NER_transformer_fine_tuunign.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ILMM6t7mbRIK",
        "outputId": "2ce7ba7a-2df8-46bb-94fe-6e79f9c938eb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting datasets\n",
            "  Downloading datasets-2.20.0-py3-none-any.whl.metadata (19 kB)\n",
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            "  Downloading requests-2.32.3-py3-none-any.whl.metadata (4.6 kB)\n",
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            "Collecting xxhash (from datasets)\n",
            "  Downloading xxhash-3.4.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (12 kB)\n",
            "Collecting multiprocess (from datasets)\n",
            "  Downloading multiprocess-0.70.16-py310-none-any.whl.metadata (7.2 kB)\n",
            "Collecting fsspec<=2024.5.0,>=2023.1.0 (from fsspec[http]<=2024.5.0,>=2023.1.0->datasets)\n",
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            "\u001b[?25hDownloading multiprocess-0.70.16-py310-none-any.whl (134 kB)\n",
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            "\u001b[?25hDownloading xxhash-3.4.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (194 kB)\n",
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            "\u001b[?25hInstalling collected packages: xxhash, requests, pyarrow, fsspec, dill, multiprocess, datasets\n",
            "  Attempting uninstall: requests\n",
            "    Found existing installation: requests 2.31.0\n",
            "    Uninstalling requests-2.31.0:\n",
            "      Successfully uninstalled requests-2.31.0\n",
            "  Attempting uninstall: pyarrow\n",
            "    Found existing installation: pyarrow 14.0.2\n",
            "    Uninstalling pyarrow-14.0.2:\n",
            "      Successfully uninstalled pyarrow-14.0.2\n",
            "  Attempting uninstall: fsspec\n",
            "    Found existing installation: fsspec 2024.6.1\n",
            "    Uninstalling fsspec-2024.6.1:\n",
            "      Successfully uninstalled fsspec-2024.6.1\n",
            "\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",
            "torch 2.3.1+cu121 requires nvidia-cublas-cu12==12.1.3.1; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n",
            "torch 2.3.1+cu121 requires nvidia-cuda-cupti-cu12==12.1.105; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n",
            "torch 2.3.1+cu121 requires nvidia-cuda-nvrtc-cu12==12.1.105; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n",
            "torch 2.3.1+cu121 requires nvidia-cuda-runtime-cu12==12.1.105; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n",
            "torch 2.3.1+cu121 requires nvidia-cudnn-cu12==8.9.2.26; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n",
            "torch 2.3.1+cu121 requires nvidia-cufft-cu12==11.0.2.54; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n",
            "torch 2.3.1+cu121 requires nvidia-curand-cu12==10.3.2.106; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n",
            "torch 2.3.1+cu121 requires nvidia-cusolver-cu12==11.4.5.107; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n",
            "torch 2.3.1+cu121 requires nvidia-cusparse-cu12==12.1.0.106; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n",
            "torch 2.3.1+cu121 requires nvidia-nccl-cu12==2.20.5; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n",
            "torch 2.3.1+cu121 requires nvidia-nvtx-cu12==12.1.105; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n",
            "cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 17.0.0 which is incompatible.\n",
            "gcsfs 2024.6.1 requires fsspec==2024.6.1, but you have fsspec 2024.5.0 which is incompatible.\n",
            "google-colab 1.0.0 requires requests==2.31.0, but you have requests 2.32.3 which is incompatible.\n",
            "ibis-framework 8.0.0 requires pyarrow<16,>=2, but you have pyarrow 17.0.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed datasets-2.20.0 dill-0.3.8 fsspec-2024.5.0 multiprocess-0.70.16 pyarrow-17.0.0 requests-2.32.3 xxhash-3.4.1\n"
          ]
        }
      ],
      "source": [
        "!pip install datasets"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UWvDwow2bS6D",
        "outputId": "def74607-5018-41ea-e8a4-fde333436e79"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.42.4)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.15.4)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.23.2 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.23.5)\n",
            "Requirement already satisfied: numpy<2.0,>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.26.4)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (24.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2024.5.15)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.32.3)\n",
            "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.4.3)\n",
            "Requirement already satisfied: tokenizers<0.20,>=0.19 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.19.1)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.4)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.23.2->transformers) (2024.5.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.23.2->transformers) (4.12.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2024.7.4)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install seqeval"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x_xwaAAUcHV2",
        "outputId": "5871e660-d0c7-4038-9f7c-1490f4f1a8bf"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting seqeval\n",
            "  Downloading seqeval-1.2.2.tar.gz (43 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/43.6 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m43.6/43.6 kB\u001b[0m \u001b[31m3.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: numpy>=1.14.0 in /usr/local/lib/python3.10/dist-packages (from seqeval) (1.26.4)\n",
            "Requirement already satisfied: scikit-learn>=0.21.3 in /usr/local/lib/python3.10/dist-packages (from seqeval) (1.3.2)\n",
            "Requirement already satisfied: scipy>=1.5.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.21.3->seqeval) (1.13.1)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.21.3->seqeval) (1.4.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.21.3->seqeval) (3.5.0)\n",
            "Building wheels for collected packages: seqeval\n",
            "  Building wheel for seqeval (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for seqeval: filename=seqeval-1.2.2-py3-none-any.whl size=16162 sha256=0b2e0679cdb5470c9a7971a87eef9695e90e78bf74af069be6d1427513cb5d8c\n",
            "  Stored in directory: /root/.cache/pip/wheels/1a/67/4a/ad4082dd7dfc30f2abfe4d80a2ed5926a506eb8a972b4767fa\n",
            "Successfully built seqeval\n",
            "Installing collected packages: seqeval\n",
            "Successfully installed seqeval-1.2.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import datasets\n",
        "import transformers\n",
        "import numpy as np\n",
        "from transformers import BertTokenizerFast\n",
        "from transformers import DataCollatorForTokenClassification\n",
        "from transformers import AutoModelForTokenClassification"
      ],
      "metadata": {
        "id": "KV4v-Gh7bTvN"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iEKacgn3bRIT"
      },
      "source": [
        "conll2003 Dataset for NER"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
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          ]
        },
        "id": "XtuOnlySbRIZ",
        "outputId": "5b78bfd3-1ac0-48a6-9ae0-715579799e1b"
      },
      "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": [
              "Downloading builder script:   0%|          | 0.00/9.57k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "51a03c208ce540c4b63741a3c28ca756"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/12.3k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5619f1160daa4fb489b4c3c5381731f8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/983k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0f2816837baf45ce8a25dd94bba1fb99"
            }
          },
          "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": "4d7704920dce40128126b9f490aa0e7b"
            }
          },
          "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": "a89768458f7d45dda01b363a96c0a9a7"
            }
          },
          "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": "80eaf04100e84acc847ca29d204abf05"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "dataset= datasets.load_dataset(\"conll2003\", trust_remote_code=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "egQgTfXCbRIa",
        "outputId": "9b213202-bf5e-4f4c-c0bf-375711016657"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags'],\n",
              "        num_rows: 14041\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags'],\n",
              "        num_rows: 3250\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags'],\n",
              "        num_rows: 3453\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hRSjbehwbRIe",
        "outputId": "70b344d7-ad33-49e8-dda2-e2ab61c7231e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sequence(feature=ClassLabel(names=['\"', \"''\", '#', '$', '(', ')', ',', '.', ':', '``', 'CC', 'CD', 'DT', 'EX', 'FW', 'IN', 'JJ', 'JJR', 'JJS', 'LS', 'MD', 'NN', 'NNP', 'NNPS', 'NNS', 'NN|SYM', 'PDT', 'POS', 'PRP', 'PRP$', 'RB', 'RBR', 'RBS', 'RP', 'SYM', 'TO', 'UH', 'VB', 'VBD', 'VBG', 'VBN', 'VBP', 'VBZ', 'WDT', 'WP', 'WP$', 'WRB'], id=None), length=-1, id=None)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "dataset['train'].features['pos_tags']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MRzYm1I5bRIg",
        "outputId": "f8fef8c3-36a8-46de-f1c2-fcc17a0579f5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sequence(feature=ClassLabel(names=['O', 'B-ADJP', 'I-ADJP', 'B-ADVP', 'I-ADVP', 'B-CONJP', 'I-CONJP', 'B-INTJ', 'I-INTJ', 'B-LST', 'I-LST', 'B-NP', 'I-NP', 'B-PP', 'I-PP', 'B-PRT', 'I-PRT', 'B-SBAR', 'I-SBAR', 'B-UCP', 'I-UCP', 'B-VP', 'I-VP'], id=None), length=-1, id=None)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "dataset['train'].features['chunk_tags']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1Jg-TPYIbRIh",
        "outputId": "a885da7d-f4d6-4bf6-8a36-fcf154275738"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sequence(feature=ClassLabel(names=['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC', 'B-MISC', 'I-MISC'], id=None), length=-1, id=None)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "dataset['train'].features['ner_tags']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QNACvqH3bRIi",
        "outputId": "850ff196-97e5-4ea4-9690-00981350a126"
      },
      "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": 9
        }
      ],
      "source": [
        "dataset['train'][0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RJde_eTBbRIk",
        "outputId": "1605246a-c225-4492-d2aa-3198dbe5278d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'train': (14041, 5), 'validation': (3250, 5), 'test': (3453, 5)}"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "dataset.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n2IiAZsgbRIm"
      },
      "source": [
        "Getting BERT Tokenizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        "id": "XvN1EINObRIo",
        "outputId": "1f495b5d-25d2-40a5-d8db-543cc6af7a0b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/48.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          },
          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          },
          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
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              "model_id": "bda968e1f7f74f55ba1ed803ebce48d3"
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          },
          "metadata": {}
        }
      ],
      "source": [
        "tokenizer= BertTokenizerFast.from_pretrained(\"bert-base-uncased\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XKXlQkPHbRIq",
        "outputId": "3616033b-5ef2-42b1-e50b-7fc5be0b4a1c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'id': '100',\n",
              " 'tokens': ['Rabinovich',\n",
              "  'is',\n",
              "  'winding',\n",
              "  'up',\n",
              "  'his',\n",
              "  'term',\n",
              "  'as',\n",
              "  'ambassador',\n",
              "  '.'],\n",
              " 'pos_tags': [21, 42, 39, 33, 29, 21, 15, 21, 7],\n",
              " 'chunk_tags': [11, 21, 22, 15, 11, 12, 13, 11, 0],\n",
              " 'ner_tags': [1, 0, 0, 0, 0, 0, 0, 0, 0]}"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "random_example= dataset['train'][100]\n",
        "random_example"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nuQRO-KDbRIt",
        "outputId": "9477ad04-1f40-4ae0-d7ba-f6956e6749a8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': [101, 10958, 21891, 12933, 2003, 12788, 2039, 2010, 2744, 2004, 6059, 1012, 102], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "tokenized_input= tokenizer(random_example['tokens'], is_split_into_words= True)\n",
        "tokenized_input"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2S891PombRIv",
        "outputId": "617f7834-f551-4484-b978-278857ffed66"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['[CLS]',\n",
              " 'ra',\n",
              " '##bino',\n",
              " '##vich',\n",
              " 'is',\n",
              " 'winding',\n",
              " 'up',\n",
              " 'his',\n",
              " 'term',\n",
              " 'as',\n",
              " 'ambassador',\n",
              " '.',\n",
              " '[SEP]']"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "tokens= tokenizer.convert_ids_to_tokens(tokenized_input['input_ids'])\n",
        "\n",
        "tokens"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U4UqOwUPbRIx",
        "outputId": "9db83623-b1ee-49a5-a791-b3c3a971b1f0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[None, 0, 0, 0, 1, 2, 3, 4, 5, 6, 7, 8, None]"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "word_ids= tokenized_input.word_ids()\n",
        "word_ids"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YVlsAAnybRIy",
        "outputId": "7a4884dd-6ef7-409d-fdb0-b65dedbfe68d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "len(random_example['ner_tags'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kVGIozovbRIz",
        "outputId": "f9bdbb2f-d3a1-4574-aad3-9a72a862d9e1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "13"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "len(tokenized_input['input_ids'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fyVgsv1xbRI0"
      },
      "source": [
        "#Problem\n",
        "\n",
        "the input ids returned by tokenizer (13) is larger than the list of labels that our dataset contain (9)\n",
        "\n",
        "reasons (the tokenizer added following changes):\n",
        "- special tokens at start CLS, and at end SEP\n",
        "- subword tokens Rabinovich= 'ra', '##bino', '##vich'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wk1NWRcSbRI1"
      },
      "source": [
        "#Solution\n",
        "\n",
        "- add -100 for special tokens (for CLS and for SEP)\n",
        "- mask the subwords"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MuF1IjJWbRI3"
      },
      "source": [
        "#why -100?\n",
        "\n",
        "cuz during training torch will ignore -100 index (In PyTorch the cross-entropy loss class torch.nn.CrossEntropyLoss has an attribute called ignore_index whose value is –100. This index is ignored during training)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "Zph8xorhbRI5"
      },
      "outputs": [],
      "source": [
        "def tokenize_and_adjust_labels(dataset, label_all_tokens=True):\n",
        "    # Tokenize the input tokens from the dataset\n",
        "    tokenized_inputs = tokenizer(dataset[\"tokens\"], truncation=True, is_split_into_words=True)\n",
        "\n",
        "    # Initialize an empty list to store the adjusted labels\n",
        "    labels = []\n",
        "\n",
        "    # Iterate over the NER tags in the dataset\n",
        "    for i, label in enumerate(dataset[\"ner_tags\"]):\n",
        "        # Get the word IDs for the current sentence in the batch\n",
        "        word_ids = tokenized_inputs.word_ids(batch_index=i)\n",
        "\n",
        "        # Initialize the previous word index to None\n",
        "        previous_word_index = None\n",
        "        # Initialize an empty list to store the labels for the current sentence\n",
        "        label_ids = []\n",
        "\n",
        "        # Iterate over the word indices in the tokenized input\n",
        "        for word_index in word_ids:\n",
        "            # If the word index is None (e.g., special tokens like [CLS] and [SEP])\n",
        "            if word_index is None:\n",
        "                label_ids.append(-100)  # Assign -100 to these tokens\n",
        "\n",
        "            # If the current word index is different from the previous one\n",
        "            elif word_index != previous_word_index:\n",
        "                label_ids.append(label[word_index])  # Assign the corresponding label\n",
        "\n",
        "            # If the current word index is the same as the previous one (subword)\n",
        "            else:\n",
        "                label_ids.append(label[word_index] if label_all_tokens else -100)  # Assign label or -100 based on the flag\n",
        "\n",
        "            # Update the previous word index\n",
        "            previous_word_index = word_index\n",
        "\n",
        "        # Append the labels for the current sentence to the labels list\n",
        "        labels.append(label_ids)\n",
        "\n",
        "    # Add the labels to the tokenized inputs\n",
        "    tokenized_inputs[\"labels\"] = labels\n",
        "    return tokenized_inputs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "Qpm9hgmwbRI8"
      },
      "outputs": [],
      "source": [
        "test = tokenize_and_adjust_labels({'tokens': [dataset['train'][50]['tokens']], 'ner_tags': [dataset['train'][50]['ner_tags']]})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MsuAWlhhbRI9",
        "outputId": "dbb9fc33-884e-407a-c6ca-6394811a65bf"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': [[101, 6728, 2884, 12943, 2362, 2007, 2236, 9693, 2234, 1999, 2117, 2173, 2007, 4749, 1010, 25717, 8819, 2015, 1010, 2385, 1012, 1018, 3867, 1997, 1996, 3452, 3275, 1012, 102]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'labels': [[-100, 3, 3, 4, 0, 0, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -100]]}"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "b96f5ff4a9ae4658bca36066ea621282",
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            "3c0120b7fd0f4a188f642c8cf1c71823",
            "eeabbc69a3514c1793630d57caff7abc",
            "5b61dab291b24684a703c13a9c14ee9b",
            "d6830e632b9447f09ab169d33c8892ad",
            "6f210d8976fa4ae7b9a4f8180e5e8566",
            "b098223ebffe42a7a6a4bc8e637ecc32",
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            "987f91d0b446479585eed76710958812",
            "6347786bd78c489b905a238b255f88c3",
            "72d76181e7d44b40971960bfe8406ac5",
            "978deb898cbd45d18bc154be70be0219",
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            "610c96bea3194253a5d88af589acbc3a",
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            "aee0947348f7462ebee3e56e116b80cf",
            "7a2bacac56674aedb47d1043ca63c8ab"
          ]
        },
        "id": "yW4ErnqzbRJA",
        "outputId": "c2a0dcc8-0bc1-46b6-8e88-0a143a00cfbc"
      },
      "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": "b96f5ff4a9ae4658bca36066ea621282"
            }
          },
          "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": "46fb214900ad4038a6a3f32c5b2f7e75"
            }
          },
          "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": "987f91d0b446479585eed76710958812"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "tokenized_dataset= dataset.map(\n",
        "    tokenize_and_adjust_labels,\n",
        "    batched=True\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y5kf4OxfbRJB",
        "outputId": "468d8b3d-c535-415c-91b8-04b53bb9879c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'id': '100',\n",
              " 'tokens': ['Rabinovich',\n",
              "  'is',\n",
              "  'winding',\n",
              "  'up',\n",
              "  'his',\n",
              "  'term',\n",
              "  'as',\n",
              "  'ambassador',\n",
              "  '.'],\n",
              " 'pos_tags': [21, 42, 39, 33, 29, 21, 15, 21, 7],\n",
              " 'chunk_tags': [11, 21, 22, 15, 11, 12, 13, 11, 0],\n",
              " 'ner_tags': [1, 0, 0, 0, 0, 0, 0, 0, 0],\n",
              " 'input_ids': [101,\n",
              "  10958,\n",
              "  21891,\n",
              "  12933,\n",
              "  2003,\n",
              "  12788,\n",
              "  2039,\n",
              "  2010,\n",
              "  2744,\n",
              "  2004,\n",
              "  6059,\n",
              "  1012,\n",
              "  102],\n",
              " 'token_type_ids': [0, 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, 1],\n",
              " 'labels': [-100, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, -100]}"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "tokenized_dataset['train'][100]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Eb4kSi-7bRJC"
      },
      "source": [
        "Adding BERT \"uncased\" model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104,
          "referenced_widgets": [
            "ee8bb7ed57fc4c53affd1758cb686337",
            "edd116067659491281fd5227ff421be3",
            "984f984bf23b437facaa21fbe95c5620",
            "877e8a8ca1e2497f9fe573e7801aea46",
            "a26b061f31104dcb8d09605baeae0b67",
            "d9029eb1237242158747e790bee01e96",
            "9b5871752a1b43e78c3332fd709123dc",
            "6f736c534e3d455094a9acfe35e7c12c",
            "075f805b2f2d4ffaa0047fac71c61313",
            "7ff4a9ddaaf7495f8b7a6feb5a0994c4",
            "3a27cd5e64314fc78af5f0ea23876e7d"
          ]
        },
        "id": "MgETEY3EbRJD",
        "outputId": "bf5b14ee-85aa-493c-83a8-fb67e9d70942"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/440M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ee8bb7ed57fc4c53affd1758cb686337"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of BertForTokenClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ],
      "source": [
        "model= AutoModelForTokenClassification.from_pretrained(\"bert-base-uncased\", num_labels=9)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1KJbXQ0jbRJE"
      },
      "source": [
        "Specifying Training Arguments"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aQPM7E9KbRJF",
        "outputId": "85470c0e-27d9-464f-b8a4-db3000a6a09b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1494: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "from transformers import TrainingArguments\n",
        "\n",
        "args= TrainingArguments(\n",
        "    \"ner-model\",\n",
        "    evaluation_strategy= \"epoch\",\n",
        "    learning_rate= 2e-5,\n",
        "    per_device_train_batch_size= 16,\n",
        "    per_device_eval_batch_size= 16,\n",
        "    num_train_epochs= 3,\n",
        "    weight_decay= 0.01\n",
        "\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N0kTuJMMbRJH"
      },
      "source": [
        "#data_collator\n",
        "\n",
        "data_collator handles padding and prepares the input sequences and labels for training. The resulting batch is a dictionary with keys like input_ids, attention_mask, and labels, each containing padded and properly formatted tensors ready for model training."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "id": "A7rF07TpbRJJ"
      },
      "outputs": [],
      "source": [
        "data_collator= DataCollatorForTokenClassification(tokenizer)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DjfbQahlbRJL",
        "outputId": "055459c0-42f8-46d4-9a70-327a901aba8b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DataCollatorForTokenClassification(tokenizer=BertTokenizerFast(name_or_path='bert-base-uncased', vocab_size=30522, model_max_length=512, is_fast=True, padding_side='right', truncation_side='right', special_tokens={'unk_token': '[UNK]', 'sep_token': '[SEP]', 'pad_token': '[PAD]', 'cls_token': '[CLS]', 'mask_token': '[MASK]'}, clean_up_tokenization_spaces=True),  added_tokens_decoder={\n",
              "\t0: AddedToken(\"[PAD]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
              "\t100: AddedToken(\"[UNK]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
              "\t101: AddedToken(\"[CLS]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
              "\t102: AddedToken(\"[SEP]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
              "\t103: AddedToken(\"[MASK]\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n",
              "}, padding=True, max_length=None, pad_to_multiple_of=None, label_pad_token_id=-100, return_tensors='pt')"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ],
      "source": [
        "data_collator"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# seqeval\n",
        "seqeval is a Python framework for sequence labeling evaluation.\n",
        "seqeval can evaluate the performance of chunking tasks such as named-entity recognition, part-of-speech tagging, semantic role labeling and so on."
      ],
      "metadata": {
        "id": "ths1_I0_cnw6"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "id": "5yVsFFi3bRJN"
      },
      "outputs": [],
      "source": [
        "metric= datasets.load_metric('seqeval', trust_remote_code=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KuoUjTQRbRJO",
        "outputId": "c059349a-013e-4875-b7f7-41f7a62aef42"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Metric(name: \"seqeval\", features: {'predictions': Sequence(feature=Value(dtype='string', id='label'), length=-1, id='sequence'), 'references': Sequence(feature=Value(dtype='string', id='label'), length=-1, id='sequence')}, usage: \"\"\"\n",
              "Produces labelling scores along with its sufficient statistics\n",
              "from a source against one or more references.\n",
              "\n",
              "Args:\n",
              "    predictions: List of List of predicted labels (Estimated targets as returned by a tagger)\n",
              "    references: List of List of reference labels (Ground truth (correct) target values)\n",
              "    suffix: True if the IOB prefix is after type, False otherwise. default: False\n",
              "    scheme: Specify target tagging scheme. Should be one of [\"IOB1\", \"IOB2\", \"IOE1\", \"IOE2\", \"IOBES\", \"BILOU\"].\n",
              "        default: None\n",
              "    mode: Whether to count correct entity labels with incorrect I/B tags as true positives or not.\n",
              "        If you want to only count exact matches, pass mode=\"strict\". default: None.\n",
              "    sample_weight: Array-like of shape (n_samples,), weights for individual samples. default: None\n",
              "    zero_division: Which value to substitute as a metric value when encountering zero division. Should be on of 0, 1,\n",
              "        \"warn\". \"warn\" acts as 0, but the warning is raised.\n",
              "\n",
              "Returns:\n",
              "    'scores': dict. Summary of the scores for overall and per type\n",
              "        Overall:\n",
              "            'accuracy': accuracy,\n",
              "            'precision': precision,\n",
              "            'recall': recall,\n",
              "            'f1': F1 score, also known as balanced F-score or F-measure,\n",
              "        Per type:\n",
              "            'precision': precision,\n",
              "            'recall': recall,\n",
              "            'f1': F1 score, also known as balanced F-score or F-measure\n",
              "Examples:\n",
              "\n",
              "    >>> predictions = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]\n",
              "    >>> references = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]\n",
              "    >>> seqeval = datasets.load_metric(\"seqeval\")\n",
              "    >>> results = seqeval.compute(predictions=predictions, references=references)\n",
              "    >>> print(list(results.keys()))\n",
              "    ['MISC', 'PER', 'overall_precision', 'overall_recall', 'overall_f1', 'overall_accuracy']\n",
              "    >>> print(results[\"overall_f1\"])\n",
              "    0.5\n",
              "    >>> print(results[\"PER\"][\"f1\"])\n",
              "    1.0\n",
              "\"\"\", stored examples: 0)"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ],
      "source": [
        "metric"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TDnmSwEQbRJQ",
        "outputId": "83840847-5717-4e12-8cbb-f50abaee42c3"
      },
      "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": 54
        }
      ],
      "source": [
        "dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "id": "PdnrrFF1bRJR"
      },
      "outputs": [],
      "source": [
        "label_list = dataset[\"train\"].features[\"ner_tags\"].feature.names"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1AOXAkl4bRJR",
        "outputId": "0104c5f8-f854-475d-acc8-e2a966c3e6ca"
      },
      "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": 56
        }
      ],
      "source": [
        "label_list"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "id": "FrZ-LRSwbRJS"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "\n",
        "def compute_metrics(eval_pred):\n",
        "    pred_logits, labels = eval_pred\n",
        "\n",
        "    # Get the predictions by taking the argmax of the logits\n",
        "    pred_logits = np.argmax(pred_logits, axis=2)\n",
        "\n",
        "    # Initialize lists to store the predictions and true labels\n",
        "    predictions = []\n",
        "    true_labels = []\n",
        "\n",
        "    # Iterate over the predictions and true labels\n",
        "    for prediction, label in zip(pred_logits, labels):\n",
        "        # Convert the indices to label names and filter out -100\n",
        "        pred_labels = [label_list[p] for p, l in zip(prediction, label) if l != -100]\n",
        "        true_labels_instance = [label_list[l] for p, l in zip(prediction, label) if l != -100]\n",
        "\n",
        "        # Append the results to the lists\n",
        "        predictions.append(pred_labels)\n",
        "        true_labels.append(true_labels_instance)\n",
        "\n",
        "    # Compute the evaluation metrics\n",
        "    results = metric.compute(predictions=predictions, references=true_labels)\n",
        "\n",
        "    return {\n",
        "        'precision': results['overall_precision'],\n",
        "        'recall': results['overall_recall'],\n",
        "        'f1': results['overall_f1'],\n",
        "        'accuracy': results['overall_accuracy']\n",
        "    }\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "id": "bB3PGmdpbRJT"
      },
      "outputs": [],
      "source": [
        "from transformers import Trainer\n",
        "\n",
        "trainer= Trainer(\n",
        "    model,\n",
        "    args,\n",
        "    train_dataset= tokenized_dataset['train'],\n",
        "    eval_dataset= tokenized_dataset['validation'],\n",
        "    data_collator= data_collator,\n",
        "    tokenizer= tokenizer,\n",
        "    compute_metrics= compute_metrics\n",
        ")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "QAco_AxAbRJ0",
        "outputId": "734de851-439c-4ad2-c39b-63517f70cf06"
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      "outputs": [
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          "output_type": "display_data",
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              "      \n",
              "      <progress value='2634' max='2634' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [2634/2634 10:53, 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>0.005600</td>\n",
              "      <td>0.080214</td>\n",
              "      <td>0.940986</td>\n",
              "      <td>0.941828</td>\n",
              "      <td>0.941407</td>\n",
              "      <td>0.985813</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.005500</td>\n",
              "      <td>0.076451</td>\n",
              "      <td>0.942150</td>\n",
              "      <td>0.949211</td>\n",
              "      <td>0.945667</td>\n",
              "      <td>0.986322</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.002900</td>\n",
              "      <td>0.076696</td>\n",
              "      <td>0.941379</td>\n",
              "      <td>0.948540</td>\n",
              "      <td>0.944946</td>\n",
              "      <td>0.986449</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
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        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=2634, training_loss=0.004120232360600158, metrics={'train_runtime': 653.5041, 'train_samples_per_second': 64.457, 'train_steps_per_second': 4.031, 'total_flos': 1020143109346326.0, 'train_loss': 0.004120232360600158, 'epoch': 3.0})"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ],
      "source": [
        "trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "id": "amWPAdUvbRJ1"
      },
      "outputs": [],
      "source": [
        "model.save_pretrained('./ner-bert-model')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iHuqNnM4bRJ2",
        "outputId": "b746100d-569a-44d8-f7d5-cf1bf4853d51"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('./ner-bert-tokenizer/tokenizer_config.json',\n",
              " './ner-bert-tokenizer/special_tokens_map.json',\n",
              " './ner-bert-tokenizer/vocab.txt',\n",
              " './ner-bert-tokenizer/added_tokens.json',\n",
              " './ner-bert-tokenizer/tokenizer.json')"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ],
      "source": [
        "tokenizer.save_pretrained('./ner-bert-tokenizer')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YMdI-kuhbRJ4",
        "outputId": "cb84692a-42ba-47fe-f232-d1d60dd037d6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Hardware accelerator e.g. GPU is available in the environment, but no `device` argument is passed to the `Pipeline` object. Model will be on CPU.\n"
          ]
        }
      ],
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "model= AutoModelForTokenClassification.from_pretrained('./ner-bert-model')\n",
        "tokenizer= BertTokenizerFast.from_pretrained('./ner-bert-tokenizer')\n",
        "\n",
        "ner_pipeline= pipeline('ner', model= model, tokenizer= tokenizer)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "asZzJeadbRJ6",
        "outputId": "31779fa7-963f-4016-f772-ea3b7e91dc48"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[{'entity': 'LABEL_1', 'score': 0.9985078, 'index': 1, 'word': 'john', 'start': 0, 'end': 4}, {'entity': 'LABEL_0', 'score': 0.99971575, 'index': 2, 'word': 'lives', 'start': 5, 'end': 10}, {'entity': 'LABEL_0', 'score': 0.99954563, 'index': 3, 'word': 'in', 'start': 11, 'end': 13}, {'entity': 'LABEL_5', 'score': 0.99972135, 'index': 4, 'word': 'new', 'start': 14, 'end': 17}, {'entity': 'LABEL_6', 'score': 0.9996569, 'index': 5, 'word': 'york', 'start': 18, 'end': 22}, {'entity': 'LABEL_0', 'score': 0.99994206, 'index': 6, 'word': 'and', 'start': 23, 'end': 26}, {'entity': 'LABEL_0', 'score': 0.99991286, 'index': 7, 'word': 'works', 'start': 27, 'end': 32}, {'entity': 'LABEL_0', 'score': 0.99976796, 'index': 8, 'word': 'for', 'start': 33, 'end': 36}, {'entity': 'LABEL_0', 'score': 0.99966156, 'index': 9, 'word': 'the', 'start': 37, 'end': 40}, {'entity': 'LABEL_3', 'score': 0.99972445, 'index': 10, 'word': 'united', 'start': 41, 'end': 47}, {'entity': 'LABEL_4', 'score': 0.9994531, 'index': 11, 'word': 'nations', 'start': 48, 'end': 55}, {'entity': 'LABEL_0', 'score': 0.99994457, 'index': 12, 'word': '.', 'start': 55, 'end': 56}]\n"
          ]
        }
      ],
      "source": [
        "test_example= 'John lives in New York and works for the United Nations.'\n",
        "ner_results= ner_pipeline(test_example)\n",
        "\n",
        "print(ner_results)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(label_list)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4a6UkAvEdcZn",
        "outputId": "093db844-0233-4e83-87ca-3cbb542637fe"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC', 'B-MISC', 'I-MISC']\n"
          ]
        }
      ]
    }
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