NLP/YT_Fine_tuning_BERT_NER_v1.ipynb

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        "---------------------------------\n",
        "\n",
        "<a href=\"https://www.youtube.com/watch?v=dzyDHMycx_c&list=PLxqBkZuBynVQEvXfJpq3smfuKq3AiNW-N&index=18\"><h1 style=\"font-size:250%; font-family:cursive; color:#ff6666;\"><b>Link YouTube Video - Fine Tuning BERT for Named Entity Recognition (NER) | NLP</b></h1></a>\n",
        "\n",
        "[![IMAGE ALT TEXT](https://imgur.com/O7UNR3C.png)](https://bit.ly/3mXnKGH \"\")\n",
        "\n",
        "\n",
        "## [Dataset in HuggingFace](https://huggingface.co/datasets/conll2003)"
      ]
    },
    {
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      "source": [
        "## First What is BERT?\n",
        "\n",
        "BERT stands for Bidirectional Encoder Representations from Transformers. The name itself gives us several clues to what BERT is all about.\n",
        "\n",
        "BERT architecture consists of several Transformer encoders stacked together. Each Transformer encoder encapsulates two sub-layers: a self-attention layer and a feed-forward layer.\n",
        "\n",
        "### There are two different BERT models:\n",
        "\n",
        "- BERT base, which is a BERT model consists of 12 layers of Transformer encoder, 12 attention heads, 768 hidden size, and 110M parameters.\n",
        "\n",
        "- BERT large, which is a BERT model consists of 24 layers of Transformer encoder,16 attention heads, 1024 hidden size, and 340 parameters.\n",
        "\n",
        "\n",
        "\n",
        "BERT Input and Output\n",
        "BERT model expects a sequence of tokens (words) as an input. In each sequence of tokens, there are two special tokens that BERT would expect as an input:\n",
        "\n",
        "- [CLS]: This is the first token of every sequence, which stands for classification token.\n",
        "- [SEP]: This is the token that makes BERT know which token belongs to which sequence. This special token is mainly important for a next sentence prediction task or question-answering task. If we only have one sequence, then this token will be appended to the end of the sequence.\n",
        "\n",
        "\n",
        "It is also important to note that the maximum size of tokens that can be fed into BERT model is 512. If the tokens in a sequence are less than 512, we can use padding to fill the unused token slots with [PAD] token. If the tokens in a sequence are longer than 512, then we need to do a truncation.\n",
        "\n",
        "And that’s all that BERT expects as input.\n",
        "\n",
        "BERT model then will output an embedding vector of size 768 in each of the tokens. We can use these vectors as an input for different kinds of NLP applications, whether it is text classification, next sentence prediction, Named-Entity-Recognition (NER), or question-answering.\n",
        "\n",
        "\n",
        "------------\n",
        "\n",
        "**For a text classification task**, we focus our attention on the embedding vector output from the special [CLS] token. This means that we’re going to use the embedding vector of size 768 from [CLS] token as an input for our classifier, which then will output a vector of size the number of classes in our classification task.\n",
        "\n",
        "-----------------------\n",
        "\n",
        "![Imgur](https://imgur.com/NpeB9vb.png)\n",
        "\n",
        "-------------------------"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "7s5qlqI-etnc"
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      "outputs": [],
      "source": [
        "!pip install transformers datasets tokenizers seqeval -q"
      ]
    },
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        "id": "SGFDaV90_AJ5"
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      "source": [
        "# Token classification\n",
        "\n",
        "The first application we’ll explore is token classification. This generic task encompasses any problem that can be formulated as “attributing a label to each token in a sentence,” such as:\n",
        "\n",
        "**Named entity recognition (NER):** Find the entities (such as persons, locations, or organizations) in a sentence. This can be formulated as attributing a label to each token by having one class per entity and one class for “no entity.”\n",
        "\n",
        "**Part-of-speech tagging (POS):** Mark each word in a sentence as corresponding to a particular part of speech (such as noun, verb, adjective, etc.).\n",
        "\n",
        "**Chunking:** Find the tokens that belong to the same entity. This task (which can be combined with POS or NER) can be formulated as attributing one label (usually B-) to any tokens that are at the beginning of a chunk, another label (usually I-) to tokens that are inside a chunk, and a third label (usually O) to tokens that don’t belong to any chunk."
      ]
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        "* O means the word doesn’t correspond to any entity.\n",
        "* B-PER/I-PER means the word corresponds to the beginning of/is inside a person entity.\n",
        "* B-ORG/I-ORG means the word corresponds to the beginning of/is inside an organization entity.\n",
        "* B-LOC/I-LOC means the word corresponds to the beginning of/is inside a location entity.\n",
        "* B-MISC/I-MISC means the word corresponds to the beginning of/is inside a miscellaneous entity."
      ]
    },
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      "source": [
        "### https://huggingface.co/course/chapter7/2"
      ]
    },
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      "source": [
        "import datasets \n",
        "import numpy as np \n",
        "from transformers import BertTokenizerFast \n",
        "from transformers import DataCollatorForTokenClassification \n",
        "from transformers import AutoModelForTokenClassification \n",
        "\n",
        "conll2003 = datasets.load_dataset(\"conll2003\") "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z9A7Jy37_AJ9",
        "outputId": "c1436dd8-5a23-4441-fc91-41ff365d8260"
      },
      "outputs": [
        {
          "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",
              "})"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "conll2003"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VSaCXJh7_AJ-",
        "outputId": "e7c168c6-b03f-47dd-db80-5c66c5fe038f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'train': (14041, 5), 'validation': (3250, 5), 'test': (3453, 5)}"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "conll2003.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HXAzwpSNe4FM",
        "outputId": "d5797e44-e3bb-4b21-a49f-b1f31a17addb"
      },
      "outputs": [
        {
          "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]}"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "conll2003[\"train\"][0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a6w_AVpbe6BZ",
        "outputId": "f8a8b799-fda8-4cdd-dfca-c729ea9157db"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Sequence(feature=ClassLabel(num_classes=9, names=['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC', 'B-MISC', 'I-MISC'], id=None), length=-1, id=None)"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "conll2003[\"train\"].features[\"ner_tags\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
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        "outputId": "e341f6cc-306b-4019-d239-5419e045b88f"
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      "outputs": [
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            "text/plain": [
              "'The shared task of CoNLL-2003 concerns language-independent named entity recognition. We will concentrate on\\nfour types of named entities: persons, locations, organizations and names of miscellaneous entities that do\\nnot belong to the previous three groups.\\n\\nThe CoNLL-2003 shared task data files contain four columns separated by a single space. Each word has been put on\\na separate line and there is an empty line after each sentence. The first item on each line is a word, the second\\na part-of-speech (POS) tag, the third a syntactic chunk tag and the fourth the named entity tag. The chunk tags\\nand the named entity tags have the format I-TYPE which means that the word is inside a phrase of type TYPE. Only\\nif two phrases of the same type immediately follow each other, the first word of the second phrase will have tag\\nB-TYPE to show that it starts a new phrase. A word with tag O is not part of a phrase. Note the dataset uses IOB2\\ntagging scheme, whereas the original dataset uses IOB1.\\n\\nFor more details see https://www.clips.uantwerpen.be/conll2003/ner/ and https://www.aclweb.org/anthology/W03-0419\\n'"
            ]
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        }
      ],
      "source": [
        "conll2003['train'].description"
      ]
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        "id": "vPdTN25De8qy",
        "outputId": "dbc85372-e4fb-49a7-a9bb-5f9bd93e0711"
      },
      "outputs": [],
      "source": [
        "tokenizer = BertTokenizerFast.from_pretrained(\"bert-base-uncased\") "
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "DWOeOxlP_AKA"
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      "source": [
        "# Problem of consecutive subwords.\n",
        "\n",
        "### Note that transformers are often pretrained with subword tokenizers, meaning that even if your inputs have been split into words already, each of those words could be split again by the tokenizer.\n",
        "\n",
        "### This means that we need to do some processing on our labels as the input ids returned by the tokenizer are longer than the lists of labels our dataset contain.\n",
        "\n",
        "This is happening, first because some special tokens might be added (we can a [CLS] and a [SEP] above) and then because of those possible splits of words in multiple tokens:\n",
        "\n",
        "## Strategy to handle above - Here we set the labels of all special tokens to -100 (the index that is ignored by PyTorch) and the labels of all other tokens to the label of the word they come from. Another strategy is to set the label only on the first token obtained from a given word, and give a label of -100 to the other subtokens from the same word. We propose the two strategies here, just change the value of the following flag:"
      ]
    },
    {
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      "metadata": {
        "id": "r-W_ZVTG_AKB"
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      "source": [
        "-----------------------------------\n",
        "\n",
        "### Setting –100 as the label for these special tokens and the subwords we wish to mask during training:\n",
        "\n",
        "Why did we choose –100 as the ID to mask subword representations? The reason is\n",
        "that in PyTorch the cross-entropy loss class torch.nn.CrossEntropyLoss has an\n",
        "attribute called ignore_index whose value is –100. This index is ignored during\n",
        "training,\n",
        "\n",
        "Also we can use it to ignore the tokens associated with consecutive subwords.\n",
        "\n",
        "-----------------------------------"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "uasGvMFo_AKB"
      },
      "source": [
        "## Below cell are just for checking the output of some variables before applying `tokenize_and_align_labels()`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
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        },
        "id": "KDVOKocl_AKC",
        "outputId": "f0837918-ac1e-43e6-a1fd-ee5f6d8789ff"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[None, 0, 1, 2, 3, 4, 5, 6, 7, 8, None]\n"
          ]
        },
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          "data": {
            "text/plain": [
              "{'input_ids': [101, 7327, 19164, 2446, 2655, 2000, 17757, 2329, 12559, 1012, 102], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "example_text = conll2003['train'][0]\n",
        "\n",
        "tokenized_input = tokenizer(example_text[\"tokens\"], is_split_into_words=True)\n",
        "\n",
        "tokens = tokenizer.convert_ids_to_tokens(tokenized_input[\"input_ids\"])\n",
        "\n",
        "word_ids = tokenized_input.word_ids()\n",
        "\n",
        "print(word_ids)\n",
        "\n",
        "''' As we can see, it returns a list with the same number of elements as our processed input ids, mapping special tokens to None and all other tokens to their respective word. This way, we can align the labels with the processed input ids. '''\n",
        "\n",
        "tokenized_input"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "gA-KLQ-D_AKC"
      },
      "source": [
        "## Problem of Sub-Token - The  input ids returned by the tokenizer are longer than the lists of labels our dataset contain."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bhQzTgKg_AKC",
        "outputId": "c322d2c2-db99-431c-83a0-f1b39bb7136a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(9, 11)"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(example_text['ner_tags']), len(tokenized_input[\"input_ids\"])\n",
        "# (9, 11)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "FsMWHr9r_AKD"
      },
      "source": [
        "## The below function `tokenize_and_align_labels` does 2 jobs\n",
        "\n",
        "1. set –100 as the label for these special tokens and the subwords we wish to mask during training\n",
        "2. mask the subword representations after the first subword\n",
        "\n",
        "\n",
        "### Then we align the labels with the token ids using the strategy we picked:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "4FX3JmnRfAv7"
      },
      "outputs": [],
      "source": [
        "def tokenize_and_align_labels(examples, label_all_tokens=True): \n",
        "    \"\"\"\n",
        "    Function to tokenize and align labels with respect to the tokens. This function is specifically designed for\n",
        "    Named Entity Recognition (NER) tasks where alignment of the labels is necessary after tokenization.\n",
        "\n",
        "    Parameters:\n",
        "    examples (dict): A dictionary containing the tokens and the corresponding NER tags.\n",
        "                     - \"tokens\": list of words in a sentence.\n",
        "                     - \"ner_tags\": list of corresponding entity tags for each word.\n",
        "                     \n",
        "    label_all_tokens (bool): A flag to indicate whether all tokens should have labels. \n",
        "                             If False, only the first token of a word will have a label, \n",
        "                             the other tokens (subwords) corresponding to the same word will be assigned -100.\n",
        "\n",
        "    Returns:\n",
        "    tokenized_inputs (dict): A dictionary containing the tokenized inputs and the corresponding labels aligned with the tokens.\n",
        "    \"\"\"\n",
        "    tokenized_inputs = tokenizer(examples[\"tokens\"], truncation=True, is_split_into_words=True) \n",
        "    labels = [] \n",
        "    for i, label in enumerate(examples[\"ner_tags\"]): \n",
        "        word_ids = tokenized_inputs.word_ids(batch_index=i) \n",
        "        # word_ids() => Return a list mapping the tokens\n",
        "        # to their actual word in the initial sentence.\n",
        "        # It Returns a list indicating the word corresponding to each token. \n",
        "        previous_word_idx = None \n",
        "        label_ids = []\n",
        "        # Special tokens like `<s>` and `<\\s>` are originally mapped to None \n",
        "        # We need to set the label to -100 so they are automatically ignored in the loss function.\n",
        "        for word_idx in word_ids: \n",
        "            if word_idx is None: \n",
        "                # set –100 as the label for these special tokens\n",
        "                label_ids.append(-100)\n",
        "            # For the other tokens in a word, we set the label to either the current label or -100, depending on\n",
        "            # the label_all_tokens flag.\n",
        "            elif word_idx != previous_word_idx:\n",
        "                # if current word_idx is != prev then its the most regular case\n",
        "                # and add the corresponding token                 \n",
        "                label_ids.append(label[word_idx]) \n",
        "            else: \n",
        "                # to take care of sub-words which have the same word_idx\n",
        "                # set -100 as well for them, but only if label_all_tokens == False\n",
        "                label_ids.append(label[word_idx] if label_all_tokens else -100) \n",
        "                # mask the subword representations after the first subword\n",
        "                 \n",
        "            previous_word_idx = word_idx \n",
        "        labels.append(label_ids) \n",
        "    tokenized_inputs[\"labels\"] = labels \n",
        "    return tokenized_inputs "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ssUnW4fyfDTc",
        "outputId": "eb0d07e8-f5da-4033-95fc-46b152e28dc9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'input_ids': [[101, 2762, 1005, 1055, 4387, 2000, 1996, 2647, 2586, 1005, 1055, 15651, 2837, 14121, 1062, 9328, 5804, 2056, 2006, 9317, 10390, 2323, 4965, 8351, 4168, 4017, 2013, 3032, 2060, 2084, 3725, 2127, 1996, 4045, 6040, 2001, 24509, 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, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'labels': [[-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"
          ]
        }
      ],
      "source": [
        "q = tokenize_and_align_labels(conll2003['train'][4:5]) \n",
        "print(q) "
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "A08S1NJn_AKD"
      },
      "source": [
        "### So before applying the `tokenize_and_align_labels()` the `tokenized_input` has 3 keys \n",
        "- input_ids\n",
        "- token_type_ids\n",
        "- attention_mask\n",
        "\n",
        "But after applying `tokenize_and_align_labels()` we have an extra key - `'labels'`\n",
        "\n",
        "\n",
        "==================================="
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gyw5ULrtfFfC",
        "outputId": "4caab7ac-69e9-4ca3-cb93-13ce235b2e5c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[CLS]___________________________________ -100\n",
            "germany_________________________________ 5\n",
            "'_______________________________________ 0\n",
            "s_______________________________________ 0\n",
            "representative__________________________ 0\n",
            "to______________________________________ 0\n",
            "the_____________________________________ 0\n",
            "european________________________________ 3\n",
            "union___________________________________ 4\n",
            "'_______________________________________ 0\n",
            "s_______________________________________ 0\n",
            "veterinary______________________________ 0\n",
            "committee_______________________________ 0\n",
            "werner__________________________________ 1\n",
            "z_______________________________________ 2\n",
            "##wing__________________________________ 2\n",
            "##mann__________________________________ 2\n",
            "said____________________________________ 0\n",
            "on______________________________________ 0\n",
            "wednesday_______________________________ 0\n",
            "consumers_______________________________ 0\n",
            "should__________________________________ 0\n",
            "buy_____________________________________ 0\n",
            "sheep___________________________________ 0\n",
            "##me____________________________________ 0\n",
            "##at____________________________________ 0\n",
            "from____________________________________ 0\n",
            "countries_______________________________ 0\n",
            "other___________________________________ 0\n",
            "than____________________________________ 0\n",
            "britain_________________________________ 5\n",
            "until___________________________________ 0\n",
            "the_____________________________________ 0\n",
            "scientific______________________________ 0\n",
            "advice__________________________________ 0\n",
            "was_____________________________________ 0\n",
            "clearer_________________________________ 0\n",
            "._______________________________________ 0\n",
            "[SEP]___________________________________ -100\n"
          ]
        }
      ],
      "source": [
        "for token, label in zip(tokenizer.convert_ids_to_tokens(q[\"input_ids\"][0]),q[\"labels\"][0]): \n",
        "    print(f\"{token:_<40} {label}\") "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
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        "id": "Kg0c2D7yfbf0",
        "outputId": "0110b5f4-d9c7-4fce-f404-e47449a35bf3"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "15195ebcadb04c85ad2fa41f525f85e9",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/15 [00:00<?, ?ba/s]"
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "  0%|          | 0/4 [00:00<?, ?ba/s]"
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        {
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              "  0%|          | 0/4 [00:00<?, ?ba/s]"
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          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "tokenized_datasets = conll2003.map(tokenize_and_align_labels, batched=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156,
          "referenced_widgets": [
            "bb30f8151e0d4d62be3a1b498b12e819",
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          ]
        },
        "id": "kSF7QRDQffPB",
        "outputId": "fd402893-085f-462e-c90f-84b308ddc102"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "bb30f8151e0d4d62be3a1b498b12e819",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/440M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForTokenClassification: ['cls.predictions.transform.LayerNorm.weight', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.dense.weight', 'cls.seq_relationship.bias', 'cls.seq_relationship.weight', 'cls.predictions.bias']\n",
            "- This IS expected if you are initializing BertForTokenClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertForTokenClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of 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": [
        "\n",
        "model = AutoModelForTokenClassification.from_pretrained(\"bert-base-uncased\", num_labels=9)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "okyF8VWWfhnv"
      },
      "outputs": [],
      "source": [
        "from transformers import TrainingArguments, Trainer \n",
        "args = TrainingArguments( \n",
        "\"test-ner\",\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",
        ") "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "D3fNcCdRfkV0"
      },
      "outputs": [],
      "source": [
        "data_collator = DataCollatorForTokenClassification(tokenizer) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104,
          "referenced_widgets": [
            "eb877d145cc84fd28e86460c29c082ea",
            "66dabdb412e3419b931d13984b46d52f",
            "e5a9d43890ef4018ab961ad2fe3cbc33",
            "f9ae7c48403a49848d71f24b58e4350a",
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            "de8817fd23d4429b944f82c94b0bc68a",
            "0527de0b8c1c4cef907a10d143a12b93"
          ]
        },
        "id": "sG9bdKiifovx",
        "outputId": "eb1fb21e-6454-4e59-9a66-b601de918890"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: FutureWarning: load_metric is deprecated and will be removed in the next major version of datasets. Use 'evaluate.load' instead, from the new library 🤗 Evaluate: https://huggingface.co/docs/evaluate\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "eb877d145cc84fd28e86460c29c082ea",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/2.47k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "metric = datasets.load_metric(\"seqeval\") "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "C-tpGx0sft-q"
      },
      "outputs": [],
      "source": [
        "example = conll2003['train'][0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vom5Yo3LfqoN",
        "outputId": "f539c7a3-c429-4cb1-c07a-fced2c37d1f3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC', 'B-MISC', 'I-MISC']"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "label_list = conll2003[\"train\"].features[\"ner_tags\"].feature.names \n",
        "\n",
        "label_list"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R11EMwO2_AKG",
        "outputId": "943ade34-1be5-45eb-cab4-c334a2adc04b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'MISC': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2},\n",
              " 'ORG': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1},\n",
              " 'overall_precision': 1.0,\n",
              " 'overall_recall': 1.0,\n",
              " 'overall_f1': 1.0,\n",
              " 'overall_accuracy': 1.0}"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "\n",
        "\n",
        "labels = [label_list[i] for i in example[\"ner_tags\"]] \n",
        "\n",
        "metric.compute(predictions=[labels], references=[labels]) "
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "2Px8K0rm_AKG"
      },
      "source": [
        "## seqeval - The way the package works by accepting list of lists\n",
        "\n",
        "The seqeval package expects the predictions and labels as lists of lists, with\n",
        "each list corresponding to a single example in our validation or test sets. To\n",
        "integrate these metrics during training, we need a function that can take the\n",
        "outputs of the model and convert them into the lists that seqeval expects. \n",
        "\n",
        "The following does the trick by ensuring we ignore the label IDs associated with\n",
        "subsequent subwords:"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "-IZiTIn3_AKG"
      },
      "source": [
        "## Compute Metrics\n",
        "\n",
        "This compute_metrics() function first takes the argmax of the logits to convert them to predictions (as usual, the logits and the probabilities are in the same order, so we don’t need to apply the softmax). Then we have to convert both labels and predictions from integers to strings. We remove all the values where the label is -100, then pass the results to the metric.compute() method:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "tySgh2eY_AKG"
      },
      "outputs": [],
      "source": [
        "def compute_metrics(eval_preds): \n",
        "    \"\"\"\n",
        "    Function to compute the evaluation metrics for Named Entity Recognition (NER) tasks.\n",
        "    The function computes precision, recall, F1 score and accuracy.\n",
        "\n",
        "    Parameters:\n",
        "    eval_preds (tuple): A tuple containing the predicted logits and the true labels.\n",
        "\n",
        "    Returns:\n",
        "    A dictionary containing the precision, recall, F1 score and accuracy.\n",
        "    \"\"\"\n",
        "    pred_logits, labels = eval_preds \n",
        "    \n",
        "    pred_logits = np.argmax(pred_logits, axis=2) \n",
        "    # the logits and the probabilities are in the same order,\n",
        "    # so we don’t need to apply the softmax\n",
        "    \n",
        "    # We remove all the values where the label is -100\n",
        "    predictions = [ \n",
        "        [label_list[eval_preds] for (eval_preds, l) in zip(prediction, label) if l != -100] \n",
        "        for prediction, label in zip(pred_logits, labels) \n",
        "    ] \n",
        "    \n",
        "    true_labels = [ \n",
        "      [label_list[l] for (eval_preds, l) in zip(prediction, label) if l != -100] \n",
        "       for prediction, label in zip(pred_logits, labels) \n",
        "   ] \n",
        "    results = metric.compute(predictions=predictions, references=true_labels) \n",
        "    return { \n",
        "   \"precision\": results[\"overall_precision\"], \n",
        "   \"recall\": results[\"overall_recall\"], \n",
        "   \"f1\": results[\"overall_f1\"], \n",
        "  \"accuracy\": results[\"overall_accuracy\"], \n",
        "  } "
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "ZieP-5MU_AKG"
      },
      "source": [
        "### `predictions` will print a long 2d tensor like below\n",
        "\n",
        "```\n",
        "[['O', 'O', 'B-ORG', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O'], ['B-LOC', 'O', 'O', 'O', 'O', 'O'], ['B-MISC', 'I-MISC', 'O', 'O', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-ORG', 'O', 'B-ORG', 'O', ['O', 'O', 'O', 'O', 'O', 'O', 'B-ORG', 'B-ORG', 'O', 'B-ORG', 'I-ORG', 'O', 'B-ORG', 'B-ORG', 'B-ORG', 'I-ORG', 'O', 'B-ORG', 'I-ORG', 'O', 'O', 'O', 'O', 'O'], ['O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O'], \n",
        "\n",
        "---\n",
        "\n",
        "---\n",
        "\n",
        ", ['O', 'O', 'B-LOC', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O']]\n",
        "\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "pNZJvlI3f3dr"
      },
      "outputs": [],
      "source": [
        "trainer = Trainer( \n",
        "    model, \n",
        "    args, \n",
        "   train_dataset=tokenized_datasets[\"train\"], \n",
        "   eval_dataset=tokenized_datasets[\"validation\"], \n",
        "   data_collator=data_collator, \n",
        "   tokenizer=tokenizer, \n",
        "   compute_metrics=compute_metrics \n",
        ") "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "4LbUh9shgD5X",
        "outputId": "d674279e-e988-4837-a0be-a1acb01ba002"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "The following columns in the training set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: id, pos_tags, ner_tags, tokens, chunk_tags. If id, pos_tags, ner_tags, tokens, chunk_tags are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/optimization.py:310: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  FutureWarning,\n",
            "***** Running training *****\n",
            "  Num examples = 14041\n",
            "  Num Epochs = 3\n",
            "  Instantaneous batch size per device = 16\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 16\n",
            "  Gradient Accumulation steps = 1\n",
            "  Total optimization steps = 2634\n",
            "You're using a BertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='2634' max='2634' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [2634/2634 08:10, 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>0.217700</td>\n",
              "      <td>0.063699</td>\n",
              "      <td>0.923307</td>\n",
              "      <td>0.931983</td>\n",
              "      <td>0.927625</td>\n",
              "      <td>0.983589</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.048500</td>\n",
              "      <td>0.055804</td>\n",
              "      <td>0.932921</td>\n",
              "      <td>0.945967</td>\n",
              "      <td>0.939399</td>\n",
              "      <td>0.985845</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.024800</td>\n",
              "      <td>0.056144</td>\n",
              "      <td>0.938073</td>\n",
              "      <td>0.950666</td>\n",
              "      <td>0.944327</td>\n",
              "      <td>0.986449</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
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              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Saving model checkpoint to test-ner/checkpoint-500\n",
            "Configuration saved in test-ner/checkpoint-500/config.json\n",
            "Model weights saved in test-ner/checkpoint-500/pytorch_model.bin\n",
            "tokenizer config file saved in test-ner/checkpoint-500/tokenizer_config.json\n",
            "Special tokens file saved in test-ner/checkpoint-500/special_tokens_map.json\n",
            "The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: id, pos_tags, ner_tags, tokens, chunk_tags. If id, pos_tags, ner_tags, tokens, chunk_tags are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 3250\n",
            "  Batch size = 16\n",
            "Saving model checkpoint to test-ner/checkpoint-1000\n",
            "Configuration saved in test-ner/checkpoint-1000/config.json\n",
            "Model weights saved in test-ner/checkpoint-1000/pytorch_model.bin\n",
            "tokenizer config file saved in test-ner/checkpoint-1000/tokenizer_config.json\n",
            "Special tokens file saved in test-ner/checkpoint-1000/special_tokens_map.json\n",
            "Saving model checkpoint to test-ner/checkpoint-1500\n",
            "Configuration saved in test-ner/checkpoint-1500/config.json\n",
            "Model weights saved in test-ner/checkpoint-1500/pytorch_model.bin\n",
            "tokenizer config file saved in test-ner/checkpoint-1500/tokenizer_config.json\n",
            "Special tokens file saved in test-ner/checkpoint-1500/special_tokens_map.json\n",
            "The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: id, pos_tags, ner_tags, tokens, chunk_tags. If id, pos_tags, ner_tags, tokens, chunk_tags are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 3250\n",
            "  Batch size = 16\n",
            "Saving model checkpoint to test-ner/checkpoint-2000\n",
            "Configuration saved in test-ner/checkpoint-2000/config.json\n",
            "Model weights saved in test-ner/checkpoint-2000/pytorch_model.bin\n",
            "tokenizer config file saved in test-ner/checkpoint-2000/tokenizer_config.json\n",
            "Special tokens file saved in test-ner/checkpoint-2000/special_tokens_map.json\n",
            "Saving model checkpoint to test-ner/checkpoint-2500\n",
            "Configuration saved in test-ner/checkpoint-2500/config.json\n",
            "Model weights saved in test-ner/checkpoint-2500/pytorch_model.bin\n",
            "tokenizer config file saved in test-ner/checkpoint-2500/tokenizer_config.json\n",
            "Special tokens file saved in test-ner/checkpoint-2500/special_tokens_map.json\n",
            "The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: id, pos_tags, ner_tags, tokens, chunk_tags. If id, pos_tags, ner_tags, tokens, chunk_tags are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 3250\n",
            "  Batch size = 16\n",
            "\n",
            "\n",
            "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
            "\n",
            "\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "TrainOutput(global_step=2634, training_loss=0.07742509429166963, metrics={'train_runtime': 493.5749, 'train_samples_per_second': 85.343, 'train_steps_per_second': 5.337, 'total_flos': 1021316467278600.0, 'train_loss': 0.07742509429166963, 'epoch': 3.0})"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "trainer.train() "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CGmSRYR6L3Ok",
        "outputId": "abde8cf0-1281-4bc3-a5e9-0e5c80302cdb"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Configuration saved in ner_model/config.json\n",
            "Model weights saved in ner_model/pytorch_model.bin\n"
          ]
        }
      ],
      "source": [
        "model.save_pretrained(\"ner_model\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NX9FrmzaR2XW",
        "outputId": "8ba9da08-69dd-4b4b-ba19-dd7da2ca5fcf"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "tokenizer config file saved in tokenizer/tokenizer_config.json\n",
            "Special tokens file saved in tokenizer/special_tokens_map.json\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "('tokenizer/tokenizer_config.json',\n",
              " 'tokenizer/special_tokens_map.json',\n",
              " 'tokenizer/vocab.txt',\n",
              " 'tokenizer/added_tokens.json',\n",
              " 'tokenizer/tokenizer.json')"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tokenizer.save_pretrained(\"tokenizer\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "qWj16dJeMj10"
      },
      "outputs": [],
      "source": [
        "id2label = {\n",
        "    str(i): label for i,label in enumerate(label_list)\n",
        "}\n",
        "label2id = {\n",
        "    label: str(i) for i,label in enumerate(label_list)\n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "s8UMh-WdMfwc"
      },
      "outputs": [],
      "source": [
        "import json"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "T5EZxnPxMjSb"
      },
      "outputs": [],
      "source": [
        "config = json.load(open(\"ner_model/config.json\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "R_p_ohBAMyQ7"
      },
      "outputs": [],
      "source": [
        "config[\"id2label\"] = id2label\n",
        "config[\"label2id\"] = label2id"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "QBagjGQfM3xo"
      },
      "outputs": [],
      "source": [
        "json.dump(config, open(\"ner_model/config.json\",\"w\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0kLZ-CYWMSNm",
        "outputId": "8e9bb61d-9973-4d45-c278-942dc00007d9"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "loading configuration file ner_model/config.json\n",
            "Model config BertConfig {\n",
            "  \"_name_or_path\": \"ner_model\",\n",
            "  \"architectures\": [\n",
            "    \"BertForTokenClassification\"\n",
            "  ],\n",
            "  \"attention_probs_dropout_prob\": 0.1,\n",
            "  \"classifier_dropout\": null,\n",
            "  \"gradient_checkpointing\": false,\n",
            "  \"hidden_act\": \"gelu\",\n",
            "  \"hidden_dropout_prob\": 0.1,\n",
            "  \"hidden_size\": 768,\n",
            "  \"id2label\": {\n",
            "    \"0\": \"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\"\n",
            "  },\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"intermediate_size\": 3072,\n",
            "  \"label2id\": {\n",
            "    \"B-LOC\": \"5\",\n",
            "    \"B-MISC\": \"7\",\n",
            "    \"B-ORG\": \"3\",\n",
            "    \"B-PER\": \"1\",\n",
            "    \"I-LOC\": \"6\",\n",
            "    \"I-MISC\": \"8\",\n",
            "    \"I-ORG\": \"4\",\n",
            "    \"I-PER\": \"2\",\n",
            "    \"O\": \"0\"\n",
            "  },\n",
            "  \"layer_norm_eps\": 1e-12,\n",
            "  \"max_position_embeddings\": 512,\n",
            "  \"model_type\": \"bert\",\n",
            "  \"num_attention_heads\": 12,\n",
            "  \"num_hidden_layers\": 12,\n",
            "  \"pad_token_id\": 0,\n",
            "  \"position_embedding_type\": \"absolute\",\n",
            "  \"torch_dtype\": \"float32\",\n",
            "  \"transformers_version\": \"4.22.1\",\n",
            "  \"type_vocab_size\": 2,\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 30522\n",
            "}\n",
            "\n",
            "loading weights file ner_model/pytorch_model.bin\n",
            "All model checkpoint weights were used when initializing BertForTokenClassification.\n",
            "\n",
            "All the weights of BertForTokenClassification were initialized from the model checkpoint at ner_model.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use BertForTokenClassification for predictions without further training.\n"
          ]
        }
      ],
      "source": [
        "model_fine_tuned = AutoModelForTokenClassification.from_pretrained(\"ner_model\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "H2OmIa1_R-Nj"
      },
      "outputs": [],
      "source": [
        "from transformers import pipeline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QH9hlOroHKyN",
        "outputId": "5c6c2739-20d3-472a-a8e5-b87e1a427645"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[{'entity': 'B-PER', 'score': 0.99551374, 'index': 1, 'word': 'bill', 'start': 0, 'end': 4}, {'entity': 'I-PER', 'score': 0.9934643, 'index': 2, 'word': 'gates', 'start': 5, 'end': 10}, {'entity': 'B-ORG', 'score': 0.9709536, 'index': 7, 'word': 'microsoft', 'start': 29, 'end': 38}]\n"
          ]
        }
      ],
      "source": [
        "nlp = pipeline(\"ner\", model=model_fine_tuned, tokenizer=tokenizer)\n",
        "\n",
        "\n",
        "example = \"Bill Gates is the Founder of Microsoft\"\n",
        "\n",
        "ner_results = nlp(example)\n",
        "\n",
        "print(ner_results)"
      ]
    }
  ],
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