NLP_Project_5_(Classification_with_BERT).ipynb

{
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    {
      "cell_type": "markdown",
      "id": "e37af394",
      "metadata": {
        "id": "e37af394"
      },
      "source": [
        "## Imports"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "45f5776c",
      "metadata": {
        "id": "45f5776c"
      },
      "outputs": [],
      "source": [
        "import random\n",
        "import torch\n",
        "import warnings\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "from transformers import BertTokenizerFast, BertForSequenceClassification\n",
        "from transformers import Trainer, TrainingArguments, DataCollatorWithPadding\n",
        "\n",
        "from sklearn.metrics import accuracy_score, classification_report\n",
        "\n",
        "from datasets import load_dataset\n",
        "\n",
        "from ray import tune\n",
        "\n",
        "pd.set_option(\"display.max_rows\", None, \"display.max_columns\", None)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "60d60792",
      "metadata": {
        "id": "60d60792"
      },
      "source": [
        "## Function definitions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b488a574",
      "metadata": {
        "id": "b488a574"
      },
      "outputs": [],
      "source": [
        "\"\"\"\n",
        "    Helper function for reproducible behavior to set the seed in ``random``, ``numpy``, ``torch`` and/or ``tf`` (if\n",
        "    installed).\n",
        "\n",
        "    Args:\n",
        "        seed (:obj:`int`): The seed to set.\n",
        "\"\"\"\n",
        "\n",
        "def set_seed(seed: int):\n",
        "    random.seed(seed)\n",
        "    np.random.seed(seed)\n",
        "    torch.manual_seed(seed)\n",
        "    torch.cuda.manual_seed_all(seed)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1c81f97a",
      "metadata": {
        "id": "1c81f97a"
      },
      "outputs": [],
      "source": [
        "\"\"\"\n",
        "    Loads the dataset.\n",
        "\"\"\"\n",
        "\n",
        "def load_ag_news_dataset():\n",
        "    dataset = load_dataset(\"ag_news\")\n",
        "    \n",
        "    train_x = []\n",
        "    train_y = []\n",
        "    dev_x = []\n",
        "    dev_y = []\n",
        "    test_x = []\n",
        "    test_y = []\n",
        "\n",
        "    for x in range(0, 1000):\n",
        "        train_x.append(dataset['train'][x]['text'])\n",
        "        train_y.append(dataset['train'][x]['label'])\n",
        "\n",
        "    for x in range(1000, 1500):\n",
        "        dev_x.append(dataset['train'][x]['text'])\n",
        "        dev_y.append(dataset['train'][x]['label'])\n",
        "\n",
        "    for x in range(0, 1500):\n",
        "        test_x.append(dataset['test'][x]['text'])\n",
        "        test_y.append(dataset['test'][x]['label'])\n",
        "    \n",
        "    return train_x, train_y, dev_x, dev_y, test_x, test_y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "97f87bec",
      "metadata": {
        "id": "97f87bec"
      },
      "outputs": [],
      "source": [
        "\"\"\"\n",
        "    Custom metrics method.\n",
        "\"\"\"\n",
        "\n",
        "def compute_metrics(pred):\n",
        "    labels = pred.label_ids\n",
        "    preds = pred.predictions.argmax(-1)\n",
        "\n",
        "    acc = accuracy_score(labels, preds)\n",
        "    \n",
        "    metrics = classification_report(\n",
        "        y_true = labels, \n",
        "        y_pred = preds, \n",
        "        labels = [0, 1, 2, 3],\n",
        "        output_dict=True\n",
        "    )\n",
        "    \n",
        "    # Metrics per class\n",
        "    class_world_precision = metrics['0']['precision']\n",
        "    class_world_recall = metrics['0']['recall']\n",
        "    class_world_f1 = metrics['0']['f1-score']\n",
        "    class_world_count = metrics['0']['support']\n",
        "    \n",
        "    class_sports_precision = metrics['1']['precision']\n",
        "    class_sports_recall = metrics['1']['recall']\n",
        "    class_sports_f1 = metrics['1']['f1-score']\n",
        "    class_sports_count = metrics['1']['support']\n",
        "    \n",
        "    class_business_precision = metrics['2']['precision']\n",
        "    class_business_recall = metrics['2']['recall']\n",
        "    class_business_f1 = metrics['2']['f1-score']\n",
        "    class_business_count = metrics['2']['support']\n",
        "    \n",
        "    class_sci_tech_precision = metrics['3']['precision']\n",
        "    class_sci_tech_recall = metrics['3']['recall']\n",
        "    class_sci_tech_f1 = metrics['3']['f1-score']\n",
        "    class_sci_tech_count = metrics['3']['support']\n",
        "    \n",
        "    # Macro averaging\n",
        "    macro_avg_precision = metrics['macro avg']['precision']\n",
        "    macro_avg_recall = metrics['macro avg']['recall']\n",
        "    macro_avg_f1 = metrics['macro avg']['f1-score']\n",
        "    \n",
        "    return {\n",
        "        'accuracy': acc,\n",
        "        'Class World (precision)': class_world_precision,\n",
        "        'Class World (recall)': class_world_recall,\n",
        "        'Class World (f1-score)': class_world_f1,\n",
        "        'Class World (count)': class_world_count,\n",
        "        'Class Sports (precision)': class_sports_precision,\n",
        "        'Class Sports (recall)': class_sports_recall,\n",
        "        'Class Sports (f1-score)': class_sports_f1,\n",
        "        'Class Sports (count)': class_sports_count,\n",
        "        'Class Business (precision)': class_business_precision,\n",
        "        'Class Business (recall)': class_business_recall,\n",
        "        'Class Business (f1-score)': class_business_f1,\n",
        "        'Class Business (count)': class_business_count,\n",
        "        'Class Sci/Tech (precision)': class_sci_tech_precision,\n",
        "        'Class Sci/Tech (recall)': class_sci_tech_recall,\n",
        "        'Class Sci/Tech (f1-score)': class_sci_tech_f1,\n",
        "        'Class Sci/Tech (count)': class_sci_tech_count,\n",
        "        'Macro-averaged precision': macro_avg_precision,\n",
        "        'Macro-averaged recall': macro_avg_recall,\n",
        "        'Macro-averaged f1-score': macro_avg_f1,\n",
        "    }"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0f706008",
      "metadata": {
        "id": "0f706008"
      },
      "outputs": [],
      "source": [
        "\"\"\"\n",
        "Returns a prediction for the text specified.\n",
        "\"\"\"\n",
        "\n",
        "def get_prediction(text, tokenizer, target_names):\n",
        "    inputs = tokenizer(text, padding=True, truncation=True, max_length=max_length, return_tensors=\"pt\")\n",
        "    outputs = model(**inputs)\n",
        "    probs = outputs[0].softmax(1)\n",
        "    return target_names[probs.argmax()]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "  Tunes the number of training epochs, and seed.\n",
        "\"\"\"\n",
        "\n",
        "def my_hp_space_ray(trial):   \n",
        "    return {\n",
        "        \"num_train_epochs\": tune.choice([1, 2]),\n",
        "        \"learning_rate\": tune.choice([1e-6, 1e-4]),\n",
        "        \"weight_decay\": tune.choice([0.01, 0.001])\n",
        "    }"
      ],
      "metadata": {
        "id": "JCdoUvdKi80Q"
      },
      "id": "JCdoUvdKi80Q",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "id": "00207f87",
      "metadata": {
        "id": "00207f87"
      },
      "source": [
        "## Class definitions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ae421b93",
      "metadata": {
        "id": "ae421b93"
      },
      "outputs": [],
      "source": [
        "class AGNewsDataset(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 = {k: torch.tensor(v[idx]) for k, v 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)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ecd11ff5",
      "metadata": {
        "id": "ecd11ff5"
      },
      "source": [
        "## Classification with BERT"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4a8fe689",
      "metadata": {
        "id": "4a8fe689"
      },
      "outputs": [],
      "source": [
        "# Disable all warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a08d624d",
      "metadata": {
        "id": "a08d624d"
      },
      "outputs": [],
      "source": [
        "# Set the seed\n",
        "set_seed(1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "53e89c78",
      "metadata": {
        "id": "53e89c78"
      },
      "outputs": [],
      "source": [
        "# Model's name, and maximum sequence length\n",
        "model_name = \"bert-base-uncased\"\n",
        "max_length = 512"
      ]
    },
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      "source": [
        "# Load the tokenizer\n",
        "tokenizer = BertTokenizerFast.from_pretrained(model_name, do_lower_case=True)"
      ]
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              "model_id": "6c58045a41bd41feb206d6e55992ff7f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/7.94k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d764d35a354b4a77ab1191bfc90c3f9a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset ag_news/default to /root/.cache/huggingface/datasets/ag_news/default/0.0.0/bc2bcb40336ace1a0374767fc29bb0296cdaf8a6da7298436239c54d79180548...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/11.0M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8dbf721a6342416c9b45a50fc795f5b4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/751k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4eb4c5d0e35e4d43b0de51c28088829c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/120000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "73bfbbc1dd61474195a8f42648e3de12"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split:   0%|          | 0/7600 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "14d5db4118874f1fa5cc024c5c35e122"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset ag_news downloaded and prepared to /root/.cache/huggingface/datasets/ag_news/default/0.0.0/bc2bcb40336ace1a0374767fc29bb0296cdaf8a6da7298436239c54d79180548. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cafd5c67be8843d88da55879aeae068e"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Load the dataset\n",
        "train_x, train_y, dev_x, dev_y, test_x, test_y = load_ag_news_dataset()\n",
        "\n",
        "# Tokenize the dataset at max_length, using truncation and padding\n",
        "train_encodings = tokenizer(train_x, truncation=True, padding=True, max_length=max_length)\n",
        "valid_encodings = tokenizer(dev_x, truncation=True, padding=True, max_length=max_length)\n",
        "test_encodings = tokenizer(test_x, truncation=True, padding=True, max_length=max_length)\n",
        "\n",
        "# And bring it into the desired format for applying classification with BERT \n",
        "train_dataset = AGNewsDataset(train_encodings, train_y)\n",
        "validation_dataset = AGNewsDataset(valid_encodings, dev_y)\n",
        "test_dataset = AGNewsDataset(test_encodings, test_y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "13ddbe4d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156,
          "referenced_widgets": [
            "3afb8340b92e41e3abf53af5f83d4388",
            "de183bd236364e63b101a523aebd607d",
            "173361501a954b07a11a543c971e17e1",
            "fedaab82dc2f46bcbd34e8dad4624969",
            "e4b6e7d40f97449fa17c6f4d2cf6649f",
            "19ae512ac80f4e22858e4e3e1dd7d90e",
            "d2b341182f70402eaa36392a7dbd9126",
            "6af3404dc50d4a4fbe969b23103821b5",
            "ea01cfac44d8449e8b94ea4660e3ce4f",
            "6321f7fed5a94b5989d58fbb6cb5c53e",
            "5f6b005c7b9d4dc283a3afd9a7755397"
          ]
        },
        "id": "13ddbe4d",
        "outputId": "2e2e06fe-ac38-4eab-c068-86b35c53f9ac"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/440M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3afb8340b92e41e3abf53af5f83d4388"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForSequenceClassification: ['cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias']\n",
            "- This IS expected if you are initializing BertForSequenceClassification 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 BertForSequenceClassification 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 BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased 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"
          ]
        }
      ],
      "source": [
        "# Load a BERT model for text classification\n",
        "target_list = [\"World\", \"Sports\", \"Business\", \"Sci/Tech\"]\n",
        "\n",
        "model = BertForSequenceClassification.from_pretrained(\n",
        "    model_name, \n",
        "    num_labels=len(target_list)\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fc2ef40a",
      "metadata": {
        "id": "fc2ef40a"
      },
      "outputs": [],
      "source": [
        "# Training arguments specification\n",
        "\n",
        "training_args = TrainingArguments(\n",
        "    output_dir='./results',          \n",
        "    num_train_epochs=2,              \n",
        "    per_device_train_batch_size=16,  \n",
        "    per_device_eval_batch_size=20,   \n",
        "    warmup_steps=500,                \n",
        "    weight_decay=0.01,               \n",
        "    logging_dir='./logs',            \n",
        "    load_best_model_at_end=True,     \n",
        "    logging_steps=50,               \n",
        "    evaluation_strategy=\"steps\",     \n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fc4f27f7",
      "metadata": {
        "id": "fc4f27f7"
      },
      "outputs": [],
      "source": [
        "# Trainer specification\n",
        "\n",
        "trainer = Trainer(\n",
        "    model=model,                         \n",
        "    args=training_args,                  \n",
        "    train_dataset=train_dataset,         \n",
        "    eval_dataset=validation_dataset,          \n",
        "    compute_metrics=compute_metrics,     \n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e73fdf84",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 575
        },
        "id": "e73fdf84",
        "outputId": "2e9550b7-9d92-40c2-9ac9-3c82d4b00645"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "***** Running training *****\n",
            "  Num examples = 1000\n",
            "  Num Epochs = 2\n",
            "  Instantaneous batch size per device = 16\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 16\n",
            "  Gradient Accumulation steps = 1\n",
            "  Total optimization steps = 126\n",
            "  Number of trainable parameters = 109485316\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='126' max='126' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [126/126 02:07, Epoch 2/2]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>Class world (precision)</th>\n",
              "      <th>Class world (recall)</th>\n",
              "      <th>Class world (f1-score)</th>\n",
              "      <th>Class world (count)</th>\n",
              "      <th>Class sports (precision)</th>\n",
              "      <th>Class sports (recall)</th>\n",
              "      <th>Class sports (f1-score)</th>\n",
              "      <th>Class sports (count)</th>\n",
              "      <th>Class business (precision)</th>\n",
              "      <th>Class business (recall)</th>\n",
              "      <th>Class business (f1-score)</th>\n",
              "      <th>Class business (count)</th>\n",
              "      <th>Class sci/tech (precision)</th>\n",
              "      <th>Class sci/tech (recall)</th>\n",
              "      <th>Class sci/tech (f1-score)</th>\n",
              "      <th>Class sci/tech (count)</th>\n",
              "      <th>Macro-averaged precision</th>\n",
              "      <th>Macro-averaged recall</th>\n",
              "      <th>Macro-averaged f1-score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>50</td>\n",
              "      <td>1.415200</td>\n",
              "      <td>1.340573</td>\n",
              "      <td>0.318000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>152</td>\n",
              "      <td>0.724138</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>0.371681</td>\n",
              "      <td>84</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>122</td>\n",
              "      <td>0.292994</td>\n",
              "      <td>0.971831</td>\n",
              "      <td>0.450245</td>\n",
              "      <td>142</td>\n",
              "      <td>0.254283</td>\n",
              "      <td>0.305458</td>\n",
              "      <td>0.205482</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>1.102400</td>\n",
              "      <td>0.935781</td>\n",
              "      <td>0.718000</td>\n",
              "      <td>0.972973</td>\n",
              "      <td>0.473684</td>\n",
              "      <td>0.637168</td>\n",
              "      <td>152</td>\n",
              "      <td>0.847826</td>\n",
              "      <td>0.928571</td>\n",
              "      <td>0.886364</td>\n",
              "      <td>84</td>\n",
              "      <td>0.764045</td>\n",
              "      <td>0.557377</td>\n",
              "      <td>0.644550</td>\n",
              "      <td>122</td>\n",
              "      <td>0.575510</td>\n",
              "      <td>0.992958</td>\n",
              "      <td>0.728682</td>\n",
              "      <td>142</td>\n",
              "      <td>0.790089</td>\n",
              "      <td>0.738148</td>\n",
              "      <td>0.724191</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "***** Running Evaluation *****\n",
            "  Num examples = 500\n",
            "  Batch size = 20\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 500\n",
            "  Batch size = 20\n",
            "\n",
            "\n",
            "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=126, training_loss=1.1630673030066112, metrics={'train_runtime': 131.9376, 'train_samples_per_second': 15.159, 'train_steps_per_second': 0.955, 'total_flos': 351506237472000.0, 'train_loss': 1.1630673030066112, 'epoch': 2.0})"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "# Train the model \n",
        "trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fe057d72",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "fe057d72",
        "outputId": "51640c70-d9fd-4f73-8bbc-4e0ab8f6a3ce"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "***** Running Evaluation *****\n",
            "  Num examples = 500\n",
            "  Batch size = 20\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='25' max='25' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [25/25 00:07]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'eval_loss': 0.7038248181343079,\n",
              " 'eval_accuracy': 0.82,\n",
              " 'eval_Class World (precision)': 0.8571428571428571,\n",
              " 'eval_Class World (recall)': 0.75,\n",
              " 'eval_Class World (f1-score)': 0.7999999999999999,\n",
              " 'eval_Class World (count)': 152,\n",
              " 'eval_Class Sports (precision)': 0.8522727272727273,\n",
              " 'eval_Class Sports (recall)': 0.8928571428571429,\n",
              " 'eval_Class Sports (f1-score)': 0.872093023255814,\n",
              " 'eval_Class Sports (count)': 84,\n",
              " 'eval_Class Business (precision)': 0.8240740740740741,\n",
              " 'eval_Class Business (recall)': 0.7295081967213115,\n",
              " 'eval_Class Business (f1-score)': 0.7739130434782607,\n",
              " 'eval_Class Business (count)': 122,\n",
              " 'eval_Class Sci/Tech (precision)': 0.7719298245614035,\n",
              " 'eval_Class Sci/Tech (recall)': 0.9295774647887324,\n",
              " 'eval_Class Sci/Tech (f1-score)': 0.8434504792332269,\n",
              " 'eval_Class Sci/Tech (count)': 142,\n",
              " 'eval_Macro-averaged precision': 0.8263548707627655,\n",
              " 'eval_Macro-averaged recall': 0.8254857010917968,\n",
              " 'eval_Macro-averaged f1-score': 0.8223641364918255,\n",
              " 'eval_runtime': 8.0895,\n",
              " 'eval_samples_per_second': 61.809,\n",
              " 'eval_steps_per_second': 3.09,\n",
              " 'epoch': 2.0}"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "# Evaluate the model after training\n",
        "trainer.evaluate() "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "753de176",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "753de176",
        "outputId": "c12e6d8c-7d51-4d95-ef4a-e53c9f0036b9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Configuration saved in ag_news-bert-base-uncased/config.json\n",
            "Model weights saved in ag_news-bert-base-uncased/pytorch_model.bin\n",
            "tokenizer config file saved in ag_news-bert-base-uncased/tokenizer_config.json\n",
            "Special tokens file saved in ag_news-bert-base-uncased/special_tokens_map.json\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('ag_news-bert-base-uncased/tokenizer_config.json',\n",
              " 'ag_news-bert-base-uncased/special_tokens_map.json',\n",
              " 'ag_news-bert-base-uncased/vocab.txt',\n",
              " 'ag_news-bert-base-uncased/added_tokens.json',\n",
              " 'ag_news-bert-base-uncased/tokenizer.json')"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "# Save the fine-tuned model and the tokenizer\n",
        "model_path = \"ag_news-bert-base-uncased\"\n",
        "model.save_pretrained(model_path)\n",
        "tokenizer.save_pretrained(model_path)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "25f5043a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "25f5043a",
        "outputId": "da5fdb22-1609-4c3d-b4ea-7a6f98a73205"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "loading configuration file ag_news-bert-base-uncased/config.json\n",
            "Model config BertConfig {\n",
            "  \"_name_or_path\": \"bert-base-uncased\",\n",
            "  \"architectures\": [\n",
            "    \"BertForSequenceClassification\"\n",
            "  ],\n",
            "  \"attention_probs_dropout_prob\": 0.1,\n",
            "  \"classifier_dropout\": null,\n",
            "  \"gradient_checkpointing\": false,\n",
            "  \"hidden_act\": \"gelu\",\n",
            "  \"hidden_dropout_prob\": 0.1,\n",
            "  \"hidden_size\": 768,\n",
            "  \"id2label\": {\n",
            "    \"0\": \"LABEL_0\",\n",
            "    \"1\": \"LABEL_1\",\n",
            "    \"2\": \"LABEL_2\",\n",
            "    \"3\": \"LABEL_3\"\n",
            "  },\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"intermediate_size\": 3072,\n",
            "  \"label2id\": {\n",
            "    \"LABEL_0\": 0,\n",
            "    \"LABEL_1\": 1,\n",
            "    \"LABEL_2\": 2,\n",
            "    \"LABEL_3\": 3\n",
            "  },\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",
            "  \"position_embedding_type\": \"absolute\",\n",
            "  \"problem_type\": \"single_label_classification\",\n",
            "  \"torch_dtype\": \"float32\",\n",
            "  \"transformers_version\": \"4.25.1\",\n",
            "  \"type_vocab_size\": 2,\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 30522\n",
            "}\n",
            "\n",
            "loading weights file ag_news-bert-base-uncased/pytorch_model.bin\n",
            "All model checkpoint weights were used when initializing BertForSequenceClassification.\n",
            "\n",
            "All the weights of BertForSequenceClassification were initialized from the model checkpoint at ag_news-bert-base-uncased.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use BertForSequenceClassification for predictions without further training.\n",
            "loading file vocab.txt\n",
            "loading file tokenizer.json\n",
            "loading file added_tokens.json\n",
            "loading file special_tokens_map.json\n",
            "loading file tokenizer_config.json\n"
          ]
        }
      ],
      "source": [
        "# Load model and tokenizer\n",
        "model = BertForSequenceClassification.from_pretrained(model_path, num_labels=len(target_list))\n",
        "tokenizer = BertTokenizerFast.from_pretrained(model_path) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d6b760c3",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "d6b760c3",
        "outputId": "86b3e415-dc08-4095-cd3f-7f215b3340d0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"Fears for T N pension after talks Unions representing workers at Turner   Newall say they are 'disappointed' after talks with stricken parent firm Federal Mogul.\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "test_x[0]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "target_list[test_y[0]]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "XK8ArFdSgdG_",
        "outputId": "0384b527-a972-436c-f636-052e4ff727d1"
      },
      "id": "XK8ArFdSgdG_",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Business'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "get_prediction(test_x[0], tokenizer, target_list)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "lcMrpvwNgfjX",
        "outputId": "6728e51e-c491-4ab3-fb39-4cd2b6c42551"
      },
      "id": "lcMrpvwNgfjX",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Business'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Hyper parameter search (tuning)"
      ],
      "metadata": {
        "id": "z-Uxwo_kjWlg"
      },
      "id": "z-Uxwo_kjWlg"
    },
    {
      "cell_type": "code",
      "source": [
        "# Hyper parameter training arguments \n",
        "\n",
        "hyper_parameter_args = TrainingArguments(\n",
        "    \"test\", \n",
        "    do_eval=True, \n",
        "    per_device_train_batch_size=16,\n",
        "    weight_decay=0.01,\n",
        "    learning_rate=1e-6,\n",
        "    eval_steps=100, \n",
        "    disable_tqdm=False\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ldA2Hg2Gjbf2",
        "outputId": "00769e4a-a93a-4649-e941-b19caef0e879"
      },
      "id": "ldA2Hg2Gjbf2",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "PyTorch: setting up devices\n",
            "The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "  Initializes the BERT model.\n",
        "\"\"\"\n",
        "\n",
        "def get_model():\n",
        "    return BertForSequenceClassification.from_pretrained(model_name, num_labels=len(target_list), return_dict=True)"
      ],
      "metadata": {
        "id": "YLReiZrPmMea"
      },
      "id": "YLReiZrPmMea",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Hyper trainer\n",
        "\n",
        "hyper_trainer = Trainer(\n",
        "    args=hyper_parameter_args, \n",
        "    data_collator=DataCollatorWithPadding(tokenizer),\n",
        "    train_dataset=train_dataset, \n",
        "    eval_dataset=validation_dataset, \n",
        "    model_init=get_model,\n",
        "    compute_metrics=compute_metrics,\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JIoj7UcQjnvE",
        "outputId": "a52a4297-4f95-4c22-94a3-7613bed94807"
      },
      "id": "JIoj7UcQjnvE",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--bert-base-uncased/snapshots/0a6aa9128b6194f4f3c4db429b6cb4891cdb421b/config.json\n",
            "Model config BertConfig {\n",
            "  \"architectures\": [\n",
            "    \"BertForMaskedLM\"\n",
            "  ],\n",
            "  \"attention_probs_dropout_prob\": 0.1,\n",
            "  \"classifier_dropout\": null,\n",
            "  \"gradient_checkpointing\": false,\n",
            "  \"hidden_act\": \"gelu\",\n",
            "  \"hidden_dropout_prob\": 0.1,\n",
            "  \"hidden_size\": 768,\n",
            "  \"id2label\": {\n",
            "    \"0\": \"LABEL_0\",\n",
            "    \"1\": \"LABEL_1\",\n",
            "    \"2\": \"LABEL_2\",\n",
            "    \"3\": \"LABEL_3\"\n",
            "  },\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"intermediate_size\": 3072,\n",
            "  \"label2id\": {\n",
            "    \"LABEL_0\": 0,\n",
            "    \"LABEL_1\": 1,\n",
            "    \"LABEL_2\": 2,\n",
            "    \"LABEL_3\": 3\n",
            "  },\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",
            "  \"position_embedding_type\": \"absolute\",\n",
            "  \"transformers_version\": \"4.25.1\",\n",
            "  \"type_vocab_size\": 2,\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 30522\n",
            "}\n",
            "\n",
            "loading weights file pytorch_model.bin from cache at /root/.cache/huggingface/hub/models--bert-base-uncased/snapshots/0a6aa9128b6194f4f3c4db429b6cb4891cdb421b/pytorch_model.bin\n",
            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.LayerNorm.bias', 'cls.predictions.bias', 'cls.predictions.transform.dense.bias', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.weight']\n",
            "- This IS expected if you are initializing BertForSequenceClassification 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 BertForSequenceClassification 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 BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased 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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Search\n",
        "best = hyper_trainer.hyperparameter_search(direction=\"maximize\", hp_space=my_hp_space_ray) "
      ],
      "metadata": {
        "id": "cxGssK63j4U9"
      },
      "id": "cxGssK63j4U9",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Predictions on the test set (100 test samples)\n",
        "\n",
        "y_pred_test = []\n",
        "for x in range(0, 100):\n",
        "  pred = get_prediction(test_x[x], tokenizer, target_list)\n",
        "  if pred == \"World\":\n",
        "    y_pred_test.append(0)\n",
        "  elif pred == \"Sports\":\n",
        "    y_pred_test.append(1)\n",
        "  elif pred == \"Business\":\n",
        "    y_pred_test.append(2)\n",
        "  else:\n",
        "    y_pred_test.append(3)"
      ],
      "metadata": {
        "id": "613iSxkinXQg"
      },
      "id": "613iSxkinXQg",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Compute the metrics \n",
        "\n",
        "metrics = classification_report(\n",
        "        y_true = test_y[:100], \n",
        "        y_pred = y_pred_test, \n",
        "        labels = [0, 1, 2, 3],\n",
        "        output_dict=True\n",
        "    )"
      ],
      "metadata": {
        "id": "URziQme-onTa"
      },
      "id": "URziQme-onTa",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Metrics per class\n",
        "\n",
        "class_world_precision = metrics['0']['precision']\n",
        "class_world_recall = metrics['0']['recall']\n",
        "class_world_f1 = metrics['0']['f1-score']\n",
        "\n",
        "class_sports_precision = metrics['1']['precision']\n",
        "class_sports_recall = metrics['1']['recall']\n",
        "class_sports_f1 = metrics['1']['f1-score']\n",
        "\n",
        "class_business_precision = metrics['2']['precision']\n",
        "class_business_recall = metrics['2']['recall']\n",
        "class_business_f1 = metrics['2']['f1-score']\n",
        "\n",
        "class_sci_tech_precision = metrics['3']['precision']\n",
        "class_sci_tech_recall = metrics['3']['recall']\n",
        "class_sci_tech_f1 = metrics['3']['f1-score']"
      ],
      "metadata": {
        "id": "Npl02qgZpVnM"
      },
      "id": "Npl02qgZpVnM",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Macro averaging\n",
        "\n",
        "macro_avg_precision = metrics['macro avg']['precision']\n",
        "macro_avg_recall = metrics['macro avg']['recall']\n",
        "macro_avg_f1 = metrics['macro avg']['f1-score']"
      ],
      "metadata": {
        "id": "GRak8C2gs9g7"
      },
      "id": "GRak8C2gs9g7",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Include the baselines\n",
        "\n",
        "pr_world_lr = 0.880342\n",
        "pr_sports_lr = 0.920904\n",
        "pr_business_lr = 0.853211\n",
        "pr_sci_tech_lr = 0.384694\n",
        "\n",
        "re_world_lr = 0.544974\n",
        "re_sports_lr = 0.406484\n",
        "re_business_lr = 0.276786\n",
        "re_sci_tech_lr = 0.979221\n",
        "\n",
        "f1_world_lr = 0.673203\n",
        "f1_sports_lr = 0.564014\n",
        "f1_business_lr = 0.417978\n",
        "f1_sci_tech_lr = 0.552381\n",
        "\n",
        "macro_avg_pr = 0.759788\n",
        "macro_avg_re = 0.551866\n",
        "macro_avg_f1 = 0.551894"
      ],
      "metadata": {
        "id": "g4xKdnWVt3lG"
      },
      "id": "g4xKdnWVt3lG",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create the dataframe\n",
        "\n",
        "data = {\n",
        "     'Metric': ['Precision (World)', 'Precision (Sports)', 'Precision (Business)', 'Precision (Sci/Tech)', \n",
        "                'Recall (World)', 'Recall (Sports)', 'Recall (Business)', 'Recall (Sci/Tech)', \n",
        "                'F1-score (World)', 'F1-score (Sports)', 'F1-score (Business)', 'F1-score (Sci/Tech)'],\n",
        "        \n",
        "     'BERT': [class_world_precision, class_sports_precision, class_business_precision, class_sci_tech_precision,\n",
        "              class_world_recall, class_sports_recall, class_business_recall, class_sci_tech_recall,\n",
        "              class_world_f1, class_sports_f1, class_business_f1, class_sci_tech_f1],\n",
        "      \n",
        "     'LR': [pr_world_lr, pr_sports_lr, pr_business_lr, pr_sci_tech_lr,\n",
        "                             re_world_lr, re_sports_lr, re_business_lr, re_sci_tech_lr,\n",
        "                             f1_world_lr, f1_sports_lr, f1_business_lr, f1_sci_tech_lr]\n",
        "}\n",
        "\n",
        "df = pd.DataFrame(data)"
      ],
      "metadata": {
        "id": "891Z59Z4uZxi"
      },
      "id": "891Z59Z4uZxi",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display\n",
        "\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        },
        "id": "6qSbcO1uwNa6",
        "outputId": "34cde600-1aef-468b-f6e8-6cc6ec0d12de"
      },
      "id": "6qSbcO1uwNa6",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  Metric      BERT        LR\n",
              "0      Precision (World)  0.928571  0.880342\n",
              "1     Precision (Sports)  0.904762  0.920904\n",
              "2   Precision (Business)  0.833333  0.853211\n",
              "3   Precision (Sci/Tech)  0.897436  0.384694\n",
              "4         Recall (World)  0.866667  0.544974\n",
              "5        Recall (Sports)  0.904762  0.406484\n",
              "6      Recall (Business)  0.833333  0.276786\n",
              "7      Recall (Sci/Tech)  0.945946  0.979221\n",
              "8       F1-score (World)  0.896552  0.673203\n",
              "9      F1-score (Sports)  0.904762  0.564014\n",
              "10   F1-score (Business)  0.833333  0.417978\n",
              "11   F1-score (Sci/Tech)  0.921053  0.552381"
            ],
            "text/html": [
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Metric</th>\n",
              "      <th>BERT</th>\n",
              "      <th>LR</th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Precision (World)</td>\n",
              "      <td>0.928571</td>\n",
              "      <td>0.880342</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Precision (Sports)</td>\n",
              "      <td>0.904762</td>\n",
              "      <td>0.920904</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Precision (Business)</td>\n",
              "      <td>0.833333</td>\n",
              "      <td>0.853211</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Precision (Sci/Tech)</td>\n",
              "      <td>0.897436</td>\n",
              "      <td>0.384694</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Recall (World)</td>\n",
              "      <td>0.866667</td>\n",
              "      <td>0.544974</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Recall (Sports)</td>\n",
              "      <td>0.904762</td>\n",
              "      <td>0.406484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Recall (Business)</td>\n",
              "      <td>0.833333</td>\n",
              "      <td>0.276786</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Recall (Sci/Tech)</td>\n",
              "      <td>0.945946</td>\n",
              "      <td>0.979221</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>F1-score (World)</td>\n",
              "      <td>0.896552</td>\n",
              "      <td>0.673203</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>F1-score (Sports)</td>\n",
              "      <td>0.904762</td>\n",
              "      <td>0.564014</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>F1-score (Business)</td>\n",
              "      <td>0.833333</td>\n",
              "      <td>0.417978</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>F1-score (Sci/Tech)</td>\n",
              "      <td>0.921053</td>\n",
              "      <td>0.552381</td>\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
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              "  "
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          "metadata": {},
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    {
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        "# Create the dataframe (macro-averaging)\n",
        "\n",
        "data_ma = {\n",
        "     'Metric': ['Macro-Averaged Precision',\n",
        "                'Macro-Averaged Recall',\n",
        "                'Macro-Averaged F1-score'],\n",
        "           \n",
        "     'BERT': [macro_avg_precision,\n",
        "              macro_avg_recall,\n",
        "              macro_avg_f1],\n",
        "           \n",
        "     'LR': [macro_avg_pr, macro_avg_re, macro_avg_f1]\n",
        "}\n",
        "\n",
        "df_ma = pd.DataFrame(data_ma)"
      ],
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      "id": "kohNG1KQwP86",
      "execution_count": null,
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        "# Display \n",
        "\n",
        "df_ma"
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          "data": {
            "text/plain": [
              "                     Metric      BERT        LR\n",
              "0  Macro-Averaged Precision  0.891026  0.759788\n",
              "1     Macro-Averaged Recall  0.887677  0.551866\n",
              "2   Macro-Averaged F1-score  0.551894  0.551894"
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              "      <th>Metric</th>\n",
              "      <th>BERT</th>\n",
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              "      <th>0</th>\n",
              "      <td>Macro-Averaged Precision</td>\n",
              "      <td>0.891026</td>\n",
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              "      <th>1</th>\n",
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              "      <td>0.887677</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Macro-Averaged F1-score</td>\n",
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