Amid the overwhelming scientific consensus and growing public demands for actions, some senior Republicans, like Majority Leader Mitch McConnell, have acknowledged that human activity is driving climate change. ': ['overwhelming', 'scientific', 'growing', 'public', 'acknowledged', 'human', 'driving'], 'Studies estimate that with climate change, water availability per capita could decrease by as much as 88 percent in places, and crop yields in coastal regions may drop by a third. ': ['estimate', 'decrease', 'coastal', 'drop'], 'The author believes this is one of the first papers in the sustainability management field to conclude that climate-induced near-term societal collapse should now be a central concern for everyone, and therefore to invite scholars to explore the implications. ': ['believes', 'first', 'induced', 'near', 'societal', 'now', 'central', 'therefore', 'explore'], \"While a more moderate emissions trajectory will still produce more intense, frequent and longer lasting droughts in most of the world's mid-latitude regions than current conditions, the shift will be less than if carbon emissions remain near the top of forecasts. \": ['more', 'moderate', 'still', 'more', 'intense', 'frequent', 'longer', 'lasting', 'mid', '-', 'latitude', 'current'], 'Malnutrition, low birth weight, nonexclusive breast-feeding, lack of measles vaccination, outdoor and indoor air pollution and crowding, mother’s education, parental smoking, vitamin A and/or zinc deficiencies are thought to influence children susceptibility to infections in developing countries. ': ['low', 'nonexclusive', 'indoor', 'parental', 'thought', 'influence', 'developing'], 'When the rain hit your face, it was like being hit by a stone,” Fatuma, whose house was severely damaged by Cyclone Kenneth in April, told Oxfam. ': ['hit', 'severely', 'damaged', 'told'], 'Wildfires, sparked by high winds and extremely dry conditions, are threatening Northern California wine country. ': ['sparked', 'high', 'extremely', 'dry', 'threatening'], 'But the report said even small cuts in meat eating could lead to significant cuts in carbon emissions, as well as benefits to health. ': ['said', 'small', 'eating', 'lead', 'significant'], 'The majority of the warming at the global scale over the past 50 years can only be explained by the effects of human influences,,, especially the emissions from burning fossil fuels (coal, oil, and natural gas) and from deforestation. ': ['global', 'past', 'only', 'explained', 'human', 'burning', 'natural'], 'They predict that the number of lightning strikes will increase by about 12% for every degree of rise in global average air temperature. ': ['predict', 'increase', 'global', 'average'], 'Hurricanes resulting in damage to infrastructure and delays in training, testing programs, and space launches at Tyndall Air Force Base, at the Atlantic Undersea Test and Evaluation Centers, and the Eastern Range. ': ['resulting'], 'It worked — the high number of people who evacuated ahead of the storm lead to a significantly lower death toll of 89. ': ['worked', 'high', 'evacuated', 'ahead', 'lead', 'significantly', 'lower'], 'Nitroglycerin becomes so sensitive when it absorbs moisture that even a slight shake can set it off. ': ['becomes', 'so', 'sensitive', 'moisture', 'slight', 'set'], 'Round-trip flights between London and Guwahati, the closest airport to North Salmara, emit over three tonnes of carbon dioxide – about twice as much as the average Indian emits in a whole year. ': ['Round', 'closest', 'emit', 'about', 'twice', 'as', 'average', 'Indian', 'whole'], 'Individuals infected with either WNV or Lyme disease may experience a range of mental health consequences following infection that can include reduced cognitive function as well depression associated with other symptoms, such as fatigue, pain, and muscle and joint aches.184, 185 ': ['infected', 'experience', 'mental', 'include', 'reduced', 'cognitive', 'associated', 'other', 'joint'], 'Ultraviolet Radiation as a Mediator of Climate Change-Induced Immune Dysfunction ': [], 'Floods kill more people in the United States than any other kind of severe weather and cause billions of dollars in damages every year. ': ['kill', 'more', 'other', 'severe', 'cause'], 'Smoke from fires has fouled Sydney’s air intermittently since November. ': ['fouled', 'intermittently'], 'Those blackouts are separate from the pre-emptive shut-offs carried out by California utilities in an effort to prevent their equipment from sparking wildfires. ': ['pre', '-', 'emptive', 'carried', 'prevent', 'sparking'], 'This weakening of wind shear is likely to result in more rapid intensification of storms as they near landfall. ': ['more', 'rapid'], 'A year ago, Dorian crawled over the Bahamas for a day and a half, causing widespread destruction from wind and storm surge. ': ['ago', 'crawled', 'causing', 'widespread'], 'That persistence, and what Francis calls the waviness of the jet stream, are fingerprints of human-caused climate change, particularly the disproportionate warming of the Arctic. ': ['calls', 'caused', 'disproportionate'], 'http://www.nasa.gov/topics/earth/features/wildfires.html> Experts in the field of climate change and biomass burning programs from NASA explained that wildfires are a result of climate change, are frequently caused by humans and that different areas are affected with varying intensity. ': ['burning', 'explained', 'frequently', 'caused', 'different', 'affected', 'varying'], 'There is growing evidence that climate change can increase the risks of conflict and violence. ': ['is', 'growing', 'increase'], 'Sherrell Thompson, a community health worker in Gilpin, said residents have high rates of asthma, diabetes and blood pressure, all conditions that can be worsened by heat. ': ['said', 'have', 'high', 'worsened'], 'Climate change, which is already causing heavier rainfall in many storms, is an important part of the mix. ': ['already', 'causing', 'heavier', 'many', 'important'], 'These events were caused by marine heatwaves – extended periods of unusually high temperatures that were themselves boosted by human-caused warming and El Niño. ': ['caused', 'marine', 'extended', 'unusually', 'high', 'boosted', 'caused'], 'Severe wildfires, like the recent Camp fire in Paradise, Calif., have been increasing across the West, devastating nearby communities. ': ['Severe', 'recent', 'increasing', 'devastating', 'nearby'], 'The Bay Area Air Quality Management District issued a spare-the-air alert for Tuesday, with air quality in the region measuring as unhealthy for sensitive groups in the eastern part of the region. ': ['issued', 'spare', 'measuring', 'unhealthy', 'sensitive', 'eastern'], 'It turns out that right here at home in the US, thousands of servicepeople suffer from heat-related illnesses like heat exhaustion and heat stroke every year, and the problem is set to grow much worse. ': ['turns', 'right', 'here', 'suffer', 'related', 'set', 'grow', 'much', 'worse'], 'The mismatch in the ventilation-perfusion ratio due to the reduced minute-ventilation increases the hypoxemia. ': ['reduced', 'increases'], 'Between 2007 and 2011 alone, urban flooding in Cook County, Illinois, resulted in over 176,000 claims or flood losses at a cost of $660m (£545m). ': ['urban', 'resulted'], 'Massive damage has resulted from our development patterns, aging and no longer adequate flood control infrastructure and extreme weather exacerbated by climate change. ': ['Massive', 'resulted', 'no', 'longer', 'adequate', 'extreme', 'exacerbated'], 'Climate change has raised the risk of a fungal disease that ravages banana crops, new research shows. ': ['raised', 'fungal', 'ravages', 'banana', 'new'], 'Researchers have found that grasses and ragweed plants increase their pollen production in response to localized surges in carbon dioxide, like from the exhaust of cars along a highway. ': ['found', 'ragweed', 'increase', 'localized'], 'Waterborne cholera and cryptosporidiosis increase with drought and flooding. ': [], 'Normally, Dr. Swain said, what finally extinguishes the fires are autumn rains and snowfall, which historically come in October or November. ': ['Normally', 'said', 'finally', 'extinguishes', 'historically', 'come'], 'What many scientists and experts agree on: As climate change increases extreme precipitation, cities will need to adapt. ': ['many', 'agree', 'increases', 'extreme', 'need', 'adapt'], 'Climate change and biodiversity loss are equally huge environmental problems that make each other worse, report chairman Robert Watson said. ': ['equally', 'huge', 'environmental', 'make', 'worse', 'said'], 'Assessing the risk from both wind and storm surge, the author of a 2017 paper concludes: “In combination, climate change and coastal development will cause hurricane damage to increase faster than the U.S. economy is expected to grow. ': ['Assessing', 'concludes', 'coastal', 'cause', 'increase', 'faster', 'grow'], 'We find strong causal evidence linking climatic events to human conflict across a range of spatial and temporal scales and across all major regions of the world. ': ['find', 'strong', 'causal', 'linking', 'climatic', 'human', 'spatial', 'temporal', 'major'], 'When people get uncomfortably hot, their tempers, irritability, and likelihood of physical aggression and violence increase. ': ['get', 'uncomfortably', 'hot', 'physical'], 'As heatwaves have increased, kelp forests, seagrass meadows and coral reefs have been lost. ': ['increased', 'coral', 'lost'], 'It predicts that in the absence of significant progress in efforts to curb emissions of temperature-raising greenhouse gases, extreme heat waves could claim thousands of lives in major U.S. cities. ': ['predicts', 'significant', 'curb', 'raising', 'extreme', 'claim', 'major'], 'One of the most common environmental allergens is ragweed, which can cause hay fever and trigger asthma attacks, especially in children and the elderly.2 ': ['most', 'common', 'environmental', 'trigger'], 'Very strong evidence from multiple studies shows a consensus that many people exposed to climate- or weather-related natural disasters experience stress reactions and serious psychological harm, which often occur simultaneously.30, 31, 32, 33, 34 ': ['Very', 'strong', 'multiple', 'shows', 'many', 'exposed', 'related', 'natural', 'experience', 'serious', 'psychological', 'often', 'occur'], 'Other trees, withering from the heat, have stopped bearing edible fruit. ': ['Other', 'withering', 'stopped', 'bearing', 'edible'], 'Naughton, M.B., A. Henderson, M.C. Mirabelli, R. Kaiser, J.L. Wilhelm, S.M. Kieszak, C.H. Rubin, and M.A. McGeehin, 2002: Heat-related mortality during a 1999 heat wave in Chicago. ': ['related'], 'Scientists and health professionals have long known that exposure to air pollution causes respiratory damage, such as asthma. ': ['long', 'known', 'causes', 'respiratory'], 'Right now, APS gets roughly a quarter of its electricity from coal, a quarter from natural gas and a quarter from nuclear power. ': ['Right', 'now', 'gets', 'natural', 'nuclear'], \"The international body estimates 80% of the Sahel's farmland has been degraded by climate change, causing scarcity of food for people and livestock. \": ['international', 'estimates', 'degraded', 'causing'], 'Air pollution is having adverse effects on plants, and some research suggests it could even impact tree mortality. ': ['having', 'adverse', 'suggests', 'even', 'impact'], 'Wireless companies whose networks are threatened by hurricanes, floods, fires and other extreme weather are rebuilding to withstand repeat disasters and factoring climate change into their long-term planning. ': ['Wireless', 'threatened', 'other', 'extreme', 'rebuilding', 'withstand', 'long'], 'Many of the blazes were sparked by an unprecedented lightning siege of nearly 12,000 strikes over a three-day period. ': ['sparked', 'unprecedented'], 'Despite the study’s relatively narrow scope, the authors argue that the results likely apply to other creatures, including those rare and vulnerable species already imperiled by habitat fragmentation, pollution, and other human-caused disruptions. ': ['relatively', 'narrow', 'argue', 'likely', 'apply', 'other', 'rare', 'vulnerable', 'already', 'imperiled', 'habitat', 'other', 'caused'], 'Already, human activities have caused an estimated 1.0 degree Celsius increase in global warming above pre-industrial levels. ': ['Already', 'human', 'caused', 'estimated', 'global', 'pre', '-', 'industrial'], 'Following Hurricanes Katrina and Rita, women in temporary housing attempted suicide 78 times more frequently than women not affected by the storms. ': ['temporary', 'attempted', 'more', 'frequently', 'affected'], 'Air Pollution in California could created a serious public health hazard. ': ['created', 'serious', 'public'], '\"As freshwater lakes get hotter in the summer, that leads to more amoebae in the water and increased human risk,\" said Sonia Altizer, an associate professor of ecology at the University of Georgia. ': ['freshwater', 'get', 'hotter', 'leads', 'more', 'increased', 'human', 'said', 'associate'], 'Climate change should cause net extinctions to increase in the south and at low elevations and to decrease in the north and at high elevations. ': ['cause', 'net', 'increase', 'low', 'decrease', 'high'], 'Another increased hazard slower storms bring is increased wind damage, due to an increase in the duration of damaging winds structures are exposed to. ': ['increased', 'slower', 'increased', 'damaging'], 'Air pollution, dust clouds, the stability of the upper atmosphere, and the relative warmth of the Atlantic Ocean compared to the Indian and Pacific Oceans can all influence the intensity and frequency of hurricanes. ': ['upper', 'relative', 'influence'], 'It’s the big, industrial dairy farms that are responsible for 55 percent of the state’s methane emissions. ': ['’s', 'big', 'industrial'], 'Warmer oceans cause hurricanes to have faster wind speeds and stronger central pressure, making the storms more intense. ': ['Warmer', 'cause', 'have', 'faster', 'stronger', 'central', 'making', 'more', 'intense'], 'One is that all that warm water allowed for the storm to ramp up quickly, undergoing what is known as rapid intensification as it exploded from a moderate category 2 to extreme category 5 over just two days. ': ['warm', 'allowed', 'ramp', 'quickly', 'known', 'rapid', 'exploded', 'moderate', 'extreme'], 'The heat, according to researchers at the University of California, Berkeley, will drive deaths that soon outpace those from car crashes or opioid overdoses. ': ['drive', 'soon', 'outpace'], 'Wildfires are also expected to increase the risk for destructive mudslides, as landscapes laid bare by fire are drenched with winter rain. ': ['also', 'expected', 'increase', 'destructive', 'laid', 'bare', 'drenched'], 'Rachel Noble, a University of North Carolina microbiologist who has monitored the bacteria in rivers and estuaries in her state for more than 20 years, notes that storm surges can cause Vibrio concentrations to spike and linger in the water. ': ['monitored', 'notes', 'cause', 'spike', 'linger'], 'Policies can improve energy efficiency, speed our transition from fossil fuels to renewable energy, and require companies to limit the amount of pollution that they produce. ': ['improve', 'speed', 'renewable', 'require', 'limit', 'produce'], 'Participation in sport improves mental and physical health,1 enhances community cohesion and well-being,2 and contributes significantly to employment.3 ': ['improves', 'mental', 'physical', 'enhances', 'contributes', 'significantly'], 'Emissions from burning coal ': ['burning'], 'Over the past century, we have become better at medicine (which lowers the risk from disease) but we also travel more (which increases the spread of diseases). ': ['past', 'become', 'better', 'lowers', 'also', 'travel', 'more', 'increases'], 'In the future, weather whiplash could mean an intense drought year followed by record rains that don’t allow planting or that wash fertilizer into waterways. ': ['mean', 'intense', 'followed', 'allow', 'wash'], 'In 2018, my father died of complications from pneumonia, after recovering from the cancer. ': ['died', 'recovering'], 'Human activities such as deforestation and the burning of fossil fuels are by far the biggest contributors to climate change. ': ['Human', 'biggest'], 'Studies show that water vapor feed back roughly doubles the amount of warming caused by CO2. ': ['show', 'feed', 'back', 'roughly', 'doubles', 'caused'], 'Last month Cyclone Idai struck central Mozambique, killing more than 600 people and leading to thousands of cases of cholera and malaria. ': ['Last', 'struck', 'killing', 'leading'], 'And with humans continuing to burn fossil fuels for energy, global warming is expected to compound the damage. ': ['continuing', 'burn', 'global', 'expected', 'compound'], 'When Butler and Gates (2012) studied income disparity in East African cattle herders, they discovered that resource asymmetries caused by extreme and adverse weather contribute to conflict in the region. ': ['studied', 'African', 'discovered', 'caused', 'extreme', 'adverse', 'contribute'], 'In Arizona, three lightning-caused wildfires forced the evacuation of multiple communities on Saturday. ': ['caused', 'forced', 'multiple'], 'In a counterpoint to the article other researchers note that savings from increasing energy efficiency and expansion of renewables is also not baked into the GDP numbers. ': ['other', 'note', 'increasing', 'also', 'baked'], 'As summers get hotter and hotter, high school football players are increasingly at risk of succumbing to heat stroke and dying during summer practice – about three players a year, on average. ': ['hotter', 'hotter', 'high', 'increasingly', 'succumbing', 'dying'], 'That means we will likely see a meter of sea level rise by 2100, from a combination of warmer water, which expands, and melting ice sheets in Greenland and Antarctica. ': ['means', 'likely', 'see', 'warmer', 'expands', 'melting'], 'Before we go, we will buy enough offsets to capture the annual methane emanations of a dozen cows — that’s several times what is needed to balance out the carbon effects of our flights. ': ['go', 'buy', 'enough', 'capture', 'annual', '’s', 'several', 'needed', 'balance'], 'These changes vary by region and time of year, but there is evidence that anthropogenic warming causes a general weakening of summertime tropical circulation1,2,3,4,5,6,7,8. ': ['vary', 'is', 'anthropogenic', 'causes', 'general', 'tropical'], 'Warmer temperatures are already causing major damage to the world’s forests. ': ['Warmer', 'already', 'causing', 'major'], 'In the 1940s, companies began offshore oil drilling projects and dredging near the island, activities which contributed to the erosion of the island and its island road.[11] ': ['began', 'offshore', 'contributed'], 'Hot pavement also transfers heat to the surrounding air, adding to the urban heat island effect. ': ['Hot', 'also', 'transfers', 'surrounding', 'adding', 'urban']}\n" + ] + } + ], + "source": [ + "ans_dict_pos = {}\n", + "for x in TEST_DATA:\n", + " sent = x[0].replace('IMPLIED_BASE', '')\n", + " doc = nlp(sent)\n", + " tmp = []\n", + " \n", + " for t in doc:\n", + " head = t.head.text\n", + " for v in pos_p:\n", + " if [t.pos_, t.dep_, t.head.pos_] == v:\n", + " #if t.text not in get_ent_from_sent(x, \"base\") and t.text.isalpha():\n", + " tmp.append(t.text)\n", + "\n", + " ans_dict_pos[sent] = tmp\n", + "print(ans_dict_pos)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "be59faf7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'\\ns = \"Antarctic glaciers have been melting at an accelerating pace over the past four decades thanks to an influx of warm ocean water — a startling new finding that researchers say could mean sea levels are poised to rise more quickly than predicted in coming decades. IMPLIED_BASE IMPLIED_BASE\"\\nwords = [\\'Antarctic\\', \\'melting\\', \\'accelerating\\', \\'past\\', \\'warm\\', \\'startling\\', \\'new\\', \\'say\\', \\'mean\\', \\'poised\\', \\'rise\\', \\'more\\', \\'quickly\\', \\'predicted\\', \\'coming\\']\\n\\nfull_word = []\\n\\nfor w in range(0, len(words)):\\n for v in range(w, len(words)):\\n new_word = \" \".join(words[w:v])\\n if new_word in s and len(new_word.split(\" \")) > 1:\\n full_word.append(new_word) \\n\\nprint(full_word)\\n'" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"\"\"\n", + "s = \"Antarctic glaciers have been melting at an accelerating pace over the past four decades thanks to an influx of warm ocean water — a startling new finding that researchers say could mean sea levels are poised to rise more quickly than predicted in coming decades. IMPLIED_BASE IMPLIED_BASE\"\n", + "words = ['Antarctic', 'melting', 'accelerating', 'past', 'warm', 'startling', 'new', 'say', 'mean', 'poised', 'rise', 'more', 'quickly', 'predicted', 'coming']\n", + "\n", + "full_word = []\n", + "\n", + "for w in range(0, len(words)):\n", + " for v in range(w, len(words)):\n", + " new_word = \" \".join(words[w:v])\n", + " if new_word in s and len(new_word.split(\" \")) > 1:\n", + " full_word.append(new_word) \n", + "\n", + "print(full_word)\n", + "\"\"\"\n" + ] + }, + { + "cell_type": "markdown", + "id": "d9c147ee", + "metadata": {}, + "source": [ + "# Calculate results" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "7aee4a60", + "metadata": {}, + "outputs": [], + "source": [ + "def del_from_list(del_arr, main_arr):\n", + " for x in del_arr:\n", + " if x in main_arr:\n", + " main_arr.remove(x)\n", + " return main_arr\n", + "\n", + "def calc_res(user_ans, answers): \n", + " final_res = []\n", + " grade_ent_res = {}\n", + " tmp_del = []\n", + " user = \"machine\"\n", + "\n", + " true_positive = 0\n", + " false_negative = 0\n", + " false_positive = 0\n", + " complete = 0\n", + " ent = \"change_direction\"\n", + " grade_ent_res[user] = {}\n", + " if ent not in grade_ent_res[user]:\n", + " grade_ent_res[user][ent] = {\"tp\": 0, \"fp\": 0, \"fn\": 0, \"complete\": 0}\n", + "\n", + " if answers == user_ans:\n", + " # 1. when both arrays are exactly the same\n", + " for ans in user_ans:\n", + " tmp_del.append(ans)\n", + " true_positive += 1\n", + " complete += 1\n", + " answers = del_from_list(tmp_del, answers)\n", + " user_ans = del_from_list(tmp_del, user_ans)\n", + " tmp_del = []\n", + " else:\n", + " # 2. when some elements are equal in both user and actual ans\n", + " for ans in user_ans:\n", + " if ans in answers:\n", + " tmp_del.append(ans)\n", + " true_positive += 1\n", + " complete += 1\n", + " answers = del_from_list(tmp_del, answers)\n", + " user_ans = del_from_list(tmp_del, user_ans)\n", + " tmp_del = []\n", + "\n", + " # 3. when user ans is partially correct\n", + " # - if user ans is contained in actual ans\n", + " if user_ans:\n", + " for ans in user_ans:\n", + " substring_user = [string for string in answers if ans in string]\n", + " if substring_user:\n", + " tmp_del.append(ans)\n", + " true_positive += 1\n", + " false_positive += 1\n", + " answers = del_from_list(tmp_del, answers)\n", + " user_ans = del_from_list(tmp_del, user_ans)\n", + " tmp_del = []\n", + "\n", + " # - if actual ans is contained in user ans\n", + " if answers:\n", + " for ans in answers:\n", + " substring_ans = [string for string in user_ans if ans in string]\n", + " if substring_ans:\n", + " tmp_del.append(ans)\n", + " true_positive += 1\n", + " false_positive += 1\n", + " answers = del_from_list(tmp_del, answers)\n", + " user_ans = del_from_list(tmp_del, user_ans)\n", + " tmp_del = []\n", + "\n", + " # 4. when actual ans is missing from user ans\n", + " if answers and user_ans:\n", + " set_difference_actual = set(answers) - set(user_ans)\n", + " for ans in list(set_difference_actual):\n", + " tmp_del.append(ans)\n", + " false_negative += 1\n", + " answers = del_from_list(tmp_del, answers)\n", + " tmp_del = []\n", + "\n", + " # 5. when user ans is missing from actual ans\n", + " if answers and user_ans:\n", + " set_difference_user = set(user_ans) - set(answers)\n", + " for ans in list(set_difference_user):\n", + " tmp_del.append(ans)\n", + " false_positive += 1\n", + " user_ans = del_from_list(tmp_del, user_ans)\n", + " tmp_del = []\n", + "\n", + " #print(\"tp: {}, fp: {}, fn: {}\".format(true_positive, false_positive, false_negative))\n", + " grade_ent_res[user][ent][\"tp\"] += true_positive\n", + " grade_ent_res[user][ent][\"fp\"] += false_positive\n", + " grade_ent_res[user][ent][\"fn\"] += false_negative\n", + " grade_ent_res[user][ent][\"complete\"] += complete\n", + "\n", + " return grade_ent_res" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "eefa0224", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "created result file /Users/ash/Desktop/custom_entity_extractor_spacy_change_dir/spacy_result_change_dir.csv\n" + ] + } + ], + "source": [ + "label_check = \"change_direction\"\n", + "result = os.path.join(output_dir, \"spacy_result_change_dir.csv\")\n", + "true_pos = 0\n", + "all_cd = 0\n", + "correct_cd = 0\n", + "false_pos = 0\n", + "false_neg = 0\n", + "\n", + "headers = [\"text\", \"predicted_ent\", \"actual_ent\"]\n", + "final_res = []\n", + "final_res.append(headers)\n", + "for sent in TEST_DATA:\n", + " #res = ans_dict_pos_cd[sent[0]]\n", + " res = ans_dict_pos[sent[0].replace('IMPLIED_BASE', '')]\n", + " actual_ent = []\n", + " for tokens in sent[1]['entities']:\n", + " if tokens[2] == label_check:\n", + " word = sent[0][tokens[0]:tokens[1]]\n", + " if len(word.split(\" \")) > 0:\n", + " actual_ent.extend(word.split(\" \"))\n", + " else:\n", + " actual_ent.append(word)\n", + " final_res.append([sent[0], res, actual_ent])\n", + " \n", + "with open(result, 'w', newline='') as csvfile:\n", + " writer = csv.writer(csvfile)\n", + " writer.writerows(final_res)\n", + "\n", + "print(\"created result file\", result)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "9ebc2a75", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "False pos: 7\n", + "False neg: 45\n", + "Marked 125 out of 175 cd correctly. Accuracy: 71.42857142857143\n" + ] + } + ], + "source": [ + "true_pos = 0\n", + "all_ents = 0\n", + "correct_ents = 0\n", + "false_pos = 0\n", + "false_neg = 0\n", + "\n", + "for sent in TEST_DATA:\n", + " #res = ans_dict_pos_cd[sent[0]]\n", + " res = ans_dict_pos[sent[0].replace('IMPLIED_BASE', '')]\n", + " actual_ent = []\n", + " for tokens in sent[1]['entities']:\n", + " if tokens[2] == label_check:\n", + " word = sent[0][tokens[0]:tokens[1]]\n", + " if len(word.split(\" \")) > 0:\n", + " actual_ent.extend(word.split(\" \"))\n", + " else:\n", + " actual_ent.append(word)\n", + " all_ents += len(actual_ent)\n", + " res_dict = calc_res(res, actual_ent)\n", + " true_pos += res_dict[\"machine\"]['change_direction']['tp']\n", + " false_pos += res_dict[\"machine\"]['change_direction']['fp']\n", + " false_neg += res_dict[\"machine\"]['change_direction']['fn']\n", + " correct_ents += res_dict[\"machine\"]['change_direction']['complete']\n", + " \n", + "acc = ((correct_ents*100)/all_ents)\n", + "print(\"False pos: \", false_pos)\n", + "print(\"False neg: \", false_neg)\n", + "print(\"Marked {} out of {} cd correctly. Accuracy: {}\".format(correct_ents, all_ents, acc))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "29793845", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "45ec0274", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: benepar in /Users/ash/opt/anaconda3/lib/python3.9/site-packages (0.2.0)\n", + "Requirement already satisfied: torch>=1.6.0 in /Users/ash/opt/anaconda3/lib/python3.9/site-packages (from benepar) (1.10.2)\n", + "Requirement already satisfied: spacy>=2.0.9 in /Users/ash/opt/anaconda3/lib/python3.9/site-packages (from benepar) (3.2.2)\n", + "Requirement already satisfied: nltk>=3.2 in /Users/ash/opt/anaconda3/lib/python3.9/site-packages (from benepar) (3.6.5)\n", + "Requirement already satisfied: tokenizers>=0.9.4 in /Users/ash/opt/anaconda3/lib/python3.9/site-packages (from benepar) (0.11.6)\n", + 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config={\"model\": \"benepar_en3\"})\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "33398ec3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Climate change can make a sizable dent on economic growth by disrupting supply chains and demand for products, and creating harsh working conditions, among other issues.\n", + "[S [NP [NN Climate] [NN change]] [VP [MD can] [VP [VB make] [NP [NP [DT a] [JJ sizable] [NN dent]] [PP [IN on] [NP [JJ economic] [NN growth]]]] [PP [IN by] [S [VP [VP [VBG disrupting] [NP [NP [NN supply] [NNS chains]] [CC and] [NP [NP [NN demand]] [PP [IN for] [NP [NNS products]]]]]] [, ,] [CC and] [VP [VBG creating] [NP [JJ harsh] [NN working] [NNS conditions]]] [, ,] [PP [IN among] [NP [JJ other] [NNS issues]]]]]]]] [. .]]\n", + "(S (NP (NN Climate) (NN change)) (VP (MD can) (VP (VB make) (NP (NP (DT a) (JJ sizable) (NN dent)) (PP (IN on) (NP (JJ economic) (NN growth)))) (PP (IN by) (S (VP (VP (VBG disrupting) (NP (NP (NN supply) (NNS chains)) (CC and) (NP (NP (NN demand)) (PP (IN for) (NP (NNS products)))))) (, ,) (CC and) (VP (VBG creating) (NP (JJ harsh) (NN working) (NNS conditions))) (, ,) (PP (IN among) (NP (JJ other) (NNS issues)))))))) (. .))\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/ash/opt/anaconda3/lib/python3.9/site-packages/torch/distributions/distribution.py:44: UserWarning: does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n", + " warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n" + ] + } + ], + "source": [ + "sent = \"Climate change can make a sizable dent on economic growth by disrupting supply chains and demand for products, and creating harsh working conditions, among other issues.\"\n", + "doc = nlp(sent)\n", + "parsed_arr = []\n", + "for sent in doc.sents:\n", + " print(sent)\n", + " parsed = list(nlp(str(sent)).sents)[0]._.parse_string\n", + " print(parsed.replace(\"(\", \"[\").replace(\")\", \"]\"))\n", + " #parsed = parsed.replace(\"(\", \"{\").replace(\")\", \"}\")\n", + " parsed_arr.append([parsed])\n", + " print(parsed)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2f47dc9c", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/model_dev/entity_extraction/to_delete/checkin_patterns.ipynb b/model_dev/entity_extraction/to_delete/checkin_patterns.ipynb new file mode 100644 index 0000000..158c45e --- /dev/null +++ b/model_dev/entity_extraction/to_delete/checkin_patterns.ipynb @@ -0,0 +1,898 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "1f30d527", + "metadata": {}, + "outputs": [], + "source": [ + "import spacy" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "7319851b", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import json\n", + "import ast" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "84e55195", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['text', '_input_hash', '_task_hash', 'spans', 'tokens', '_session_id',\n", + " '_view_id', 'relations', 'answer', 'flagged', 'relation count',\n", + " 'complexity', 'original_text', 'document_index', 'md_sentence_index',\n", + " 'classification_origin', 'url', 'order', 'Used?', 'Dataset name',\n", + " 'ignore?', 'reason', 'original_md_text', 'characters'],\n", + " dtype='object')" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "answers = pd.read_json('checkin_answers.jsonl', lines=True)\n", + "answers.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "948ab86a", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'start': 0, 'end': 4, 'token_start': 0, 'token_end': 0, 'label': 'type_of'},\n", + " {'start': 5, 'end': 11, 'token_start': 1, 'token_end': 1, 'label': 'base'},\n", + " {'start': 16,\n", + " 'end': 26,\n", + " 'token_start': 3,\n", + " 'token_end': 3,\n", + " 'label': 'type_of'},\n", + " {'start': 27, 'end': 38, 'token_start': 4, 'token_end': 4, 'label': 'base'},\n", + " {'start': 39,\n", + " 'end': 42,\n", + " 'token_start': 5,\n", + " 'token_end': 5,\n", + " 'label': 'confidence'},\n", + " {'start': 43,\n", + " 'end': 48,\n", + " 'token_start': 6,\n", + " 'token_end': 6,\n", + " 'label': 'predicate'},\n", + " {'start': 49, 'end': 55, 'token_start': 7, 'token_end': 7, 'label': 'base'},\n", + " {'start': 56,\n", + " 'end': 62,\n", + " 'token_start': 8,\n", + " 'token_end': 8,\n", + " 'label': 'aspect_changing'}]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "answers['spans'].iloc[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "d61078a9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Heat stress and persistent dehydration can cause kidney damage. IMPLIED_BASE IMPLIED_BASE'" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "answers['text'].iloc[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "8136a27c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'head': 0,\n", + " 'child': 1,\n", + " 'head_span': {'start': 0,\n", + " 'end': 4,\n", + " 'token_start': 0,\n", + " 'token_end': 0,\n", + " 'label': 'type_of'},\n", + " 'child_span': {'start': 5,\n", + " 'end': 11,\n", + " 'token_start': 1,\n", + " 'token_end': 1,\n", + " 'label': 'base'},\n", + " 'color': '#c5bdf4',\n", + " 'label': 'Concept_Member'},\n", + " {'head': 3,\n", + " 'child': 4,\n", + " 'head_span': {'start': 16,\n", + " 'end': 26,\n", + " 'token_start': 3,\n", + " 'token_end': 3,\n", + " 'label': 'type_of'},\n", + " 'child_span': {'start': 27,\n", + " 'end': 38,\n", + " 'token_start': 4,\n", + " 'token_end': 4,\n", + " 'label': 'base'},\n", + " 'color': '#c5bdf4',\n", + " 'label': 'Concept_Member'},\n", + " {'head': 8,\n", + " 'child': 7,\n", + " 'head_span': {'start': 56,\n", + " 'end': 62,\n", + " 'token_start': 8,\n", + " 'token_end': 8,\n", + " 'label': 'aspect_changing'},\n", + " 'child_span': {'start': 49,\n", + " 'end': 55,\n", + " 'token_start': 7,\n", + " 'token_end': 7,\n", + " 'label': 'base'},\n", + " 'color': '#c5bdf4',\n", + " 'label': 'Concept_Member'},\n", + " {'head': 1,\n", + " 'child': 7,\n", + " 'head_span': {'start': 5,\n", + " 'end': 11,\n", + " 'token_start': 1,\n", + " 'token_end': 1,\n", + " 'label': 'base'},\n", + " 'child_span': {'start': 49,\n", + " 'end': 55,\n", + " 'token_start': 7,\n", + " 'token_end': 7,\n", + " 'label': 'base'},\n", + " 'color': '#ffd882',\n", + " 'label': 'Contributes_To'},\n", + " {'head': 4,\n", + " 'child': 7,\n", + " 'head_span': {'start': 27,\n", + " 'end': 38,\n", + " 'token_start': 4,\n", + " 'token_end': 4,\n", + " 'label': 'base'},\n", + " 'child_span': {'start': 49,\n", + " 'end': 55,\n", + " 'token_start': 7,\n", + " 'token_end': 7,\n", + " 'label': 'base'},\n", + " 'color': '#ffd882',\n", + " 'label': 'Contributes_To'}]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "answers['relations'].iloc[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "ec130f7a", + "metadata": {}, + "outputs": [], + "source": [ + "from spacy.matcher import Matcher\n", + "import networkx as nx\n", + "from tqdm import tqdm\n", + "tqdm.pandas()" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "725520b5", + "metadata": {}, + "outputs": [], + "source": [ + "def get_dependency_patterns(answers, head_type='type_of', tail_type='base'):\n", + " patterns = {}\n", + " for _, answer in tqdm(answers.iterrows(), total=answers.shape[0]):\n", + " spans = answer['spans']\n", + " text = answer['text']\n", + " relations = answer['relations']\n", + " nlp = spacy.load('en_core_web_md')\n", + " doc = nlp(text)\n", + " for relation in relations:\n", + " if relation['label'] != 'Concept_Member':\n", + " continue\n", + " head_span = relation['head_span']\n", + " child_span = relation['child_span']\n", + " if head_span['label'] == head_type and child_span['label'] == tail_type:\n", + " src_span = doc[head_span['token_start']: head_span['token_end'] + 1]\n", + " tgt_span = doc[child_span['token_start']: child_span['token_end'] + 1]\n", + " src_root = src_span.root\n", + " tgt_root = tgt_span.root\n", + " if tgt_span.text.lower() == 'implied_base':\n", + " continue\n", + " edges = []\n", + " for token in doc:\n", + " for child in token.children:\n", + " edges.append(('{}-{}'.format(token.lower_,token.i), '{}-{}'.format(child.lower_,child.i))) \n", + " graph = nx.Graph(edges) \n", + " path = None\n", + " source = '{}-{}'.format(src_root.lower_, src_root.i)\n", + " target = '{}-{}'.format(tgt_root.lower_, tgt_root.i)\n", + " if nx.has_path(graph, source=source, target=target):\n", + " path = nx.shortest_path(graph, source=source, target=target)\n", + " if path is not None:\n", + " for t in src_span:\n", + " n = '{}-{}'.format(t.lower_, t.i) \n", + " if n not in path:\n", + " path.append(n)\n", + " for t in tgt_span:\n", + " n = '{}-{}'.format(t.lower_, t.i)\n", + " if n not in path:\n", + " path.append(n)\n", + " path_nodes = {}\n", + " for p in path:\n", + " t, i = p.rsplit('-', 1)\n", + " i = int(i)\n", + " if i in range(head_span['token_start'], head_span['token_end'] + 1):\n", + " t = ''\n", + " elif i in range(child_span['token_start'], child_span['token_end'] + 1):\n", + " t = ''\n", + " path_nodes[i] = t\n", + " path_nodes = sorted(path_nodes.items(), key=lambda x: x[0])\n", + " pattern = ' '.join([p[1] for p in path_nodes])\n", + " patterns[pattern] = patterns.get(pattern, 0) + 1\n", + " patterns = {k:v for k,v in patterns.items()}\n", + " patterns = sorted(patterns.items(), key=lambda x: x[1], reverse=True)\n", + " return patterns" + ] + }, + { + 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head_type='type_of', tail_type='base'):\n", + " patterns = {}\n", + " nlp = spacy.load('en_core_web_md')\n", + " for _, answer in tqdm(answers.iterrows(), total=answers.shape[0]):\n", + " spans = answer['spans']\n", + " text = answer['text']\n", + " relations = answer['relations']\n", + " doc = nlp(text)\n", + " token_tags = {}\n", + " for span in spans:\n", + " if 'token_start' not in span or 'token_end' not in span:\n", + " continue\n", + " token_start = span['token_start']\n", + " token_end = span['token_end']\n", + " tag = span['label']\n", + " for i in range(token_start, token_end+1):\n", + " token_tags[i] = tag\n", + " for relation in relations:\n", + " if relation['label'] != 'Concept_Member':\n", + " continue\n", + " head_span = relation['head_span']\n", + " child_span = relation['child_span']\n", + " if head_span['label'] == head_type and child_span['label'] == tail_type:\n", + " src_span = doc[head_span['token_start']: head_span['token_end'] + 1]\n", + " tgt_span = doc[child_span['token_start']: child_span['token_end'] + 1]\n", + "\n", + " start_idx = min(head_span['token_start'], child_span['token_start'])\n", + " end_idx = max(head_span['token_end'], child_span['token_end']) + 1\n", + "\n", + " doc_span = doc[start_idx: end_idx]\n", + " pattern = []\n", + " for token in doc_span:\n", + " tag = token_tags.get(token.i, 'UNK')\n", + " elem = '{}_{}'.format(token.pos_, tag)\n", + " pattern.append(elem)\n", + " pattern = ' '.join(pattern)\n", + " patterns[pattern] = patterns.get(pattern, 0) + 1\n", + " \n", + " patterns = {k:v for k,v in patterns.items()}\n", + " patterns = sorted(patterns.items(), key=lambda x: x[1], reverse=True)\n", + " return patterns" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "id": "01bbe57d", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████████████████████████████████████| 60/60 [00:00<00:00, 100.85it/s]\n" + ] + }, + { + "data": { + 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"code", + "execution_count": null, + "id": "b8d4790a", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/src/utils/model/data_setup/custom_bert_data_setup.py b/model_dev/entity_extraction/to_delete/custom_bert_data_setup.py similarity index 51% rename from src/utils/model/data_setup/custom_bert_data_setup.py rename to model_dev/entity_extraction/to_delete/custom_bert_data_setup.py index 67df847..1fb2647 100644 --- a/src/utils/model/data_setup/custom_bert_data_setup.py +++ b/model_dev/entity_extraction/to_delete/custom_bert_data_setup.py @@ -1,13 +1,30 @@ import srsly import pandas as pd +import os -file_name_answers = "checkin_answers" -file_path_answers = "C://Users//buchh//OneDrive/Desktop//"+file_name_answers+".jsonl" +file_name_answers = "main_3_per_cluster_download.66081fcf-3ef5-48ea-97e0-49298d29b477" +file_path_answers = "/Users/ash/Desktop/"+file_name_answers+".jsonl" data = srsly.read_jsonl(file_path_answers) -sent_dict = [] +data1 = srsly.read_jsonl(file_path_answers) +sent_dict_train = [] +sent_dict_test = [] count = 1 +tot_len = 0 +for n in data1: + if n['answer'] == "accept": + tot_len += 1 + +print(tot_len) + +# 70% training, 30% testing +training_data_index = int(tot_len*0.7) +print() for entry in data: + #print(count) + #print(training_data_index) + #print("-----") + if "text" in entry: text = entry["text"] @@ -28,13 +45,18 @@ else: sent_dict_tmp["word"] = text[child_span_start: child_span_end] sent_dict_tmp["tag"] = "O" + if training_data_index != 0: + sent_dict_train.append(sent_dict_tmp) else: - sent_dict_tmp["word"] = text[child_span_start: child_span_end] - sent_dict_tmp["tag"] = "O" - sent_dict.append(sent_dict_tmp) + sent_dict_test.append(sent_dict_tmp) count += 1 + if training_data_index > 0: + training_data_index -= 1 + +df_train = pd.DataFrame.from_dict(sent_dict_train) +print(df_train.iloc[-1]) +df_train.to_csv(r'custom_ent_bert_train_data.csv', index = False, header=True) -print(sent_dict) +df_test = pd.DataFrame.from_dict(sent_dict_test) +df_test.to_csv(r'custom_ent_bert_test_data.csv', index = False, header=True) -df = pd.DataFrame.from_dict(sent_dict) -df.to_csv (r'custom_ent_bert_data.csv', index = False, header=True) \ No newline at end of file diff --git a/model_dev/entity_extraction/to_delete/custom_entity_extractor_bert.ipynb b/model_dev/entity_extraction/to_delete/custom_entity_extractor_bert.ipynb new file mode 100644 index 0000000..c0c8058 --- /dev/null +++ b/model_dev/entity_extraction/to_delete/custom_entity_extractor_bert.ipynb @@ -0,0 +1,12437 @@ +{ + 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"executionInfo": { + "elapsed": 194, + "status": "ok", + "timestamp": 1644787979491, + "user": { + "displayName": "Himesh Buch", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiV8OCq9wFOhYmGL1qBY-DHltq27lamDhcBltE_HpE=s64", + "userId": "10202248199877876561" + }, + "user_tz": 300 + }, + "id": "WOI08I9LodsM" + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from sklearn.preprocessing import LabelEncoder\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import accuracy_score\n", + "import pickle\n", + "from pathlib import Path\n", + "import time, os, csv, srsly, collections" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "file_name_answers = \"main_3_per_cluster_download.66081fcf-3ef5-48ea-97e0-49298d29b477\"\n", + "file_path_answers = \"/Users/ash/Desktop/\"+file_name_answers+\".jsonl\"\n", + "data = srsly.read_jsonl(file_path_answers)\n", + "data1 = srsly.read_jsonl(file_path_answers)\n", + "sent_dict_train = []\n", + "sent_dict_test = []\n", + "count = 1\n", + "tot_len = 0\n", + "\n", + "for n in data1:\n", + " if n['answer'] == \"accept\":\n", + " if n['_session_id'] == \"main_3_per_cluster-Kameron\":\n", + " tot_len += 1\n", + "\n", + "# 70% training, 30% testing\n", + "training_data_index = int(tot_len*0.7)\n", + "\n", + "for entry in data:\n", + " if \"text\" in entry:\n", + " text = entry[\"text\"]\n", + " \n", + " if entry['answer'] == \"accept\":\n", + " if entry['_session_id'] == \"main_3_per_cluster-Kameron\":\n", + " for relation in entry['spans']:\n", + " sent_dict_tmp = {}\n", + " sent_dict_tmp[\"Sentence #\"] = \"Sentence \" + str(count)\n", + " if (\"label\" in relation) and (\"start\" in relation) and (\"end\" in relation):\n", + " sent_dict_tmp[\"text\"] = text\n", + " child_span_start = relation[\"start\"]\n", + " child_span_end = relation[\"end\"]\n", + " word = text[child_span_start:child_span_end]\n", + " if relation[\"label\"] == \"base\":\n", + " sent_dict_tmp[\"word\"] = text[child_span_start: child_span_end]\n", + " sent_dict_tmp[\"tag\"] = relation[\"label\"]\n", + " else:\n", + " sent_dict_tmp[\"word\"] = text[child_span_start: child_span_end]\n", + " sent_dict_tmp[\"tag\"] = \"O\"\n", + " if training_data_index != 0:\n", + " sent_dict_train.append(sent_dict_tmp)\n", + " else:\n", + " sent_dict_test.append(sent_dict_tmp)\n", + " if training_data_index > 0:\n", + " training_data_index -= 1\n", + " count += 1\n", + " \n", + "\n", + "train_data = pd.DataFrame.from_dict(sent_dict_train)\n", + "train_data = train_data.fillna(method =\"ffill\")\n", + "train_data[\"Sentence #\"] = LabelEncoder().fit_transform(train_data[\"Sentence #\"] )\n", + "train_data.rename(columns={\"Sentence #\":\"sentence_id\", \"word\":\"words\", \"tag\":\"labels\"}, inplace = True)\n", + "train_data[\"labels\"] = train_data[\"labels\"].str.upper()\n", + "\n", + "test_data = pd.DataFrame.from_dict(sent_dict_test) \n", + "test_data = test_data.fillna(method =\"ffill\")\n", + "test_data[\"Sentence #\"] = LabelEncoder().fit_transform(test_data[\"Sentence #\"] )\n", + "test_data.rename(columns={\"Sentence #\":\"sentence_id\", \"word\":\"words\", \"tag\":\"labels\"}, inplace = True)\n", + "test_data[\"labels\"] = test_data[\"labels\"].str.upper()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "defaultdict(, {'Around the wine-growing world, smart producers have contemplated and experimented with adaptations, not only to hotter summers, but also to warmer winters, droughts and the sort of unexpected, sometimes violent events that stem from climate change: freak hailstorms, spring frosts, flooding and forest fires, just to name a few. IMPLIED_BASE IMPLIED_BASE': ['summers', 'winters', 'droughts', 'climate change', 'hailstorms', 'frosts', 'flooding', 'forest fires'], '“Extreme heat, powerful storms and floods, year-round wildfires, droughts, and other climate-related events have already caused thousands of deaths and displaced tens of thousands of people in the U.S. from their homes, with significant personal loss and mental health impacts especially for first responders and children,” the letter warns. IMPLIED_BASE IMPLIED_BASE': ['heat', 'storms', 'floods', 'wildfires', 'droughts', 'deaths', 'people', 'personal loss', 'health'], 'Historical warming has likely increased flood magnitude for mixed‐regime watersheds IMPLIED_BASE IMPLIED_BASE': ['watersheds', 'IMPLIED_BASE'], 'Antarctic glaciers have been melting at an accelerating pace over the past four decades thanks to an influx of warm ocean water — a startling new finding that researchers say could mean sea levels are poised to rise more quickly than predicted in coming decades. IMPLIED_BASE IMPLIED_BASE': ['glaciers', 'water', 'sea'], 'For Suzanne Drake, a historical-society volunteer who cobbled together enough money during the recession to buy “the ugliest house on the prettiest street in town,” talk of managed retreat has kicked up an anger she didn’t know she had. IMPLIED_BASE IMPLIED_BASE': ['managed retreat', 'anger'], 'Amid the overwhelming scientific consensus and growing public demands for actions, some senior Republicans, like Majority Leader Mitch McConnell, have acknowledged that human activity is driving climate change. IMPLIED_BASE IMPLIED_BASE': ['human activity', 'climate change'], 'Studies estimate that with climate change, water availability per capita could decrease by as much as 88 percent in places, and crop yields in coastal regions may drop by a third. IMPLIED_BASE IMPLIED_BASE': ['climate change', 'water', 'crop'], 'The author believes this is one of the first papers in the sustainability management field to conclude that climate-induced near-term societal collapse should now be a central concern for everyone, and therefore to invite scholars to explore the implications. IMPLIED_BASE IMPLIED_BASE': ['climate', 'societal collapse'], \"While a more moderate emissions trajectory will still produce more intense, frequent and longer lasting droughts in most of the world's mid-latitude regions than current conditions, the shift will be less than if carbon emissions remain near the top of forecasts. IMPLIED_BASE IMPLIED_BASE\": ['droughts', 'carbon', 'IMPLIED_BASE'], 'Malnutrition, low birth weight, nonexclusive breast-feeding, lack of measles vaccination, outdoor and indoor air pollution and crowding, mother’s education, parental smoking, vitamin A and/or zinc deficiencies are thought to influence children susceptibility to infections in developing countries. IMPLIED_BASE IMPLIED_BASE': ['Malnutrition', 'birth', 'breast-feeding', 'vaccination', 'air pollution', 'crowding', 'education', 'smoking', 'infections', 'IMPLIED_BASE'], 'When the rain hit your face, it was like being hit by a stone,” Fatuma, whose house was severely damaged by Cyclone Kenneth in April, told Oxfam. IMPLIED_BASE IMPLIED_BASE': ['house', 'Cyclone'], 'Wildfires, sparked by high winds and extremely dry conditions, are threatening Northern California wine country. IMPLIED_BASE IMPLIED_BASE': ['Wildfires', 'winds', 'country', 'IMPLIED_BASE'], 'But the report said even small cuts in meat eating could lead to significant cuts in carbon emissions, as well as benefits to health. IMPLIED_BASE IMPLIED_BASE': ['eating', 'carbon', 'health'], 'The majority of the warming at the global scale over the past 50 years can only be explained by the effects of human influences,,, especially the emissions from burning fossil fuels (coal, oil, and natural gas) and from deforestation. IMPLIED_BASE IMPLIED_BASE': ['warming', 'human influences', 'fuels', 'coal', 'oil', 'gas', 'deforestation', 'IMPLIED_BASE'], 'They predict that the number of lightning strikes will increase by about 12% for every degree of rise in global average air temperature. IMPLIED_BASE IMPLIED_BASE': ['lightning', 'air'], 'Hurricanes resulting in damage to infrastructure and delays in training, testing programs, and space launches at Tyndall Air Force Base, at the Atlantic Undersea Test and Evaluation Centers, and the Eastern Range. IMPLIED_BASE IMPLIED_BASE': ['Hurricanes', 'infrastructure', 'training', 'testing programs', 'IMPLIED_BASE'], 'It worked — the high number of people who evacuated ahead of the storm lead to a significantly lower death toll of 89. IMPLIED_BASE IMPLIED_BASE': ['people', 'IMPLIED_BASE'], 'Nitroglycerin becomes so sensitive when it absorbs moisture that even a slight shake can set it off. IMPLIED_BASE IMPLIED_BASE': ['Nitroglycerin', 'shake', 'IMPLIED_BASE', 'IMPLIED_BASE'], 'Round-trip flights between London and Guwahati, the closest airport to North Salmara, emit over three tonnes of carbon dioxide – about twice as much as the average Indian emits in a whole year. IMPLIED_BASE IMPLIED_BASE': ['flights', 'carbon dioxide'], 'Individuals infected with either WNV or Lyme disease may experience a range of mental health consequences following infection that can include reduced cognitive function as well depression associated with other symptoms, such as fatigue, pain, and muscle and joint aches.184, 185 IMPLIED_BASE IMPLIED_BASE': ['WNV', 'Lyme disease', 'health', 'cognitive', 'depression', 'fatigue', 'pain', 'aches.184'], 'Ultraviolet Radiation as a Mediator of Climate Change-Induced Immune Dysfunction IMPLIED_BASE IMPLIED_BASE': ['Radiation', 'as a Mediator of', 'Climate Change', 'Dysfunction'], 'Floods kill more people in the United States than any other kind of severe weather and cause billions of dollars in damages every year. IMPLIED_BASE IMPLIED_BASE': ['Floods', 'people', 'weather', 'IMPLIED_BASE'], 'Smoke from fires has fouled Sydney’s air intermittently since November. IMPLIED_BASE IMPLIED_BASE': ['Smoke', 'fires', 'air'], 'Those blackouts are separate from the pre-emptive shut-offs carried out by California utilities in an effort to prevent their equipment from sparking wildfires. IMPLIED_BASE IMPLIED_BASE': ['equipment', 'wildfires', 'IMPLIED_BASE'], 'This weakening of wind shear is likely to result in more rapid intensification of storms as they near landfall. IMPLIED_BASE IMPLIED_BASE': ['wind', 'storms'], 'A year ago, Dorian crawled over the Bahamas for a day and a half, causing widespread destruction from wind and storm surge. IMPLIED_BASE IMPLIED_BASE': ['wind', 'storm', 'IMPLIED_BASE'], 'That persistence, and what Francis calls the waviness of the jet stream, are fingerprints of human-caused climate change, particularly the disproportionate warming of the Arctic. IMPLIED_BASE IMPLIED_BASE': ['jet stream', 'climate change', 'IMPLIED_BASE'], 'http://www.nasa.gov/topics/earth/features/wildfires.html> Experts in the field of climate change and biomass burning programs from NASA explained that wildfires are a result of climate change, are frequently caused by humans and that different areas are affected with varying intensity. IMPLIED_BASE IMPLIED_BASE': ['wildfires', 'climate change', 'humans'], 'There is growing evidence that climate change can increase the risks of conflict and violence. IMPLIED_BASE IMPLIED_BASE': ['climate change', 'conflict', 'violence'], 'Sherrell Thompson, a community health worker in Gilpin, said residents have high rates of asthma, diabetes and blood pressure, all conditions that can be worsened by heat. IMPLIED_BASE IMPLIED_BASE': ['asthma', 'diabetes', 'blood', 'heat'], 'Climate change, which is already causing heavier rainfall in many storms, is an important part of the mix. IMPLIED_BASE IMPLIED_BASE': ['Climate change', 'storms'], 'These events were caused by marine heatwaves – extended periods of unusually high temperatures that were themselves boosted by human-caused warming and El Niño. IMPLIED_BASE IMPLIED_BASE': ['heatwaves', 'warming', 'El Niño', 'IMPLIED_BASE'], 'Severe wildfires, like the recent Camp fire in Paradise, Calif., have been increasing across the West, devastating nearby communities. IMPLIED_BASE IMPLIED_BASE': ['wildfires', 'fire', 'communities'], 'The Bay Area Air Quality Management District issued a spare-the-air alert for Tuesday, with air quality in the region measuring as unhealthy for sensitive groups in the eastern part of the region. IMPLIED_BASE IMPLIED_BASE': ['alert', 'air'], 'It turns out that right here at home in the US, thousands of servicepeople suffer from heat-related illnesses like heat exhaustion and heat stroke every year, and the problem is set to grow much worse. IMPLIED_BASE IMPLIED_BASE': ['heat', 'illnesses', 'heat exhaustion', 'heat stroke'], 'The mismatch in the ventilation-perfusion ratio due to the reduced minute-ventilation increases the hypoxemia. IMPLIED_BASE IMPLIED_BASE': ['hypoxemia', 'IMPLIED_BASE', 'IMPLIED_BASE'], 'Between 2007 and 2011 alone, urban flooding in Cook County, Illinois, resulted in over 176,000 claims or flood losses at a cost of $660m (£545m). IMPLIED_BASE IMPLIED_BASE': ['flooding', 'claims', 'losses'], 'Massive damage has resulted from our development patterns, aging and no longer adequate flood control infrastructure and extreme weather exacerbated by climate change. IMPLIED_BASE IMPLIED_BASE': ['development', 'infrastructure', 'extreme weather', 'climate change', 'IMPLIED_BASE'], 'Climate change has raised the risk of a fungal disease that ravages banana crops, new research shows. IMPLIED_BASE IMPLIED_BASE': ['Climate change', 'has', 'disease', 'crops'], 'Researchers have found that grasses and ragweed plants increase their pollen production in response to localized surges in carbon dioxide, like from the exhaust of cars along a highway. IMPLIED_BASE IMPLIED_BASE': ['plants', 'carbon dioxide', 'cars'], 'Waterborne cholera and cryptosporidiosis increase with drought and flooding. IMPLIED_BASE IMPLIED_BASE': ['cholera', 'cryptosporidiosis', 'drought', 'flooding'], 'Normally, Dr. Swain said, what finally extinguishes the fires are autumn rains and snowfall, which historically come in October or November. IMPLIED_BASE IMPLIED_BASE': ['fires', 'rains', 'snowfall'], 'What many scientists and experts agree on: As climate change increases extreme precipitation, cities will need to adapt. IMPLIED_BASE IMPLIED_BASE': ['climate change', 'precipitation', 'precipitation', 'to adapt'], 'Climate change and biodiversity loss are equally huge environmental problems that make each other worse, report chairman Robert Watson said. IMPLIED_BASE IMPLIED_BASE': ['Climate change', 'biodiversity'], 'Assessing the risk from both wind and storm surge, the author of a 2017 paper concludes: “In combination, climate change and coastal development will cause hurricane damage to increase faster than the U.S. economy is expected to grow. IMPLIED_BASE IMPLIED_BASE': ['wind', 'storm surge', 'climate change', 'development', 'hurricane'], 'We find strong causal evidence linking climatic events to human conflict across a range of spatial and temporal scales and across all major regions of the world. IMPLIED_BASE IMPLIED_BASE': ['climatic', 'conflict'], 'When people get uncomfortably hot, their tempers, irritability, and likelihood of physical aggression and violence increase. IMPLIED_BASE IMPLIED_BASE': ['people', 'tempers', 'aggression', 'violence', 'IMPLIED_BASE'], 'As heatwaves have increased, kelp forests, seagrass meadows and coral reefs have been lost. IMPLIED_BASE IMPLIED_BASE': ['heatwaves', 'forests', 'meadows', 'reefs'], 'It predicts that in the absence of significant progress in efforts to curb emissions of temperature-raising greenhouse gases, extreme heat waves could claim thousands of lives in major U.S. cities. IMPLIED_BASE IMPLIED_BASE': ['gases', 'heat waves', 'lives'], 'One of the most common environmental allergens is ragweed, which can cause hay fever and trigger asthma attacks, especially in children and the elderly.2 IMPLIED_BASE IMPLIED_BASE': ['allergens', 'ragweed', 'hay fever', 'asthma'], 'Very strong evidence from multiple studies shows a consensus that many people exposed to climate- or weather-related natural disasters experience stress reactions and serious psychological harm, which often occur simultaneously.30, 31, 32, 33, 34 IMPLIED_BASE IMPLIED_BASE': ['stress', 'natural disasters', 'psychological'], 'Other trees, withering from the heat, have stopped bearing edible fruit. IMPLIED_BASE IMPLIED_BASE': ['trees', 'heat', 'fruit'], 'Naughton, M.B., A. Henderson, M.C. Mirabelli, R. Kaiser, J.L. Wilhelm, S.M. Kieszak, C.H. Rubin, and M.A. McGeehin, 2002: Heat-related mortality during a 1999 heat wave in Chicago. IMPLIED_BASE IMPLIED_BASE': ['Heat', 'mortality'], 'Scientists and health professionals have long known that exposure to air pollution causes respiratory damage, such as asthma. IMPLIED_BASE IMPLIED_BASE': ['pollution', 'respiratory', 'asthma'], 'Right now, APS gets roughly a quarter of its electricity from coal, a quarter from natural gas and a quarter from nuclear power. IMPLIED_BASE IMPLIED_BASE': ['electricity', 'coal', 'gas', 'nuclear power'], \"The international body estimates 80% of the Sahel's farmland has been degraded by climate change, causing scarcity of food for people and livestock. IMPLIED_BASE IMPLIED_BASE\": ['farmland', 'climate change', 'food'], 'Air pollution is having adverse effects on plants, and some research suggests it could even impact tree mortality. IMPLIED_BASE IMPLIED_BASE': ['pollution', 'plants', 'tree'], 'Wireless companies whose networks are threatened by hurricanes, floods, fires and other extreme weather are rebuilding to withstand repeat disasters and factoring climate change into their long-term planning. IMPLIED_BASE IMPLIED_BASE': ['networks', 'hurricanes', 'floods', 'fires', 'weather', 'disasters', 'climate change', 'planning'], 'Many of the blazes were sparked by an unprecedented lightning siege of nearly 12,000 strikes over a three-day period. IMPLIED_BASE IMPLIED_BASE': ['blazes', 'lightning'], 'Despite the study’s relatively narrow scope, the authors argue that the results likely apply to other creatures, including those rare and vulnerable species already imperiled by habitat fragmentation, pollution, and other human-caused disruptions. IMPLIED_BASE IMPLIED_BASE': ['species', 'habitat', 'pollution', 'disruptions'], 'Already, human activities have caused an estimated 1.0 degree Celsius increase in global warming above pre-industrial levels. IMPLIED_BASE IMPLIED_BASE': ['human activities', 'global warming'], 'Following Hurricanes Katrina and Rita, women in temporary housing attempted suicide 78 times more frequently than women not affected by the storms. IMPLIED_BASE IMPLIED_BASE': ['Hurricanes', 'suicide'], 'Air Pollution in California could created a serious public health hazard. IMPLIED_BASE IMPLIED_BASE': ['Pollution', 'health'], '\"As freshwater lakes get hotter in the summer, that leads to more amoebae in the water and increased human risk,\" said Sonia Altizer, an associate professor of ecology at the University of Georgia. IMPLIED_BASE IMPLIED_BASE': ['lakes', 'amoebae', 'IMPLIED_BASE'], 'Climate change should cause net extinctions to increase in the south and at low elevations and to decrease in the north and at high elevations. IMPLIED_BASE IMPLIED_BASE': ['Climate change', 'extinctions', 'IMPLIED_BASE'], 'Another increased hazard slower storms bring is increased wind damage, due to an increase in the duration of damaging winds structures are exposed to. IMPLIED_BASE IMPLIED_BASE': ['storms', 'wind', 'structures', 'IMPLIED_BASE'], 'Air pollution, dust clouds, the stability of the upper atmosphere, and the relative warmth of the Atlantic Ocean compared to the Indian and Pacific Oceans can all influence the intensity and frequency of hurricanes. IMPLIED_BASE IMPLIED_BASE': ['pollution', 'clouds', 'atmosphere', 'Ocean', 'hurricanes'], 'It’s the big, industrial dairy farms that are responsible for 55 percent of the state’s methane emissions. IMPLIED_BASE IMPLIED_BASE': ['farms', 'methane'], 'Warmer oceans cause hurricanes to have faster wind speeds and stronger central pressure, making the storms more intense. IMPLIED_BASE IMPLIED_BASE': ['oceans', 'hurricanes', 'storms', 'IMPLIED_BASE'], 'One is that all that warm water allowed for the storm to ramp up quickly, undergoing what is known as rapid intensification as it exploded from a moderate category 2 to extreme category 5 over just two days. IMPLIED_BASE IMPLIED_BASE': ['water', 'storm', 'rapid intensification'], 'The heat, according to researchers at the University of California, Berkeley, will drive deaths that soon outpace those from car crashes or opioid overdoses. IMPLIED_BASE IMPLIED_BASE': ['heat', 'deaths'], 'Wildfires are also expected to increase the risk for destructive mudslides, as landscapes laid bare by fire are drenched with winter rain. IMPLIED_BASE IMPLIED_BASE': ['Wildfires', 'mudslides', 'landscapes', 'drenched with winter rain'], 'Rachel Noble, a University of North Carolina microbiologist who has monitored the bacteria in rivers and estuaries in her state for more than 20 years, notes that storm surges can cause Vibrio concentrations to spike and linger in the water. IMPLIED_BASE IMPLIED_BASE': ['storm', 'Vibrio', 'IMPLIED_BASE'], 'Policies can improve energy efficiency, speed our transition from fossil fuels to renewable energy, and require companies to limit the amount of pollution that they produce. IMPLIED_BASE IMPLIED_BASE': ['Policies', 'energy', 'transition from fossil fuels to renewable energy', 'pollution'], 'Participation in sport improves mental and physical health,1 enhances community cohesion and well-being,2 and contributes significantly to employment.3 IMPLIED_BASE IMPLIED_BASE': ['sport', 'health,1', 'community', 'employment.3'], 'Emissions from burning coal IMPLIED_BASE IMPLIED_BASE': ['coal', 'IMPLIED_BASE'], 'Over the past century, we have become better at medicine (which lowers the risk from disease) but we also travel more (which increases the spread of diseases). IMPLIED_BASE IMPLIED_BASE': ['medicine', 'disease', 'travel', 'diseases'], 'In the future, weather whiplash could mean an intense drought year followed by record rains that don’t allow planting or that wash fertilizer into waterways. IMPLIED_BASE IMPLIED_BASE': ['weather whiplash', 'drought', 'rains', 'fertilizer', 'IMPLIED_BASE'], 'In 2018, my father died of complications from pneumonia, after recovering from the cancer. IMPLIED_BASE IMPLIED_BASE': ['father', 'pneumonia'], 'Human activities such as deforestation and the burning of fossil fuels are by far the biggest contributors to climate change. IMPLIED_BASE IMPLIED_BASE': ['Human activities', 'deforestation', 'fuels', 'climate change'], 'Studies show that water vapor feed back roughly doubles the amount of warming caused by CO2. IMPLIED_BASE IMPLIED_BASE': ['water vapor feed back', 'CO2', 'IMPLIED_BASE'], 'Last month Cyclone Idai struck central Mozambique, killing more than 600 people and leading to thousands of cases of cholera and malaria. IMPLIED_BASE IMPLIED_BASE': ['Cyclone', 'people', 'people', 'cholera', 'malaria'], 'And with humans continuing to burn fossil fuels for energy, global warming is expected to compound the damage. IMPLIED_BASE IMPLIED_BASE': ['fuels', 'energy', 'global warming', 'IMPLIED_BASE'], 'When Butler and Gates (2012) studied income disparity in East African cattle herders, they discovered that resource asymmetries caused by extreme and adverse weather contribute to conflict in the region. IMPLIED_BASE IMPLIED_BASE': ['income', 'weather', 'conflict', 'IMPLIED_BASE'], 'In Arizona, three lightning-caused wildfires forced the evacuation of multiple communities on Saturday. IMPLIED_BASE IMPLIED_BASE': ['lightning', 'wildfires', 'evacuation'], 'In a counterpoint to the article other researchers note that savings from increasing energy efficiency and expansion of renewables is also not baked into the GDP numbers. IMPLIED_BASE IMPLIED_BASE': ['savings', 'energy', 'renewables', 'GDP'], 'As summers get hotter and hotter, high school football players are increasingly at risk of succumbing to heat stroke and dying during summer practice – about three players a year, on average. IMPLIED_BASE IMPLIED_BASE': ['summers', 'heat stroke', 'heat stroke', 'dying'], 'That means we will likely see a meter of sea level rise by 2100, from a combination of warmer water, which expands, and melting ice sheets in Greenland and Antarctica. IMPLIED_BASE IMPLIED_BASE': ['sea', 'water', 'ice sheets', 'IMPLIED_BASE'], 'Before we go, we will buy enough offsets to capture the annual methane emanations of a dozen cows — that’s several times what is needed to balance out the carbon effects of our flights. IMPLIED_BASE IMPLIED_BASE': ['offsets', 'methane', 'cows', 'carbon', 'IMPLIED_BASE', 'IMPLIED_BASE'], 'These changes vary by region and time of year, but there is evidence that anthropogenic warming causes a general weakening of summertime tropical circulation1,2,3,4,5,6,7,8. IMPLIED_BASE IMPLIED_BASE': ['anthropogenic warming', 'IMPLIED_BASE'], 'Warmer temperatures are already causing major damage to the world’s forests. IMPLIED_BASE IMPLIED_BASE': ['forests', 'IMPLIED_BASE'], 'In the 1940s, companies began offshore oil drilling projects and dredging near the island, activities which contributed to the erosion of the island and its island road.[11] IMPLIED_BASE IMPLIED_BASE': ['oil', 'island', 'road.[11'], 'Hot pavement also transfers heat to the surrounding air, adding to the urban heat island effect. IMPLIED_BASE IMPLIED_BASE': ['pavement', 'air', 'urban heat island effect']})\n" + ] + } + ], + "source": [ + "ans_dict = collections.defaultdict(list)\n", + "for index, row in test_data.iterrows():\n", + " if row[\"labels\"] == \"BASE\":\n", + " ans_dict[row[\"text\"]].append(row[\"words\"])\n", + "\n", + "print(ans_dict)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "214\n", + "93\n" + ] + } + ], + "source": [ + "print(len(train_data.text.unique()))\n", + "print(len(test_data.text.unique()))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zPP_tp8-SFe3" + }, + "source": [ + "# Model Training\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: torchvision in /Users/ash/opt/anaconda3/lib/python3.9/site-packages (0.11.3)\r\n", + "Requirement already satisfied: numpy in /Users/ash/opt/anaconda3/lib/python3.9/site-packages (from torchvision) (1.20.3)\r\n", + "Requirement already satisfied: torch==1.10.2 in /Users/ash/opt/anaconda3/lib/python3.9/site-packages (from torchvision) (1.10.2)\r\n", + "Requirement already satisfied: pillow!=8.3.0,>=5.3.0 in /Users/ash/opt/anaconda3/lib/python3.9/site-packages (from torchvision) (8.4.0)\r\n", + "Requirement already satisfied: typing-extensions in /Users/ash/opt/anaconda3/lib/python3.9/site-packages (from torch==1.10.2->torchvision) (3.10.0.2)\r\n" + ] + } + ], + "source": [ + "!pip install torchvision" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "executionInfo": { + "elapsed": 12448, + "status": "ok", + "timestamp": 1644787993439, + "user": { + "displayName": "Himesh Buch", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiV8OCq9wFOhYmGL1qBY-DHltq27lamDhcBltE_HpE=s64", + "userId": "10202248199877876561" + }, + "user_tz": 300 + }, + "id": "VGgAcra70Imh" + }, + "outputs": [], + "source": [ + "from simpletransformers.ner import NERModel,NERArgs" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "created /Users/ash/Desktop/custom_entity_extractor_bert1\n" + ] + } + ], + "source": [ + "model_dir_name = \"custom_entity_extractor_bert\"\n", + "model_dir_parent = \"/Users/ash/Desktop\"\n", + "output_dir = os.path.join(model_dir_parent, model_dir_name)\n", + "os.makedirs(output_dir, exist_ok=True)\n", + "new_model_name = \"custom_entity_extraction_bert_model\"\n", + "print(\"created\", output_dir)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "executionInfo": { + "elapsed": 114, + "status": "ok", + "timestamp": 1644788112377, + "user": { + "displayName": "Himesh Buch", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiV8OCq9wFOhYmGL1qBY-DHltq27lamDhcBltE_HpE=s64", + "userId": "10202248199877876561" + }, + "user_tz": 300 + }, + "id": "MNIc73Z5Wa8W" + }, + "outputs": [], + "source": [ + "args = NERArgs()\n", + "args.num_train_epochs = 10\n", + "args.learning_rate = 1e-4\n", + "args.overwrite_output_dir = True\n", + "args.train_batch_size = 32\n", + "args.eval_batch_size = 32" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 4290, + "status": "ok", + "timestamp": 1644788117438, + "user": { + "displayName": "Himesh Buch", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiV8OCq9wFOhYmGL1qBY-DHltq27lamDhcBltE_HpE=s64", + "userId": "10202248199877876561" + }, + "user_tz": 300 + }, + "id": "k8HD5x8T0cyk", + "outputId": "9ed8ecce-1410-4750-ef1c-974deaf5f0b2" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "starting time: 03/05/2022 12:23:42\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Some weights of the model checkpoint at bert-base-cased were not used when initializing BertForTokenClassification: ['cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.dense.weight', 'cls.seq_relationship.bias']\n", + "- This IS expected if you are initializing BertForTokenClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n", + "- This IS NOT expected if you are initializing BertForTokenClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n", + "Some weights of BertForTokenClassification were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['classifier.bias', 'classifier.weight']\n", + "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "36bd140741c34bbe9955aa2544abd226", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/1 [00:00 0:\n"," label_tup = (text, {\"entities\": label_arr})\n"," final_rel_sent.append(label_tup)\n"," else:\n"," pass\n"," #print(text)\n"," return final_rel_sent\n"," \n","final_rel_sent = prep_rel_data(rel_label)\n","#print(final_rel_sent)"]},{"cell_type":"code","execution_count":543,"id":"c5711add","metadata":{"id":"c5711add","executionInfo":{"status":"ok","timestamp":1650927954016,"user_tz":240,"elapsed":210,"user":{"displayName":"Himesh Buch","userId":"10202248199877876561"}}},"outputs":[],"source":["def prep_ent_data(ents):\n"," \n"," file_name_answers = \"checkin_answers\"\n"," file_path_answers = \"/content/\" + file_name_answers + \".jsonl\"\n"," data = srsly.read_jsonl(file_path_answers)\n"," label_dict = {}\n","\n"," for entry in data:\n"," if \"text\" in entry:\n"," text = entry[\"text\"]\n"," base_arr = []\n"," cd_arr = []\n"," pred_arr = []\n"," ac_arr = []\n"," if entry['answer'] == \"accept\":\n"," if entry['_session_id'] == \"main_3_per_cluster-Kameron\":\n"," for relation in entry['spans']:\n"," if (\"label\" in relation) and (\"start\" in relation) and (\"end\" in relation):\n"," child_span_start = relation[\"start\"]\n"," child_span_end = relation[\"end\"]\n"," word = text[child_span_start:child_span_end]\n"," if relation[\"label\"] in ents:\n"," if relation[\"label\"] == \"base\":\n"," base_arr.append(word)\n"," elif relation[\"label\"] == \"change_direction\":\n"," cd_arr.append(word)\n"," elif relation[\"label\"] == \"predicate\":\n"," pred_arr.append(word)\n"," elif relation[\"label\"] == \"aspect_changing\":\n"," ac_arr.append(word)\n","\n"," label_dict[text] = {\"base\": base_arr, \"change_direction\": cd_arr, \"predicate\": pred_arr, \"aspect_changing\": ac_arr}\n"," return label_dict\n","\n","final_sent_ent = prep_ent_data(ents)\n","#print(final_sent_ent)"]},{"cell_type":"code","execution_count":544,"id":"1525537f","metadata":{"id":"1525537f","executionInfo":{"status":"ok","timestamp":1650927954016,"user_tz":240,"elapsed":15,"user":{"displayName":"Himesh Buch","userId":"10202248199877876561"}}},"outputs":[],"source":["def get_direction_counter(with_implied_base=False):\n"," left_to_right = 0\n"," right_to_left = 0\n"," for x in final_rel_sent:\n"," sent = x[0]\n"," for y in x[1]['entities']:\n"," head = y['head']\n"," tail = y['child']\n"," start_index = sent.find(head)\n"," end_index = sent.find(tail)\n"," if with_implied_base:\n"," if head != \"IMPLIED_BASE\" and tail != \"IMPLIED_BASE\":\n"," if start_index < end_index:\n"," right_to_left += 1\n"," else:\n"," left_to_right += 1\n"," else:\n"," if start_index < end_index:\n"," right_to_left += 1\n"," else:\n"," left_to_right += 1\n"," return {\"left_to_right\": left_to_right, \"right_to_left\": right_to_left}"]},{"cell_type":"code","execution_count":545,"id":"1d2443b9","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1d2443b9","executionInfo":{"status":"ok","timestamp":1650927954017,"user_tz":240,"elapsed":14,"user":{"displayName":"Himesh Buch","userId":"10202248199877876561"}},"outputId":"430a3b6c-f98f-477e-f6d2-e4292a175677"},"outputs":[{"output_type":"stream","name":"stdout","text":["{'left_to_right': 308, 'right_to_left': 514}\n","{'left_to_right': 246, 'right_to_left': 447}\n"]}],"source":["print(get_direction_counter())\n","print(get_direction_counter(True))"]},{"cell_type":"code","source":["\"\"\"\n","#!pip install benepar\n","#!python -m spacy download en_core_web_md\n","import benepar, spacy\n","#benepar.download('benepar_en3')\n","nlp = spacy.load('en')\n","\n","if spacy.__version__.startswith('2'):\n"," nlp.add_pipe(benepar.BeneparComponent(\"benepar_en3\"))\n","else:\n"," nlp.add_pipe(\"benepar\", config={\"model\": \"benepar_en3\"})\n","\n","def get_dep_tree():\n"," dep_tree = {}\n"," for x in final_rel_sent:\n"," doc = nlp(x[0])\n"," sent = list(doc.sents)[0]\n"," dep_tree[x[0]] = sent\n"," return dep_tree\n","\n","#dep_tree = get_dep_tree()\n","\"\"\""],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":89},"id":"nM0sWgju26Ow","executionInfo":{"status":"ok","timestamp":1650927954017,"user_tz":240,"elapsed":12,"user":{"displayName":"Himesh Buch","userId":"10202248199877876561"}},"outputId":"f31c1535-f3a8-4f74-c9dc-de02a3d11722"},"id":"nM0sWgju26Ow","execution_count":546,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'\\n#!pip install benepar\\n#!python -m spacy download en_core_web_md\\nimport benepar, spacy\\n#benepar.download(\\'benepar_en3\\')\\nnlp = spacy.load(\\'en\\')\\n\\nif spacy.__version__.startswith(\\'2\\'):\\n nlp.add_pipe(benepar.BeneparComponent(\"benepar_en3\"))\\nelse:\\n nlp.add_pipe(\"benepar\", config={\"model\": \"benepar_en3\"})\\n\\ndef get_dep_tree():\\n dep_tree = {}\\n for x in final_rel_sent:\\n doc = nlp(x[0])\\n sent = list(doc.sents)[0]\\n dep_tree[x[0]] = sent\\n return dep_tree\\n\\n#dep_tree = get_dep_tree()\\n'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":546}]},{"cell_type":"code","execution_count":547,"id":"68b9c88d","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"68b9c88d","executionInfo":{"status":"ok","timestamp":1650927954017,"user_tz":240,"elapsed":10,"user":{"displayName":"Himesh Buch","userId":"10202248199877876561"}},"outputId":"4fcdfaed-5825-4adf-ed57-59972a947199"},"outputs":[{"output_type":"stream","name":"stdout","text":["694\n"]}],"source":["csv = []\n","#features = [\"sent\", \"base1\", \"base2\", \"all_bases\", \"all_cd\", \"all_pred\", \"all_ac\", \"left_to_right\"]\n","features = [\"sent\", \"base1\", \"base2\", \"all_bases\", \"all_cd\", \"all_pred\", \"left_to_right\"]\n","csv.append(features)\n","\n","for x in final_rel_sent:\n"," sent = x[0]\n"," for y in x[1]['entities']:\n"," if y[\"head\"] != \"IMPLIED_BASE\" and y[\"child\"] != \"IMPLIED_BASE\":\n"," start_index = sent.find(y[\"head\"])\n"," end_index = sent.find(y[\"child\"])\n"," if start_index < end_index:\n"," left_to_right = 0\n"," else:\n"," left_to_right = 1\n","\n"," #tmp = [sent, y[\"head\"], y[\"child\"], \", \".join(final_sent_ent[sent][\"base\"]), \", \".join(final_sent_ent[sent][\"change_direction\"]), \n"," # \", \".join(final_sent_ent[sent][\"predicate\"]), \", \".join(final_sent_ent[sent][\"aspect_changing\"]), left_to_right]\n"," tmp = [sent, y[\"head\"], y[\"child\"], \", \".join(final_sent_ent[sent][\"base\"]), \", \".join(final_sent_ent[sent][\"change_direction\"]), \n"," \", \".join(final_sent_ent[sent][\"predicate\"]), left_to_right]\n"," \n"," csv.append(tmp)\n"," tmp = []\n"," \n","print(len(csv))"]},{"cell_type":"code","execution_count":548,"id":"34434937","metadata":{"id":"34434937","executionInfo":{"status":"ok","timestamp":1650927954017,"user_tz":240,"elapsed":8,"user":{"displayName":"Himesh Buch","userId":"10202248199877876561"}}},"outputs":[],"source":["import pandas as pd\n","\n","df = pd.DataFrame(i for i in csv)\n","df.drop(0, axis=0, inplace=True)\n","df.columns = csv[0]\n","#df[10:20]\n","#df.to_csv('data.csv')\n","df['left_to_right'] = df['left_to_right'].astype(float)"]},{"cell_type":"code","execution_count":549,"id":"899826a3","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"899826a3","executionInfo":{"status":"ok","timestamp":1650927954017,"user_tz":240,"elapsed":8,"user":{"displayName":"Himesh Buch","userId":"10202248199877876561"}},"outputId":"b194fd90-c41c-4e88-ea36-b6d632b4f456"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" sent base1 \\\n","1 These risks are crop insurance, health care, w... climate change.23 \n","2 These risks are crop insurance, health care, w... climate change.23 \n","3 These risks are crop insurance, health care, w... climate change.23 \n","4 These risks are crop insurance, health care, w... climate change.23 \n","5 These risks are crop insurance, health care, w... climate change.23 \n","\n"," base2 all_bases \\\n","1 insurance insurance, health care, wildfire suppression, ... \n","2 health care insurance, health care, wildfire suppression, ... \n","3 wildfire suppression insurance, health care, wildfire suppression, ... \n","4 disaster relief insurance, health care, wildfire suppression, ... \n","5 flood insurance, health care, wildfire suppression, ... \n","\n"," all_cd all_pred left_to_right \n","1 more due to 1.0 \n","2 more due to 1.0 \n","3 more due to 1.0 \n","4 more due to 1.0 \n","5 more due to 1.0 "],"text/html":["\n","
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sentbase1base2all_basesall_cdall_predleft_to_right
1These risks are crop insurance, health care, w...climate change.23insuranceinsurance, health care, wildfire suppression, ...moredue to1.0
2These risks are crop insurance, health care, w...climate change.23health careinsurance, health care, wildfire suppression, ...moredue to1.0
3These risks are crop insurance, health care, w...climate change.23wildfire suppressioninsurance, health care, wildfire suppression, ...moredue to1.0
4These risks are crop insurance, health care, w...climate change.23disaster reliefinsurance, health care, wildfire suppression, ...moredue to1.0
5These risks are crop insurance, health care, w...climate change.23floodinsurance, health care, wildfire suppression, ...moredue to1.0
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sentbase1base2all_basesall_cdall_predleft_to_right
122064181173132691.0
222064156173132691.0
322064316173132691.0
42206498173132691.0
522064138173132691.0
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\n"," "]},"metadata":{},"execution_count":551}],"source":["df[\"sent\"] = df[\"sent\"].astype('category')\n","df[\"base1\"] = df[\"base1\"].astype('category')\n","df[\"base2\"] = df[\"base2\"].astype('category')\n","df[\"all_bases\"] = df[\"all_bases\"].astype('category')\n","df[\"all_cd\"] = df[\"all_cd\"].astype('category')\n","df[\"all_pred\"] = df[\"all_pred\"].astype('category')\n","#df[\"dep_tree\"] = df[\"dep_tree\"].astype('category')\n","#df[\"all_ac\"] = df[\"all_ac\"].astype('category')\n","\n","# Second, replace the strings with their code values.\n","df[\"sent\"] = df[\"sent\"].cat.codes\n","df[\"base1\"] = df[\"base1\"].cat.codes\n","df[\"base2\"] = df[\"base2\"].cat.codes\n","df[\"all_bases\"] = df[\"all_bases\"].cat.codes\n","df[\"all_cd\"] = df[\"all_cd\"].cat.codes\n","df[\"all_pred\"] = df[\"all_pred\"].cat.codes\n","#df[\"dep_tree\"] = df[\"dep_tree\"].cat.codes\n","#df[\"all_ac\"] = df[\"all_ac\"].cat.codes\n","\n","# Display the table--notice how the above columns are all integers now.\n","df.head()"]},{"cell_type":"code","source":["from sklearn.model_selection import train_test_split\n","from sklearn.preprocessing import OneHotEncoder\n","\n","feature_cols = [\"sent\", \"base1\", \"base2\", \"all_bases\", \"all_cd\", \"all_pred\"]\n","\n","X = df[feature_cols]\n","y = df.left_to_right\n","\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=0)\n","print(\"Training size: {}\".format(len(X_train)))\n","print(\"Testing size: {}\".format(len(X_test)))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"D2uX1yMU7tA1","executionInfo":{"status":"ok","timestamp":1650927954212,"user_tz":240,"elapsed":5,"user":{"displayName":"Himesh Buch","userId":"10202248199877876561"}},"outputId":"7bf28f13-6193-4412-9e9f-31db8d23e5af"},"id":"D2uX1yMU7tA1","execution_count":552,"outputs":[{"output_type":"stream","name":"stdout","text":["Training size: 623\n","Testing size: 70\n"]}]},{"cell_type":"code","source":["X_train"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":423},"id":"0URDCek_778a","executionInfo":{"status":"ok","timestamp":1650927954213,"user_tz":240,"elapsed":5,"user":{"displayName":"Himesh Buch","userId":"10202248199877876561"}},"outputId":"47a246e5-c205-4d0d-adfc-03770bf26de0"},"id":"0URDCek_778a","execution_count":553,"outputs":[{"output_type":"execute_result","data":{"text/plain":[" sent base1 base2 all_bases all_cd all_pred\n","464 165 270 140 155 29 26\n","264 228 34 95 43 139 0\n","645 19 27 155 35 0 64\n","314 10 247 78 241 157 108\n","171 229 100 155 139 20 30\n",".. ... ... ... ... ... ...\n","360 183 63 43 67 187 81\n","193 9 131 111 192 98 104\n","630 160 186 112 105 0 80\n","560 116 34 71 42 147 0\n","685 102 223 258 225 110 79\n","\n","[623 rows x 6 columns]"],"text/html":["\n","
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sentbase1base2all_basesall_cdall_pred
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.....................
36018363436718781
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623 rows × 6 columns

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\n","
\n"," "]},"metadata":{},"execution_count":553}]},{"cell_type":"code","execution_count":554,"id":"fe1941e3","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"fe1941e3","executionInfo":{"status":"ok","timestamp":1650927954381,"user_tz":240,"elapsed":172,"user":{"displayName":"Himesh Buch","userId":"10202248199877876561"}},"outputId":"55231777-c291-4a9e-e37f-fc7b44230c4b"},"outputs":[{"output_type":"stream","name":"stdout","text":["F1: 0.909090909090909\n"]}],"source":["from xgboost import XGBClassifier\n","from sklearn.metrics import f1_score\n","\n","# Create an instance of the classifier\n","model = XGBClassifier(early_stopping_rounds = 10, n_estimators=300, learning_rate = 0.1)\n","\n","# Train it on the training set.\n","model.fit(X_train, y_train)\n","\n","# Use the trained model to predict the labels for the test set.\n","predictions = model.predict(X_test)\n","\n","# Calculate the F1 score.\n","f1 = f1_score(y_true = y_test,\n"," y_pred = predictions)\n","\n","print('F1: {}'.format(f1))"]},{"cell_type":"code","execution_count":555,"id":"19f0f148","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"19f0f148","executionInfo":{"status":"ok","timestamp":1650927954381,"user_tz":240,"elapsed":3,"user":{"displayName":"Himesh Buch","userId":"10202248199877876561"}},"outputId":"f86bdda8-f6e8-4c7f-fda9-6a187427b000"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[46, 3],\n"," [ 1, 20]])"]},"metadata":{},"execution_count":555}],"source":["from sklearn import metrics\n","cnf_matrix = metrics.confusion_matrix(y_test, predictions)\n","cnf_matrix"]},{"cell_type":"code","execution_count":556,"id":"76488d6a","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"76488d6a","executionInfo":{"status":"ok","timestamp":1650927954381,"user_tz":240,"elapsed":2,"user":{"displayName":"Himesh Buch","userId":"10202248199877876561"}},"outputId":"3fbe85c6-d701-4872-ba13-21f4b1b74536"},"outputs":[{"output_type":"stream","name":"stdout","text":["0.9428571428571428\n"]}],"source":["from sklearn.metrics import accuracy_score\n","print(accuracy_score(y_test, predictions))"]},{"cell_type":"code","execution_count":557,"id":"71b4ba06","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"71b4ba06","executionInfo":{"status":"ok","timestamp":1650927954381,"user_tz":240,"elapsed":2,"user":{"displayName":"Himesh Buch","userId":"10202248199877876561"}},"outputId":"e65ebd81-0a6b-4932-c725-5b1e3c7eaae5"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0., 1., 1., 1., 0., 0., 1., 0., 0., 0., 0., 0., 0., 1., 0., 0., 1.,\n"," 1., 1., 1., 0., 1., 0., 1., 0., 0., 0., 1., 0., 0., 0., 1., 0., 0.,\n"," 0., 0., 1., 0., 0., 0., 1., 0., 0., 0., 0., 1., 0., 0., 0., 0., 1.,\n"," 1., 0., 0., 1., 0., 0., 1., 0., 0., 0., 0., 0., 0., 1., 1., 1., 0.,\n"," 0., 0.])"]},"metadata":{},"execution_count":557}],"source":["predictions"]},{"cell_type":"code","execution_count":558,"id":"fe69c8fa","metadata":{"scrolled":true,"colab":{"base_uri":"https://localhost:8080/","height":295},"id":"fe69c8fa","executionInfo":{"status":"ok","timestamp":1650927954613,"user_tz":240,"elapsed":233,"user":{"displayName":"Himesh Buch","userId":"10202248199877876561"}},"outputId":"87f7b467-79a4-495f-bdf3-db63f7f8c0bb"},"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{"needs_background":"light"}}],"source":["from xgboost import plot_importance\n","from matplotlib import pyplot\n","\n","# plot feature importance\n","plot_importance(model)\n","\n","pyplot.show()"]}],"metadata":{"kernelspec":{"display_name":"Python 3 (ipykernel)","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.9.7"},"colab":{"name":"xgboost_rel_classifier.ipynb","provenance":[],"collapsed_sections":[]}},"nbformat":4,"nbformat_minor":5} \ No newline at end of file diff --git a/model_dev/relationship_extraction/xgboost/xgboost_rel_classifier_modified_features (1).ipynb b/model_dev/relationship_extraction/xgboost/xgboost_rel_classifier_modified_features (1).ipynb new file mode 100644 index 0000000..5a81a05 --- /dev/null +++ b/model_dev/relationship_extraction/xgboost/xgboost_rel_classifier_modified_features (1).ipynb @@ -0,0 +1,2146 @@ +{ + "cells": [ + { + "cell_type": "code", + "source": [ + "import srsly\n", + "import numpy as np\n", + "import spacy\n", + "import pandas as pd\n", + "\n", + "!pip install benepar\n", + "#!python -m spacy download en_core_web_md\n", + "#!python -m spacy download en\n", + "nlp = spacy.load('en')\n", + "\n", + "import benepar\n", + "benepar.download('benepar_en3')\n", + "if spacy.__version__.startswith('2'):\n", + " nlp.add_pipe(benepar.BeneparComponent(\"benepar_en3\"))\n", + "else:\n", + " nlp.add_pipe(\"benepar\", config={\"model\": \"benepar_en3\"})\n", + "\n", + "!pip install node2vec\n", + "#!pip install xgboost\n", + "\n", + "!pip install -U sentence-transformers\n", + "from sentence_transformers import SentenceTransformer\n", + "sbert_model = SentenceTransformer('bert-base-nli-mean-tokens')\n", + "\n", + "!pip install stellargraph[demos]" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": "Requirement already satisfied: benepar in 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(0.12.1)\nRequirement already satisfied: protobuf in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (from benepar) (3.19.4)\nRequirement already satisfied: tqdm in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (from nltk>=3.2->benepar) (4.64.0)\nRequirement already satisfied: regex>=2021.8.3 in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (from nltk>=3.2->benepar) (2022.3.15)\nRequirement already satisfied: click in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (from nltk>=3.2->benepar) (7.1.2)\nRequirement already satisfied: joblib in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (from nltk>=3.2->benepar) (1.1.0)\nRequirement already satisfied: typing-extensions in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (from torch>=1.6.0->benepar) (4.1.1)\nRequirement already satisfied: pyyaml>=5.1 in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (from transformers[tokenizers,torch]>=4.2.2->benepar) (6.0)\nRequirement 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satisfied: tqdm<5.0.0,>=4.55.1 in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (from node2vec) (4.64.0)\nRequirement already satisfied: smart-open>=1.8.1 in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (from gensim<5.0.0,>=4.1.2->node2vec) (6.0.0)\nRequirement already satisfied: scipy>=0.18.1 in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (from gensim<5.0.0,>=4.1.2->node2vec) (1.5.2)\nRequirement already satisfied: decorator>=4.3.0 in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (from networkx<3.0,>=2.5->node2vec) (5.1.1)\nCollecting sentence-transformers\n Downloading sentence-transformers-2.2.2.tar.gz (85 kB)\n\u001b[K |████████████████████████████████| 85 kB 2.2 MB/s eta 0:00:01\n\u001b[?25hRequirement already satisfied, skipping upgrade: transformers<5.0.0,>=4.6.0 in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (from sentence-transformers) (4.19.4)\nRequirement already satisfied, skipping upgrade: tqdm in 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sentence-transformers) (3.7)\nRequirement already satisfied, skipping upgrade: sentencepiece in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (from sentence-transformers) (0.1.96)\nRequirement already satisfied, skipping upgrade: huggingface-hub>=0.4.0 in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (from sentence-transformers) (0.7.0)\nRequirement already satisfied, skipping upgrade: requests in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers) (2.27.1)\nRequirement already satisfied, skipping upgrade: filelock in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers) (3.6.0)\nRequirement already satisfied, skipping upgrade: regex!=2019.12.17 in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers) (2022.3.15)\nRequirement already satisfied, skipping upgrade: pyyaml>=5.1 in 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scikit-learn->sentence-transformers) (1.1.0)\nRequirement already satisfied, skipping upgrade: threadpoolctl>=2.0.0 in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (from scikit-learn->sentence-transformers) (2.1.0)\nRequirement already satisfied, skipping upgrade: click in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (from nltk->sentence-transformers) (7.1.2)\nRequirement already satisfied, skipping upgrade: charset-normalizer~=2.0.0; python_version >= \"3\" in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (from requests->transformers<5.0.0,>=4.6.0->sentence-transformers) (2.0.12)\nRequirement already satisfied, skipping upgrade: certifi>=2017.4.17 in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (from requests->transformers<5.0.0,>=4.6.0->sentence-transformers) (2021.10.8)\nRequirement already satisfied, skipping upgrade: urllib3<1.27,>=1.21.1 in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (from requests->transformers<5.0.0,>=4.6.0->sentence-transformers) (1.26.7)\nRequirement already satisfied, skipping upgrade: idna<4,>=2.5; python_version >= \"3\" in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (from requests->transformers<5.0.0,>=4.6.0->sentence-transformers) (3.3)\nRequirement already satisfied, skipping upgrade: pyparsing!=3.0.5,>=2.0.2 in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (from packaging>=20.0->transformers<5.0.0,>=4.6.0->sentence-transformers) (3.0.7)\nBuilding wheels for collected packages: sentence-transformers\n Building wheel for sentence-transformers (setup.py) ... \u001b[?25l-\b \b\\\b \b|\b \bdone\n\u001b[?25h Created wheel for sentence-transformers: filename=sentence_transformers-2.2.2-py3-none-any.whl size=125937 sha256=c3e793ae5a17572605874772f339a2824abe5caaf682d00e233b9eac5f9f7b5a\n Stored in directory: /home/azureuser/.cache/pip/wheels/5e/6f/8c/d88aec621f3f542d26fac0342bef5e693335d125f4e54aeffe\nSuccessfully built sentence-transformers\nInstalling collected packages: sentence-transformers\n Attempting uninstall: sentence-transformers\n Found existing installation: sentence-transformers 2.2.0\n Uninstalling sentence-transformers-2.2.0:\n Successfully uninstalled sentence-transformers-2.2.0\nSuccessfully installed sentence-transformers-2.2.2\nRequirement already satisfied: stellargraph[demos] in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (1.2.1)\nRequirement already satisfied: scikit-learn>=0.20 in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (from stellargraph[demos]) (1.0.2)\nRequirement already satisfied: networkx>=2.2 in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (from stellargraph[demos]) (2.5)\nRequirement already satisfied: scipy>=1.1.0 in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (from stellargraph[demos]) (1.5.2)\nRequirement already satisfied: numpy>=1.14 in /anaconda/envs/azureml_py38/lib/python3.8/site-packages (from 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"2baf699b-4e78-423c-8cdf-8651861733d0", + "gather": { + "logged": 1657225799917 + } + }, + "id": "6fa48c3c" + }, + { + "cell_type": "code", + "source": [ + "rel_label = [\"Contributes_To\"]\n", + "ents = [\"base\", \"change_direction\", \"predicate\", \"aspect_changing\"]" + ], + "outputs": [], + "execution_count": 2, + "metadata": { + "id": "ff7a6974", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 17 + }, + "outputId": "992a29c9-649a-4372-e691-4079394dcdb4", + "gather": { + "logged": 1655865064600 + } + }, + "id": "ff7a6974" + }, + { + "cell_type": "code", + "source": [ + "def prep_rel_data(rel_labels):\n", + " \n", + " file_name_answers = \"checkin_answers\"\n", + " file_path_answers = file_name_answers + \".jsonl\"\n", + " data = srsly.read_jsonl(file_path_answers)\n", + " final_rel_sent = []\n", + "\n", + " for entry in data:\n", + " if \"text\" in entry:\n", + " text = entry[\"text\"]\n", + " label_arr = []\n", + " label_dict = {}\n", + " if entry['answer'] == \"accept\":\n", + " if entry['_session_id'] == \"main_3_per_cluster-Kameron\":\n", + " for relation in entry['relations']:\n", + " if (\"label\" in relation) and (\"head_span\" in relation) and (\"child_span\" in relation):\n", + " if relation[\"label\"] in rel_labels:\n", + " head_span_start = relation[\"head_span\"][\"start\"]\n", + " head_span_end = relation[\"head_span\"][\"end\"]\n", + " child_span_start = relation[\"child_span\"][\"start\"]\n", + " child_span_end = relation[\"child_span\"][\"end\"]\n", + " head_word = text[head_span_start:head_span_end]\n", + " child_word = text[child_span_start:child_span_end]\n", + " tmp_dict = {\"head\": head_word, \"child\": child_word, \"label\": relation[\"label\"]}\n", + " label_arr.append(tmp_dict)\n", + " if len(label_arr) > 0:\n", + " label_tup = (text, {\"entities\": label_arr})\n", + " final_rel_sent.append(label_tup)\n", + " else:\n", + " pass\n", + " #print(text)\n", + " return final_rel_sent\n", + " \n", + "final_rel_sent = prep_rel_data(rel_label)\n", + "#print(final_rel_sent)" + ], + "outputs": [], + "execution_count": 3, + "metadata": { + "id": "1e76999b", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 17 + }, + "outputId": "50d947d7-1455-4174-8fc7-deeb77ea6acb", + "gather": { + "logged": 1655865064756 + } + }, + "id": "1e76999b" + }, + { + "cell_type": "code", + "source": [ + "def prep_ent_data(ents):\n", + " \n", + " file_name_answers = \"checkin_answers\"\n", + " file_path_answers = file_name_answers + \".jsonl\"\n", + " data = srsly.read_jsonl(file_path_answers)\n", + " label_dict = {}\n", + "\n", + " for entry in data:\n", + " if \"text\" in entry:\n", + " text = entry[\"text\"]\n", + " base_arr = []\n", + " cd_arr = []\n", + " pred_arr = []\n", + " ac_arr = []\n", + " if entry['answer'] == \"accept\":\n", + " if entry['_session_id'] == \"main_3_per_cluster-Kameron\":\n", + " for relation in entry['spans']:\n", + " if (\"label\" in relation) and (\"start\" in relation) and (\"end\" in relation):\n", + " child_span_start = relation[\"start\"]\n", + " child_span_end = relation[\"end\"]\n", + " word = text[child_span_start:child_span_end]\n", + " if relation[\"label\"] in ents:\n", + " if relation[\"label\"] == \"base\":\n", + " base_arr.append(word)\n", + " elif relation[\"label\"] == \"change_direction\":\n", + " cd_arr.append(word)\n", + " elif relation[\"label\"] == \"predicate\":\n", + " pred_arr.append(word)\n", + " elif relation[\"label\"] == \"aspect_changing\":\n", + " ac_arr.append(word)\n", + "\n", + " label_dict[text] = {\"base\": base_arr, \"change_direction\": cd_arr, \"predicate\": pred_arr, \"aspect_changing\": ac_arr}\n", + " return label_dict\n", + "\n", + "final_sent_ent = prep_ent_data(ents)\n", + "#print(final_sent_ent)" + ], + "outputs": [], + "execution_count": 4, + "metadata": { + "id": "c5711add", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 17 + }, + "outputId": "fecb1f25-c0bb-4c06-bc16-830df58c9534", + "gather": { + "logged": 1655865064870 + } + }, + "id": "c5711add" + }, + { + "cell_type": "code", + "source": [ + "def get_direction_counter():\n", + " left_to_right = 0\n", + " right_to_left = 0\n", + " for x in final_rel_sent:\n", + " sent = x[0]\n", + " for y in x[1]['entities']:\n", + " head = y['head']\n", + " tail = y['child']\n", + " start_index = sent.find(head)\n", + " end_index = sent.find(tail)\n", + " if head != \"IMPLIED_BASE\" and tail != \"IMPLIED_BASE\":\n", + " if start_index < end_index:\n", + " right_to_left += 1\n", + " else:\n", + " left_to_right += 1 \n", + " return {\"left_to_right\": left_to_right, \"right_to_left\": right_to_left}\n", + "\n", + "def get_num_unique_sent():\n", + " sentences = set()\n", + " for x in final_rel_sent:\n", + " sent = x[0]\n", + " sentences.add(sent)\n", + " return len(sentences)" + ], + "outputs": [], + "execution_count": 5, + "metadata": { + "id": "1525537f", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 17 + }, + "outputId": "0bc453d5-8940-4654-e25f-675c7467e1d5", + "gather": { + "logged": 1655865064941 + } + }, + "id": "1525537f" + }, + { + "cell_type": "code", + "source": [ + "print(get_direction_counter())\n", + "print(get_num_unique_sent())" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": "{'left_to_right': 246, 'right_to_left': 447}\n305\n" + } + ], + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 53 + }, + "id": "1d2443b9", + "outputId": "f9c57aab-c9f4-4d62-f64e-35f6fbedfded", + "gather": { + "logged": 1655865065004 + } + }, + "id": "1d2443b9" + }, + { + "cell_type": "code", + "source": [ + "from nltk.tree import Tree\n", + "import networkx as nx\n", + "from collections import defaultdict\n", + "import itertools\n", + "\n", + "def convertToGraph(edges):\n", + " #G = defaultdict(list)\n", + " #G = nx.Graph()\n", + " #for a, b in tree_to_edges(edges):\n", + " #G[a] += [b]\n", + " #G[b] += [a]\n", + " # G.add_node(a)\n", + " # G.add_node(b)\n", + " # G.add_edge(a, b)\n", + " #return G\n", + " \n", + " G = nx.Graph()\n", + " for node_tuple in tree_to_edges(edges):\n", + " G.add_edges_from(itertools.product(node_tuple, node_tuple))\n", + "\n", + " G.remove_edges_from(nx.selfloop_edges(G))\n", + "\n", + " return G\n", + " \n", + "\n", + "\n", + "def get_edges(tree, i):\n", + " from_str = f\"{tree.label()}{i}\"\n", + " children = [f\"{child.label()} {child.leaves()[0]}\" for child in tree if isinstance(child, Tree) and child.height() == 2]\n", + " children.extend([f\"{child.label()}{i+1}\" for child in tree if isinstance(child, Tree) and child.height() > 2])\n", + " return [(from_str, child) for child in children]\n", + "\n", + "def tree_to_edges(tree):\n", + " height = 0\n", + " rv = []\n", + " to_check = [tree]\n", + " while to_check:\n", + " tree_to_check = to_check.pop(0)\n", + " rv.extend(get_edges(tree_to_check, height))\n", + " height += 1\n", + " to_check.extend([child for child in tree_to_check if isinstance(child, Tree) and child.height() > 2])\n", + " return rv\n", + "\n", + "def generate_graph(tmp_ans):\n", + " #t = Tree.fromstring(tmp_ans._.parse_string)\n", + " t = Tree.fromstring(tmp_ans)\n", + " #final_graph = convertToGraph(tree_to_edges(t))\n", + " final_graph = convertToGraph(t)\n", + " return final_graph\n", + "\n", + "\"\"\"\n", + "tmp_sent = 'As freshwater lakes get hotter in the summer that leads to more amoebae in the water and increased human risk said Sonia Altizer an associate professor of ecology at the University of Georgia'\n", + "tmp_doc = nlp(tmp_sent) \n", + "tmp_ans = list(tmp_doc.sents)[0]\n", + "parse = tmp_ans._.parse_string\n", + "final_graph = generate_graph(parse)\n", + "print(final_graph)\n", + "\"\"\"" + ], + "outputs": [ + { + "output_type": "execute_result", + "execution_count": 7, + "data": { + "text/plain": "\"\\ntmp_sent = 'As freshwater lakes get hotter in the summer that leads to more amoebae in the water and increased human risk said Sonia Altizer an associate professor of ecology at the University of Georgia'\\ntmp_doc = nlp(tmp_sent) \\ntmp_ans = list(tmp_doc.sents)[0]\\nparse = tmp_ans._.parse_string\\nfinal_graph = generate_graph(parse)\\nprint(final_graph)\\n\"" + }, + "metadata": {} + } + ], + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 17 + }, + "id": "8R7vT3u1Ll3o", + "outputId": "7038dfb1-e6e2-4449-f30c-5e14690ed538", + "gather": { + "logged": 1655865065632 + } + }, + "id": "8R7vT3u1Ll3o" + }, + { + "cell_type": "code", + "source": [ + "from node2vec import Node2Vec\n", + "from stellargraph import StellarGraph\n", + "from stellargraph.data import BiasedRandomWalk\n", + "from gensim.models import Word2Vec\n", + "\n", + "def generate_embeddings(final_graph):\n", + " node2vec = Node2Vec(final_graph, dimensions=64, walk_length=30, num_walks=200, workers=4)\n", + " model = node2vec.fit(window=10, min_count=1, batch_words=4)\n", + " return model.wv.vectors\n", + "\n", + "def generate_embeddings_new(final_graph):\n", + " G = StellarGraph.from_networkx(final_graph)\n", + " rw = BiasedRandomWalk(G)\n", + "\n", + " walks = rw.run(\n", + " nodes=list(G.nodes()), # root nodes\n", + " length=100, # maximum length of a random walk\n", + " n=10, # number of random walks per root node\n", + " p=0.5, # Defines (unormalised) probability, 1/p, of returning to source node\n", + " q=2.0, # Defines (unormalised) probability, 1/q, for moving away from source node\n", + " )\n", + " #print(\"Number of random walks: {}\".format(len(walks)))\n", + "\n", + " str_walks = [[str(n) for n in walk] for walk in walks]\n", + " model = Word2Vec(str_walks, vector_size=128, window=5, min_count=0, sg=1, workers=2, epochs=1)\n", + "\n", + " # Retrieve node embeddings and corresponding subjects\n", + " node_ids = model.wv.index_to_key # list of node IDs\n", + " node_embeddings = (\n", + " model.wv.vectors\n", + " ) # numpy.ndarray of size number of nodes times embeddings dimensionality\n", + " return node_embeddings\n", + "\n", + "\"\"\"\n", + "# Create a graph\n", + "graph = nx.fast_gnp_random_graph(n=100, p=0.5)\n", + "\n", + "# Precompute probabilities and generate walks - **ON WINDOWS ONLY WORKS WITH workers=1**\n", + "node2vec = Node2Vec(graph, dimensions=64, walk_length=30, num_walks=200, workers=4) # Use temp_folder for big graphs\n", + "\n", + "# Embed nodes\n", + "#model = node2vec.fit(window=10, min_count=1, batch_words=2) # Any keywords acceptable by gensim.Word2Vec can be passed, `dimensions` and `workers` are automatically passed (from the Node2Vec constructor)\n", + "\n", + "# Look for most similar nodes\n", + "model.wv.most_similar('2') # Output node names are always strings\n", + "\n", + "# Save embeddings for later use\n", + "model.wv.save_word2vec_format(EMBEDDING_FILENAME)\n", + "\n", + "# Save model for later use\n", + "model.save(EMBEDDING_MODEL_FILENAME)\n", + "\n", + "# Embed edges using Hadamard method\n", + "from node2vec.edges import HadamardEmbedder\n", + "\n", + "edges_embs = HadamardEmbedder(keyed_vectors=model.wv)\n", + "\n", + "# Look for embeddings on the fly - here we pass normal tuples\n", + "edges_embs[('1', '2')]\n", + "''' OUTPUT\n", + "array([ 5.75068220e-03, -1.10937878e-02, 3.76693785e-01, 2.69105062e-02,\n", + " ... ... ....\n", + " ..................................................................],\n", + " dtype=float32)\n", + "'''\n", + "\n", + "# Get all edges in a separate KeyedVectors instance - use with caution could be huge for big networks\n", + "edges_kv = edges_embs.as_keyed_vectors()\n", + "\n", + "# Look for most similar edges - this time tuples must be sorted and as str\n", + "edges_kv.most_similar(str(('1', '2')))\n", + "\n", + "# Save embeddings for later use\n", + "edges_kv.save_word2vec_format(EDGES_EMBEDDING_FILENAME)\n", + "\"\"\"" + ], + "outputs": [ + { + "output_type": "execute_result", + "execution_count": 8, + "data": { + "text/plain": "\"\\n# Create a graph\\ngraph = nx.fast_gnp_random_graph(n=100, p=0.5)\\n\\n# Precompute probabilities and generate walks - **ON WINDOWS ONLY WORKS WITH workers=1**\\nnode2vec = Node2Vec(graph, dimensions=64, walk_length=30, num_walks=200, workers=4) # Use temp_folder for big graphs\\n\\n# Embed nodes\\n#model = node2vec.fit(window=10, min_count=1, batch_words=2) # Any keywords acceptable by gensim.Word2Vec can be passed, `dimensions` and `workers` are automatically passed (from the Node2Vec constructor)\\n\\n# Look for most similar nodes\\nmodel.wv.most_similar('2') # Output node names are always strings\\n\\n# Save embeddings for later use\\nmodel.wv.save_word2vec_format(EMBEDDING_FILENAME)\\n\\n# Save model for later use\\nmodel.save(EMBEDDING_MODEL_FILENAME)\\n\\n# Embed edges using Hadamard method\\nfrom node2vec.edges import HadamardEmbedder\\n\\nedges_embs = HadamardEmbedder(keyed_vectors=model.wv)\\n\\n# Look for embeddings on the fly - here we pass normal tuples\\nedges_embs[('1', '2')]\\n''' OUTPUT\\narray([ 5.75068220e-03, -1.10937878e-02, 3.76693785e-01, 2.69105062e-02,\\n ... ... ....\\n ..................................................................],\\n dtype=float32)\\n'''\\n\\n# Get all edges in a separate KeyedVectors instance - use with caution could be huge for big networks\\nedges_kv = edges_embs.as_keyed_vectors()\\n\\n# Look for most similar edges - this time tuples must be sorted and as str\\nedges_kv.most_similar(str(('1', '2')))\\n\\n# Save embeddings for later use\\nedges_kv.save_word2vec_format(EDGES_EMBEDDING_FILENAME)\\n\"" + }, + "metadata": {} + } + ], + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 143 + }, + "id": "6Zs_GeZmpxK5", + "outputId": "e0882d72-9a39-4a47-a76a-a526db57a911", + "gather": { + "logged": 1655865067683 + } + }, + "id": "6Zs_GeZmpxK5" + }, + { + "cell_type": "code", + "source": [ + "def get_dep_tree(new_sent):\n", + " dep_tree = {}\n", + " doc = nlp(new_sent) \n", + " sent = list(doc.sents)[0]\n", + " #dep_tree[new_sent] = sent._.parse_string.replace(\"(\", \"[\").replace(\")\", \"]\")\n", + " dep_tree[new_sent] = sent._.parse_string\n", + " return dep_tree" + ], + "outputs": [], + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 381, + "referenced_widgets": [ + "77c2a3cb30444547917ef705d627b34e", + "c34a6c42c93d4854aa7f4b4cbf190f18", + "5f8cc9ceecb941949d7859c9a6a50e87", + "f21d6f69293e4c968f0eb05fd5a3dab3", + "1ec8e0b1f9b445f6af1d4524a4207b7f", + "3e0584decb65406b9a9ab2d62d1ee757", + "09abeb999394429daa67b1755fff502a", + "00361e5c64844938922ab37c06b61115", + "b9b4d1807321492e8a9add7aae3009ef", + "f894fefe5d0a45a789418a4bc217a056", + "bbf33695cf864f1a9d4705b415bc873f", + "359ee725a949490c973600d9f1ed5719", + "c011c79124964473a52c58b0ea434413", + "a371cb1e2f4d40c48a5b7aab516c16c2", + "3d26eb2d68ed47a49b61aeaf9106783f", + "304d30f139184d0091d8ead7e1173e1a", + "f994bc53e75444d3a6bbd88446457f45", + "6a6f9ea1e61040bd840ab6f7e59ae28b", + "5193ca43f561481f90e007e4ee624321", + "e3322237cd514b86a94d28a738976e48", + "ea94833057fd465faf90516d550911de", + "cea473f5f7064c9194b6292dd134fdf4", + "bf350c098f31480ab5cf418eea1fb883", + "ec415743f79047deaeaa7c5226bae996", + "fde50b8314174daa9ce7bd08e0d437f8", + "cf719f7a35e94f12abe80dff415276c4", + "77df230def2f4235965291f8e27cf7a3", + "40224d9f1641453aa79b9d84e8b58e9b", + "3523b39469df4de7a7a9d86b78489642", + "abaf12b3b70b4e17bca8ed491a92bfea", + "922641e2b0d84641987cd4e870cf2486", + "f022f39316bf45ffa4731872751a7540", + "f88c2cb94d1f48a686fb7b30aac4c2c9" + ] + }, + "id": "45kv9ni6CAvn", + "outputId": "dd3537e0-13d7-4ec7-fe78-de8ac513ddc3", + "gather": { + "logged": 1657226126829 + } + }, + "id": "45kv9ni6CAvn" + }, + { + "cell_type": "code", + "source": [ + "tmp_sent = 'As freshwater lakes get hotter in the summer that leads to more amoebae in the water and increased human risk said Sonia Altizer an associate professor of ecology at the University of Georgia'\n", + "res = get_dep_tree(tmp_sent)[tmp_sent]\n", + "print(res.replace(\"(\", \"[\").replace(\")\", \"]\"))" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": "[S [SBAR [IN As] [S [NP [NN freshwater] [NNS lakes]] [VP [VBP get] [ADJP [JJR hotter]] [PP [IN in] [NP [DT the] [NN summer]]]]]] [NP [WDT that]] [VP [VBZ leads] [PP [PP [IN to] [NP [NP [NP [JJR more] [NNS amoebae]] [PP [IN in] [NP [DT the] [NN water]]]] [CC and] [NP [VBN increased] [JJ human] [NN risk]]]] [PP [VP [VBD said]] [NP [NP [NNP Sonia] [NNP Altizer]] [NP [NP [DT an] [JJ associate] [NN professor]] [PP [IN of] [NP [NN ecology]]] [PP [IN at] [NP [NP [DT the] [NNP University]] [PP [IN of] [NP [NNP Georgia]]]]]]]]]]]\n" + } + ], + "execution_count": 7, + "metadata": { + "jupyter": { + "source_hidden": false, + "outputs_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + }, + "gather": { + "logged": 1657226188992 + } + }, + "id": "7de6b8fa-87b2-49d8-8315-c1a1445f47a8" + }, + { + "cell_type": "code", + "source": [ + "csv = []\n", + "#features = [\"sent\", \"base1\", \"base2\", \"all_bases\", \"all_cd\", \"all_pred\", \"all_ac\", \"left_to_right\"]\n", + "#features = [\"sent\", \"base1\", \"base2\", \"all_bases\", \"all_cd\", \"all_pred\", \"dep_tree\", \"left_to_right\"]\n", + "features = [\"sent\", \"base1\", \"base2\", \"dep_tree\", \"left_to_right\"]\n", + "csv.append(features)\n", + "\n", + "for x in final_rel_sent:\n", + " print(final_rel_sent.index(x))\n", + " sent = x[0]\n", + " for y in x[1]['entities']:\n", + " if y[\"head\"] != \"IMPLIED_BASE\" and y[\"child\"] != \"IMPLIED_BASE\":\n", + " start_index = sent.find(y[\"head\"])\n", + " end_index = sent.find(y[\"child\"])\n", + " if start_index < end_index:\n", + " left_to_right = 0\n", + " else:\n", + " left_to_right = 1\n", + " \n", + " new_sent = sent\n", + " new_sent = new_sent.replace(y[\"head\"], \"BASE\")\n", + " new_sent = new_sent.replace(y[\"child\"], \"BASE\")\n", + "\n", + " #tmp = [sent, y[\"head\"], y[\"child\"], \", \".join(final_sent_ent[sent][\"base\"]), \", \".join(final_sent_ent[sent][\"change_direction\"]), \n", + " # \", \".join(final_sent_ent[sent][\"predicate\"]), \", \".join(final_sent_ent[sent][\"aspect_changing\"]), left_to_right]\n", + " #tmp = [sent, y[\"head\"], y[\"child\"], \", \".join(final_sent_ent[sent][\"base\"]), \", \".join(final_sent_ent[sent][\"change_direction\"]), \n", + " # \", \".join(final_sent_ent[sent][\"predicate\"]), left_to_right]\n", + " \n", + " # passing in dep tree/sentence grammar as a feature\n", + " #tmp = [sent, y[\"head\"], y[\"child\"], \", \".join(final_sent_ent[sent][\"base\"]), \", \".join(final_sent_ent[sent][\"change_direction\"]), \n", + " # \", \".join(final_sent_ent[sent][\"predicate\"]), dep_tree[sent], left_to_right]\n", + "\n", + " # swapping the actual words base to \"BASE\" to understand if the model is actually learning the grammar or not\n", + " #tmp = [new_sent, \"BASE\", \"BASE\", \", \".join(final_sent_ent[sent][\"base\"]), \", \".join(final_sent_ent[sent][\"change_direction\"]), \n", + " # \", \".join(final_sent_ent[sent][\"predicate\"]), left_to_right]\n", + " #tmp = [new_sent, \"BASE\", \"BASE\", \", \".join(final_sent_ent[sent][\"change_direction\"]), \n", + " # \", \".join(final_sent_ent[sent][\"predicate\"]), left_to_right]\n", + "\n", + " dep_tree = get_dep_tree(new_sent)[new_sent]\n", + " #graph_generated = generate_graph(dep_tree)\n", + " #g_embeddings = generate_embeddings_new(graph_generated)\n", + "\n", + " #sentence_embeddings_model = sbert_model.encode(new_sent)\n", + " #s_embeddings = list(sentence_embeddings_model)\n", + " tmp = [new_sent, \"BASE\", \"BASE\", dep_tree, left_to_right]\n", + " \n", + " #for e in g_embeddings:\n", + " # tmp = [s_embeddings, \"BASE\", \"BASE\", e, left_to_right]\n", + " # csv.append(tmp)\n", + " csv.append(tmp)\n", + " tmp = [] \n", + " \n", + "print(len(csv))" + ], + "outputs": [], + "execution_count": null, + "metadata": { + "id": "68b9c88d", + "gather": { + "logged": 1655865232104 + } + }, + "id": "68b9c88d" + }, + { + "cell_type": "code", + "source": [ + "len(csv)" + ], + "outputs": [ + { + "output_type": "execute_result", + "execution_count": 11, + "data": { + "text/plain": "694" + }, + "metadata": {} + } + ], + "execution_count": 11, + "metadata": { + "jupyter": { + "source_hidden": false, + "outputs_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + }, + "gather": { + "logged": 1655865165791 + } + }, + "id": "cd86adc9-d6f6-4934-8797-f6066f8d981e" + }, + { + "cell_type": "code", + "source": [ + "df = pd.DataFrame(i for i in csv)\n", + "df.drop(0, axis=0, inplace=True)\n", + "df.columns = csv[0]\n", + "df['left_to_right'] = df['left_to_right'].astype(int)\n", + "#df.to_csv('data.csv')" + ], + "outputs": [], + "execution_count": 12, + "metadata": { + "id": "34434937", + "gather": { + "logged": 1655865165863 + } + }, + "id": "34434937" + }, + { + "cell_type": "code", + "source": [ + "df.head()" + ], + "outputs": [ + { + "output_type": "execute_result", + "execution_count": 13, + "data": { + "text/plain": " sent base1 base2 \\\n1 [-0.3926697, 0.29174235, 0.34444395, 0.1115106... BASE BASE \n2 [-0.4879627, 0.20637694, 0.35121828, 0.1713416... BASE BASE \n3 [-0.2728444, 0.3277352, 0.4151958, 0.036221836... BASE BASE \n4 [-0.2765118, 0.2526166, 0.36175925, 0.10105675... BASE BASE \n5 [-0.2971289, 0.28096542, 0.47723004, 0.0929106... BASE BASE \n\n dep_tree left_to_right \n1 (S (NP (DT These) (NNS risks)) (VP (VBP are) (... 1 \n2 (S (NP (DT These) (NNS risks)) (VP (VBP are) (... 1 \n3 (S (NP (DT These) (NNS risks)) (VP (VBP are) (... 1 \n4 (S (NP (DT These) (NNS risks)) (VP (VBP are) (... 1 \n5 (S (NP (DT These) (NNS risks)) (VP (VBP are) (... 1 ", + "text/html": "
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sentbase1base2dep_treeleft_to_right
1[-0.3926697, 0.29174235, 0.34444395, 0.1115106...BASEBASE(S (NP (DT These) (NNS risks)) (VP (VBP are) (...1
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\n
" + }, + "metadata": {} + } + ], + "execution_count": 13, + "metadata": { + "id": "899826a3", + "gather": { + "logged": 1655865165952 + } + }, + "id": "899826a3" + }, + { + "cell_type": "code", + "source": [ + "\"\"\"\n", + "square_edges = pd.DataFrame(\n", + " {\"source\": df.dep_tree, \"target\": df.left_to_right}\n", + ")\n", + "square = StellarGraph(edges=square_edges)\n", + "print(square.info())\n", + "\"\"\"" + ], + "outputs": [], + "execution_count": 16, + "metadata": { + "jupyter": { + "source_hidden": false, + "outputs_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + }, + "gather": { + "logged": 1655865409383 + } + }, + "id": "8e0622f0-07b7-4aeb-972a-30c41da92c19" + }, + { + "cell_type": "code", + "source": [ + "#df[\"sent\"] = df[\"sent\"].astype('category')\n", + "df[\"base1\"] = df[\"base1\"].astype('category')\n", + "df[\"base2\"] = df[\"base2\"].astype('category')\n", + "#df[\"all_bases\"] = df[\"all_bases\"].astype('category')\n", + "#df[\"all_cd\"] = df[\"all_cd\"].astype('category')\n", + "#df[\"all_pred\"] = df[\"all_pred\"].astype('category')\n", + "#df[\"dep_tree\"] = df[\"dep_tree\"][:0,].astype('category')\n", + "#df[\"all_ac\"] = df[\"all_ac\"].astype('category')\n", + "\n", + "# Second, replace the strings with their code values.\n", + "#df[\"sent\"] = df[\"sent\"].cat.codes\n", + "df[\"base1\"] = df[\"base1\"].cat.codes\n", + "df[\"base2\"] = df[\"base2\"].cat.codes\n", + "#df[\"all_bases\"] = df[\"all_bases\"].cat.codes\n", + "#df[\"all_cd\"] = df[\"all_cd\"].cat.codes\n", + "#df[\"all_pred\"] = df[\"all_pred\"].cat.codes\n", + "#df[\"dep_tree\"] = df[\"dep_tree\"].cat.codes\n", + "#df[\"all_ac\"] = df[\"all_ac\"].cat.codes\n", + "\n", + "# Display the table--notice how the above columns are all integers now.\n", + "df.head()" + ], + "outputs": [], + "execution_count": null, + "metadata": { + "id": "1e350bbc", + "gather": { + "logged": 1655865166488 + } + }, + "id": "1e350bbc" + }, + { + "cell_type": "code", + "source": [], + "outputs": [], + "execution_count": null, + "metadata": { + "jupyter": { + "source_hidden": false, + "outputs_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + } + }, + "id": "24f5066d-6133-4ae7-a887-efe6524f7f89" + }, + { + "cell_type": "code", + "source": [ + "from sklearn.model_selection import train_test_split\n", + "from sklearn.preprocessing import OneHotEncoder\n", + "\n", + "feature_cols = [\"sent\", \"dep_tree\"]\n", + "#feature_cols = [\"sent\", \"base1\", \"base2\", \"all_bases\", \"all_cd\", \"dep_tree\", \"all_pred\"]\n", + "#feature_cols = [\"sent\", \"base1\", \"base2\", \"all_bases\", \"all_cd\", \"all_pred\", \"all_ac\"]\n", + "\n", + "X = df[feature_cols]\n", + "y = df.left_to_right\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=10)\n", + "print(\"Training size: {}\".format(len(X_train)))\n", + "print(\"Testing size: {}\".format(len(X_test)))" + ], + "outputs": [], + "execution_count": null, + "metadata": { + "id": "D2uX1yMU7tA1", + "gather": { + "logged": 1655865166498 + } + }, + "id": "D2uX1yMU7tA1" + }, + { + "cell_type": "code", + "source": [ + "#X_train[\"dep_tree\"].str.len().max()\n", + "new_list = []\n", + "padded_list = []\n", + "for x in range(0, len(X_train[\"dep_tree\"])):\n", + " #print(np.asarray(X_train[\"dep_tree\"].iloc[x]).flatten())\n", + " new_list.append(np.asarray(X_train[\"dep_tree\"].iloc[x]).flatten())\n", + "\n", + "max_len = max(len(x) for x in new_list)\n", + "\n", + "for n in new_list:\n", + " pad = int(max_len - len(n))\n", + " if pad > 0:\n", + " new_arr = [0.00]*pad\n", + " #print(np.concatenate(n, np.array(new_arr)))\n", + " n = np.concatenate((n, np.array(new_arr)))\n", + " padded_list.append(n)\n", + "\n", + "X_train[\"dep_tree_flatten\"] = padded_list" + ], + "outputs": [], + "execution_count": null, + "metadata": { + "jupyter": { + "source_hidden": false, + "outputs_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + }, + "gather": { + "logged": 1655865166507 + } + }, + "id": "98989355-2a81-441a-93aa-665d850346c6" + }, + { + "cell_type": "code", + "source": [ + "X_train.drop('dep_tree', axis=1, inplace=True)\n", + "\n", + "X_train" + ], + "outputs": [], + "execution_count": null, + "metadata": { + "jupyter": { + "source_hidden": false, + "outputs_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + }, + "gather": { + "logged": 1655865166518 + } + }, + "id": "a5339bef-4a97-4558-92b7-1863ea5613ca" + }, + { + "cell_type": "code", + "source": [ + "\n", + "import xgboost as xgb\n", + "\n", + "#new_ser = pd.Series(X_train[\"sent\"])\n", + "#new_new = np.stack(new_ser)\n", + "#print(new_new)\n", + "\n", + "new_dep_ser = pd.Series(X_train[\"dep_tree_flatten\"])\n", + "new_dep_new = np.stack(new_dep_ser)\n", + "print(type(new_dep_ser))\n", + "\n", + "#new_dep_new = np.column_stack(new_dep_ser)\n", + "#print(new_dep_new)\n", + "\n", + "#print(type(new_new))\n", + "my_dmat = xgb.DMatrix(np.asarray(new_dep_new))\n", + "my_dmat\n", + "X_train[\"dep_dmat\"] = my_dmat" + ], + "outputs": [], + "execution_count": null, + "metadata": { + "jupyter": { + "source_hidden": false, + "outputs_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + }, + "gather": { + "logged": 1655865166525 + } + }, + "id": "042615f7-264d-4b24-9669-ea8fa99fbdb9" + }, + { + "cell_type": "code", + "source": [ + "#np.vstack(X_train[\"dep_tree\"])\n", + "#np.array(X_train[\"dep_tree_flatten\"], dtype=object).reshape((1,-1))\n", + "X_train" + ], + "outputs": [], + "execution_count": null, + "metadata": { + "jupyter": { + "source_hidden": false, + "outputs_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + }, + "gather": { + "logged": 1655865166532 + } + }, + "id": "eea35e68-3993-471c-b0fb-6e2ce9daf695" + }, + { + "cell_type": "code", + "source": [ + "xgb.DMatrix(X_train, y_train)" + ], + "outputs": [], + "execution_count": null, + "metadata": { + "jupyter": { + "source_hidden": false, + "outputs_hidden": false + }, + "nteract": { + "transient": { + "deleting": false + } + }, + "gather": { + "logged": 1655865166538 + } + }, + "id": "d73f2ee7-21a9-47a4-b38e-4dba42ca921a" + }, + { + "cell_type": "code", + "source": [ + "import xgboost\n", + "from xgboost import XGBClassifier\n", + "from sklearn.metrics import f1_score\n", + "\n", + "# Create an instance of the classifier\n", + "#model = XGBClassifier(early_stopping_rounds = 10, n_estimators=300, learning_rate = 0.1)\n", + "model = XGBClassifier(early_stopping_rounds = 10, n_estimators=300, learning_rate = 0.1, max_depth=5, use_label_encoder=False)\n", + "#model = XGBClassifier(use_label_encoder=False)\n", + "# Train it on the training set.\n", + "#model.fit(X_train, y_train)\n", + "#model.fit(np.array(X_train[\"dep_dmat\"]).reshape((1, -1)), y_train)\n", + "model.fit(np.array(my_dmat).reshape(1, -1), y_train)\n", + "\n", + "#model.fit(np.array(X_train[\"dep_tree_flatten\"], dtype=object).reshape((1, -1)), y_train)\n", + "\n", + "# Use the trained model to predict the labels for the test set.\n", + "predictions = model.predict(X_test[\"dep_tree_flatten\"])\n", + "print(predictions)\n", + "# Calculate the F1 score.\n", + "f1 = f1_score(y_true = y_test,\n", + " y_pred = predictions)\n", + "\n", + "print('F1: {}'.format(f1))" + ], + "outputs": [], + "execution_count": null, + "metadata": { + "id": "fe1941e3", + "gather": { + "logged": 1655865166545 + } + }, + "id": "fe1941e3" + }, + { + "cell_type": "code", + "source": [ + "from sklearn import metrics\n", + "cnf_matrix = metrics.confusion_matrix(y_test, predictions)\n", + "cnf_matrix" + ], + "outputs": [], + "execution_count": null, + "metadata": { + "id": "19f0f148", + "gather": { + "logged": 1655865166552 + } + }, + "id": "19f0f148" + }, + { + "cell_type": "code", + "source": [ + "from sklearn.metrics import accuracy_score\n", + "print(accuracy_score(y_test, predictions))" + ], + "outputs": [], + "execution_count": null, + "metadata": { + "id": "76488d6a", + "gather": { + "logged": 1655865166560 + } + }, + "id": "76488d6a" + }, + { + "cell_type": "code", + "source": [ + "predictions" + ], + "outputs": [], + "execution_count": null, + "metadata": { + "id": "71b4ba06", + "gather": { + "logged": 1655865166566 + } + }, + "id": "71b4ba06" + }, + { + "cell_type": "code", + "source": [ + "from xgboost import plot_importance\n", + "from matplotlib import pyplot\n", + "\n", + "# plot feature importance\n", + "plot_importance(model)\n", + "pyplot.show()" + ], + "outputs": [], + "execution_count": null, + "metadata": { + "scrolled": true, + "id": "fe69c8fa", + "gather": { + "logged": 1655865166571 + } + }, + "id": "fe69c8fa" + }, + { + "cell_type": "code", + "source": [ + "feat_gains = model.get_booster().get_score(importance_type=\"gain\")\n", + "\n", + "pyplot.bar(feat_gains.keys(), feat_gains.values())\n", + 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/dev/null +++ b/model_dev/scripts/data_analysis.py @@ -0,0 +1,31 @@ +import srsly + +file_name_answers = "gold_standard" +file_path_answers = "/Users/ash/Desktop/PURE/PURE/new_data/"+file_name_answers+".jsonl" + +def get_all_sessions(): + data = srsly.read_jsonl(file_path_answers) + sessions = [] + for entry in data: + if entry["_session_id"] and entry["_session_id"] not in sessions: + sessions.append(entry["_session_id"]) + return sessions + +def get_sentences_per_user(user): + data = srsly.read_jsonl(file_path_answers) + sentence_count = 0 + for entry in data: + text = entry["text"] + if text: + if entry['answer'] == "accept": + if entry['_session_id'] == user: + sentence_count += 1 + return sentence_count + +if __name__ == "__main__": + users = get_all_sessions() + for u in users: + sentences = get_sentences_per_user(u) + print("{}: {} sentences".format(u, sentences)) + + diff --git a/model_dev/scripts/data_to_pure.py b/model_dev/scripts/data_to_pure.py new file mode 100644 index 0000000..e2ef1fb --- /dev/null +++ b/model_dev/scripts/data_to_pure.py @@ -0,0 +1,70 @@ +import srsly +import json + +file_name_answers = "checkin_answers" +file_path_answers = "/Users/ash/Desktop/PURE/PURE/"+file_name_answers+".jsonl" +rel_labels = ["Contributes_To"] + +def get_rel(user): + tmp_index = 0 + final_sent = {} + data = srsly.read_jsonl(file_path_answers) + final_sent_arr = [] + for entry in data: + text = entry["text"] + if text: + sentences = [] + if entry['answer'] == "accept": + if entry['_session_id'] == user: + ner = [] + rel = [] + token_sent = [] + for t in entry["tokens"]: + token_sent.append(t["text"]) + sentences.append(token_sent) + for relation in entry['relations']: + if ("label" in relation) and ("head_span" in relation) and ("child_span" in relation): + if relation["label"] in rel_labels: + + ner.append([relation["head_span"]["token_start"] + 1, relation["head_span"]["token_end"] + 1, "base"]) + ner.append([relation["child_span"]["token_start"] + 1, relation["child_span"]["token_end"] + 1, "base"]) + + rel.append([relation["head_span"]["token_start"] + 1, relation["head_span"]["token_end"] + 1, + relation["child_span"]["token_start"] + 1, relation["child_span"]["token_end"] + 1, + "Contributes_To"]) + + final_sent['doc_key'] = str(tmp_index) + final_sent['sentences'] = sentences + final_sent['ner'] = [ner] + final_sent['predicted_ner'] = [ner] + final_sent['relations'] = [rel] + + if final_sent: + final_sent_arr.append(final_sent) + final_sent = {} + tmp_index += 1 + + return final_sent_arr + +def gen_preds(user): + kameron_rel = get_rel("main_3_per_cluster-Kameron") + user_rel = get_rel("main_3_per_cluster-" + user) + + for k in kameron_rel: + for u in user_rel: + if k["sentences"] == u["sentences"]: + u["predicted_relations"] = u["relations"] + u["relations"] = k["relations"] + + for ur in user_rel: + if "predicted_relations" not in ur.keys(): + user_rel.remove(ur) + + for ud in user_rel: + json_object = json.dumps(ud) + with open("predictions_"+user+".json", "a") as outfile: + outfile.write(json_object + "\n") + + +gen_preds("Nikita") +gen_preds("Sampath") \ No newline at end of file diff --git a/model_dev/scripts/human_eval.ipynb b/model_dev/scripts/human_eval.ipynb new file mode 100644 index 0000000..8537ee7 --- /dev/null +++ b/model_dev/scripts/human_eval.ipynb @@ -0,0 +1,382 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 3, + "id": "e3f71a5a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2022-07-09 13:48:26-- http://nlp.cs.washington.edu/sciIE/data/sciERC_processed.tar.gz\n", + "Resolving nlp.cs.washington.edu (nlp.cs.washington.edu)... 128.208.3.120\n", + "Connecting to nlp.cs.washington.edu (nlp.cs.washington.edu)|128.208.3.120|:80... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 695340151 (663M) [application/x-gzip]\n", + "Saving to: ‘sciERC_processed.tar.gz’\n", + "\n", + "sciERC_processed.ta 100%[===================>] 663.13M 10.6MB/s in 1m 49s \n", + "\n", + "2022-07-09 13:50:15 (6.10 MB/s) - ‘sciERC_processed.tar.gz’ saved [695340151/695340151]\n", + "\n" + ] + }, + { + "data": { + "text/plain": [ + "'\\n!rm -f sciERC_processed.tar.gz\\n\\nscierc_dataset=\"scierc_data/processed_data/json/\"\\n\\n# Download the pre-trained models (single-sentence)\\n!mkdir scierc_models; cd scierc_models\\n\\n# Download the pre-trained entity model\\n!wget https://nlp.cs.princeton.edu/projects/pure/scierc_models/ent-scib-ctx0.zip\\n!unzip ent-scib-ctx0.zip; \\n!rm -f ent-scib-ctx0.zip\\nscierc_ent_model=scierc_models/ent-scib-ctx0/\\n\\n# Download the pre-trained full relation model\\n!wget https://nlp.cs.princeton.edu/projects/pure/scierc_models/rel-scib-ctx0.zip\\n!unzip rel-scib-ctx0.zip; \\n!rm -f rel-scib-ctx0.zip\\nscierc_rel_model=scierc_models/rel-scib-ctx0/\\n'" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Download the SciERC dataset\n", + "!wget http://nlp.cs.washington.edu/sciIE/data/sciERC_processed.tar.gz\n", + "!mkdir scierc_data \n", + "!tar -xf sciERC_processed.tar.gz -C scierc_data\n", + "!rm -f sciERC_processed.tar.gz\n", + "scierc_dataset=\"scierc_data/processed_data/json/\"\n", + "\n", + "# Download the pre-trained models (single-sentence)\n", + "!mkdir scierc_models \n", + "!cd scierc_models\n", + "\n", + "# Download the pre-trained entity model\n", + "!wget https://nlp.cs.princeton.edu/projects/pure/scierc_models/ent-scib-ctx0.zip\n", + "!unzip ent-scib-ctx0.zip \n", + "!rm -f ent-scib-ctx0.zip\n", + "scierc_ent_model=\"scierc_models/ent-scib-ctx0/\"\n", + "\n", + "# Download the pre-trained full relation model\n", + "!wget https://nlp.cs.princeton.edu/projects/pure/scierc_models/rel-scib-ctx0.zip\n", + "!unzip rel-scib-ctx0.zip \n", + "!rm -f rel-scib-ctx0.zip\n", + "scierc_rel_model=\"scierc_models/rel-scib-ctx0/\"" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "b7652003", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2022-07-09 13:51:16-- https://nlp.cs.princeton.edu/projects/pure/scierc_models/ent-scib-ctx0.zip\n", + "Resolving nlp.cs.princeton.edu (nlp.cs.princeton.edu)... 128.112.136.61\n", + "Connecting to nlp.cs.princeton.edu (nlp.cs.princeton.edu)|128.112.136.61|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 409227718 (390M) [application/zip]\n", + "Saving to: ‘ent-scib-ctx0.zip’\n", + "\n", + "ent-scib-ctx0.zip 100%[===================>] 390.27M 14.8MB/s in 25s \n", + "\n", + "2022-07-09 13:51:42 (15.4 MB/s) - ‘ent-scib-ctx0.zip’ saved [409227718/409227718]\n", + "\n", + "Archive: ent-scib-ctx0.zip\n", + " creating: ent-scib-ctx0/\n", + " inflating: ent-scib-ctx0/special_tokens_map.json \n", + " inflating: ent-scib-ctx0/vocab.txt \n", + " inflating: ent-scib-ctx0/config.json \n", + " inflating: ent-scib-ctx0/tokenizer_config.json \n", + " inflating: ent-scib-ctx0/pytorch_model.bin \n", + "--2022-07-09 13:51:46-- https://nlp.cs.princeton.edu/projects/pure/scierc_models/rel-scib-ctx0.zip\n", + "Resolving nlp.cs.princeton.edu (nlp.cs.princeton.edu)... 128.112.136.61\n", + "Connecting to nlp.cs.princeton.edu (nlp.cs.princeton.edu)|128.112.136.61|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 408246037 (389M) [application/zip]\n", + "Saving to: ‘rel-scib-ctx0.zip’\n", + "\n", + "rel-scib-ctx0.zip 100%[===================>] 389.33M 17.2MB/s in 26s \n", + "\n", + "2022-07-09 13:52:13 (14.8 MB/s) - ‘rel-scib-ctx0.zip’ saved [408246037/408246037]\n", + "\n", + "Archive: rel-scib-ctx0.zip\n", + " creating: rel-scib-ctx0/\n", + " inflating: rel-scib-ctx0/vocab.txt \n", + " inflating: rel-scib-ctx0/config.json \n", + " inflating: rel-scib-ctx0/pytorch_model.bin \n", + " inflating: rel-scib-ctx0/special_tokens.json \n", + " inflating: rel-scib-ctx0/label_list.json \n" + ] + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "e7df2748", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting allennlp\n", + " Downloading allennlp-2.9.3-py3-none-any.whl (719 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m719.6/719.6 kB\u001b[0m \u001b[31m1.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n", + "\u001b[?25hRequirement already satisfied: transformers<4.19,>=4.1 in /Users/ash/opt/anaconda3/lib/python3.9/site-packages (from allennlp) (4.16.2)\n", + "Requirement already satisfied: nltk in /Users/ash/opt/anaconda3/lib/python3.9/site-packages (from allennlp) (3.6.5)\n", + "Requirement already satisfied: h5py in /Users/ash/opt/anaconda3/lib/python3.9/site-packages (from allennlp) (3.2.1)\n", + "Collecting lmdb\n", + " Downloading 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cached-path, allennlp\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Attempting uninstall: typer\n", + " Found existing installation: typer 0.4.0\n", + " Uninstalling typer-0.4.0:\n", + " Successfully uninstalled typer-0.4.0\n", + " Attempting uninstall: filelock\n", + " Found existing installation: filelock 3.3.1\n", + " Uninstalling filelock-3.3.1:\n", + " Successfully uninstalled filelock-3.3.1\n", + " Attempting uninstall: huggingface-hub\n", + " Found existing installation: huggingface-hub 0.4.0\n", + " Uninstalling huggingface-hub-0.4.0:\n", + " Successfully uninstalled huggingface-hub-0.4.0\n", + "Successfully installed allennlp-2.9.3 boto3-1.24.26 botocore-1.27.26 cached-path-1.1.5 commonmark-0.9.1 fairscale-0.4.6 filelock-3.6.0 google-api-core-2.8.2 google-cloud-core-2.3.1 google-cloud-storage-2.4.0 google-crc32c-1.3.0 google-resumable-media-2.3.3 googleapis-common-protos-1.56.3 huggingface-hub-0.8.1 jmespath-1.0.1 jsonnet-0.18.0 lmdb-1.3.0 rich-12.4.4 s3transfer-0.6.0 tensorboardX-2.5.1 typer-0.4.2\n", + "\u001b[33mWARNING: There was an error checking the latest version of pip.\u001b[0m\u001b[33m\n", + "\u001b[0m" + ] + } + ], + "source": [ + "!pip install allennlp" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "6e5fb8fd", + "metadata": {}, + "outputs": [], + "source": [ + "!cd .." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "6b34c96f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Traceback (most recent call last):\r\n", + " File \"/Users/ash/Desktop/PURE/PURE/run_eval.py\", line 12, in \r\n", + " data = Dataset(args.prediction_file)\r\n", + " File \"/Users/ash/Desktop/PURE/PURE/shared/data_structures.py\", line 36, in __init__\r\n", + " self.js = self._read(json_file, pred_file)\r\n", + " File \"/Users/ash/Desktop/PURE/PURE/shared/data_structures.py\", line 46, in _read\r\n", + " gold_docs = [json.loads(line) for line in open(json_file)]\r\n", + "FileNotFoundError: [Errno 2] No such file or directory: '/rel-scib-ctx0/predictions.json'\r\n" + ] + } + ], + "source": [ + "# Run the pre-trained full relation model\n", + "!python run_relation.py \\\n", + " --task scierc \\\n", + " --do_eval --eval_test \\\n", + " --model allenai/scibert_scivocab_uncased \\\n", + " --do_lower_case \\\n", + " --context_window 0\\\n", + " --max_seq_length 128 \\\n", + " --entity_output_dir ${scierc_ent_model} \\\n", + " --output_dir ${scierc_rel_model}\n", + " \n", + "# Output end-to-end evaluation results\n", + "!python run_eval.py --prediction_file ${scierc_rel_model}predictions.json" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c01cc7e9", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/model_dev/scripts/human_performance_evaluation.py b/model_dev/scripts/human_performance_evaluation.py new file mode 100644 index 0000000..bb3645e --- /dev/null +++ b/model_dev/scripts/human_performance_evaluation.py @@ -0,0 +1,41 @@ +import json +from pathlib import Path + +from nervaluate import Evaluator + +#base_path = Path(__file__).parents[2] / "data" +#data_path = base_path / "main_3_per_cluster_download.cba617d8-a055-4622-97a3-c194a148cbed.jsonl" + +base_path = "/Users/ash/Desktop/PURE/PURE/" +data_path = base_path+"main_3_per_cluster_download.cba617d8-a055-4622-97a3-c194a148cbed.jsonl" + +with open(data_path, "r") as f: + lines = [json.loads(x) for x in f.readlines()] + +gold_standard_session = "main_3_per_cluster-Kameron" +other_sessions = ["main_3_per_cluster-Sampath", "main_3_per_cluster-Nikita"] + +all_labels = ['base', 'change_direction', 'type_of', 'aspect_changing'] + +for other_session in other_sessions: + other_session_subset = [x for x in lines if x["_session_id"] == other_session] + gold_session_subset = [x for x in lines if x["_session_id"] == gold_standard_session] + + other_session_subset.sort(key=lambda x: x["document_index"]) + gold_session_subset.sort(key=lambda x: x["document_index"]) + + assert [x["document_index"] for x in other_session_subset] == [x["document_index"] for x in gold_session_subset] + + other_spans = [[a for a in x["spans"] if (all(k in a for k in ["start", "end", "label"]) and a["label"] in all_labels)] for x in + other_session_subset] + gold_spans = [[a for a in x["spans"] if (all(k in a for k in ["start", "end", "label"]) and a["label"] in all_labels)] for x in + gold_session_subset] + + evaluator = Evaluator(gold_spans, other_spans, tags=all_labels) + + results, results_per_tag = evaluator.evaluate() + + with open(base_path + f"results_{other_session}.json", "w") as f: + json.dump(results, f, indent=4) + with open(base_path + f"results_per_tag_{other_session}.json", "w") as f: + json.dump(results_per_tag, f, indent=4) diff --git a/src/utils/model/custom_entity_extractor_PURE.py b/src/utils/model/custom_entity_extractor_PURE.py deleted file mode 100644 index 0393c9a..0000000 --- a/src/utils/model/custom_entity_extractor_PURE.py +++ /dev/null @@ -1,16 +0,0 @@ -json_string = '{"clusters": [[[6, 17], [32, 32]], [[4, 4], [55, 55], [91, 91]], [[58, 62], [64, 64], [79, 79]]], "sentences": [["This", "paper", "presents", "an", "algorithm", "for", "computing", "optical", "flow", ",", "shape", ",", "motion", ",", "lighting", ",", "and", "albedo", "from", "an", "image", "sequence", "of", "a", "rigidly-moving", "Lambertian", "object", "under", "distant", "illumination", "."], ["The", "problem", "is", "formulated", "in", "a", "manner", "that", "subsumes", "structure", "from", "motion", ",", "multi-view", "stereo", ",", "and", "photo-metric", "stereo", "as", "special", "cases", "."], ["The", "algorithm", "utilizes", "both", "spatial", "and", "temporal", "intensity", "variation", "as", "cues", ":", "the", "former", "constrains", "flow", "and", "the", "latter", "constrains", "surface", "orientation", ";", "combining", "both", "cues", "enables", "dense", "reconstruction", "of", "both", "textured", "and", "texture-less", "surfaces", "."], ["The", "algorithm", "works", "by", "iteratively", "estimating", "affine", "camera", "parameters", ",", "illumination", ",", "shape", ",", "and", "albedo", "in", "an", "alternating", "fashion", "."], ["Results", "are", "demonstrated", "on", "videos", "of", "hand-held", "objects", "moving", "in", "front", "of", "a", "fixed", "light", "and", "camera", "."]], "ner": [[[4, 4, "Generic"], [6, 17, "Task"], [20, 21, "Material"], [24, 26, "Material"], [28, 29, "OtherScientificTerm"]], [[32, 32, "Generic"], [42, 42, "Material"], [44, 45, "Material"], [48, 49, "Material"]], [[55, 55, "Generic"], [58, 62, "OtherScientificTerm"], [64, 64, "Generic"], [67, 67, "Generic"], [69, 69, "OtherScientificTerm"], [72, 72, "Generic"], [74, 75, "OtherScientificTerm"], [79, 79, "Generic"], [81, 88, "Task"]], [[91, 91, "Generic"], [95, 105, "Method"]], [[115, 118, "Material"]]], "relations": [[[4, 4, 6, 17, "USED-FOR"], [20, 21, 4, 4, "USED-FOR"], [24, 26, 20, 21, "FEATURE-OF"], [28, 29, 24, 26, "FEATURE-OF"]], [[42, 42, 44, 45, "CONJUNCTION"], [44, 45, 48, 49, "CONJUNCTION"]], [[58, 62, 55, 55, "USED-FOR"], [67, 67, 64, 64, "HYPONYM-OF"], [67, 67, 69, 69, "USED-FOR"], [67, 67, 72, 72, "CONJUNCTION"], [72, 72, 64, 64, "HYPONYM-OF"], [72, 72, 74, 75, "USED-FOR"], [79, 79, 81, 88, "USED-FOR"]], [[95, 105, 91, 91, "USED-FOR"]], []], "doc_key": "ICCV_2003_158_abs"}' - - -import itertools -import json - -obj = json.loads(json_string) -doc = list(itertools.chain(*obj['sentences'])) - -for sent in obj['ner']: - for e in sent: - 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)"],"execution_count":2,"outputs":[]},{"cell_type":"code","metadata":{"id":"qZvPqWXjsSzB","colab":{"base_uri":"https://localhost:8080/","height":990},"executionInfo":{"status":"ok","timestamp":1644787979493,"user_tz":300,"elapsed":24,"user":{"displayName":"Himesh Buch","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiV8OCq9wFOhYmGL1qBY-DHltq27lamDhcBltE_HpE=s64","userId":"10202248199877876561"}},"outputId":"c708efcc-e72d-4998-97b3-2e1c8c7858a2"},"source":["data.head(30)"],"execution_count":3,"outputs":[{"output_type":"execute_result","data":{"text/html":["\n","
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Sentence #textwordtag
0Sentence 1Heat stress and persistent dehydration can cau...HeatO
1Sentence 1Heat stress and persistent dehydration can cau...stressbase
2Sentence 1Heat stress and persistent dehydration can cau...persistentO
3Sentence 1Heat stress and persistent dehydration can cau...dehydrationbase
4Sentence 1Heat stress and persistent dehydration can cau...canO
5Sentence 1Heat stress and persistent dehydration can cau...causeO
6Sentence 1Heat stress and persistent dehydration can cau...kidneybase
7Sentence 1Heat stress and persistent dehydration can cau...damageO
8Sentence 2This summer, the heat was intensified by a lac...This summerO
9Sentence 2This summer, the heat was intensified by a lac...heatbase
10Sentence 2This summer, the heat was intensified by a lac...wasO
11Sentence 2This summer, the heat was intensified by a lac...intensifiedO
12Sentence 2This summer, the heat was intensified by a lac...byO
13Sentence 2This summer, the heat was intensified by a lac...lack ofO
14Sentence 2This summer, the heat was intensified by a lac...cloudsbase
15Sentence 2This summer, the heat was intensified by a lac...rainbase
16Sentence 2This summer, the heat was intensified by a lac...stormbase
17Sentence 2This summer, the heat was intensified by a lac...activityO
18Sentence 2This summer, the heat was intensified by a lac...because ofO
19Sentence 2This summer, the heat was intensified by a lac...weaker-than-usualO
20Sentence 2This summer, the heat was intensified by a lac...summerO
21Sentence 2This summer, the heat was intensified by a lac...monsoonbase
22Sentence 2This summer, the heat was intensified by a lac...from July into early fallO
23Sentence 3Using these rates, the study's authors project...projectO
24Sentence 3Using these rates, the study's authors project...climate changebase
25Sentence 3Using these rates, the study's authors project...current courseO
26Sentence 3Using these rates, the study's authors project...couldO
27Sentence 3Using these rates, the study's authors project...lead toO
28Sentence 3Using these rates, the study's authors project...between 9,000 and 40,000O
29Sentence 3Using these rates, the study's authors project...additionalO
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Sentence #textwordtag
0Sentence 1Heat stress and persistent dehydration can cau...HeatO
1Sentence 1Heat stress and persistent dehydration can cau...stressbase
2Sentence 1Heat stress and persistent dehydration can cau...persistentO
3Sentence 1Heat stress and persistent dehydration can cau...dehydrationbase
4Sentence 1Heat stress and persistent dehydration can cau...canO
5Sentence 1Heat stress and persistent dehydration can cau...causeO
6Sentence 1Heat stress and persistent dehydration can cau...kidneybase
7Sentence 1Heat stress and persistent dehydration can cau...damageO
8Sentence 2This summer, the heat was intensified by a lac...This summerO
9Sentence 2This summer, the heat was intensified by a lac...heatbase
10Sentence 2This summer, the heat was intensified by a lac...wasO
11Sentence 2This summer, the heat was intensified by a lac...intensifiedO
12Sentence 2This summer, the heat was intensified by a lac...byO
13Sentence 2This summer, the heat was intensified by a lac...lack ofO
14Sentence 2This summer, the heat was intensified by a lac...cloudsbase
15Sentence 2This summer, the heat was intensified by a lac...rainbase
16Sentence 2This summer, the heat was intensified by a lac...stormbase
17Sentence 2This summer, the heat was intensified by a lac...activityO
18Sentence 2This summer, the heat was intensified by a lac...because ofO
19Sentence 2This summer, the heat was intensified by a lac...weaker-than-usualO
20Sentence 2This summer, the heat was intensified by a lac...summerO
21Sentence 2This summer, the heat was intensified by a lac...monsoonbase
22Sentence 2This summer, the heat was intensified by a lac...from July into early fallO
23Sentence 3Using these rates, the study's authors project...projectO
24Sentence 3Using these rates, the study's authors project...climate changebase
25Sentence 3Using these rates, the study's authors project...current courseO
26Sentence 3Using these rates, the study's authors project...couldO
27Sentence 3Using these rates, the study's authors project...lead toO
28Sentence 3Using these rates, the study's authors project...between 9,000 and 40,000O
29Sentence 3Using these rates, the study's authors project...additionalO
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Sentence #textwordtag
00Heat stress and persistent dehydration can cau...HeatO
10Heat stress and persistent dehydration can cau...stressbase
20Heat stress and persistent dehydration can cau...persistentO
30Heat stress and persistent dehydration can cau...dehydrationbase
40Heat stress and persistent dehydration can cau...canO
50Heat stress and persistent dehydration can cau...causeO
60Heat stress and persistent dehydration can cau...kidneybase
70Heat stress and persistent dehydration can cau...damageO
811This summer, the heat was intensified by a lac...This summerO
911This summer, the heat was intensified by a lac...heatbase
1011This summer, the heat was intensified by a lac...wasO
1111This summer, the heat was intensified by a lac...intensifiedO
1211This summer, the heat was intensified by a lac...byO
1311This summer, the heat was intensified by a lac...lack ofO
1411This summer, the heat was intensified by a lac...cloudsbase
1511This summer, the heat was intensified by a lac...rainbase
1611This summer, the heat was intensified by a lac...stormbase
1711This summer, the heat was intensified by a lac...activityO
1811This summer, the heat was intensified by a lac...because ofO
1911This summer, the heat was intensified by a lac...weaker-than-usualO
2011This summer, the heat was intensified by a lac...summerO
2111This summer, the heat was intensified by a lac...monsoonbase
2211This summer, the heat was intensified by a lac...from July into early fallO
2322Using these rates, the study's authors project...projectO
2422Using these rates, the study's authors project...climate changebase
2522Using these rates, the study's authors project...current courseO
2622Using these rates, the study's authors project...couldO
2722Using these rates, the study's authors project...lead toO
2822Using these rates, the study's authors project...between 9,000 and 40,000O
2922Using these rates, the study's authors project...additionalO
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sentence_idtextwordslabels
00Heat stress and persistent dehydration can cau...HeatO
10Heat stress and persistent dehydration can cau...stressbase
20Heat stress and persistent dehydration can cau...persistentO
30Heat stress and persistent dehydration can cau...dehydrationbase
40Heat stress and persistent dehydration can cau...canO
...............
69056As heatwaves have increased, kelp forests, sea...meadowsbase
69156As heatwaves have increased, kelp forests, sea...coralO
69256As heatwaves have increased, kelp forests, sea...reefsbase
69356As heatwaves have increased, kelp forests, sea...haveO
69456As heatwaves have increased, kelp forests, sea...lostO
\n","

695 rows × 4 columns

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\n","
\n"," "],"text/plain":[" sentence_id ... labels\n","0 0 ... O\n","1 0 ... base\n","2 0 ... O\n","3 0 ... base\n","4 0 ... O\n",".. ... ... ...\n","690 56 ... base\n","691 56 ... O\n","692 56 ... base\n","693 56 ... O\n","694 56 ... O\n","\n","[695 rows x 4 columns]"]},"metadata":{},"execution_count":10}]},{"cell_type":"code","metadata":{"id":"poOfND4wR-CH","executionInfo":{"status":"ok","timestamp":1644787980644,"user_tz":300,"elapsed":55,"user":{"displayName":"Himesh Buch","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiV8OCq9wFOhYmGL1qBY-DHltq27lamDhcBltE_HpE=s64","userId":"10202248199877876561"}}},"source":["data[\"labels\"] = data[\"labels\"].str.upper()"],"execution_count":11,"outputs":[]},{"cell_type":"code","metadata":{"id":"9_cPSerc3Dlq","executionInfo":{"status":"ok","timestamp":1644787980647,"user_tz":300,"elapsed":59,"user":{"displayName":"Himesh Buch","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiV8OCq9wFOhYmGL1qBY-DHltq27lamDhcBltE_HpE=s64","userId":"10202248199877876561"}}},"source":["X = data[[\"sentence_id\", \"words\"]]\n","Y = data[\"labels\"]"],"execution_count":12,"outputs":[]},{"cell_type":"code","metadata":{"id":"9F2-B0Fc3Dos","executionInfo":{"status":"ok","timestamp":1644787980648,"user_tz":300,"elapsed":58,"user":{"displayName":"Himesh Buch","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiV8OCq9wFOhYmGL1qBY-DHltq27lamDhcBltE_HpE=s64","userId":"10202248199877876561"}}},"source":["x_train, x_test, y_train, y_test = train_test_split(X,Y, test_size =0.2)"],"execution_count":13,"outputs":[]},{"cell_type":"code","metadata":{"id":"7uNU471x3bn1","executionInfo":{"status":"ok","timestamp":1644787980650,"user_tz":300,"elapsed":59,"user":{"displayName":"Himesh Buch","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiV8OCq9wFOhYmGL1qBY-DHltq27lamDhcBltE_HpE=s64","userId":"10202248199877876561"}}},"source":["#building up train data and test data\n","train_data = pd.DataFrame({\"sentence_id\":x_train[\"sentence_id\"],\"words\":x_train[\"words\"],\"labels\":y_train})\n","test_data = pd.DataFrame({\"sentence_id\":x_test[\"sentence_id\"],\"words\":x_test[\"words\"],\"labels\":y_test})"],"execution_count":14,"outputs":[]},{"cell_type":"code","metadata":{"id":"SM7czCDG3oPz","colab":{"base_uri":"https://localhost:8080/","height":424},"executionInfo":{"status":"ok","timestamp":1644787980998,"user_tz":300,"elapsed":406,"user":{"displayName":"Himesh Buch","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiV8OCq9wFOhYmGL1qBY-DHltq27lamDhcBltE_HpE=s64","userId":"10202248199877876561"}},"outputId":"5814f0a3-8dfe-4a1d-9917-8fb5bfb6a448"},"source":["train_data"],"execution_count":15,"outputs":[{"output_type":"execute_result","data":{"text/html":["\n","
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sentence_idwordslabels
43035neurologicalO
31123hotterO
60448levelO
38029strokesBASE
10059$8 trillionO
............
68556haveO
34826increasingO
3944pollutionBASE
48739groundwaterBASE
61548tradeBASE
\n","

556 rows × 3 columns

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\n"," "],"text/plain":[" sentence_id words labels\n","430 35 neurological O\n","311 23 hotter O\n","604 48 level O\n","380 29 strokes BASE\n","100 59 $8 trillion O\n",".. ... ... ...\n","685 56 have O\n","348 26 increasing O\n","39 44 pollution BASE\n","487 39 groundwater BASE\n","615 48 trade BASE\n","\n","[556 rows x 3 columns]"]},"metadata":{},"execution_count":15}]},{"cell_type":"markdown","metadata":{"id":"zPP_tp8-SFe3"},"source":["# Model Training\n"]},{"cell_type":"code","metadata":{"id":"VGgAcra70Imh","executionInfo":{"status":"ok","timestamp":1644787993439,"user_tz":300,"elapsed":12448,"user":{"displayName":"Himesh Buch","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiV8OCq9wFOhYmGL1qBY-DHltq27lamDhcBltE_HpE=s64","userId":"10202248199877876561"}}},"source":["from simpletransformers.ner import NERModel,NERArgs"],"execution_count":16,"outputs":[]},{"cell_type":"code","metadata":{"id":"88QG-bE3XNRd","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1644787993440,"user_tz":300,"elapsed":20,"user":{"displayName":"Himesh Buch","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiV8OCq9wFOhYmGL1qBY-DHltq27lamDhcBltE_HpE=s64","userId":"10202248199877876561"}},"outputId":"6a6422b7-d13e-4d7d-fcd5-e26f12f3281c"},"source":["label = data[\"labels\"].unique().tolist()\n","label"],"execution_count":17,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['O', 'BASE']"]},"metadata":{},"execution_count":17}]},{"cell_type":"code","metadata":{"id":"MNIc73Z5Wa8W","executionInfo":{"status":"ok","timestamp":1644788112377,"user_tz":300,"elapsed":114,"user":{"displayName":"Himesh Buch","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiV8OCq9wFOhYmGL1qBY-DHltq27lamDhcBltE_HpE=s64","userId":"10202248199877876561"}}},"source":["args = NERArgs()\n","args.num_train_epochs = 1000\n","args.learning_rate = 1e-4\n","args.overwrite_output_dir =True\n","args.train_batch_size = 32\n","args.eval_batch_size = 32"],"execution_count":27,"outputs":[]},{"cell_type":"code","metadata":{"id":"k8HD5x8T0cyk","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1644788117438,"user_tz":300,"elapsed":4290,"user":{"displayName":"Himesh Buch","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiV8OCq9wFOhYmGL1qBY-DHltq27lamDhcBltE_HpE=s64","userId":"10202248199877876561"}},"outputId":"9ed8ecce-1410-4750-ef1c-974deaf5f0b2"},"source":["model = NERModel('bert', 'bert-base-cased',labels=label,args =args)"],"execution_count":28,"outputs":[{"output_type":"stream","name":"stderr","text":["Some weights of the model checkpoint at bert-base-cased were not used when initializing BertForTokenClassification: ['cls.predictions.decoder.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.bias']\n","- This IS expected if you are initializing BertForTokenClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n","- This IS NOT expected if you are initializing BertForTokenClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n","Some weights of BertForTokenClassification were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['classifier.weight', 'classifier.bias']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"]}]},{"cell_type":"code","metadata":{"id":"rB2rmemV1PVM","colab":{"base_uri":"https://localhost:8080/","height":0,"referenced_widgets":["b902e162488e46acb176f7068bf5f1b7","b43f65d6d1ac4e619b77e14a87ff7c23","ffc24d18dd394b91a82a2b8c45c37278","52b79dbe82eb4da68976d48a6e360f9f","333a74d7c53d49e5b4277e7bcbaa0937","0759fa826dd443e2b218117601c940d7","e1d06f041eed4c6588faaa62d3b6938d","0088db6bae4d4097b697380dc3765ef6","41440e8b8946463ca67fab2468fa705e","1f0fd5c972344f65b335bc188b07f5b1","390b4ab10ca44cf08edad0dc0775e397","a6fd6e1bff1d4551a7022c954d5133c1","2fa134c51b7f43e397c7f4e87ef31728","dcd06cc297c54e4e81b7c757c8f1fb63","1613d7fef1d6487fb9868a19f9e917af","9dbec98fe35849ac8894f888b1771580","606ba737ad9d43b7ac5c16784ddd4d57","8be4b69078d84f188bc6ccf951a8716a","2a561d918c7847e29c413e83e96750ce","7e42c501b92542868743bc50f31cecaa","8eca8af04e54452f9cdfbbed801ccd7c","b153669921f34b65977c02e65126c4f3","e62c9ff121834c91900b94a11f6134e3","0fca2a1a9f1c4ca9a3849e5e2460de7f","86306403a5c3488292f334262a30a19d","646d66568eb94f43a5d9a759be81f668","1399e86be8664326a79c2321e2363e45","0b93d336951943c7a095a0eb76976df0","1c66d924993e4a82ad0c555ffba9549c","45b041c8fe4c42e780848b34d881e2e4","26b287cc246f4a658e7e08ceabbc57b4","d1d01653a51d4db3b0bcac8cc9600f4a","f73348f859724e28a6e644d6e99d2973","d2ed354ea802484faa11c9eeabcc5ca8","9da98d63e03146b0a1d44be7f0d1f137","44e721ad26c04111ba2a182c2cf4d655","dbf47511b0574e7db5f55bd67806db3b","c1dddb3d3e6b4be885c6ab43eb33896f","8c3de1a2a8514aa4ad68bc40d461ce96","30ed9d15e00e4ca3bf8b04b0ecbbd137","46ec05d26d964cad9913a567cb716f91","7cccc58683154ac5bbffd2ae5b7bbf9c","0cf448371055463380fa65173e07310a","fc36df2dc8f74c9b9595a622976e3616","16279e8916f14e1996b4575bbe330dc7","a3107027f6354a6993fe09d7aeea150c","ac25d6b001a34779b6f61f8d88ea0cc2","fa552168f18d464a98dbd956aadd903f","69bcf738e47e4b0b9f429b14e1e458d1","3c768b30e5ca418cab76a05f5c92cd4f","e87a0e11ff7248f687a526fea64497a6","30004fb3d5774950a6b9b38725944b47","1b06d6e6c5834b36ac0fbd1b10555dda","0c591dc1e0db4a2286b502b0d598be6b","59709277fc924cd2a8e293d7159d6735","3b15dfbd909541379db4d199f05e9e50","e9d32b1fe9b64a5385ff8385be28225d","7881b0356c5b416086f2da43fab329df","3748910234f34920b2f600e8f7805d65","65793c0f465b4ec8989ebd5833b45341","aab5c27b72f54e2cbc949f109c088a7d","8544fc603d2f41e8bfe403da5d9338e9","cef01c6f2e3d4223a3d8326eac313e90","7771c19f911b486393cfbfd40fee706e","66bc7e1e6f4c44a2bb154fb2bc48dd43","d1842bd5d4d14a668a600752af56b701","adce621fcee84c15927f0de3846b8567","cee6a71ec0964f52beb3a0a49d9492ed","8cda8584be0f4b52ad94d2aec84b9725","0c5545750db6418196bdad054b1a04a2","8fdf92de3f684b2aa559908052737c60","c55082fe0aed42a29ded43a00a76803b","b4266c8e3b804a039d559b87d456900a","e92bcf29a7b140458f397881f929167a","343273b9f81f46ba947267f156554e11","f91b42bff5c043f0a03756d3e443fc23","f2b14b2445cd42458325997e0d7eec0e","b53855b0066940ed902f6098782825e0","231ae2ab99c94510a1beb21e1591274d","338e6ad0d2fb4da4b1b8e43845086424","5ea8ba279644433b9250b615d6e63c82","1deadf6fcc6041528da6b7b8d893d6db","a90bf27611da40729f06eef42cfa42d9","3f103457342342d0843f45d391fb1ae3","4246679eb5544f34aef8632d8bcf70c6","7ac332cf31524299875c52f0b072ceca","a70cf6671a8d4a0b8380c946d2a6e5ce","346b01754eff4310b9ae29f5d98bad00","7e13e2507f0840ecb18f2102685ba1fa","4deadb35652f49e5b82ab01dc782bc8c","101d4fd3af3a4130ba042252a6246e1b","e9f4db7b9de24709bf8e1d47a5b49477","b2da0615063c479284b9139108e37c75","898e0aaca8a949ff908d945d7ed723be","283177bf9fde4df1abde27190aa2d0fb","1717447dc6644342a7b29a1c093e0514","2c7d2a7e99974acca7cdba9b287bc3ae","6c34ed705a8346af813e77e32e854bbd","ce13287fe2c14318bca05320b0ba48bb","610addf048254469b01387831de16c5a","248d739ec34d4394a45b8d51118b6674","6cad071e001f4d1e91e5c2fdf694cea6","86cbae4cdf7643cca613e4ba8269444f","edb3c84c0cdb48d5b27b270d2ebd3531","26decb51073142beb10af01ee78aedea","67de48aaba7e4d2bbeb19446613b2f2f","a5ddcee2e5bd4dad975d214a7391475f","6bd0b465b0c44a47ae98299e250734fc","cc419ba227824391b06e98fa14ef564a","a1fa3ab09b0c42538ddd7cbbc7abeaf3","8a709c31605e4f2195132b86425d2f24","b612bbe44a2e43cb8a65391070bc505d","30ef0fdeb7ae4c0ba48e56b00eeb9b16","7a9b5038fc2f498f9a2f81b35ef6f65c","fd6ae046a57c47c19ecf3fb47a157d75","2032cb08ad6f49a596ac6dde37f854bf","d6a4402bd6e244ccae6b4a91561aa81b","8cca2a55b25f4ad798ac377fbf371dc4","7269f346b7734c459c758230190c4dcd","5197f774835e4763bf242567c3d36f0a","a39ab6944870427bb816492b23ef4604","c55149941d2840a79793c20b19a605e3","9b75328e5ea0423989d9e2c7b01c0843","ea721401a9474970a3a8f09d232ebe4c","95c7f0b7d8f54f57adedebcac36e45b7","6090b42ff599437da0dd420de25a650f","def2c9def1704968bd169d2d301cab92","e2d07ef1d80048ad8d51d74da435b58b","0f849063e7aa4eaa92b54c2b0fefa298","c90827893f884b17b3b735a148d5bc3d","d9f146902ebc4074a39da9b0f8deea68","597ccf830cda460f9c6b78bd479ddd52","b0751298449c4725816ec2d7c3331d9c","e4d63d1a9d35449eb88c57ff6781d695","b7a7e912697a4909b8ce6539537ce473","64355dfa3f6c4e669e03c17301a2c168","658345a258e2492791ecfbd66191f753","25394093660a4253a39f3b59dc84fb28","2961977048044b889f5a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= test_data,acc=accuracy_score)"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"b902e162488e46acb176f7068bf5f1b7","version_minor":0,"version_major":2},"text/plain":[" 0%| | 0/1 [00:00\n","
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sentence_idwordslabels
36028killO
37029SmokeBASE
26418IMPLIED_BASEBASE
50041climate changeBASE
59348concentrationsO
............
63350haveO
68356AsO
24617causedO
52342violenceBASE
21613lessO
\n","

139 rows × 3 columns

\n","
\n"," \n"," \n"," \n","\n"," \n","
\n"," \n"," "],"text/plain":[" sentence_id words labels\n","360 28 kill O\n","370 29 Smoke BASE\n","264 18 IMPLIED_BASE BASE\n","500 41 climate change BASE\n","593 48 concentrations O\n",".. ... ... ...\n","633 50 have O\n","683 56 As O\n","246 17 caused O\n","523 42 violence BASE\n","216 13 less O\n","\n","[139 rows x 3 columns]"]},"metadata":{},"execution_count":25}]},{"cell_type":"code","metadata":{"id":"L_1yDDYMLgpo","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1644788019493,"user_tz":300,"elapsed":9,"user":{"displayName":"Himesh Buch","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiV8OCq9wFOhYmGL1qBY-DHltq27lamDhcBltE_HpE=s64","userId":"10202248199877876561"}},"outputId":"392417b0-b436-4cd3-c2ec-349ab9feec34"},"source":["prediction[0]\n","#for x in prediction[0]:\n","# for key, val in x.items():\n","# if val == \"BASE\":\n","# print(key)"],"execution_count":26,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[{'In': 'O'},\n"," {'recent': 'O'},\n"," {'years,': 'O'},\n"," {'disease': 'O'},\n"," {'and': 'O'},\n"," {'other': 'O'},\n"," {'disturbances': 'O'},\n"," {'have': 'O'},\n"," {'caused': 'O'},\n"," {'forests': 'O'},\n"," {'to': 'O'},\n"," {'die,': 'O'},\n"," {'emitting': 'O'},\n"," {'carbon': 'O'},\n"," {'dioxide': 'O'},\n"," {'instead': 'O'},\n"," {'as': 'O'},\n"," {'they': 'O'},\n"," {'rot.': 'O'},\n"," {'IMPLIED_BASE': 'O'},\n"," {'IMPLIED_BASE': 'O'}]"]},"metadata":{},"execution_count":26}]},{"cell_type":"code","metadata":{"id":"rG_gJHXC8nU1","executionInfo":{"status":"ok","timestamp":1644788019495,"user_tz":300,"elapsed":8,"user":{"displayName":"Himesh Buch","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiV8OCq9wFOhYmGL1qBY-DHltq27lamDhcBltE_HpE=s64","userId":"10202248199877876561"}}},"source":[""],"execution_count":26,"outputs":[]}]} \ No newline at end of file diff --git a/src/utils/model/custom_entity_extractor_spacy.ipynb b/src/utils/model/custom_entity_extractor_spacy.ipynb deleted file mode 100644 index 96474d6..0000000 --- a/src/utils/model/custom_entity_extractor_spacy.ipynb +++ /dev/null @@ -1,271 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "56eb7e8d", - "metadata": {}, - "outputs": [], - "source": [ - "from __future__ import unicode_literals, print_function\n", - "import pickle\n", - "import plac\n", - "import random\n", - "from pathlib import Path\n", - "import spacy\n", - "from spacy.util import minibatch, compounding\n", - "import srsly\n", - "import pickle\n", - "import os\n", - "import csv\n", - "import numpy as np\n", - "#!python -m spacy download en_core_web_md " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ad0baade", - "metadata": {}, - "outputs": [], - "source": [ - "# creating a folder to hold the data files, model results, output etc.\n", - "model_dir_name = \"custom_entity_extractor\"\n", - "model_dir_parent = \"\"\n", - "output_dir = os.path.join(model_dir_parent, model_dir_name)\n", - "os.makedirs(output_dir, exist_ok=True)\n", - "print(\"created\", output_dir)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e162d778", - "metadata": {}, - "outputs": [], - "source": [ - "# below code is to convert a jsonl file in this format to spacy format:\n", - "# {\"order\":10,\"Used?\":\"staged\",\"Dataset name\":\"checkin-three\",\"ignore?\":\"\",\"reason\":\"\",\"relation count\":\"many\",\"complexity\":\"complex\",\"original_md_text\":\"We also know that emotional distress during pregnancy and mood disorders, which can be triggered as women face the uncertainty that climate change brings to their lives and the lives of their families, can affect not only the physical but also the neurological and cognitive development of the unborn child.\\n\",\"text\":\"We also know that emotional distress during pregnancy and mood disorders, which can be triggered as women face the uncertainty that climate change brings to their lives and the lives of their families, can affect not only the physical but also the neurological and cognitive development of the unborn child. IMPLIED_BASE IMPLIED_BASE\",\"characters\":308,\"document_index\":14,\"md_sentence_index\":35,\"classification_origin\":\"cm-label-eval\",\"url\":\"https://www.climaterealityproject.org/blog/dose-reality-how-climate-change-affects-our-kids-straight-pediatrician\",\"_input_hash\":690545709,\"_task_hash\":1928498526,\"spans\":[{\"start\":8,\"end\":12,\"token_start\":2,\"token_end\":2,\"label\":\"confidence\"},{\"start\":18,\"end\":27,\"token_start\":4,\"token_end\":4,\"label\":\"type_of\"},{\"start\":28,\"end\":36,\"token_start\":5,\"token_end\":5,\"label\":\"base\"},{\"start\":37,\"end\":53,\"token_start\":6,\"token_end\":7,\"label\":\"when\"},{\"start\":58,\"end\":62,\"token_start\":9,\"token_end\":9,\"label\":\"type_of\"},{\"start\":63,\"end\":72,\"token_start\":10,\"token_end\":10,\"label\":\"base\"},{\"start\":80,\"end\":83,\"token_start\":13,\"token_end\":13,\"label\":\"confidence\"},{\"start\":87,\"end\":96,\"token_start\":15,\"token_end\":15,\"label\":\"change_direction\"},{\"start\":97,\"end\":99,\"token_start\":16,\"token_end\":16,\"label\":\"predicate\"},{\"start\":100,\"end\":105,\"token_start\":17,\"token_end\":17,\"label\":\"to_whom\"},{\"start\":115,\"end\":126,\"token_start\":20,\"token_end\":20,\"label\":\"base\"},{\"start\":132,\"end\":146,\"token_start\":22,\"token_end\":23,\"label\":\"base\"},{\"start\":147,\"end\":153,\"token_start\":24,\"token_end\":24,\"label\":\"predicate\"},{\"start\":192,\"end\":200,\"token_start\":33,\"token_end\":33,\"label\":\"to_whom\"},{\"start\":202,\"end\":205,\"token_start\":35,\"token_end\":35,\"label\":\"confidence\"},{\"start\":206,\"end\":212,\"token_start\":36,\"token_end\":36,\"label\":\"predicate\"},{\"start\":226,\"end\":234,\"token_start\":40,\"token_end\":40,\"label\":\"type_of\"},{\"start\":248,\"end\":260,\"token_start\":44,\"token_end\":44,\"label\":\"type_of\"},{\"start\":265,\"end\":274,\"token_start\":46,\"token_end\":46,\"label\":\"type_of\"},{\"start\":275,\"end\":306,\"token_start\":47,\"token_end\":51,\"label\":\"base\"}],\"tokens\":[{\"text\":\"We\",\"start\":0,\"end\":2,\"id\":0,\"ws\":true,\"disabled\":false},{\"text\":\"also\",\"start\":3,\"end\":7,\"id\":1,\"ws\":true,\"disabled\":false},{\"text\":\"know\",\"start\":8,\"end\":12,\"id\":2,\"ws\":true,\"disabled\":false},{\"text\":\"that\",\"start\":13,\"end\":17,\"id\":3,\"ws\":true,\"disabled\":false},{\"text\":\"emotional\",\"start\":18,\"end\":27,\"id\":4,\"ws\":true,\"disabled\":false},{\"text\":\"distress\",\"start\":28,\"end\":36,\"id\":5,\"ws\":true,\"disabled\":false},{\"text\":\"during\",\"start\":37,\"end\":43,\"id\":6,\"ws\":true,\"disabled\":false},{\"text\":\"pregnancy\",\"start\":44,\"end\":53,\"id\":7,\"ws\":true,\"disabled\":false},{\"text\":\"and\",\"start\":54,\"end\":57,\"id\":8,\"ws\":true,\"disabled\":false},{\"text\":\"mood\",\"start\":58,\"end\":62,\"id\":9,\"ws\":true,\"disabled\":false},{\"text\":\"disorders\",\"start\":63,\"end\":72,\"id\":10,\"ws\":false,\"disabled\":false},{\"text\":\",\",\"start\":72,\"end\":73,\"id\":11,\"ws\":true,\"disabled\":false},{\"text\":\"which\",\"start\":74,\"end\":79,\"id\":12,\"ws\":true,\"disabled\":false},{\"text\":\"can\",\"start\":80,\"end\":83,\"id\":13,\"ws\":true,\"disabled\":false},{\"text\":\"be\",\"start\":84,\"end\":86,\"id\":14,\"ws\":true,\"disabled\":false},{\"text\":\"triggered\",\"start\":87,\"end\":96,\"id\":15,\"ws\":true,\"disabled\":false},{\"text\":\"as\",\"start\":97,\"end\":99,\"id\":16,\"ws\":true,\"disabled\":false},{\"text\":\"women\",\"start\":100,\"end\":105,\"id\":17,\"ws\":true,\"disabled\":false},{\"text\":\"face\",\"start\":106,\"end\":110,\"id\":18,\"ws\":true,\"disabled\":false},{\"text\":\"the\",\"start\":111,\"end\":114,\"id\":19,\"ws\":true,\"disabled\":false},{\"text\":\"uncertainty\",\"start\":115,\"end\":126,\"id\":20,\"ws\":true,\"disabled\":false},{\"text\":\"that\",\"start\":127,\"end\":131,\"id\":21,\"ws\":true,\"disabled\":false},{\"text\":\"climate\",\"start\":132,\"end\":139,\"id\":22,\"ws\":true,\"disabled\":false},{\"text\":\"change\",\"start\":140,\"end\":146,\"id\":23,\"ws\":true,\"disabled\":false},{\"text\":\"brings\",\"start\":147,\"end\":153,\"id\":24,\"ws\":true,\"disabled\":false},{\"text\":\"to\",\"start\":154,\"end\":156,\"id\":25,\"ws\":true,\"disabled\":false},{\"text\":\"their\",\"start\":157,\"end\":162,\"id\":26,\"ws\":true,\"disabled\":false},{\"text\":\"lives\",\"start\":163,\"end\":168,\"id\":27,\"ws\":true,\"disabled\":false},{\"text\":\"and\",\"start\":169,\"end\":172,\"id\":28,\"ws\":true,\"disabled\":false},{\"text\":\"the\",\"start\":173,\"end\":176,\"id\":29,\"ws\":true,\"disabled\":false},{\"text\":\"lives\",\"start\":177,\"end\":182,\"id\":30,\"ws\":true,\"disabled\":false},{\"text\":\"of\",\"start\":183,\"end\":185,\"id\":31,\"ws\":true,\"disabled\":false},{\"text\":\"their\",\"start\":186,\"end\":191,\"id\":32,\"ws\":true,\"disabled\":false},{\"text\":\"families\",\"start\":192,\"end\":200,\"id\":33,\"ws\":false,\"disabled\":false},{\"text\":\",\",\"start\":200,\"end\":201,\"id\":34,\"ws\":true,\"disabled\":false},{\"text\":\"can\",\"start\":202,\"end\":205,\"id\":35,\"ws\":true,\"disabled\":false},{\"text\":\"affect\",\"start\":206,\"end\":212,\"id\":36,\"ws\":true,\"disabled\":false},{\"text\":\"not\",\"start\":213,\"end\":216,\"id\":37,\"ws\":true,\"disabled\":false},{\"text\":\"only\",\"start\":217,\"end\":221,\"id\":38,\"ws\":true,\"disabled\":false},{\"text\":\"the\",\"start\":222,\"end\":225,\"id\":39,\"ws\":true,\"disabled\":false},{\"text\":\"physical\",\"start\":226,\"end\":234,\"id\":40,\"ws\":true,\"disabled\":false},{\"text\":\"but\",\"start\":235,\"end\":238,\"id\":41,\"ws\":true,\"disabled\":false},{\"text\":\"also\",\"start\":239,\"end\":243,\"id\":42,\"ws\":true,\"disabled\":false},{\"text\":\"the\",\"start\":244,\"end\":247,\"id\":43,\"ws\":true,\"disabled\":false},{\"text\":\"neurological\",\"start\":248,\"end\":260,\"id\":44,\"ws\":true,\"disabled\":false},{\"text\":\"and\",\"start\":261,\"end\":264,\"id\":45,\"ws\":true,\"disabled\":false},{\"text\":\"cognitive\",\"start\":265,\"end\":274,\"id\":46,\"ws\":true,\"disabled\":false},{\"text\":\"development\",\"start\":275,\"end\":286,\"id\":47,\"ws\":true,\"disabled\":false},{\"text\":\"of\",\"start\":287,\"end\":289,\"id\":48,\"ws\":true,\"disabled\":false},{\"text\":\"the\",\"start\":290,\"end\":293,\"id\":49,\"ws\":true,\"disabled\":false},{\"text\":\"unborn\",\"start\":294,\"end\":300,\"id\":50,\"ws\":true,\"disabled\":false},{\"text\":\"child\",\"start\":301,\"end\":306,\"id\":51,\"ws\":false,\"disabled\":false},{\"text\":\".\",\"start\":306,\"end\":307,\"id\":52,\"ws\":true,\"disabled\":false},{\"text\":\"IMPLIED_BASE\",\"start\":308,\"end\":320,\"id\":53,\"ws\":true,\"disabled\":false},{\"text\":\"IMPLIED_BASE\",\"start\":321,\"end\":333,\"id\":54,\"ws\":false,\"disabled\":false}],\"_session_id\":\"checkin_three_all_labels-answers\",\"_view_id\":\"relations\",\"relations\":[{\"head\":4,\"child\":5,\"head_span\":{\"start\":18,\"end\":27,\"token_start\":4,\"token_end\":4,\"label\":\"type_of\"},\"child_span\":{\"start\":28,\"end\":36,\"token_start\":5,\"token_end\":5,\"label\":\"base\"},\"color\":\"#c5bdf4\",\"label\":\"Concept_Member\"},{\"head\":9,\"child\":10,\"head_span\":{\"start\":58,\"end\":62,\"token_start\":9,\"token_end\":9,\"label\":\"type_of\"},\"child_span\":{\"start\":63,\"end\":72,\"token_start\":10,\"token_end\":10,\"label\":\"base\"},\"color\":\"#c5bdf4\",\"label\":\"Concept_Member\"},{\"head\":46,\"child\":51,\"head_span\":{\"start\":265,\"end\":274,\"token_start\":46,\"token_end\":46,\"label\":\"type_of\"},\"child_span\":{\"start\":275,\"end\":306,\"token_start\":47,\"token_end\":51,\"label\":\"base\"},\"color\":\"#c5bdf4\",\"label\":\"Concept_Member\"},{\"head\":44,\"child\":51,\"head_span\":{\"start\":248,\"end\":260,\"token_start\":44,\"token_end\":44,\"label\":\"type_of\"},\"child_span\":{\"start\":275,\"end\":306,\"token_start\":47,\"token_end\":51,\"label\":\"base\"},\"color\":\"#c5bdf4\",\"label\":\"Concept_Member\"},{\"head\":40,\"child\":51,\"head_span\":{\"start\":226,\"end\":234,\"token_start\":40,\"token_end\":40,\"label\":\"type_of\"},\"child_span\":{\"start\":275,\"end\":306,\"token_start\":47,\"token_end\":51,\"label\":\"base\"},\"color\":\"#c5bdf4\",\"label\":\"Concept_Member\"},{\"head\":7,\"child\":5,\"head_span\":{\"start\":37,\"end\":53,\"token_start\":6,\"token_end\":7,\"label\":\"when\"},\"child_span\":{\"start\":28,\"end\":36,\"token_start\":5,\"token_end\":5,\"label\":\"base\"},\"color\":\"#c5bdf4\",\"label\":\"Concept_Member\"},{\"head\":7,\"child\":10,\"head_span\":{\"start\":37,\"end\":53,\"token_start\":6,\"token_end\":7,\"label\":\"when\"},\"child_span\":{\"start\":63,\"end\":72,\"token_start\":10,\"token_end\":10,\"label\":\"base\"},\"color\":\"#c5bdf4\",\"label\":\"Concept_Member\"},{\"head\":15,\"child\":10,\"head_span\":{\"start\":87,\"end\":96,\"token_start\":15,\"token_end\":15,\"label\":\"change_direction\"},\"child_span\":{\"start\":63,\"end\":72,\"token_start\":10,\"token_end\":10,\"label\":\"base\"},\"color\":\"#c5bdf4\",\"label\":\"Concept_Member\"},{\"head\":15,\"child\":5,\"head_span\":{\"start\":87,\"end\":96,\"token_start\":15,\"token_end\":15,\"label\":\"change_direction\"},\"child_span\":{\"start\":28,\"end\":36,\"token_start\":5,\"token_end\":5,\"label\":\"base\"},\"color\":\"#c5bdf4\",\"label\":\"Concept_Member\"},{\"head\":17,\"child\":5,\"head_span\":{\"start\":100,\"end\":105,\"token_start\":17,\"token_end\":17,\"label\":\"to_whom\"},\"child_span\":{\"start\":28,\"end\":36,\"token_start\":5,\"token_end\":5,\"label\":\"base\"},\"color\":\"#c5bdf4\",\"label\":\"Concept_Member\"},{\"head\":17,\"child\":10,\"head_span\":{\"start\":100,\"end\":105,\"token_start\":17,\"token_end\":17,\"label\":\"to_whom\"},\"child_span\":{\"start\":63,\"end\":72,\"token_start\":10,\"token_end\":10,\"label\":\"base\"},\"color\":\"#c5bdf4\",\"label\":\"Concept_Member\"},{\"head\":17,\"child\":20,\"head_span\":{\"start\":100,\"end\":105,\"token_start\":17,\"token_end\":17,\"label\":\"to_whom\"},\"child_span\":{\"start\":115,\"end\":126,\"token_start\":20,\"token_end\":20,\"label\":\"base\"},\"color\":\"#c5bdf4\",\"label\":\"Concept_Member\"},{\"head\":33,\"child\":20,\"head_span\":{\"start\":192,\"end\":200,\"token_start\":33,\"token_end\":33,\"label\":\"to_whom\"},\"child_span\":{\"start\":115,\"end\":126,\"token_start\":20,\"token_end\":20,\"label\":\"base\"},\"color\":\"#c5bdf4\",\"label\":\"Concept_Member\"},{\"head\":23,\"child\":20,\"head_span\":{\"start\":132,\"end\":146,\"token_start\":22,\"token_end\":23,\"label\":\"base\"},\"child_span\":{\"start\":115,\"end\":126,\"token_start\":20,\"token_end\":20,\"label\":\"base\"},\"color\":\"#ffd882\",\"label\":\"Contributes_To\"},{\"head\":20,\"child\":5,\"head_span\":{\"start\":115,\"end\":126,\"token_start\":20,\"token_end\":20,\"label\":\"base\"},\"child_span\":{\"start\":28,\"end\":36,\"token_start\":5,\"token_end\":5,\"label\":\"base\"},\"color\":\"#ffd882\",\"label\":\"Contributes_To\"},{\"head\":20,\"child\":10,\"head_span\":{\"start\":115,\"end\":126,\"token_start\":20,\"token_end\":20,\"label\":\"base\"},\"child_span\":{\"start\":63,\"end\":72,\"token_start\":10,\"token_end\":10,\"label\":\"base\"},\"color\":\"#ffd882\",\"label\":\"Contributes_To\"},{\"head\":5,\"child\":51,\"head_span\":{\"start\":28,\"end\":36,\"token_start\":5,\"token_end\":5,\"label\":\"base\"},\"child_span\":{\"start\":275,\"end\":306,\"token_start\":47,\"token_end\":51,\"label\":\"base\"},\"color\":\"#ffd882\",\"label\":\"Contributes_To\"},{\"head\":10,\"child\":51,\"head_span\":{\"start\":63,\"end\":72,\"token_start\":10,\"token_end\":10,\"label\":\"base\"},\"child_span\":{\"start\":275,\"end\":306,\"token_start\":47,\"token_end\":51,\"label\":\"base\"},\"color\":\"#ffd882\",\"label\":\"Contributes_To\"}],\"answer\":\"accept\"}\n", - "# above is only one entry from the jsonl file" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b2382b8e", - "metadata": {}, - "outputs": [], - "source": [ - "# Create Training data\n", - "# 70% training data and 30% validation/testing data\n", - "\n", - "# spacy requires data to be in a specific format which is what the code below does. Here is the acceptable formart:\n", - "# [('Heat stress and persistent dehydration can cause kidney damage. IMPLIED_BASE IMPLIED_BASE', {'entities': [(5, 11, 'base'), (27, 38, 'base'), (49, 55, 'base')]})]\n", - "\n", - "file_name_answers = \"checkin_answers\"\n", - "file_path_answers = \"\"+file_name_answers+\".jsonl\"\n", - "data = srsly.read_jsonl(file_path_answers)\n", - "final_sent = []\n", - "\n", - "for entry in data:\n", - " if \"text\" in entry:\n", - " text = entry[\"text\"]\n", - " else:\n", - " text = \"\"\n", - " throw(\"NO 'text' field encountered!\")\n", - "\n", - " label_arr = []\n", - " label_tup = ()\n", - " if entry['answer'] == \"accept\":\n", - " for relation in entry['spans']:\n", - " if (\"label\" in relation) and (\"start\" in relation) and (\"end\" in relation):\n", - " child_span_start = relation[\"start\"]\n", - " child_span_end = relation[\"end\"]\n", - " word = text[child_span_start:child_span_end]\n", - " if relation[\"label\"] == \"base\":\n", - " tmp_tuple = (child_span_start, child_span_end, relation[\"label\"])\n", - " label_arr.append(tmp_tuple)\n", - "\n", - " label_tup = (text, {\"entities\": label_arr})\n", - " final_sent.append(label_tup)\n", - "\n", - "training_data_index = int(len(final_sent)*0.3)\n", - "\n", - "with open(os.path.join(output_dir, 'training_data.txt'), 'wb') as file:\n", - " pickle.dump(final_sent[:training_data_index], file)\n", - " \n", - "with open(os.path.join(output_dir, 'testing_data.txt'), 'wb') as file:\n", - " pickle.dump(final_sent[training_data_index:], file)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "191260bc", - "metadata": {}, - "outputs": [], - "source": [ - "LABEL = ['base']\n", - "\n", - "with open (os.path.join(output_dir, 'training_data.txt'), 'rb') as file:\n", - " TRAIN_DATA = pickle.load(file)\n", - "\n", - "with open (os.path.join(output_dir, 'testing_data.txt'), 'rb') as file:\n", - " TEST_DATA = pickle.load(file)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2de44e61", - "metadata": {}, - "outputs": [], - "source": [ - "model = \"en_core_web_md\"\n", - "new_model_name = \"custom_entity_extraction\"\n", - "#n_iter = 10000\n", - "n_iter = 1000" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fdb0e821", - "metadata": {}, - "outputs": [], - "source": [ - "if model is not None:\n", - " nlp = spacy.load(model)\n", - " print(\"Loaded model '%s'\" % model)\n", - "else:\n", - " nlp = spacy.blank('en')\n", - " print(\"Created blank 'en' model\")\n", - " \n", - "if 'ner' not in nlp.pipe_names:\n", - " ner = nlp.create_pipe('ner')\n", - " nlp.add_pipe(ner)\n", - "else:\n", - " ner = nlp.get_pipe('ner')\n", - "\n", - "for i in LABEL:\n", - " # Adding new entity\n", - " ner.add_label(i)\n", - "\n", - "if model is None:\n", - " optimizer = nlp.begin_training()\n", - "else:\n", - " optimizer = nlp.entity.create_optimizer()\n", - "\n", - "# Get names of other pipes to disable them during training to train only NER\n", - "other_pipes = [pipe for pipe in nlp.pipe_names if pipe != 'ner']\n", - "with nlp.disable_pipes(*other_pipes): # only train NER\n", - " for itn in range(n_iter):\n", - " random.shuffle(TRAIN_DATA)\n", - " losses = {}\n", - " batches = minibatch(TRAIN_DATA, size=compounding(4., 32., 1.001))\n", - " for batch in batches:\n", - " texts, annotations = zip(*batch)\n", - " nlp.update(texts, annotations, sgd=optimizer, drop=0.2, losses=losses)\n", - " print('Losses', losses)\n", - "\n", - "# Save model \n", - "if output_dir is not None:\n", - " output_dir = Path(output_dir)\n", - " if not output_dir.exists():\n", - " output_dir.mkdir()\n", - " nlp.meta['name'] = new_model_name # rename model\n", - " nlp.to_disk(output_dir)\n", - " print(\"Saved model to\", output_dir)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "48674ab2", - "metadata": {}, - "outputs": [], - "source": [ - "# Test the saved model\n", - "print(\"Loading from\", output_dir)\n", - "nlp_test = spacy.load(output_dir)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0157898a", - "metadata": {}, - "outputs": [], - "source": [ - "result = os.path.join(output_dir, \"result.csv\")\n", - "all_bases = 0\n", - "correct_bases = 0\n", - "false_pos = 0\n", - "false_neg = 0\n", - "\n", - "headers = [\"text\", \"predicted_base\", \"actual_base\"]\n", - "final_res = []\n", - "final_res.append(headers)\n", - "for sent in TEST_DATA:\n", - " doc2 = nlp_test(sent[0])\n", - " res = [i.text for i in list(doc2.ents)]\n", - " actual_base = []\n", - " for tokens in sent[1]['entities']:\n", - " actual_base.append(sent[0][tokens[0]:tokens[1]])\n", - " all_bases += len(actual_base)\n", - " correct_bases += len(np.intersect1d(actual_base, res))\n", - " false_pos += len(list(sorted(set(actual_base) - set(res))))\n", - " false_neg += len(list(sorted(set(res) - set(actual_base))))\n", - " final_res.append([sent, res, actual_base])\n", - "\n", - "acc = ((correct_bases*100)/all_bases)\n", - "print(\"False pos: \", false_pos)\n", - "print(\"False neg: \", false_neg)\n", - "print(\"Marked {} out of {} bases correctly. Accuracy: {}\".format(correct_bases, all_bases, acc))\n", - " \n", - "with open(result, 'w', newline='') as csvfile:\n", - " writer = csv.writer(csvfile)\n", - " writer.writerows(final_res)\n", - "\n", - "print(\"created result file\", result)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3560bcec", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "876a8538", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/src/utils/model/data_setup/PURE_data_setup.py b/src/utils/model/data_setup/PURE_data_setup.py deleted file mode 100644 index 8a9df15..0000000 --- a/src/utils/model/data_setup/PURE_data_setup.py +++ /dev/null @@ -1,87 +0,0 @@ -""" -repo to install and setup PURE: https://github.com/princeton-nlp/PURE - -command used to train: -python run_entity.py \ - --do_train --do_eval --eval_test \ - --learning_rate=1e-5 \ - --task_learning_rate=5e-4 \ - --train_batch_size=16 \ - --context_window 100 \ - --task ace05 \ - --data_dir /content/ \ - --model bert-base-uncased \ - --output_dir /content/ -""" - -from nltk.util import pr -import srsly -import json -#from nltk.tokenize import word_tokenize -from sklearn.model_selection import train_test_split -from nltk.tokenize import RegexpTokenizer -tokenizer = RegexpTokenizer(r"\w+") - -file_name_answers = "checkin_answers" -file_path_answers = "C://Users//buchh//OneDrive/Desktop//"+file_name_answers+".jsonl" -data = srsly.read_jsonl(file_path_answers) -final_sent = {} -final_sent_arr = [] - -for entry in data: - if "text" in entry: - text = entry["text"] - else: - text = "" - throw("NO 'text' field encountered! This field is necessary for the rest of the script to work! Please fix this and then run this script.") - - if "document_index" in entry: - doc_index = entry['document_index'] - else: - doc_index = -1 - sentences = [tokenizer.tokenize(text)] - - if entry['answer'] == "accept": - for relation in entry['spans']: - ner = [] - if ("label" in relation) and ("start" in relation) and ("end" in relation): - child_span_start = relation["start"] - child_span_end = relation["end"] - word = text[child_span_start:child_span_end] - if relation["label"] == "base": - tokenized_word = tokenizer.tokenize(word) - #ner.append([child_span_start, child_span_end, str(relation["label"])]) - if len(tokenized_word) == 1: - ner.append([sentences[0].index(tokenized_word[0]), sentences[0].index(tokenized_word[0]), "PER"]) - else: - ner.append([sentences[0].index(tokenized_word[0]), sentences[0].index(tokenized_word[-1]), "PER"]) - - # this will remove all the sentences with no labels (base) - if ner: - final_sent['doc_key'] = str(doc_index) - final_sent['sentences'] = sentences - final_sent['ner'] = [ner] - final_sent['relations'] = [[]] - - if final_sent: - final_sent_arr.append(final_sent) - final_sent = {} - -train, test = train_test_split(final_sent_arr, test_size=0.3) -val, test = train_test_split(test, test_size=0.5) - -with open('dev.json', 'w') as file: - for item in val: - file.write("%s\n" % json.dumps(item)) - -with open('train.json', 'w') as file: - for item in train: - file.write("%s\n" % json.dumps(item)) - -with open('test.json', 'w') as file: - for item in test: - file.write("%s\n" % json.dumps(item)) - - -print("Done!") - diff --git a/src/tools/README.md b/tools/README.md similarity index 100% rename from src/tools/README.md rename to tools/README.md diff --git a/src/tools/ontology_graph/climate_mind_OWL_to_networkx.py b/tools/ontology_graph/climate_mind_OWL_to_networkx.py similarity index 100% rename from src/tools/ontology_graph/climate_mind_OWL_to_networkx.py rename to tools/ontology_graph/climate_mind_OWL_to_networkx.py diff --git a/src/tools/ontology_graph/install_graphviz.py b/tools/ontology_graph/install_graphviz.py similarity index 100% rename from src/tools/ontology_graph/install_graphviz.py rename to tools/ontology_graph/install_graphviz.py diff --git a/src/tools/ontology_graph/tests_output_folder/Climate_Mind_DiGraph.gpickle b/tools/ontology_graph/tests_output_folder/Climate_Mind_DiGraph.gpickle similarity index 100% rename from src/tools/ontology_graph/tests_output_folder/Climate_Mind_DiGraph.gpickle rename to tools/ontology_graph/tests_output_folder/Climate_Mind_DiGraph.gpickle diff --git a/src/tools/ontology_graph/tests_output_folder/Climate_Mind_Digraph_Test_Ont.json b/tools/ontology_graph/tests_output_folder/Climate_Mind_Digraph_Test_Ont.json similarity index 100% rename from src/tools/ontology_graph/tests_output_folder/Climate_Mind_Digraph_Test_Ont.json rename to tools/ontology_graph/tests_output_folder/Climate_Mind_Digraph_Test_Ont.json diff --git 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a/src/tools/preprocess_data/example_data.jsonl b/tools/preprocess_data/example_data.jsonl similarity index 100% rename from src/tools/preprocess_data/example_data.jsonl rename to tools/preprocess_data/example_data.jsonl diff --git a/src/tools/preprocess_data/helper_functions.py b/tools/preprocess_data/helper_functions.py similarity index 100% rename from src/tools/preprocess_data/helper_functions.py rename to tools/preprocess_data/helper_functions.py diff --git a/src/tools/prodigy_deployments/entity_checkin_one/Dockerfile b/tools/prodigy_deployments/entity_checkin_one/Dockerfile similarity index 100% rename from src/tools/prodigy_deployments/entity_checkin_one/Dockerfile rename to tools/prodigy_deployments/entity_checkin_one/Dockerfile diff --git a/src/tools/prodigy_deployments/entity_checkin_one/checkin_one.jsonl b/tools/prodigy_deployments/entity_checkin_one/checkin_one.jsonl similarity index 100% rename from src/tools/prodigy_deployments/entity_checkin_one/checkin_one.jsonl rename 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