ner_finetuning.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/loganathanspr/nlp_course/blob/main/ner_finetuning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pzUvMvpMPVH8"
      },
      "source": [
        "# Token classification (PyTorch)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LW6MoHLAPVH-"
      },
      "source": [
        "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "DZQ_fFfHPVH_",
        "outputId": "0535fe0c-2a84-4d35-c2a8-5bacbd7aaa7f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting datasets\n",
            "  Downloading datasets-2.14.5-py3-none-any.whl (519 kB)\n",
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            "\u001b[?25hCollecting evaluate\n",
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            "\u001b[?25hCollecting sentencepiece!=0.1.92,>=0.1.91 (from transformers[sentencepiece])\n",
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            "Installing collected packages: sentencepiece, xxhash, safetensors, dill, responses, multiprocess, huggingface-hub, tokenizers, transformers, datasets, evaluate\n",
            "Successfully installed datasets-2.14.5 dill-0.3.7 evaluate-0.4.1 huggingface-hub-0.17.3 multiprocess-0.70.15 responses-0.18.0 safetensors-0.4.0 sentencepiece-0.1.99 tokenizers-0.14.1 transformers-4.34.0 xxhash-3.4.1\n",
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            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.10.0->accelerate) (1.3.0)\n",
            "Installing collected packages: accelerate\n",
            "Successfully installed accelerate-0.23.0\n",
            "Reading package lists... Done\n",
            "Building dependency tree... Done\n",
            "Reading state information... Done\n",
            "git-lfs is already the newest version (3.0.2-1ubuntu0.2).\n",
            "0 upgraded, 0 newly installed, 0 to remove and 18 not upgraded.\n"
          ]
        }
      ],
      "source": [
        "!pip install datasets evaluate transformers[sentencepiece]\n",
        "!pip install accelerate\n",
        "# To run the training on TPU, you will need to uncomment the following line:\n",
        "# !pip install cloud-tpu-client==0.10 torch==1.9.0 https://storage.googleapis.com/tpu-pytorch/wheels/torch_xla-1.9-cp37-cp37m-linux_x86_64.whl\n",
        "!apt install git-lfs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BU4YqX97PVH_"
      },
      "source": [
        "You will need to setup git, adapt your email and name in the following cell."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "cK6Et4JcPVIA"
      },
      "outputs": [],
      "source": [
        "!git config --global user.email \"loganathanspr@gmail.com\"\n",
        "!git config --global user.name \"Loganathan Ramasamy\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ufOeaDNTPVIA"
      },
      "source": [
        "You will also need to be logged in to the Hugging Face Hub. Execute the following and enter your credentials."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "TbF6SJ6XPVIA",
        "outputId": "f38306f0-6175-423a-8676-02f8154cc755",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "397b0d7742354190889345b1643413f1",
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            "a6f8fc77c9144db0b3b6bd4e5d3481ee",
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            "3e76107d7baa486fbee3c53f36c8cf4e",
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            "1b9790afd229442b87f952f1f432cad2",
            "130a20530784453385f64a84a86a79f7",
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            "d3b0ec918ca0410e8596ae0106f5a41c",
            "5197e4e83f2f4180b02ddc1e30d8695b",
            "e38c2e8cd76544edb93a6e326ba03632",
            "f2d775df9cfc42019323e4086a4cd802",
            "63706497e9f34a808bc47490f426165f",
            "4018eaf28ccb4df3bd818586fc2028f0",
            "6fa6fe1c8e60425db7197a80844c2383",
            "dc1a4cfc07684e34812ef5d1b90fbf4e",
            "937d4b5ced0b486ea7170fe9f6c3dd26",
            "08da630618f24af8aa614e434528b2a1",
            "5f308ff35a9a4183afbeef40c69927cc"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "397b0d7742354190889345b1643413f1"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "U8ZlbMagPVIA",
        "outputId": "698b2ccf-5f18-46e4-e1b0-1f081ae505b5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241,
          "referenced_widgets": [
            "9d629eef9e384f5b8852e89cf44ad16c",
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            "1a066d0cb61a48b6b57400ca2afd9578",
            "ddb48fba40ff4237ab29803c524096fa",
            "1461ac1c097a4b33b9bbb432ed5f5d0f",
            "4920421028cb4e4a8aef8de93c665202",
            "e03e53a0047043ef91d57ddea13ae915",
            "16376571be7d4be3a4fc99bcab39c553",
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            "54d78fad037d4a9992efe84878b3862e",
            "b4aa5ba103d8430fa241358a22974ea7"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/9.57k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9d629eef9e384f5b8852e89cf44ad16c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading metadata:   0%|          | 0.00/3.73k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e03e53a0047043ef91d57ddea13ae915"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/12.3k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fc215d08559147ea9ecfb6ffcb97955b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/983k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "32072dc9b2dc45819eed7d32174d33b6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/14041 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "402a557fcbe745eb9d127df7f8331061"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation split:   0%|          | 0/3250 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e4cc5bba518e4533a35c3d8d509570eb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split:   0%|          | 0/3453 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b793f3957e3d42918f3297d02bc6caf7"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "raw_datasets = load_dataset(\"conll2003\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "XAvct2iJPVIB",
        "outputId": "91da1ee0-0f15-4cba-ece8-2ca635a47ae9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags'],\n",
              "        num_rows: 14041\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags'],\n",
              "        num_rows: 3250\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags'],\n",
              "        num_rows: 3453\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "raw_datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "oq9_cu64PVIC",
        "outputId": "766716d4-be81-43bf-d083-23b7230ea980",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['EU', 'rejects', 'German', 'call', 'to', 'boycott', 'British', 'lamb', '.']"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "raw_datasets[\"train\"][0][\"tokens\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "IKyXOBT-PVIC",
        "outputId": "52d7d4b2-3e4f-4f2c-e4c5-e09fb0f8e1f5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[3, 0, 7, 0, 0, 0, 7, 0, 0]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "raw_datasets[\"train\"][0][\"ner_tags\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "7PbFbhklPVIC",
        "outputId": "7ee66746-0ab3-4d94-a9d0-477fc54c92b4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sequence(feature=ClassLabel(names=['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC', 'B-MISC', 'I-MISC'], id=None), length=-1, id=None)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "ner_feature = raw_datasets[\"train\"].features[\"ner_tags\"]\n",
        "ner_feature"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "Ls2LD0NJPVIC",
        "outputId": "e6ec9871-7f64-4377-9224-a8f8e6636f50",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC', 'B-MISC', 'I-MISC']"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "label_names = ner_feature.feature.names\n",
        "label_names"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "6Q2ISK9yPVIC",
        "outputId": "9adaf030-f7b2-4365-9efa-1ebc740747d3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "EU    rejects German call to boycott British lamb . \n",
            "B-ORG O       B-MISC O    O  O       B-MISC  O    O \n"
          ]
        }
      ],
      "source": [
        "words = raw_datasets[\"train\"][0][\"tokens\"]\n",
        "labels = raw_datasets[\"train\"][0][\"ner_tags\"]\n",
        "line1 = \"\"\n",
        "line2 = \"\"\n",
        "for word, label in zip(words, labels):\n",
        "    full_label = label_names[label]\n",
        "    max_length = max(len(word), len(full_label))\n",
        "    line1 += word + \" \" * (max_length - len(word) + 1)\n",
        "    line2 += full_label + \" \" * (max_length - len(full_label) + 1)\n",
        "\n",
        "print(line1)\n",
        "print(line2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "TRnc30p2PVID",
        "outputId": "df7800af-65cc-40de-ce78-cb2ded6b960d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "41d552bacb474e4a9b01eaf26cf6770b",
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            "a0a8197bd18447438133a55e883e8e15",
            "b135cd04459b4caa83ad3c3614bef558",
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            "17bf0d9ba7584febb1656c1015c05166",
            "1545aeefc3e04967a22ab05e8ad04970",
            "ff96cf73b07c4c29bef57896cc4a06e0",
            "ba177fd42423414faeca8e21b4cdfc08",
            "48c3afc8994646c6af893db4f170ae11"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/29.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "41d552bacb474e4a9b01eaf26cf6770b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bb88ca19ca49414e9cc83dbd226585fd"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/213k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "513f0c8ec88f47fd867310a8fea15097"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/436k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bc04a649e6be471a960b329b934c66c6"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from transformers import AutoTokenizer\n",
        "\n",
        "model_checkpoint = \"bert-base-cased\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "xfM0ZXRvPVID",
        "outputId": "cc40e55a-3717-4ced-8f4d-9090f7169566",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "tokenizer.is_fast"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "2nmBgaAfPVID",
        "outputId": "e215f38f-a072-49e9-cbb3-5b2308488ccf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['[CLS]',\n",
              " 'EU',\n",
              " 'rejects',\n",
              " 'German',\n",
              " 'call',\n",
              " 'to',\n",
              " 'boycott',\n",
              " 'British',\n",
              " 'la',\n",
              " '##mb',\n",
              " '.',\n",
              " '[SEP]']"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "inputs = tokenizer(raw_datasets[\"train\"][0][\"tokens\"], is_split_into_words=True)\n",
        "inputs.tokens()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "oID2jiwfPVID",
        "outputId": "74534ecf-2586-47e8-b8be-9055cae960e2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[None, 0, 1, 2, 3, 4, 5, 6, 7, 7, 8, None]"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "inputs.word_ids()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "pBJvGdbGPVID"
      },
      "outputs": [],
      "source": [
        "def align_labels_with_tokens(labels, word_ids):\n",
        "    new_labels = []\n",
        "    current_word = None\n",
        "    for word_id in word_ids:\n",
        "        if word_id != current_word:\n",
        "            # Start of a new word!\n",
        "            current_word = word_id\n",
        "            label = -100 if word_id is None else labels[word_id]\n",
        "            new_labels.append(label)\n",
        "        elif word_id is None:\n",
        "            # Special token\n",
        "            new_labels.append(-100)\n",
        "        else:\n",
        "            # Same word as previous token\n",
        "            label = labels[word_id]\n",
        "            # If the label is B-XXX we change it to I-XXX\n",
        "            if label % 2 == 1:\n",
        "                label += 1\n",
        "            new_labels.append(label)\n",
        "\n",
        "    return new_labels"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "HvNokb95PVID",
        "outputId": "aff26f40-346a-42ff-d3da-486fd7a35a2d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[3, 0, 7, 0, 0, 0, 7, 0, 0]\n",
            "[-100, 3, 0, 7, 0, 0, 0, 7, 0, 0, 0, -100]\n"
          ]
        }
      ],
      "source": [
        "labels = raw_datasets[\"train\"][0][\"ner_tags\"]\n",
        "word_ids = inputs.word_ids()\n",
        "print(labels)\n",
        "print(align_labels_with_tokens(labels, word_ids))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "3kG2X0LRPVID"
      },
      "outputs": [],
      "source": [
        "def tokenize_and_align_labels(examples):\n",
        "    tokenized_inputs = tokenizer(\n",
        "        examples[\"tokens\"], truncation=True, is_split_into_words=True\n",
        "    )\n",
        "    all_labels = examples[\"ner_tags\"]\n",
        "    new_labels = []\n",
        "    for i, labels in enumerate(all_labels):\n",
        "        word_ids = tokenized_inputs.word_ids(i)\n",
        "        new_labels.append(align_labels_with_tokens(labels, word_ids))\n",
        "\n",
        "    tokenized_inputs[\"labels\"] = new_labels\n",
        "    return tokenized_inputs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "Pc0ToicwPVID",
        "outputId": "7e79e1e1-888c-4a34-be44-258d95249866",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "fb645c028d244613b7d55e575bd00116",
            "c83fcf055da548729b0e1ed61f617c3e",
            "a6c17a4b673e444e93a5e96cec427220",
            "63f8dd52da8f42f89b47f46185d38d63",
            "c6461a1341b0494a89023094bef4e040",
            "ac272448f3084fac84a95dcfabce04db",
            "f40a9261e5e54e2d92e65ced4fd4e4f2",
            "361449397e5145aa96a285a2a4de0372",
            "90929e34b0be4ff7b3a5c401cef4f7f1",
            "da5268a30d6c40a0af871aa387c7058b",
            "ed8e2f6c60fe4af1bc90e6c52e36e4fe",
            "a9afe54f0413434b95319fb617404dd6",
            "b42083011fa54d899df265198dcbad2b",
            "37efa3a33ed744e5a6cbb02702196d8d",
            "c6673b7131db45b3a2411904d4ece662",
            "e54037a92bf44dec9530a21d0f6100de",
            "b2e530afe66247b2ba1d46460afcda1f",
            "3fc5720c122a4ecc9dedae7029cb7980",
            "1b3fc2fe9688457187d9f223f00e0ac1",
            "7de9c439fefc47e0a551f10f43f8a028",
            "492921ace80648f4bc334254c5e08bf8",
            "2f44e9c335bd46449074b6ac83bcc973",
            "613c135ea1bf4715b9d28b511390742a",
            "080f54bb8afb4bfeaa49e48a25a4e270",
            "cc3d39d9911643c9b46e6213ddd6932c",
            "6923313f2d7b40018594eda68c4fc3be",
            "09afc81a52eb41e38a7935b9e9eada46",
            "4241c02596264e89bec65c923f33b2f6",
            "f4ee0ea83509446ba34b784fac77ec6f",
            "7e9e7597b5af4fcdb7c88b26c6384aad",
            "b9f1ab99cfcc47c282396b2b54df5765",
            "ae3ca1b532824a758766ed08f14cf23e",
            "f590e65031384a2aaeb2d4091faa8541"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/14041 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fb645c028d244613b7d55e575bd00116"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/3250 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a9afe54f0413434b95319fb617404dd6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/3453 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "613c135ea1bf4715b9d28b511390742a"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "tokenized_datasets = raw_datasets.map(\n",
        "    tokenize_and_align_labels,\n",
        "    batched=True,\n",
        "    remove_columns=raw_datasets[\"train\"].column_names,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "Y7jDlIFgPVID"
      },
      "outputs": [],
      "source": [
        "from transformers import DataCollatorForTokenClassification\n",
        "data_collator = DataCollatorForTokenClassification(tokenizer=tokenizer)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "3YYQ9qRGPVID",
        "outputId": "815dc98e-774f-4abe-e232-6376535919eb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You're using a BertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[-100,    3,    0,    7,    0,    0,    0,    7,    0,    0,    0, -100],\n",
              "        [-100,    1,    2, -100, -100, -100, -100, -100, -100, -100, -100, -100]])"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "batch = data_collator([tokenized_datasets[\"train\"][i] for i in range(2)])\n",
        "batch[\"labels\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "fGd3HcFSPVID",
        "outputId": "12b51d08-9463-4878-960a-67731c1c7764",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[-100, 3, 0, 7, 0, 0, 0, 7, 0, 0, 0, -100]\n",
            "[-100, 1, 2, -100]\n"
          ]
        }
      ],
      "source": [
        "for i in range(2):\n",
        "    print(tokenized_datasets[\"train\"][i][\"labels\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "AswxYGgVPVIE",
        "outputId": "1bb0ebd0-c765-4ea8-c2a6-46716bc7ba8f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting seqeval\n",
            "  Downloading seqeval-1.2.2.tar.gz (43 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/43.6 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m43.6/43.6 kB\u001b[0m \u001b[31m1.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: numpy>=1.14.0 in /usr/local/lib/python3.10/dist-packages (from seqeval) (1.23.5)\n",
            "Requirement already satisfied: scikit-learn>=0.21.3 in /usr/local/lib/python3.10/dist-packages (from seqeval) (1.2.2)\n",
            "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.21.3->seqeval) (1.11.3)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.21.3->seqeval) (1.3.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.21.3->seqeval) (3.2.0)\n",
            "Building wheels for collected packages: seqeval\n",
            "  Building wheel for seqeval (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for seqeval: filename=seqeval-1.2.2-py3-none-any.whl size=16162 sha256=125c5533160015705ed52ed226ae76b7c87c7959ba8fa97b2ce508a1e0501efc\n",
            "  Stored in directory: /root/.cache/pip/wheels/1a/67/4a/ad4082dd7dfc30f2abfe4d80a2ed5926a506eb8a972b4767fa\n",
            "Successfully built seqeval\n",
            "Installing collected packages: seqeval\n",
            "Successfully installed seqeval-1.2.2\n"
          ]
        }
      ],
      "source": [
        "!pip install seqeval"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "4Cx23Vi5PVIE",
        "outputId": "a475c0fc-f818-43c2-f055-8c374f2cc868",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "13b42062139743788be77feee3dd6c6f",
            "ea55227faea54400a417c37fee838956",
            "dd88e1b2efc348638fd8fa150b62d57b",
            "b40e7de09a904215be2c7e6d2038596f",
            "41389e53ba494b219b6fecdd1a126fdb",
            "f4552e6a3aa14e7cb1c4f5659b65433f",
            "583e9d3f2e50440b87d5ae69d7a82281",
            "3508100480f94a5a955040dd6f02ea54",
            "e3a74c487b6446f8ad116ba938d507b5",
            "3430842ec6cd4b18988a73df13a14ce2",
            "0fd2adf79caa4b53ad3872a576aa882e"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/6.34k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "13b42062139743788be77feee3dd6c6f"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "import evaluate\n",
        "metric = evaluate.load(\"seqeval\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "4PzXkBo7PVIE",
        "outputId": "99ca0e13-7ca1-4b3e-f925-249bccf1f3d9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['B-ORG', 'O', 'B-MISC', 'O', 'O', 'O', 'B-MISC', 'O', 'O']"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "labels = raw_datasets[\"train\"][0][\"ner_tags\"]\n",
        "labels = [label_names[i] for i in labels]\n",
        "labels"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "Uy-gmLLLPVIE",
        "outputId": "3d734449-9da5-44fc-8583-975bc6270d0d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'MISC': {'precision': 1.0,\n",
              "  'recall': 0.5,\n",
              "  'f1': 0.6666666666666666,\n",
              "  'number': 2},\n",
              " 'ORG': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1},\n",
              " 'overall_precision': 1.0,\n",
              " 'overall_recall': 0.6666666666666666,\n",
              " 'overall_f1': 0.8,\n",
              " 'overall_accuracy': 0.8888888888888888}"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "predictions = labels.copy()\n",
        "predictions[2] = \"O\"\n",
        "metric.compute(predictions=[predictions], references=[labels])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "iSFb9M-oPVIE"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "\n",
        "def compute_metrics(eval_preds):\n",
        "    logits, labels = eval_preds\n",
        "    predictions = np.argmax(logits, axis=-1)\n",
        "\n",
        "    # Remove ignore index (special tokens) and convert to labels\n",
        "    true_labels = [[label_names[l] for l in label if l != -100] for label in labels]\n",
        "    true_predictions = [\n",
        "        [label_names[p] for (p, l) in zip(prediction, label) if l != -100] for prediction, label in zip(predictions, labels)\n",
        "    ]\n",
        "    all_metrics = metric.compute(predictions=true_predictions, references=true_labels)\n",
        "    return {\n",
        "        \"precision\": all_metrics[\"overall_precision\"],\n",
        "        \"recall\": all_metrics[\"overall_recall\"],\n",
        "        \"f1\": all_metrics[\"overall_f1\"],\n",
        "        \"accuracy\": all_metrics[\"overall_accuracy\"],\n",
        "    }"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "GtEvzZ24PVIE"
      },
      "outputs": [],
      "source": [
        "id2label = {i: label for i, label in enumerate(label_names)}\n",
        "label2id = {v: k for k, v in id2label.items()}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "88idOzMtPVIE",
        "outputId": "507f940a-7ab4-4a89-bdfc-7929f1ea4fdf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84,
          "referenced_widgets": [
            "96be863b635849cd992bf240d09d7c0a",
            "7a997a1a4e70469a845a84740e9e554d",
            "5aa01c0eb6a544b6a991005f3d0a9518",
            "df471900bf6d48b1bfc879f9aea01afa",
            "05c2d58c10ae41a49750a37fbd7d2338",
            "b0c88da1d03f440da5657ab9a300a8b7",
            "53b06bbd00ff4f6884bfdf366bf5112e",
            "138aa61d577645aeb62f1646b03db029",
            "5f791ad4c61747d4907996ee62c4d489",
            "0bfb90feeb92466fb6a39cce24cd4940",
            "25332519fd13457f829420dab5aeb6ac"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading model.safetensors:   0%|          | 0.00/436M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "96be863b635849cd992bf240d09d7c0a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "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"
          ]
        }
      ],
      "source": [
        "from transformers import AutoModelForTokenClassification\n",
        "\n",
        "model = AutoModelForTokenClassification.from_pretrained(\n",
        "    model_checkpoint,\n",
        "    id2label = id2label,\n",
        "    label2id = label2id\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "VPSJFOSTPVIE",
        "outputId": "42052ae4-3ad9-4dbe-db74-6f91f6003f26",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "model.config.num_labels"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "ZpI74CX5PVIE",
        "outputId": "8dbbc06f-2bff-4b09-d4cf-0c872e6afec4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "65edd8acd7d0480ea569040206d31867",
            "f195908013e443ceac9270565996a634",
            "862cb786c1d34232a77bb0f20869d73c",
            "959ef9d4aec447489fd827bd36fe30dc",
            "205bfd8c97bd40c683f0588447e85cc5",
            "87ef17dee68f4eb2b225123417d98b63",
            "86606f975c014091bbc5e1b4173a6458",
            "cf700a485ef947f08ddcef2cd52e0ce3",
            "3597f808daf64206957e87a67d7c1659",
            "f9ec7d73dece468a810e3e3b81470a02",
            "092174c9b7e5446d926da3f0f4b931d2",
            "2a7efe95916340078ec99cd80376982d",
            "bd26a6b6c33b4660a72d17e706bc8275",
            "5036ae20a42541cba65a854a9ada925f",
            "ca6a3d18e3664940994619ea443596eb",
            "2561f843cb7d45568c6ec7655e3d0f84",
            "93ae2ee37b494c41ba53e73435d1faf3",
            "9f965acfecdb41a2a2de957562ee4009",
            "e11ad0a11b0242eaae3d56b2735ff2b9",
            "edaaedf7412143828b925d1dfdd1d00d",
            "45751569e3c8488eb255f8d3ab313d6d",
            "84c6cbc1442e4029a74ca51fb83c2b75",
            "a7ef91c3bd72475b8edd0460dfeb84aa",
            "954d37c5e5f34da397e5096d2f8c82a9",
            "66c824578e224916ae4b59a74cde5672",
            "cb4f21d2c3c040efb208cd7a505d89e6",
            "67130a035e2b43e9ac75e5e9de4e8aa3",
            "e3ad57a035cd4c89a55eb14f70cb9501",
            "4dd506b2c39b496094d2065bac006bbb"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "65edd8acd7d0480ea569040206d31867"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "cv6_xQLXPVIE"
      },
      "outputs": [],
      "source": [
        "from transformers import TrainingArguments\n",
        "\n",
        "args = TrainingArguments(\n",
        "    \"bert-finetuned-ner\",\n",
        "    evaluation_strategy=\"epoch\",\n",
        "    save_strategy=\"epoch\",\n",
        "    learning_rate=2e-5,\n",
        "    num_train_epochs=3,\n",
        "    weight_decay=0.01,\n",
        "    push_to_hub=True\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_datasets"
      ],
      "metadata": {
        "id": "8WjaoH5wEiUw",
        "outputId": "3548b851-20fa-44b9-ec38-79eeb560acba",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['input_ids', 'token_type_ids', 'attention_mask', 'labels'],\n",
              "        num_rows: 14041\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['input_ids', 'token_type_ids', 'attention_mask', 'labels'],\n",
              "        num_rows: 3250\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['input_ids', 'token_type_ids', 'attention_mask', 'labels'],\n",
              "        num_rows: 3453\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_datasets[\"train\"].shuffle().select(range(1000))"
      ],
      "metadata": {
        "id": "d4uQ9UVwEn_1",
        "outputId": "50522e21-a578-4fee-ae46-934042ee8816",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['input_ids', 'token_type_ids', 'attention_mask', 'labels'],\n",
              "    num_rows: 1000\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "OOjD7_ajPVIE"
      },
      "outputs": [],
      "source": [
        "from transformers import Trainer\n",
        "\n",
        "trainer = Trainer(\n",
        "    model = model,\n",
        "    args = args,\n",
        "    train_dataset = tokenized_datasets[\"train\"].shuffle().select(range(2000)),\n",
        "    eval_dataset = tokenized_datasets[\"validation\"].shuffle().select(range(500)),\n",
        "    data_collator = data_collator,\n",
        "    compute_metrics = compute_metrics,\n",
        "    tokenizer = tokenizer\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "id": "h5w-Pa6yGT6o",
        "outputId": "c7edd8b7-c84e-42a2-f991-f600ed1a153e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='750' max='750' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [750/750 01:44, Epoch 3/3]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Precision</th>\n",
              "      <th>Recall</th>\n",
              "      <th>F1</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.164726</td>\n",
              "      <td>0.776842</td>\n",
              "      <td>0.841505</td>\n",
              "      <td>0.807882</td>\n",
              "      <td>0.958579</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.250300</td>\n",
              "      <td>0.133968</td>\n",
              "      <td>0.814103</td>\n",
              "      <td>0.868871</td>\n",
              "      <td>0.840596</td>\n",
              "      <td>0.966464</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.250300</td>\n",
              "      <td>0.127027</td>\n",
              "      <td>0.823592</td>\n",
              "      <td>0.883694</td>\n",
              "      <td>0.852585</td>\n",
              "      <td>0.967794</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=750, training_loss=0.1861228624979655, metrics={'train_runtime': 108.628, 'train_samples_per_second': 55.234, 'train_steps_per_second': 6.904, 'total_flos': 133857734499072.0, 'train_loss': 0.1861228624979655, 'epoch': 3.0})"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "g9TBDQbhPVIE",
        "outputId": "ac012381-729c-44c2-e6ea-14dfaf9cf979",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'https://huggingface.co/loganathanspr/bert-finetuned-ner/tree/main/'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "trainer.push_to_hub(commit_message=\"Training complete\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Custom training loop"
      ],
      "metadata": {
        "id": "Z4i3lhNpG5dQ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "7-lY86PYPVIE"
      },
      "outputs": [],
      "source": [
        "from torch.utils.data import DataLoader\n",
        "\n",
        "train_dataloader = DataLoader(\n",
        "    tokenized_datasets[\"train\"].shuffle().select(range(2000)),\n",
        "    shuffle=True,\n",
        "    collate_fn=data_collator,\n",
        "    batch_size=8\n",
        ")\n",
        "\n",
        "eval_dataloader = DataLoader(\n",
        "    tokenized_datasets[\"validation\"].select(range(500)),\n",
        "    collate_fn=data_collator,\n",
        "    batch_size=8\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "BxvkUDlCPVIE",
        "outputId": "9f4d256d-6e3a-4350-9721-7f21ba68cd21",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "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"
          ]
        }
      ],
      "source": [
        "model = AutoModelForTokenClassification.from_pretrained(\n",
        "    model_checkpoint,\n",
        "    id2label=id2label,\n",
        "    label2id=label2id\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "61EN_XrKPVIJ"
      },
      "outputs": [],
      "source": [
        "from torch.optim import AdamW\n",
        "optimizer = AdamW(model.parameters(), lr=2e-5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "id": "w6p7aPtyPVIJ"
      },
      "outputs": [],
      "source": [
        "from accelerate import Accelerator\n",
        "\n",
        "accelerator = Accelerator()\n",
        "model, optimizer, train_dataloader, eval_dataloader = accelerator.prepare(model, optimizer, train_dataloader, eval_dataloader)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "id": "6ykmNtQtPVIJ"
      },
      "outputs": [],
      "source": [
        "from transformers import get_scheduler\n",
        "\n",
        "num_train_epochs = 3\n",
        "num_update_steps_per_epoch = len(train_dataloader)\n",
        "num_training_steps = num_train_epochs * num_update_steps_per_epoch\n",
        "\n",
        "lr_scheduler = get_scheduler(\n",
        "    \"linear\",\n",
        "    optimizer = optimizer,\n",
        "    num_warmup_steps = 0,\n",
        "    num_training_steps = num_training_steps\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "id": "yBPDKA2YPVIJ",
        "outputId": "5e6442af-7167-4aac-cf3c-fb832d9a3e83",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'loganathanspr/bert-finetuned-ner-accelerate'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 62
        }
      ],
      "source": [
        "from huggingface_hub import Repository, get_full_repo_name\n",
        "\n",
        "model_name = \"bert-finetuned-ner-accelerate\"\n",
        "repo_name = get_full_repo_name(model_name)\n",
        "repo_name"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!ls"
      ],
      "metadata": {
        "id": "faYiCl36X5zU",
        "outputId": "dab1f1ad-419f-4f92-f959-e0bdb2cc8647",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "bert-finetuned-ner  sample_data\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!rm -rf bert-finetuned-ner-accelerate"
      ],
      "metadata": {
        "id": "6or3-vl3X8Nu"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!ls"
      ],
      "metadata": {
        "id": "iICSJoV3X_xI",
        "outputId": "c85a4080-b09c-4263-be0d-0397b1b2dce0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "bert-finetuned-ner  sample_data\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "id": "V4L5n5CkPVIJ",
        "outputId": "d92cae60-9329-478b-a8a2-3708ccb0aad2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Cloning https://huggingface.co/loganathanspr/bert-finetuned-ner-accelerate into local empty directory.\n",
            "WARNING:huggingface_hub.repository:Cloning https://huggingface.co/loganathanspr/bert-finetuned-ner-accelerate into local empty directory.\n"
          ]
        }
      ],
      "source": [
        "output_dir = \"bert-finetuned-ner-accelerate\"\n",
        "repo = Repository(output_dir, clone_from=repo_name)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "id": "Ls_q05wHPVIJ"
      },
      "outputs": [],
      "source": [
        "def postprocess(predictions, labels):\n",
        "    predictions = predictions.detach().cpu().clone().numpy()\n",
        "    labels = labels.detach().cpu().clone().numpy()\n",
        "\n",
        "    # Remove ignored index (special tokens) convert to labels\n",
        "    true_labels = [[label_names[l] for l in label if l != -100] for label in labels]\n",
        "    true_predictions = [\n",
        "        [label_names[p] for (p,l) in zip(prediction, label) if l != -100]\n",
        "        for prediction, label in zip(predictions, labels)\n",
        "    ]\n",
        "    return true_labels, true_predictions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "id": "U8qNnEWqPVIJ",
        "outputId": "5cf58216-6ecd-4b5c-d699-fed83358cd77",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101,
          "referenced_widgets": [
            "e44064610b1d4603bf9a284dd48a7850",
            "7494615b16b64e43ac748e8acf008126",
            "c2e1bf3e5e7d41c4abce7f54ecfdf327",
            "67c5692462af4c6cbbe480d4b26b77d2",
            "c9ef9e701b1747ac86c20c90a578253a",
            "1884d8f9fd854352be8c7186a5d362d1",
            "e0bf88838a934bb58fdb3cef7e54ceda",
            "69fc3d9b3f47412da3c0646e2034d157",
            "573eb214c77642e1873126f8856feaa8",
            "e53c950b0fd041038262e4291a4aafbf",
            "ff449e402c64443da8e3b89ed7a52ce0"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/750 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e44064610b1d4603bf9a284dd48a7850"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 0:  {'precision': 0.8977272727272727, 'recall': 0.8467309753483387, 'f1': 0.8714837286265857, 'accuracy': 0.9702061732808962}\n",
            "epoch 1:  {'precision': 0.8977272727272727, 'recall': 0.8467309753483387, 'f1': 0.8714837286265857, 'accuracy': 0.9702061732808962}\n",
            "epoch 2:  {'precision': 0.8977272727272727, 'recall': 0.8467309753483387, 'f1': 0.8714837286265857, 'accuracy': 0.9702061732808962}\n"
          ]
        }
      ],
      "source": [
        "from tqdm.auto import tqdm\n",
        "import torch\n",
        "\n",
        "progress_bar = tqdm(range(num_training_steps))\n",
        "\n",
        "for epoch in range(num_train_epochs):\n",
        "    # Training\n",
        "    model.train()\n",
        "    for batch in train_dataloader:\n",
        "        outputs = model(**batch)\n",
        "        loss = outputs.loss\n",
        "        accelerator.backward(loss)\n",
        "\n",
        "        optimizer.step()\n",
        "        lr_scheduler.step()\n",
        "        optimizer.zero_grad()\n",
        "        progress_bar.update(1)\n",
        "\n",
        "    # Evaluation\n",
        "    model.eval()\n",
        "    for batch in eval_dataloader:\n",
        "        with torch.no_grad():\n",
        "            outputs = model(**batch)\n",
        "        predictions = outputs.logits.argmax(dim=-1)\n",
        "        labels = batch[\"labels\"]\n",
        "\n",
        "        # Necessary to pad predictions and labels for being gathered\n",
        "        predictions = accelerator.pad_across_processes(predictions, dim=1, pad_index=-100)\n",
        "        labels = accelerator.pad_across_processes(labels, dim=1, pad_index=-100)\n",
        "\n",
        "        predictions_gathered = accelerator.gather(predictions)\n",
        "        labels_gathered = accelerator.gather(labels)\n",
        "\n",
        "        true_predictions, true_labels = postprocess(predictions_gathered, labels_gathered)\n",
        "        metric.add_batch(predictions=true_predictions, references=true_labels)\n",
        "\n",
        "    results = metric.compute()\n",
        "    print(\n",
        "        f\"epoch {epoch}: \",\n",
        "        {\n",
        "            key: results[f\"overall_{key}\"] for key in [\"precision\", \"recall\", \"f1\", \"accuracy\"]\n",
        "        }\n",
        "    )\n",
        "\n",
        "    # Save and upload\n",
        "    accelerator.wait_for_everyone()\n",
        "    unwrapped_model = accelerator.unwrap_model(model)\n",
        "    unwrapped_model.save_pretrained(output_dir, save_function=accelerator.save)\n",
        "    if accelerator.is_main_process:\n",
        "        tokenizer.save_pretrained(output_dir)\n",
        "        repo.push_to_hub(commit_message=f\"Training in progress epoch {epoch}\", blocking=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "id": "K9I0LdD9PVIJ"
      },
      "outputs": [],
      "source": [
        "accelerator.wait_for_everyone()\n",
        "unwrapped_model = accelerator.unwrap_model(model)\n",
        "unwrapped_model.save_pretrained(output_dir, save_function=accelerator.save)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Using the fine-tuned model"
      ],
      "metadata": {
        "id": "WrzJw8ESYs1b"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "id": "OoF5I1viPVIJ",
        "outputId": "aa9cfc9a-6847-4cd8-f852-c67424d00f2c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519,
          "referenced_widgets": [
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            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
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              "[{'entity_group': 'PER',\n",
              "  'score': 0.98475045,\n",
              "  'word': 'Loganathan Ramasamy',\n",
              "  'start': 11,\n",
              "  'end': 30},\n",
              " {'entity_group': 'LOC',\n",
              "  'score': 0.98940265,\n",
              "  'word': 'Prague',\n",
              "  'start': 45,\n",
              "  'end': 51},\n",
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              "  'word': 'Czech Republic',\n",
              "  'start': 53,\n",
              "  'end': 67}]"
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        "from transformers import pipeline\n",
        "model_checkpoint = \"loganathanspr/bert-finetuned-ner-accelerate\"\n",
        "token_classifier = pipeline(\n",
        "    \"token-classification\", model=model_checkpoint, aggregation_strategy=\"simple\"\n",
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