System_A.ipynb

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            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "918b6f7b422444a7bd24ca75f9a7a9f6": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "To run the following code in Google Colab you need these libraries. More details in requirements.txt."
      ],
      "metadata": {
        "id": "MK2DM_HYJk_z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install datasets transformers torch seqeval"
      ],
      "metadata": {
        "id": "zsz0KdvUJjw1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install accelerate -U"
      ],
      "metadata": {
        "id": "zEDkRRBBJ4P0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data preparation"
      ],
      "metadata": {
        "id": "2GKxXND6-CuW"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NXN2oxf698oS"
      },
      "outputs": [],
      "source": [
        "# Load the dataset.\n",
        "\n",
        "from datasets import load_dataset\n",
        "\n",
        "dataset = load_dataset(\"Babelscape/multinerd\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the structure of the dataset.\n",
        "\n",
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wjSWaLmw-2A2",
        "outputId": "e502a16b-fa63-4a04-bb6f-860564474fbd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['tokens', 'ner_tags', 'lang'],\n",
              "        num_rows: 2678400\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['tokens', 'ner_tags', 'lang'],\n",
              "        num_rows: 334800\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['tokens', 'ner_tags', 'lang'],\n",
              "        num_rows: 335986\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Filter out non-English data.\n",
        "\n",
        "en_dataset = dataset.filter(lambda example: example['lang'] == 'en')"
      ],
      "metadata": {
        "id": "3sHaetnYcPxi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "3f97812b4a7542579d0ee34b9f7c381c",
            "48a20ef0eda4440e92be83797565d54e",
            "9261c4b89a4b4da0ab4bb757ff8cb743",
            "755d9dd80edc4075a2f07f2cb5b4664b",
            "321a39e1cd834a3c8d8e8a5b1b91ee5f",
            "8edc3b19a0cd4d2f9a1abf57b1ee9b7d",
            "6c05d8abbce34293a97403e227a821e4",
            "d4ff1af08da546048bdea65459c20f50",
            "314ce1d7c82d4529b2191e7e22fefcb1",
            "b9b41c6bdcdf4a158af1d64e55b2f2f6",
            "b53a5bbdd0374a0bb1d156203f5ade63",
            "713112aa26994d8189b3da41aed540ab",
            "db1da095359b40feac5eea8eefae4f69",
            "c0e00a9176194701ac1c2665cf5cf1a5",
            "0e75306d8ef3493b803ffd4f7eef5859",
            "c3912dde3ea5445e83513c24d1fd037f",
            "70308eae7bed470b800e73161a1d75a4",
            "4350e8efd3d74e46b13f16d7c815894b",
            "d6956e859d814895b819bc108a7544b7",
            "d148dd7828af44ffbf911cd31d35e96f",
            "ea467904284c48ee8c05776409ae2d55",
            "fe6bece2ad654501ae3e6f4bad8bc023",
            "e08439412fac42199e224d338483e993",
            "fbff6e51d39e4108b356055eef496bc6",
            "ddc71c4efaac43a4bf9ed70959a0ce18",
            "4ef2321f4b6048f8a50f6b6d9a862ff3",
            "396becb7796141088e316f5d4a37081e",
            "930aacda0afc4bb4a1c312d26e69fb59",
            "6841439f762f4308bddf0ad0ea1bf2ba",
            "583eecc37ac9468bb758de1f0aaec5e0",
            "709818be193b4d519d61ad7a9956f8e1",
            "8e3d4cadbc344c79b0f9dc46b3952fe8",
            "918b6f7b422444a7bd24ca75f9a7a9f6"
          ]
        },
        "outputId": "1e487cf0-d1fe-4cbc-a96d-3bbd9338ac64"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Filter:   0%|          | 0/2678400 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3f97812b4a7542579d0ee34b9f7c381c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Filter:   0%|          | 0/334800 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "713112aa26994d8189b3da41aed540ab"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Filter:   0%|          | 0/335986 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e08439412fac42199e224d338483e993"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the number of examples in the train, validation, and test sets of the English sub-dataset.\n",
        "\n",
        "print('Number of examples in train, validation, and test sets:',\n",
        "      len(en_dataset[\"train\"]), len(en_dataset[\"validation\"]), len(en_dataset[\"test\"]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b5eimShjMFq7",
        "outputId": "9a214a48-3084-4109-d56c-07e82c5cd118"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of examples in train, validation, and test sets: 262560 32820 32908\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the content of an example\n",
        "\n",
        "en_dataset[\"train\"][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bh6duNdEeIuo",
        "outputId": "71a00f3c-4fe9-4650-f65b-d697f582a3f5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'tokens': ['The', 'type', 'locality', 'is', 'Kīlauea', '.'],\n",
              " 'ner_tags': [0, 0, 0, 0, 5, 0],\n",
              " 'lang': 'en'}"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot out the number of tokens in each example in the training set\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "sample_lengths = [len(sample[\"tokens\"]) for sample in en_dataset[\"train\"]]\n",
        "\n",
        "plt.hist(sample_lengths, bins=30, edgecolor='black')\n",
        "plt.title(\"Sample Lengths Distribution\")\n",
        "plt.xlabel(\"Length\")\n",
        "plt.ylabel(\"Frequency\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "SbMzI-IGmbcO",
        "outputId": "b606a605-25f4-4919-efe5-c55dac3aa2b4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the maximum length\n",
        "\n",
        "max_length = np.max(sample_lengths)\n",
        "max_length"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PIydAp4kpJe_",
        "outputId": "51f58a91-fd0c-4dec-fb0c-d5467e2e412d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "237"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Copy the label2id dictionary from https://huggingface.co/datasets/Babelscape/multinerd?row=0\n",
        "label2id = {\n",
        "    \"O\": 0,\n",
        "    \"B-PER\": 1,\n",
        "    \"I-PER\": 2,\n",
        "    \"B-ORG\": 3,\n",
        "    \"I-ORG\": 4,\n",
        "    \"B-LOC\": 5,\n",
        "    \"I-LOC\": 6,\n",
        "    \"B-ANIM\": 7,\n",
        "    \"I-ANIM\": 8,\n",
        "    \"B-BIO\": 9,\n",
        "    \"I-BIO\": 10,\n",
        "    \"B-CEL\": 11,\n",
        "    \"I-CEL\": 12,\n",
        "    \"B-DIS\": 13,\n",
        "    \"I-DIS\": 14,\n",
        "    \"B-EVE\": 15,\n",
        "    \"I-EVE\": 16,\n",
        "    \"B-FOOD\": 17,\n",
        "    \"I-FOOD\": 18,\n",
        "    \"B-INST\": 19,\n",
        "    \"I-INST\": 20,\n",
        "    \"B-MEDIA\": 21,\n",
        "    \"I-MEDIA\": 22,\n",
        "    \"B-MYTH\": 23,\n",
        "    \"I-MYTH\": 24,\n",
        "    \"B-PLANT\": 25,\n",
        "    \"I-PLANT\": 26,\n",
        "    \"B-TIME\": 27,\n",
        "    \"I-TIME\": 28,\n",
        "    \"B-VEHI\": 29,\n",
        "    \"I-VEHI\": 30,\n",
        "  }\n",
        "\n",
        "# Create an id2label dictionary\n",
        "id2label = {id: label for label, id in label2id.items()}"
      ],
      "metadata": {
        "id": "4zTrtYUU3sK9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the proportions of different labels in the train set now.\n",
        "\n",
        "from collections import Counter\n",
        "\n",
        "# Flat the labels into one list\n",
        "all_labels = [label for example in en_dataset['train'] for label in example['ner_tags']]\n",
        "\n",
        "# Count the labels in this list\n",
        "label_counts = Counter(all_labels)\n",
        "\n",
        "# Group the labels by entitiy type (Entity A - E and O)\n",
        "label_groups = {\n",
        "    'A': [1, 2],\n",
        "    'B': [3, 4],\n",
        "    'C': [5, 6],\n",
        "    'D': [7, 8],\n",
        "    'E': [9, 10],\n",
        "    'F': [11, 12],\n",
        "    'G': [13, 14],\n",
        "    'H': [15, 16],\n",
        "    'I': [17, 18],\n",
        "    'J': [19, 20],\n",
        "    'K': [21, 22],\n",
        "    'L': [23, 24],\n",
        "    'M': [25, 26],\n",
        "    'N': [27, 28],\n",
        "    'P': [29, 30],\n",
        "    'O': [0]\n",
        "}\n",
        "\n",
        "# Count the labels per group and get the proportions of each group\n",
        "grouped_label_counts = {group: sum(label_counts[label] for label in labels) for group, labels in label_groups.items()}\n",
        "grouped_total_labels = sum(grouped_label_counts.values())\n",
        "grouped_label_proportions = {group: count / grouped_total_labels for group, count in grouped_label_counts.items()}\n",
        "\n",
        "# Print the proportions of each group\n",
        "for group, count in grouped_label_counts.items():\n",
        "    proportion = grouped_label_proportions[group]\n",
        "    print(f\"Label Group: {group}, Count: {count}, Proportion: {proportion:.2%}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u_UuT13oLJWc",
        "outputId": "22d4ba31-8a9b-4d83-e7ee-4435133d1da3"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Label Group: A, Count: 258350, Proportion: 4.55%\n",
            "Label Group: B, Count: 127280, Proportion: 2.24%\n",
            "Label Group: C, Count: 166130, Proportion: 2.92%\n",
            "Label Group: D, Count: 36086, Proportion: 0.63%\n",
            "Label Group: E, Count: 350, Proportion: 0.01%\n",
            "Label Group: F, Count: 8342, Proportion: 0.15%\n",
            "Label Group: G, Count: 29012, Proportion: 0.51%\n",
            "Label Group: H, Count: 13456, Proportion: 0.24%\n",
            "Label Group: I, Count: 22618, Proportion: 0.40%\n",
            "Label Group: J, Count: 1484, Proportion: 0.03%\n",
            "Label Group: K, Count: 32232, Proportion: 0.57%\n",
            "Label Group: L, Count: 1340, Proportion: 0.02%\n",
            "Label Group: M, Count: 19574, Proportion: 0.34%\n",
            "Label Group: N, Count: 9022, Proportion: 0.16%\n",
            "Label Group: P, Count: 1764, Proportion: 0.03%\n",
            "Label Group: O, Count: 4957198, Proportion: 87.21%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Align the labels in tokenized_inputs with the labels of words in dataset.\n",
        "# Here DistilBertTokenizerFast is used as the tokenizer,\n",
        "# initialized with huggingface's 'distilbert-base-cased' model's tokenizer config.\n",
        "\n",
        "from transformers import DistilBertTokenizerFast\n",
        "\n",
        "tokenizer = DistilBertTokenizerFast.from_pretrained('distilbert-base-cased')\n",
        "\n",
        "def tokenize_and_align_labels(examples):\n",
        "    tokenized_inputs = tokenizer(examples[\"tokens\"], truncation=True, is_split_into_words=True)\n",
        "\n",
        "    labels = []\n",
        "    for i, label in enumerate(examples[f\"ner_tags\"]):\n",
        "        word_ids = tokenized_inputs.word_ids(batch_index=i)\n",
        "        previous_word_idx = None\n",
        "        label_ids = []\n",
        "        for word_idx in word_ids:\n",
        "            if word_idx is None:\n",
        "                label_ids.append(-100)  # Special token\n",
        "            elif word_idx != previous_word_idx:\n",
        "                label_ids.append(label[word_idx])\n",
        "            else:\n",
        "                label_ids.append(-100)  # Duplicate token\n",
        "            previous_word_idx = word_idx\n",
        "        labels.append(label_ids)\n",
        "\n",
        "    tokenized_inputs[\"labels\"] = labels\n",
        "    return tokenized_inputs\n",
        "\n",
        "tokenized_en_dataset = en_dataset.map(tokenize_and_align_labels, batched=True)\n",
        "tokenized_en_dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 383,
          "referenced_widgets": [
            "acd30b07b047404a9b27a8aafd05d5c3",
            "84a65646c02d4ad0990fb3f1341c514a",
            "96f8bce52d0f4ae8b93c89d5aca077e3",
            "405090d221a3493c8e46d660d0b88930",
            "857073a874dc4a1baeed89ef2e9cb22f",
            "ef837608316a42c5806848f3af7ba129",
            "c579818964e948bb94fbf9019a66820c",
            "59f44378fd0f446781c519764ccc7322",
            "5560bd0227324f01bc1d6d10077727ac",
            "eba94af5b3c74016a2f4490377719e8b",
            "66e92ea365864d5f907c46f954ac88b1"
          ]
        },
        "id": "H42x8A7VgUTc",
        "outputId": "60d75511-1be2-4e9b-beeb-bd0517eaa4f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/32908 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "acd30b07b047404a9b27a8aafd05d5c3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['tokens', 'ner_tags', 'lang', 'input_ids', 'attention_mask', 'labels'],\n",
              "        num_rows: 262560\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['tokens', 'ner_tags', 'lang', 'input_ids', 'attention_mask', 'labels'],\n",
              "        num_rows: 32820\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['tokens', 'ner_tags', 'lang', 'input_ids', 'attention_mask', 'labels'],\n",
              "        num_rows: 32908\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Retain the necessary columns for training the model.\n",
        "\n",
        "tokenized_en_dataset = tokenized_en_dataset.remove_columns([\"tokens\", \"ner_tags\", \"lang\"])\n",
        "tokenized_en_dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zwAUVdVq_OA-",
        "outputId": "7c5a7d65-1e97-4b94-f5d8-cd4106b43988"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['input_ids', 'attention_mask', 'labels'],\n",
              "        num_rows: 262560\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['input_ids', 'attention_mask', 'labels'],\n",
              "        num_rows: 32820\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['input_ids', 'attention_mask', 'labels'],\n",
              "        num_rows: 32908\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model training"
      ],
      "metadata": {
        "id": "mSh-mUPf-IVm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the evaluation metric: seqeval F1\n",
        "\n",
        "from seqeval.metrics import f1_score\n",
        "\n",
        "def metrics_func(eval_arg):\n",
        "  preds = np.argmax(eval_arg.predictions, axis=2)\n",
        "  batch_size, seq_len = preds.shape\n",
        "  y_true, y_pred = [], []\n",
        "  for b in range(batch_size):\n",
        "    true_label, pred_label = [], []\n",
        "    for s in range(seq_len):\n",
        "      if eval_arg.label_ids[b, s] != -100:  # -100 must be ignored\n",
        "        true_label.append(id2label[eval_arg.label_ids[b][s]])\n",
        "        pred_label.append(id2label[preds[b][s]])\n",
        "    y_true.append(true_label)\n",
        "    y_pred.append(pred_label)\n",
        "  return {\"f1\": f1_score(y_true, y_pred)}"
      ],
      "metadata": {
        "id": "abueObb21DhK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize the model \"distilbert-base-cased\" with seed=42.\n",
        "\n",
        "from transformers import DistilBertForTokenClassification\n",
        "\n",
        "import random\n",
        "import torch\n",
        "import numpy as np\n",
        "\n",
        "seed = 42\n",
        "torch.manual_seed(seed)\n",
        "torch.cuda.manual_seed_all(seed)\n",
        "torch.backends.cudnn.deterministic = True\n",
        "np.random.seed(seed)\n",
        "random.seed(seed)\n",
        "\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "model = DistilBertForTokenClassification.from_pretrained('distilbert-base-cased', num_labels=31, id2label=id2label, label2id=label2id).to(device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6KGSZksHpyxM",
        "outputId": "26891ab1-07dc-42bb-aed8-0044874bba8f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of DistilBertForTokenClassification were not initialized from the model checkpoint at distilbert-base-cased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the training arguments for the trainer.\n",
        "\n",
        "from transformers import TrainingArguments\n",
        "\n",
        "training_args = TrainingArguments(\n",
        "    output_dir=\"./results\",\n",
        "    per_device_train_batch_size=100,\n",
        "    per_device_eval_batch_size=100,\n",
        "    num_train_epochs=3,\n",
        "    fp16 = True,\n",
        "    evaluation_strategy = \"epoch\",\n",
        "    save_strategy=\"epoch\"\n",
        ")"
      ],
      "metadata": {
        "id": "Oi55fb_kqBS8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Set up a data collator to pad the tokenized input into the same length\n",
        "\n",
        "from transformers import DataCollatorForTokenClassification\n",
        "\n",
        "data_collator = DataCollatorForTokenClassification(\n",
        "  tokenizer,\n",
        "  return_tensors=\"pt\")"
      ],
      "metadata": {
        "id": "06nVSJtBQtEg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Set up the trainer\n",
        "\n",
        "from transformers import Trainer\n",
        "\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=tokenized_en_dataset[\"train\"],\n",
        "    eval_dataset=tokenized_en_dataset[\"validation\"],\n",
        "    tokenizer=tokenizer,\n",
        "    compute_metrics = metrics_func,\n",
        "    data_collator = data_collator\n",
        ")"
      ],
      "metadata": {
        "id": "3qK4kg7qsEXx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the model\n",
        "\n",
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "GFiRFRMRtxsB",
        "outputId": "2b0666c3-15a7-4da2-d37d-b9d1a103f34e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You're using a DistilBertTokenizerFast 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": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='7878' max='7878' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [7878/7878 28:38, 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>F1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.029100</td>\n",
              "      <td>0.044334</td>\n",
              "      <td>0.900379</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.014700</td>\n",
              "      <td>0.050704</td>\n",
              "      <td>0.905978</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.007100</td>\n",
              "      <td>0.060216</td>\n",
              "      <td>0.907564</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=7878, training_loss=0.026563344857631586, metrics={'train_runtime': 1719.92, 'train_samples_per_second': 457.975, 'train_steps_per_second': 4.58, 'total_flos': 1.656045678604056e+16, 'train_loss': 0.026563344857631586, 'epoch': 3.0})"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model testing\n",
        "\n",
        "Test the model on the test set"
      ],
      "metadata": {
        "id": "FQo0k17L-MDD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_result = trainer.predict(test_dataset=tokenized_en_dataset[\"test\"])[-1]['test_f1']\n",
        "test_result"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "_qp5YhUVS8ys",
        "outputId": "fb303379-229c-4d92-9abf-579b13cb008b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9438389313716"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "If someone one to test the model from other checkpoints:"
      ],
      "metadata": {
        "id": "kvzl2NuGU4L5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load model from a certain checkpoint\n",
        "model = (DistilBertForTokenClassification.from_pretrained(\n",
        "    \"./results/checkpoint-15756\", num_labels=31, id2label=id2label, label2id=label2id\n",
        ").to(device))\n",
        "\n",
        "# Reset the trainer\n",
        "trainer = Trainer(\n",
        "  model = model,\n",
        "  args = training_args,\n",
        "  data_collator = data_collator,\n",
        "  compute_metrics = metrics_func,\n",
        "  train_dataset = tokenized_en_dataset[\"train\"],\n",
        "  eval_dataset = tokenized_en_dataset[\"test\"],\n",
        "  tokenizer = tokenizer\n",
        ")\n",
        "\n",
        "# Evaluate on the test set\n",
        "trainer.evaluate()"
      ],
      "metadata": {
        "id": "NvCE7XWgCher"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}