token_classification.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "https://huggingface.co/learn/nlp-course/en/chapter7/2\n",
        "\n"
      ],
      "metadata": {
        "id": "MApUjQUTdISF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Token classification\n",
        "\n",
        "\n",
        "*   Named entity recognition (NER)\n",
        "*   Part-of-speech tagging (POS)\n",
        "*   Chunking  \n",
        "\n"
      ],
      "metadata": {
        "id": "9E7ar3_kdQtY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Preparing the data"
      ],
      "metadata": {
        "id": "vwC7vYnXfObq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q datasets transformers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "Dm0eZLypfMMN",
        "outputId": "512ea5d2-1d0d-40bd-88be-cda9ed526f3d"
      },
      "execution_count": 1,
      "outputs": [
        {
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          "name": "stdout",
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            "\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/480.6 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m \u001b[32m471.0/480.6 kB\u001b[0m \u001b[31m20.8 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m480.6/480.6 kB\u001b[0m \u001b[31m7.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/116.3 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m5.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/179.3 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m179.3/179.3 kB\u001b[0m \u001b[31m13.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/134.8 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m7.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/194.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m7.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "gcsfs 2024.10.0 requires fsspec==2024.10.0, but you have fsspec 2024.9.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "raw_datasets = load_dataset(\"conll2003\")"
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        },
        "collapsed": true,
        "id": "8FSXgeJrdQIJ",
        "outputId": "a50d9702-1340-4d26-8095-f28f72e170a2"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: 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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "README.md:   0%|          | 0.00/12.3k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fac51a7373e44644b7a2dc943389e951"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "conll2003.py:   0%|          | 0.00/9.57k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b0ee0d7fc2bd4502bf76dc1a71d23989"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The repository for conll2003 contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/conll2003.\n",
            "You can avoid this prompt in future by passing the argument `trust_remote_code=True`.\n",
            "\n",
            "Do you wish to run the custom code? [y/N] y\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/983k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "965402b731964a3eb610023622120bbf"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/14041 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b8b1d926b52344cb8a96075b6f00361e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation split:   0%|          | 0/3250 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b45a4a36e93c4697b43e9d5f44db3b5a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split:   0%|          | 0/3453 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "563f7496c6d44fde9ba9c397996179c6"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "raw_datasets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4qXFPjezgifE",
        "outputId": "f2dc5753-5f22-40a8-e98f-d55b5b8d36df"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags'],\n",
              "        num_rows: 14041\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags'],\n",
              "        num_rows: 3250\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags'],\n",
              "        num_rows: 3453\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "raw_datasets['train'][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oUYHNYPNhJbS",
        "outputId": "cd1acd11-ff46-41ba-d943-d77e0508c6b9"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'id': '0',\n",
              " 'tokens': ['EU',\n",
              "  'rejects',\n",
              "  'German',\n",
              "  'call',\n",
              "  'to',\n",
              "  'boycott',\n",
              "  'British',\n",
              "  'lamb',\n",
              "  '.'],\n",
              " 'pos_tags': [22, 42, 16, 21, 35, 37, 16, 21, 7],\n",
              " 'chunk_tags': [11, 21, 11, 12, 21, 22, 11, 12, 0],\n",
              " 'ner_tags': [3, 0, 7, 0, 0, 0, 7, 0, 0]}"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "raw_datasets['train'][0]['ner_tags']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OU-Ehhy3hSCm",
        "outputId": "1f2defa9-9424-4b59-fa62-a48de91d306f"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[3, 0, 7, 0, 0, 0, 7, 0, 0]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ner_feature = raw_datasets['train'].features['ner_tags']\n",
        "ner_feature"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "whXkdoxrh1Yi",
        "outputId": "fc6ee778-c8e7-438a-da07-746f819fcb8c"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sequence(feature=ClassLabel(names=['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC', 'B-MISC', 'I-MISC'], id=None), length=-1, id=None)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "label_names = ner_feature.feature.names\n",
        "label_names"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yKhQojrpibTX",
        "outputId": "176ae736-1634-45c6-f93a-3421735a8d68"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC', 'B-MISC', 'I-MISC']"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ner_feature.feature.names"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "278XKCqpijtr",
        "outputId": "b9cdbf7f-ee1c-4765-fd12-9cd70ef7ae97"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC', 'B-MISC', 'I-MISC']"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "words = raw_datasets['train'][4]['tokens']\n",
        "labels = raw_datasets['train'][4]['ner_tags']\n",
        "line1 = ''\n",
        "line2 = ''\n",
        "\n",
        "z = list(zip(words, labels))"
      ],
      "metadata": {
        "id": "N2ogFloHilgO"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for word, label in z:\n",
        "  full_label = label_names[label]\n",
        "  max_length = max(len(word), len(full_label))\n",
        "  line1 += word + \" \" * (max_length - len(word) + 1)\n",
        "  line2 += full_label + \" \" * (max_length - len(full_label) + 1)\n",
        "\n",
        "print(line1)\n",
        "print(line2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xmv_4F8djaOM",
        "outputId": "aa5bddf6-163c-4aff-f0e3-8f75fb3112de"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Germany 's representative to the European Union 's veterinary committee Werner Zwingmann said on Wednesday consumers should buy sheepmeat from countries other than Britain until the scientific advice was clearer . \n",
            "B-LOC   O  O              O  O   B-ORG    I-ORG O  O          O         B-PER  I-PER     O    O  O         O         O      O   O         O    O         O     O    B-LOC   O     O   O          O      O   O       O \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Print the same two sentences with their POS or chunking labels."
      ],
      "metadata": {
        "id": "8QEBXhLdpFt0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pos_feature = raw_datasets['train'].features['pos_tags']\n",
        "label_names = pos_feature.feature.names\n",
        "words = raw_datasets['train'][4]['tokens']\n",
        "labels = raw_datasets['train'][4]['pos_tags']\n",
        "line1 = ''\n",
        "line2 = ''\n",
        "\n",
        "z = list(zip(words, labels))\n",
        "\n",
        "for word, number in z:\n",
        "  full_label = label_names[number]\n",
        "  max_length = max(len(word), len(full_label))\n",
        "  line1 += word + \" \" * (max_length - len(word) + 1)\n",
        "  line2 += full_label + \" \" * (max_length - len(full_label) + 1)\n",
        "\n",
        "print(line1)\n",
        "print(line2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vjr-eE30kBka",
        "outputId": "88affe0c-fc86-4cc3-9ee5-4b98d96a922c"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Germany 's  representative to the European Union 's  veterinary committee Werner Zwingmann said on Wednesday consumers should buy sheepmeat from countries other than Britain until the scientific advice was clearer . \n",
            "NNP     POS NN             TO DT  NNP      NNP   POS JJ         NN        NNP    NNP       VBD  IN NNP       NNS       MD     VB  NN        IN   NNS       JJ    IN   NNP     IN    DT  JJ         NN     VBD JJR     . \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Texts need to be converted to token IDs before the model can use them. But we already have pre-tokenized inputs. Because of this, we need to warn the tokenizer with a special flag.\n",
        "\n"
      ],
      "metadata": {
        "id": "dz5ktPvNrYWg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer\n",
        "\n",
        "model_checkpoint = \"bert-base-cased\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248,
          "referenced_widgets": [
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            "1d6fddf1d0aa457bac56c76eabdd0bf1",
            "75473df4354a4b3ebcc45e9164d8d504",
            "4a1bfad5adf443c5afee7211b8c52192",
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            "eccfd7dbec374abda110ac39d9be5db3",
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            "51edd6f87d494d1f910c503d53253293",
            "e37622a8b7df44b290f316b87dbb1a7d",
            "42a33520ba4b47f2bd88941b77868f93",
            "2161a996cfc04af4845cf3615496f230",
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            "4dbfa66026a94dd58115ff5f257c7047",
            "618e22facb094efdae375afc4ace6bdf",
            "0bd17177d0b74e64a97e4271aa91b95d",
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            "227ad32caad146e2a331f0c266e826e5",
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            "ec7dba30cd944b42a4f38d63fbc46a1a",
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            "ab80fa171aea47af964dd68497ae7698",
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            "193c211c595046d1ba138eba27075922",
            "b78ab2fb4a704af68242898b8a824899",
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            "19caea62155141f6ae844215d50fa0c6",
            "227b58fcf0db4e1a91f1188884aab493",
            "85877175ddd74fbd82e7ea91e633c3e0",
            "6eaef441e13640bb80b7f69a70daa81b",
            "3c4fa84185ed4994a6f95c5d200610c7",
            "e7b4e22c016b4e16a27a4440a58cf95d",
            "708738317b5b43b9b6cb5827222bca1e",
            "b4db4504ed424289864921242648dbaa",
            "4a4af8d743d84c5aaab0f09480470687",
            "b1059953bce14cc9888e48bc70aeb444",
            "c3e6425148ba4daab82e5cdc9ad99b42",
            "304e9fa004d8403d8588f96f44114eba"
          ]
        },
        "collapsed": true,
        "id": "_Fbe9T2YrX4i",
        "outputId": "70e8bf80-f1ed-4f97-aabc-f470ab544558"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The cache for model files in Transformers v4.22.0 has been updated. Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling `transformers.utils.move_cache()`.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b537dae259d84fb4bb6e0f211f5bb4a1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/49.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "51edd6f87d494d1f910c503d53253293"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "227ad32caad146e2a331f0c266e826e5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/213k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "42d09b7569be48ed81c30f03ad5bdc6b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/436k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "227b58fcf0db4e1a91f1188884aab493"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer.is_fast"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tr2ilhLDrUk6",
        "outputId": "bd9360ac-b694-4af4-8935-97f8fa07a3c6"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "To tokenize pre-tokenized input, we use ``` is_split_into_words=True ```:"
      ],
      "metadata": {
        "id": "5s00xTL4shwe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "inputs = tokenizer(raw_datasets['train'][0]['tokens'], is_split_into_words=True)\n",
        "inputs.tokens()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5ttzBKq7sk-Z",
        "outputId": "0743124e-4974-4750-e3ca-9e2c72d57d91"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['[CLS]',\n",
              " 'EU',\n",
              " 'rejects',\n",
              " 'German',\n",
              " 'call',\n",
              " 'to',\n",
              " 'boycott',\n",
              " 'British',\n",
              " 'la',\n",
              " '##mb',\n",
              " '.',\n",
              " '[SEP]']"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "But now there's a mismatch between our inputs and the labels: the list of labels has 9 elements, whereas the list of tokens has 12. We need to fix this."
      ],
      "metadata": {
        "id": "Iu7vHR1Kt3Nw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "inputs.word_ids()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kOGaaQjiuLkC",
        "outputId": "0f1de3f2-c7f2-4d00-e680-aacf0c3ac665"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[None, 0, 1, 2, 3, 4, 5, 6, 7, 7, 8, None]"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def align_labels_with_tokens(labels, word_ids):\n",
        "  new_labels = []\n",
        "  current_word = None\n",
        "  for word_id in word_ids:\n",
        "    if word_id != current_word:\n",
        "      current_word = word_id\n",
        "      label = -100 if word_id is None else labels[word_id]\n",
        "      new_labels.append(label)\n",
        "    elif word_id is None:\n",
        "      new_labels.append(-100)\n",
        "    else:\n",
        "      label = labels[word_id]\n",
        "      if label % 2 == 1:\n",
        "        label += 1\n",
        "      new_labels.append(label)\n",
        "  return new_labels"
      ],
      "metadata": {
        "id": "q4ERoivPuZHR"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "inputs = tokenizer(raw_datasets['train'][4]['tokens'], is_split_into_words=True)\n",
        "\n",
        "labels = raw_datasets[\"train\"][4][\"ner_tags\"]\n",
        "word_ids = inputs.word_ids()\n",
        "print(raw_datasets['train'][4]['tokens'])\n",
        "print(labels)\n",
        "print(inputs.tokens())\n",
        "print(align_labels_with_tokens(labels, word_ids))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0QEwvbMc0ihE",
        "outputId": "156a73a5-e91d-4158-971a-7e9beb58af06"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Germany', \"'s\", 'representative', 'to', 'the', 'European', 'Union', \"'s\", 'veterinary', 'committee', 'Werner', 'Zwingmann', 'said', 'on', 'Wednesday', 'consumers', 'should', 'buy', 'sheepmeat', 'from', 'countries', 'other', 'than', 'Britain', 'until', 'the', 'scientific', 'advice', 'was', 'clearer', '.']\n",
            "[5, 0, 0, 0, 0, 3, 4, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0]\n",
            "['[CLS]', 'Germany', \"'\", 's', 'representative', 'to', 'the', 'European', 'Union', \"'\", 's', 'veterinary', 'committee', 'Werner', 'Z', '##wing', '##mann', 'said', 'on', 'Wednesday', 'consumers', 'should', 'buy', 'sheep', '##me', '##at', 'from', 'countries', 'other', 'than', 'Britain', 'until', 'the', 'scientific', 'advice', 'was', 'clearer', '.', '[SEP]']\n",
            "[-100, 5, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 1, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, -100]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(raw_datasets['train'][4]['tokens']), len(labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-6YlzxYB1Vjz",
        "outputId": "810b469c-4e68-41d3-cd6f-c19552c57c2a"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(31, 31)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Some researchers prefer to attribute only one label per word, and assign -100 to the other subtokens in a given word. This is to avoid long words that split into lots of subtokens contributing heavily to the loss. Change the previous function to align labels with input IDs by following this rule."
      ],
      "metadata": {
        "id": "2MzChouA6Jo4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# def align_labels_with_tokens(labels, word_ids):\n",
        "#   new_labels = []\n",
        "#   current_word = None\n",
        "#   for word_id in word_ids:\n",
        "#     if word_id != current_word:\n",
        "#       current_word = word_id\n",
        "#       label = -100 if word_id is None else labels[word_id]\n",
        "#       new_labels.append(label)\n",
        "#     elif word_id is None:\n",
        "#       new_labels.append(-100)\n",
        "#     else:\n",
        "#       label = -100\n",
        "#       if label % 2 == 1:\n",
        "#         label += 1\n",
        "#       new_labels.append(label)\n",
        "#   return new_labels"
      ],
      "metadata": {
        "id": "G_AMvRxy6LC6"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# inputs = tokenizer(raw_datasets['train'][4]['tokens'], is_split_into_words=True)\n",
        "\n",
        "# labels = raw_datasets[\"train\"][4][\"ner_tags\"]\n",
        "# word_ids = inputs.word_ids()\n",
        "# print(word_ids)\n",
        "# print(raw_datasets['train'][4]['tokens'])\n",
        "# print(labels)\n",
        "# print(inputs.tokens())\n",
        "# print(align_labels_with_tokens(labels, word_ids))"
      ],
      "metadata": {
        "id": "JDnGwtFi6gKc"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "For the above, we did only one input sentence (e.g., 4). However, we need to tokenize all input sentences and apply ``` align_labels_with_tokens()``` on all the labels (e.g., ner_tags)."
      ],
      "metadata": {
        "id": "SffoHz_C8zaa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def tokenize_and_align_labels(examples):\n",
        "    tokenized_inputs = tokenizer(\n",
        "        examples[\"tokens\"], truncation=True, is_split_into_words=True\n",
        "    )\n",
        "    all_labels = examples[\"ner_tags\"]\n",
        "    new_labels = []\n",
        "    for i, labels in enumerate(all_labels):\n",
        "        word_ids = tokenized_inputs.word_ids(i)\n",
        "        new_labels.append(align_labels_with_tokens(labels, word_ids))\n",
        "\n",
        "    tokenized_inputs[\"labels\"] = new_labels\n",
        "    return tokenized_inputs"
      ],
      "metadata": {
        "id": "3Rg0JL5J8kQN"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_datasets = raw_datasets.map(\n",
        "    tokenize_and_align_labels,\n",
        "    batched=True,\n",
        "    remove_columns=raw_datasets['train'].column_names,\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "db2a1118ea8c4fd9b6d853d5365695d1",
            "845d72a4ba4b471e9600914528005161",
            "104529207b4744b28ef3f71c2143b0ed",
            "5dc6f1ff7f6242cf947ccd10a9bc0f17",
            "8346fcaa372846399613baa002a3df0a",
            "95a2a81b3ec041c3a73401138044941c",
            "e5741b3a426e4c0981e9eb4a4a72f080",
            "193478dfe1c14901a696f6ae7767db9f",
            "bc3d16f1bab6416299ca480cf844ea10",
            "2e2e4c611dc84b7a86a7c562fff38746",
            "16d911548d5643278787348cce486151",
            "5d5a8e2524e14cb086221de5977f99c4",
            "bdeea32633a0465a910e7e582d408e23",
            "fffe50fc2440477e9dcaadc7f33e048c",
            "bbe59501e84543bc8f5217d3adeb00ae",
            "35ec9fdf03ae43aebf8dd42288cd9451",
            "192310d959bb41aa89b7e7b1b2ada017",
            "f33fd76c5ed240f194b63f8cbee8a945",
            "97f00b108d4f46fb9f0d22b11035bf30",
            "7804a658def0481396ea004da2e97e55",
            "8720df9962d54bc7af9e2da2c9695ccd",
            "acd2e1008ef84f6bbe5f150c361435a8",
            "1181f7e0a5314b949b7cf219d7fa1bb8",
            "9f3a398c95b745b89b4e526425e84433",
            "f46364858884420eac334217df342d12",
            "940bf93a2929459fa9c5bba31cab8c12",
            "bad20f22983e47e484c659192b4214ef",
            "3e286cceb88e4d149e3b658ded67d751",
            "27fa4d17a6b5404590d741bb55d9268e",
            "e9f64c9492294e2786daf7486037a1c9",
            "0f065cd87e6f426cba6cdce995d3a009",
            "3c68a7af566644aa806edcad5ff7fea1",
            "bcd6bbfef230412aac63cdf0bc86871d"
          ]
        },
        "collapsed": true,
        "id": "v12NH_ji_TdN",
        "outputId": "42ddf44a-923b-4236-d471-eb419a0d2c72"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/14041 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "db2a1118ea8c4fd9b6d853d5365695d1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/3250 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5d5a8e2524e14cb086221de5977f99c4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/3453 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1181f7e0a5314b949b7cf219d7fa1bb8"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "raw_datasets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ABGZl8-FRcpq",
        "outputId": "257e1cf9-e082-4ec0-8326-ad97fdd771eb"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags'],\n",
              "        num_rows: 14041\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags'],\n",
              "        num_rows: 3250\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags'],\n",
              "        num_rows: 3453\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_datasets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qVT9CRC0Q5VT",
        "outputId": "2483747f-df57-4d19-c100-67a395b0a115"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['input_ids', 'token_type_ids', 'attention_mask', 'labels'],\n",
              "        num_rows: 14041\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['input_ids', 'token_type_ids', 'attention_mask', 'labels'],\n",
              "        num_rows: 3250\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['input_ids', 'token_type_ids', 'attention_mask', 'labels'],\n",
              "        num_rows: 3453\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_datasets['train'][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2xlNSuReSq0E",
        "outputId": "d6c193e5-a2c9-4b80-8fb1-e4eff86f595d"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': [101,\n",
              "  7270,\n",
              "  22961,\n",
              "  1528,\n",
              "  1840,\n",
              "  1106,\n",
              "  21423,\n",
              "  1418,\n",
              "  2495,\n",
              "  12913,\n",
              "  119,\n",
              "  102],\n",
              " 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
              " 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
              " 'labels': [-100, 3, 0, 7, 0, 0, 0, 7, 0, 0, 0, -100]}"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "raw_datasets['train'][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zsSqCJHGSybb",
        "outputId": "b51d5619-81d1-4640-cbeb-7a8d6b8ed1a0"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'id': '0',\n",
              " 'tokens': ['EU',\n",
              "  'rejects',\n",
              "  'German',\n",
              "  'call',\n",
              "  'to',\n",
              "  'boycott',\n",
              "  'British',\n",
              "  'lamb',\n",
              "  '.'],\n",
              " 'pos_tags': [22, 42, 16, 21, 35, 37, 16, 21, 7],\n",
              " 'chunk_tags': [11, 21, 11, 12, 21, 22, 11, 12, 0],\n",
              " 'ner_tags': [3, 0, 7, 0, 0, 0, 7, 0, 0]}"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now that we've finished with preprocessing the data (the hardest part), we can begin training."
      ],
      "metadata": {
        "id": "RtoS9zW1TLWc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Fine-tuning the model with the Trainer API"
      ],
      "metadata": {
        "id": "1pW0llVbTXGz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Data collation"
      ],
      "metadata": {
        "id": "OY0xRENjWR1-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import DataCollatorForTokenClassification\n",
        "\n",
        "data_collator = DataCollatorForTokenClassification(tokenizer=tokenizer)"
      ],
      "metadata": {
        "id": "BJuv4M7VTIYt"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "[tokenized_datasets[\"train\"][i] for i in range(2)]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bECXFETsUpfQ",
        "outputId": "2cff5f1e-54a2-48d2-dea8-1de4c61a3c51"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'input_ids': [101,\n",
              "   7270,\n",
              "   22961,\n",
              "   1528,\n",
              "   1840,\n",
              "   1106,\n",
              "   21423,\n",
              "   1418,\n",
              "   2495,\n",
              "   12913,\n",
              "   119,\n",
              "   102],\n",
              "  'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
              "  'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
              "  'labels': [-100, 3, 0, 7, 0, 0, 0, 7, 0, 0, 0, -100]},\n",
              " {'input_ids': [101, 1943, 14428, 102],\n",
              "  'token_type_ids': [0, 0, 0, 0],\n",
              "  'attention_mask': [1, 1, 1, 1],\n",
              "  'labels': [-100, 1, 2, -100]}]"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "batch = data_collator([tokenized_datasets[\"train\"][i] for i in range(2)])\n",
        "batch[\"labels\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4ISD_1QrU-sU",
        "outputId": "2208c849-df7c-4048-97dc-a96ceea78d68"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[-100,    3,    0,    7,    0,    0,    0,    7,    0,    0,    0, -100],\n",
              "        [-100,    1,    2, -100, -100, -100, -100, -100, -100, -100, -100, -100]])"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_datasets['train'][0]['labels'], tokenized_datasets['train'][1]['labels']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sebMxvYMVHqT",
        "outputId": "fd4a1264-e198-48ff-b801-6fefa77713fd"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "([-100, 3, 0, 7, 0, 0, 0, 7, 0, 0, 0, -100], [-100, 1, 2, -100])"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As you can see, each element is now the same length because padding was added. Compare the two cells above."
      ],
      "metadata": {
        "id": "meIEXbEzVe-n"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Metrics"
      ],
      "metadata": {
        "id": "w8bR7SLoWMKi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We'd like to compute a metric every epoch, so we will need to define a ``` compute_metrics() ``` function. The function will take the arrays of predictions and labels, and it will return a dictionary with the metric names and values.\n",
        "\n",
        "Seqeval is used to evaluate token classification prediction. So let's install it."
      ],
      "metadata": {
        "id": "nPCh0a_9Whbf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q seqeval scikit-learn evaluate"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "6gqtHnCZVmRC",
        "outputId": "b2a163ab-fab1-4a3e-e645-4a8f4c6fbe2c"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/43.6 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m43.6/43.6 kB\u001b[0m \u001b[31m2.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.0/84.0 kB\u001b[0m \u001b[31m4.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for seqeval (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import evaluate\n",
        "\n",
        "metric = evaluate.load(\"seqeval\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "bac6c7c814ef4a8c92be73bcf448fbe3",
            "d8558b998f7c4d4989398b78fa2de01c",
            "9e99fb1910c34dc285645c791770552b",
            "5b85457b79584720923f4fab4d1fb05d",
            "001b458e79234b15bcc4ee81347c8ca7",
            "a3dcbb650ca541a3bf7881331ef5aaf8",
            "4a498c643d794189a2cb94f7b82c7772",
            "83c6e3aa317642849cd6124699237213",
            "6c6f6276d690479cb9c9cb22cefdc36e",
            "c04aa53d5d844694b9d9fc383627500d",
            "b2ebcf4c95104277bb2ac4f6f689279d"
          ]
        },
        "id": "xIhTiKXgVWb3",
        "outputId": "77eda0ae-6b21-42e3-c85e-3299bc09e0af"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/6.34k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bac6c7c814ef4a8c92be73bcf448fbe3"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Because this metric does not behave like the standard accuracy and takes the lists of labels as strings instead of integers, we need to decode the predictions and labels before passing them to the metric."
      ],
      "metadata": {
        "id": "JXOJYiXUZiFc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "label_names = raw_datasets['train'].features['ner_tags'].feature.names\n",
        "label_names"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RVc-Z30pYf6X",
        "outputId": "e92577db-e7f2-4719-d812-cc7f8acc3bad"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC', 'B-MISC', 'I-MISC']"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "labels = raw_datasets['train'][0]['ner_tags']\n",
        "labels = [label_names[i] for i in labels]\n",
        "labels"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J1KeMO8jZZNS",
        "outputId": "9360095c-db16-41fb-f462-1a47d8db2c8e"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['B-ORG', 'O', 'B-MISC', 'O', 'O', 'O', 'B-MISC', 'O', 'O']"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's create a fake prediction by changing the value at index 2:"
      ],
      "metadata": {
        "id": "jpl9BB0eeRbs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = labels.copy()\n",
        "predictions[2] = \"O\"\n",
        "predictions"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0NSmq4xteQ06",
        "outputId": "c2d8fa51-570a-4d20-cdd2-a6b1223dd01e"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['B-ORG', 'O', 'O', 'O', 'O', 'O', 'B-MISC', 'O', 'O']"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "metric.compute(predictions=[predictions], references=[labels])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sPCL0ir7eL9P",
        "outputId": "dd9e2cd1-9955-4969-bb22-1d736e5f376e"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'MISC': {'precision': 1.0,\n",
              "  'recall': 0.5,\n",
              "  'f1': 0.6666666666666666,\n",
              "  'number': 2},\n",
              " 'ORG': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1},\n",
              " 'overall_precision': 1.0,\n",
              " 'overall_recall': 0.6666666666666666,\n",
              " 'overall_f1': 0.8,\n",
              " 'overall_accuracy': 0.8888888888888888}"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The above cells use the raw_datasets (I don't know why HF thinks this isn't confusing), but we should be using the tokenized_datasets. When we do this, we will need to remove all the values where the label is -100 before we pass the results to the ``` metric.compute() ``` method. This is because there is no -100 in the true predictions."
      ],
      "metadata": {
        "id": "ZyWZjfuBi9CQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_datasets['train'][0]['labels']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pD6y3ykPfPMp",
        "outputId": "06737bf5-f1ba-4c45-e163-81ea1eab7fdd"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[-100, 3, 0, 7, 0, 0, 0, 7, 0, 0, 0, -100]"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "def compute_metrics(eval_preds):\n",
        "  logits, labels = eval_preds\n",
        "  predictions = np.argmax(logits, axis=-1)\n",
        "\n",
        "  true_labels = [[label_names[l] for l in label if l != -100] for label in labels]\n",
        "  true_predictions = [\n",
        "      [label_names[p] for (p, l) in zip(prediction, label) if l != -100]\n",
        "      for prediction, label in zip(predictions, labels)\n",
        "  ]\n",
        "\n",
        "  all_metrics = metric.compute(predictions=true_predictions, references=true_labels)\n",
        "\n",
        "  return {\n",
        "      \"precision\": all_metrics[\"overall_precision\"],\n",
        "      \"recall\": all_metrics[\"overall_recall\"],\n",
        "      \"f1\": all_metrics[\"overall_f1\"],\n",
        "      \"accuracy\": all_metrics[\"overall_accuracy\"]\n",
        "  }"
      ],
      "metadata": {
        "id": "02PdbU6_hOcn"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Defining the model"
      ],
      "metadata": {
        "id": "8tFXXO8ynJyj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We have to pass some information on the number of labels we have to the model we define. It's easy to do this with the ``` num_labels ``` argument. But it's better to set the correct label correspondences instead with ``` id2label ``` and ``` label2id ```."
      ],
      "metadata": {
        "id": "CS556q1Hom5K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "label_names"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ia9ziyK4nMm0",
        "outputId": "9fd6e7d2-fcd1-4b12-e3bc-fadc98778f1d"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC', 'B-MISC', 'I-MISC']"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "id2label = {i: label for i, label in enumerate(label_names) }\n",
        "id2label"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8bJySOJmnu_I",
        "outputId": "7ca6b780-b692-40bd-e246-0d9f269c23d3"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{0: 'O',\n",
              " 1: 'B-PER',\n",
              " 2: 'I-PER',\n",
              " 3: 'B-ORG',\n",
              " 4: 'I-ORG',\n",
              " 5: 'B-LOC',\n",
              " 6: 'I-LOC',\n",
              " 7: 'B-MISC',\n",
              " 8: 'I-MISC'}"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "id2label.items()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z3RfebOaoO6F",
        "outputId": "32e008b7-f7bf-4ce9-8fe8-2f4ee1fb8c10"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_items([(0, 'O'), (1, 'B-PER'), (2, 'I-PER'), (3, 'B-ORG'), (4, 'I-ORG'), (5, 'B-LOC'), (6, 'I-LOC'), (7, 'B-MISC'), (8, 'I-MISC')])"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "label2id = {v:k for k, v in id2label.items()}\n",
        "label2id"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zjq0wm7loTjL",
        "outputId": "253332c1-078b-447f-8d19-3445e2523539"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'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-MISC': 7,\n",
              " 'I-MISC': 8}"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we can pass them to our model. We use the class ``` AutoModelForTokenClassification ```."
      ],
      "metadata": {
        "id": "veeNq8lHpGwe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModelForTokenClassification\n",
        "\n",
        "model = AutoModelForTokenClassification.from_pretrained(\n",
        "    model_checkpoint,\n",
        "    id2label=id2label,\n",
        "    label2id=label2id\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 103,
          "referenced_widgets": [
            "c0a90d872c9341a3b8358502e2141b38",
            "e05ae5a3fdc9497a86ba4f0d1d67e081",
            "6445f7bcb32044b3969df14578de5b72",
            "c83018a5b1d44836ae176adbe180d2af",
            "768ed9f748094f6f9787047ad39f29b0",
            "c4ecd0286d3d482fb69487c1d394dc78",
            "e6f47fd87d9d4f1eadd6b8288f5ac6df",
            "b2946d58b2ca47a393ab54b5357c4e9f",
            "044e1fc74a8841a0b30870562ea58915",
            "a772ad8ba92d4b3593898b6cc41ea8e4",
            "3278933993f04ae4978814f1f3518922"
          ]
        },
        "id": "BWhMxRhzoezB",
        "outputId": "cd9e5fab-1765-4239-88ab-a650e9815d9d"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/436M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c0a90d872c9341a3b8358502e2141b38"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of BertForTokenClassification were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['classifier.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": [
        "model.config.num_labels"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZfUOlH4IptER",
        "outputId": "5656da02-236e-487c-cffc-6047e6a985ce"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "It's important to have the correct number of labels. Otherwise you will get an obscure error. This error is the number one cause of bugs reported by users, so make sure to do this check (``` model.config.num_labels ```) to ensure you have the correct number of labels."
      ],
      "metadata": {
        "id": "MLKyMQsgqA_X"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Fine-tuning the model"
      ],
      "metadata": {
        "id": "v9fgWdb_qr3Q"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "After we define our model, we are ready to train it. But first, we need to log in to Hugging Face."
      ],
      "metadata": {
        "id": "8bIShlp9qu7v"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "6e2fde932cf4484fadb4ceffad67eee4",
            "36055ef7f2024f3ab8a8295ca39f465e",
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            "6686000a241f4d5fa6e3fa5fe78b3d5f",
            "6f09d3c826544958b409f1863e4e2c3e",
            "0ace34ff06134cc99de9586a91f42614",
            "c40735381a734e94932ef1e80171d47d",
            "f078455a3f554c2ca1c0a1477756f59a",
            "893229173f684948b7bacd140a509fb0",
            "cdd9bf7a7d2c4771a2b1ef0a0a53b759",
            "199d292d134f4078a74829da921e50ab",
            "db0c4fce52dd43dc8528889fb7cd2b3a",
            "25eccb8d4dbc47029efe92b5c69c7289",
            "4619f13090e9429eb5fcf5d17f8db4e0",
            "5c821928e31b42b5a95e6069c54ae00b",
            "047733a04e77476a93877950e0c3f97d",
            "2ef6d1dd0ce94a8ba8663b2f6a8fc69f",
            "f569e10a1587488680176f7ef7010d45",
            "99f32dcc2daf4de9bde22bbe5b92a244",
            "9edeb063bfcc4be4a948beb5a45a7ac3",
            "e408f60b40554bf59c42d51f8689852e",
            "07eea606ca1549b28a5c6c5859198acd",
            "621459a9aa48462c9acd83b75b46667d",
            "cd60f3406cf646e4a607de5418de24ba",
            "2c2a337440654503a924b9747b2db2f5",
            "4c6f198c0ff3427a9d59e045b1fc0c7e",
            "3c5f18d10e5543b999c239bab06d20e9",
            "5c2e549d853b4458a306fdc5b917eac6",
            "87bb341735a049fe908e36960bf5d075",
            "8d007c85cc5c4a89b0fa0b0fe3c7160a",
            "cc7652edec544653a89b6cdc98d57772",
            "00a9cf1afda0475f808232288cdc95ec"
          ]
        },
        "id": "xD_ZwfE_q6kq",
        "outputId": "a8873ae2-f86c-4b1e-998a-16065cae7bcd"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6e2fde932cf4484fadb4ceffad67eee4"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import TrainingArguments\n",
        "\n",
        "# evaluation_strategy is depreacted\n",
        "# use eval_strategy instead\n",
        "\n",
        "args = TrainingArguments(\n",
        "    \"bert-finetuned-ner\",\n",
        "    eval_strategy=\"epoch\",\n",
        "    save_strategy=\"epoch\",\n",
        "    learning_rate=2e-5,\n",
        "    num_train_epochs=3,\n",
        "    weight_decay=0.01,\n",
        "    push_to_hub=True,\n",
        ")"
      ],
      "metadata": {
        "id": "yyDVryWwsOlF"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import Trainer\n",
        "\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=args,\n",
        "    train_dataset=tokenized_datasets[\"train\"],\n",
        "    eval_dataset=tokenized_datasets[\"validation\"],\n",
        "    data_collator=data_collator,\n",
        "    compute_metrics=compute_metrics,\n",
        "    tokenizer=tokenizer\n",
        ")\n",
        "\n",
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "id": "XroJ126BwUU5",
        "outputId": "a81f38d9-6b53-438d-da26-d4166530e661"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='5268' max='5268' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [5268/5268 10:03, Epoch 3/3]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Precision</th>\n",
              "      <th>Recall</th>\n",
              "      <th>F1</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.023700</td>\n",
              "      <td>0.071843</td>\n",
              "      <td>0.918852</td>\n",
              "      <td>0.943285</td>\n",
              "      <td>0.930908</td>\n",
              "      <td>0.983752</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.020500</td>\n",
              "      <td>0.080226</td>\n",
              "      <td>0.934176</td>\n",
              "      <td>0.945809</td>\n",
              "      <td>0.939957</td>\n",
              "      <td>0.984871</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.009800</td>\n",
              "      <td>0.073689</td>\n",
              "      <td>0.935233</td>\n",
              "      <td>0.950185</td>\n",
              "      <td>0.942650</td>\n",
              "      <td>0.985783</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=5268, training_loss=0.024435627388067108, metrics={'train_runtime': 603.3999, 'train_samples_per_second': 69.809, 'train_steps_per_second': 8.731, 'total_flos': 920771584279074.0, 'train_loss': 0.024435627388067108, 'epoch': 3.0})"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.push_to_hub(commit_message=\"Training complete\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "id": "udgXnnVHxEED",
        "outputId": "d4369850-322a-461e-dae1-6b11fe1b87bb"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/shoowadoo/bert-finetuned-ner/commit/05f7552e16184f838f96c0a19338006a4e04ae6e', commit_message='Training complete', commit_description='', oid='05f7552e16184f838f96c0a19338006a4e04ae6e', pr_url=None, pr_revision=None, pr_num=None)"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## A custom training loop"
      ],
      "metadata": {
        "id": "Iun9AUfj1QLj"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Fo_1oExs1VjS"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}