deBERTa_1e_6_8.ipynb

{
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    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "Kxvk4OamxxVz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1a38bf1e-ecf1-4235-d1f6-e022d759c8f7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Installing collected packages: nlpaug\n",
            "Successfully installed nlpaug-1.1.11\n"
          ]
        }
      ],
      "source": [
        "!pip install transformers[torch]\n",
        "!pip install accelerate -U\n",
        "!pip install nlpaug"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 528
        },
        "id": "nbkEulidY9Sx",
        "outputId": "ecbde114-74b1-4fc4-f7b6-61b68b37e123"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data] Downloading package omw-1.4 to /root/nltk_data...\n"
          ]
        },
        {
          "output_type": "execute_result",
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              "       par_id      art_id     keyword country  \\\n",
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              "10467  \" Anja Ringgren Loven I ca n't find a word to ...      1  \n",
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
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              "  }\n",
              "\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "        document.querySelector('#' + key + ' button');\n",
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              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 10469,\n  \"fields\": [\n    {\n      \"column\": \"par_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3022,\n        \"min\": 1,\n        \"max\": 10469,\n        \"num_unique_values\": 10469,\n        \"samples\": [\n          7567,\n          7992,\n          2115\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"art_id\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10093,\n        \"samples\": [\n          \"@@15858151\",\n          \"@@25187013\",\n          \"@@896186\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"keyword\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"women\",\n          \"migrant\",\n          \"in-need\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"country\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \"ph\",\n          \"lk\",\n          \"bd\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10468,\n        \"samples\": [\n          \"Ekso -- formerly known as Berkeley Bionics -- has been developing the Ekso exoskeleton for about seven years , beginning in 2005 with the Exohiker , a suit designed to assist load bearing . After discovering that energy efficiency meant a restriction of what they could do with their power , the company began looking at the disabled market to try and create a product that would help paraplegics walk again .\",\n          \"Thirty seven percent of the offences registered on Central Railway from January to May this year pertain to illegal travel in coaches reserved for the disabled .\",\n          \"Old buildings , coastal areas most vulnerable to major earthquake in B.C.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import torch\n",
        "import os\n",
        "import random\n",
        "\n",
        "from transformers import DebertaForSequenceClassification, DebertaTokenizer, Trainer, TrainingArguments\n",
        "import nltk\n",
        "from nltk.tokenize import word_tokenize\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.corpus import wordnet\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import f1_score, accuracy_score\n",
        "nltk.download('punkt')\n",
        "nltk.download('stopwords')\n",
        "nltk.download('wordnet')\n",
        "nltk.download('omw-1.4')\n",
        "\n",
        "cuda_available = torch.cuda.is_available()\n",
        "device = torch.device('cuda') if cuda_available else torch.device('cpu')\n",
        "\n",
        "torch.manual_seed(0)\n",
        "\n",
        "data = pd.read_csv(\"dontpatronizeme_pcl.tsv\",\n",
        "                       sep=\"\\t\",\n",
        "                       names=['par_id', 'art_id', 'keyword', 'country', 'text', 'label'],\n",
        "                       skiprows=4)\n",
        "\n",
        "data['label'] = data['label'].apply(lambda x: 0 if x in [0, 1] else 1)\n",
        "\n",
        "trids = pd.read_csv('train_semeval_parids-labels.csv')\n",
        "teids = pd.read_csv('dev_semeval_parids-labels.csv')\n",
        "\n",
        "trids.par_id = trids.par_id\n",
        "teids.par_id = teids.par_id\n",
        "\n",
        "data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "MzzbZTz-xxV4"
      },
      "outputs": [],
      "source": [
        "# Rebuild training set\n",
        "\n",
        "rows = [] # will contain par_id, label and text\n",
        "for idx in range(len(trids)):\n",
        "  parid = trids.par_id[idx]\n",
        "  #print(parid)\n",
        "  # select row from original dataset to retrieve `text` and binary label\n",
        "  keyword = data.loc[data.par_id == parid].keyword.values[0]\n",
        "  text = data.loc[data.par_id == parid].text.values[0]\n",
        "  label = data.loc[data.par_id == parid].label.values[0]\n",
        "  rows.append({\n",
        "      'par_id':parid,\n",
        "      'community':keyword,\n",
        "      'text':text,\n",
        "      'label':label\n",
        "  })\n",
        "\n",
        "train_set = pd.DataFrame(rows)\n",
        "# Split train into train and internal validation set 80:20\n",
        "\n",
        "val_size = int(len(train_set) * 0.2)\n",
        "\n",
        "train_set = train_set.sample(frac=1)\n",
        "val_set = train_set.iloc[0:val_size].reset_index(drop=True).copy()\n",
        "train_set = train_set.iloc[val_size:].reset_index(drop=True).copy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "0Dz6kGVSaHws"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Assuming 'train_set' is your DataFrame and 'label' is the class column\n",
        "# First, separate the majority and minority classes\n",
        "non_pcl = train_set[train_set.label == 0]\n",
        "pcl = train_set[train_set.label == 1]\n",
        "\n",
        "# Duplicate minority class instances\n",
        "pcl_upsampled = pcl.sample(n=len(non_pcl), replace=True)\n",
        "\n",
        "# Concatenate the majority class with the upsampled minority class\n",
        "train_set_upsampled = pd.concat([non_pcl, pcl_upsampled])\n",
        "\n",
        "# Shuffle the dataset\n",
        "train_set_upsampled_shuffled = train_set_upsampled.sample(frac=1).reset_index(drop=True)"
      ]
    },
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          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
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              "tokenizer_config.json:   0%|          | 0.00/52.0 [00:00<?, ?B/s]"
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      "source": [
        "tokenizer = DebertaTokenizer.from_pretrained(\"microsoft/deberta-base\")\n",
        "train_encodings = tokenizer(list(train_set_upsampled_shuffled[\"text\"]), return_tensors=\"pt\", truncation=True, padding=True).to(device)\n",
        "val_encodings = tokenizer(list(val_set[\"text\"]), return_tensors=\"pt\", truncation=True, padding=True).to(device)\n",
        "train_labels = list(train_set_upsampled_shuffled[\"label\"])\n",
        "val_labels = list(val_set[\"label\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "pytorch_model.bin:   0%|          | 0.00/559M [00:00<?, ?B/s]"
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          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
            "  return self.fget.__get__(instance, owner)()\n",
            "Some weights of DebertaForSequenceClassification were not initialized from the model checkpoint at microsoft/deberta-base and are newly initialized: ['classifier.bias', 'classifier.weight', 'pooler.dense.bias', 'pooler.dense.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ],
      "source": [
        "class Dataset(torch.utils.data.Dataset):\n",
        "    def __init__(self, encodings, labels):\n",
        "        self.encodings = encodings\n",
        "        self.labels = labels\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}\n",
        "        item['labels'] = torch.tensor(self.labels[idx])\n",
        "        return item\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.labels)\n",
        "\n",
        "train_dataset = Dataset(train_encodings, train_labels)\n",
        "val_dataset = Dataset(val_encodings, val_labels)\n",
        "\n",
        "# define model\n",
        "\n",
        "def compute_metrics(input):\n",
        "    y_pred = np.argmax(input.predictions, axis=1)\n",
        "    y_true = input.label_ids\n",
        "    accuracy = accuracy_score(y_true, y_pred)\n",
        "    f1score = f1_score(y_true, y_pred)\n",
        "    return {'accuracy': accuracy, 'f1 score': f1score}\n",
        "\n",
        "model = DebertaForSequenceClassification.from_pretrained(\"microsoft/deberta-base\", num_labels=2).to(device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
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        "id": "_eTHAvZSxxV7",
        "outputId": "44303a6e-27c1-4c3e-d491-2b6e202713d0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-6-a8ef8bcd7fd3>:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='7585' max='7585' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [7585/7585 49:51, Epoch 5/5]\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>Accuracy</th>\n",
              "      <th>F1 score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.541900</td>\n",
              "      <td>0.785786</td>\n",
              "      <td>0.770149</td>\n",
              "      <td>0.378029</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.385800</td>\n",
              "      <td>0.330590</td>\n",
              "      <td>0.864478</td>\n",
              "      <td>0.475751</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.325600</td>\n",
              "      <td>0.344339</td>\n",
              "      <td>0.872239</td>\n",
              "      <td>0.483092</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.275900</td>\n",
              "      <td>0.422019</td>\n",
              "      <td>0.857910</td>\n",
              "      <td>0.475771</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.265900</td>\n",
              "      <td>0.407902</td>\n",
              "      <td>0.868060</td>\n",
              "      <td>0.480000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
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        },
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          "text": [
            "<ipython-input-6-a8ef8bcd7fd3>:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}\n",
            "<ipython-input-6-a8ef8bcd7fd3>:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}\n",
            "<ipython-input-6-a8ef8bcd7fd3>:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}\n",
            "<ipython-input-6-a8ef8bcd7fd3>:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=7585, training_loss=0.39185171108459593, metrics={'train_runtime': 2993.3391, 'train_samples_per_second': 20.265, 'train_steps_per_second': 2.534, 'total_flos': 1.859821036941312e+16, 'train_loss': 0.39185171108459593, 'epoch': 5.0})"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "training_args = TrainingArguments(\n",
        "    output_dir=\"Deberta/\",\n",
        "    learning_rate=1e-6,\n",
        "    weight_decay=0.05,\n",
        "    per_device_train_batch_size=8,\n",
        "    per_device_eval_batch_size=8,\n",
        "    num_train_epochs=5,\n",
        "    do_eval=True,\n",
        "    evaluation_strategy=\"epoch\",\n",
        "    save_strategy=\"epoch\",\n",
        "    load_best_model_at_end=True,\n",
        "    metric_for_best_model=\"f1 score\",\n",
        "    greater_is_better=True,\n",
        "    report_to=\"none\"\n",
        ")\n",
        "\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    tokenizer=tokenizer,\n",
        "    train_dataset=train_dataset,\n",
        "    eval_dataset=val_dataset,\n",
        "    compute_metrics=compute_metrics\n",
        ")\n",
        "\n",
        "trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "uXI-XOfbboRB"
      },
      "outputs": [],
      "source": [
        "trainer.save_model(f\"deberta_upsample\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "id": "pobvE6zRxxV8",
        "outputId": "4e7b500f-53fd-45b4-b95f-3512477ff1ae"
      },
      "outputs": [
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          "text": [
            "<ipython-input-6-a8ef8bcd7fd3>:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}\n"
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          "text": [
            "F1 Score: 0.48309178743961356\n"
          ]
        }
      ],
      "source": [
        "predictions = trainer.predict(val_dataset)\n",
        "\n",
        "# Step 1: Extract predictions and true labels\n",
        "raw_predictions = predictions.predictions\n",
        "true_labels = predictions.label_ids\n",
        "\n",
        "# Step 2: Convert logits to predicted labels\n",
        "predicted_labels = np.argmax(raw_predictions, axis=1)\n",
        "\n",
        "# Step 3: Compute the F1 score\n",
        "f1 = f1_score(true_labels, predicted_labels, average='binary')\n",
        "\n",
        "print(f\"F1 Score: {f1}\")\n",
        "# run predictions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
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        "id": "_YIJ5zuesGgt",
        "outputId": "1259ac72-2685-4115-910e-7aaab40f5f86"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    }
  ],
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    "accelerator": "GPU",
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      "gpuType": "V100",
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