NER_BERT.ipynb

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  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XjLnNeAzpCOF",
        "outputId": "0da85738-5fc1-431e-b00a-94c842e561ab"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.0/7.0 MB\u001b[0m \u001b[31m22.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: accelerate in /usr/local/lib/python3.10/dist-packages (0.21.0)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from accelerate) (1.23.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (23.1)\n",
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            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from accelerate) (6.0.1)\n",
            "Requirement already satisfied: torch>=1.10.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (2.0.1+cu118)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.12.2)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (4.7.1)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.1.2)\n",
            "Requirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2.0.0)\n",
            "Requirement already satisfied: cmake in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch>=1.10.0->accelerate) (3.27.2)\n",
            "Requirement already satisfied: lit in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch>=1.10.0->accelerate) (16.0.6)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.10.0->accelerate) (2.1.3)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.10.0->accelerate) (1.3.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install datasets -q\n",
        "!pip install tokenizers -q\n",
        "!pip install transformers==4.28.0 -q\n",
        "!pip install seqeval -q\n",
        "!pip install accelerate -U"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "from transformers import AutoTokenizer"
      ],
      "metadata": {
        "id": "eSF3hR-9pKSC"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "def create_dataset(path):\n",
        "    datasets = {'data': []}\n",
        "    with open(path, 'r') as f:\n",
        "        datas = json.load(f)\n",
        "\n",
        "    for data in datas:\n",
        "        txt = data['annotations'][0]\n",
        "        bboxes, labels = [], []\n",
        "        for entity in data['annotations'][1]['entities']:\n",
        "            bboxes.append([entity[0], entity[1]])\n",
        "            labels.append(entity[2])\n",
        "\n",
        "        initial_idx = 0\n",
        "        temp_data = {}\n",
        "        for idx in range(len(labels)):\n",
        "            bbox = bboxes[idx]\n",
        "            final_idx = bbox[0]\n",
        "            sub_txts = txt[initial_idx:final_idx].split()\n",
        "            initial_idx = bbox[1]+1\n",
        "            for sub_txt in sub_txts:\n",
        "                temp_data[sub_txt] = \"0\"\n",
        "            temp_data[txt[bbox[0]:bbox[1]]] = labels[idx]\n",
        "        datasets['data'].append(temp_data)\n",
        "\n",
        "    with open(path, 'w') as f:\n",
        "        json.dump(f, datasets)\n",
        "\n",
        "\n",
        "path = \"/content/dex_annotations.json\"\n",
        "create_dataset(path)"
      ],
      "metadata": {
        "id": "673TghboY8fu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# dataset = load_dataset('wikiann', 'en')\n",
        "# label_names = dataset['train'].features['ner_tags'].feature.names\n",
        "dataset = load_dataset('json', data_files=path, field='data')\n",
        "label_names = [\"ORG NAME\",\n",
        "        \"PHONE/FAX\",\n",
        "        \"DATE\",\n",
        "        \"EMAIL\",\n",
        "        \"TXN\"]\n",
        "model_name = \"distilbert-base-uncased\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)"
      ],
      "metadata": {
        "id": "8xACybCFtTIi"
      },
      "execution_count": null,
      "outputs": []
    },
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      "cell_type": "code",
      "source": [
        "def tokenize_function(examples):\n",
        "    return tokenizer(examples[\"tokens\"], padding=\"max_length\", truncation=True, is_split_into_words=True)\n",
        "tokenized_datasets_ = dataset.map(tokenize_function, batched=True)"
      ],
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    },
    {
      "cell_type": "code",
      "source": [
        "def tokenize_adjust_labels(all_samples_per_split):\n",
        "  tokenized_samples = tokenizer.batch_encode_plus(all_samples_per_split[\"tokens\"], is_split_into_words=True, truncation=True)\n",
        "\n",
        "  total_adjusted_labels = []\n",
        "\n",
        "  for k in range(0, len(tokenized_samples[\"input_ids\"])):\n",
        "    prev_wid = -1\n",
        "    word_ids_list = tokenized_samples.word_ids(batch_index=k)\n",
        "    existing_label_ids = all_samples_per_split[\"ner_tags\"][k]\n",
        "    i = -1\n",
        "    adjusted_label_ids = []\n",
        "\n",
        "    for word_idx in word_ids_list:\n",
        "      # Special tokens have a word id that is None. We set the label to -100 so they are automatically\n",
        "      # ignored in the loss function.\n",
        "      if(word_idx is None):\n",
        "        adjusted_label_ids.append(-100)\n",
        "      elif(word_idx!=prev_wid):\n",
        "        i = i + 1\n",
        "        adjusted_label_ids.append(existing_label_ids[i])\n",
        "        prev_wid = word_idx\n",
        "      else:\n",
        "        label_name = label_names[existing_label_ids[i]]\n",
        "        adjusted_label_ids.append(existing_label_ids[i])\n",
        "\n",
        "    total_adjusted_labels.append(adjusted_label_ids)\n",
        "\n",
        "  #add adjusted labels to the tokenized samples\n",
        "  tokenized_samples[\"labels\"] = total_adjusted_labels\n",
        "  return tokenized_samples\n",
        "\n",
        "tokenized_dataset = dataset.map(tokenize_adjust_labels, batched=True, remove_columns=['tokens', 'ner_tags', 'langs', 'spans'])"
      ],
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import DataCollatorForTokenClassification\n",
        "data_collator = DataCollatorForTokenClassification(tokenizer)\n",
        "\n",
        "import torch\n",
        "from torch.utils.data import DataLoader\n",
        "from transformers import AutoModelForTokenClassification, AdamW\n",
        "\n",
        "device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')"
      ],
      "metadata": {
        "id": "6Kaq2W_Dpk0j"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = AutoModelForTokenClassification.from_pretrained(\"distilbert-base-uncased\", num_labels=len(label_names))\n",
        "model.to(device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 677,
          "referenced_widgets": [
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        "outputId": "df311053-6ef3-4337-a360-033173302920"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading model.safetensors:   0%|          | 0.00/268M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of DistilBertForTokenClassification were not initialized from the model checkpoint at distilbert-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"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DistilBertForTokenClassification(\n",
              "  (distilbert): DistilBertModel(\n",
              "    (embeddings): Embeddings(\n",
              "      (word_embeddings): Embedding(30522, 768, padding_idx=0)\n",
              "      (position_embeddings): Embedding(512, 768)\n",
              "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "      (dropout): Dropout(p=0.1, inplace=False)\n",
              "    )\n",
              "    (transformer): Transformer(\n",
              "      (layer): ModuleList(\n",
              "        (0-5): 6 x TransformerBlock(\n",
              "          (attention): MultiHeadSelfAttention(\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "            (q_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (k_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (v_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (out_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "          )\n",
              "          (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "          (ffn): FFN(\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "            (lin1): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (lin2): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (activation): GELUActivation()\n",
              "          )\n",
              "          (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (dropout): Dropout(p=0.1, inplace=False)\n",
              "  (classifier): Linear(in_features=768, out_features=7, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from datasets import load_metric\n",
        "metric = load_metric(\"seqeval\")\n",
        "\n",
        "def compute_metrics(p):\n",
        "    predictions, labels = p\n",
        "\n",
        "    #select predicted index with maximum logit for each token\n",
        "    predictions = np.argmax(predictions, axis=2)\n",
        "\n",
        "    # Remove ignored index (special tokens)\n",
        "    true_predictions = [\n",
        "        [label_names[p] for (p, l) in zip(prediction, label) if l != -100]\n",
        "        for prediction, label in zip(predictions, labels)\n",
        "    ]\n",
        "    true_labels = [\n",
        "        [label_names[l] for (p, l) in zip(prediction, label) if l != -100]\n",
        "        for prediction, label in zip(predictions, labels)\n",
        "    ]\n",
        "\n",
        "    results = metric.compute(predictions=true_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",
        "    }"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104,
          "referenced_widgets": [
            "7f0e1b940dc341f7b9bbef1d614ebd9d",
            "ed1a885d973c40efa34c1809b232d631",
            "d407abc303ff4f5d8c9226108ea5c322",
            "b588dd50945a430992b3e7adfd916d91",
            "88924815a20a40f6b1f6d45f29b5f99b",
            "4ddebfba8b4f4d51bf09ebb88b661c7c",
            "f970af3bf5434a2aa123c7b547e31b37",
            "68e6de83dede45a3bd8ccae85e16e2fb",
            "38be4d1268dc4c29b165897f7fe521d0",
            "7992597a1dd44c2b814d300971ef8487",
            "23fcafb0db2943f9a130917b6c2c7ca4"
          ]
        },
        "id": "d1juvXM-rKy9",
        "outputId": "a9399333-0094-4a9b-d2f4-2cb6e3d0e0a6"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-11-02b457ce3401>:3: 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",
            "  metric = load_metric(\"seqeval\")\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/2.47k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7f0e1b940dc341f7b9bbef1d614ebd9d"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import TrainingArguments, Trainer\n",
        "\n",
        "batch_size = 16\n",
        "logging_steps = len(tokenized_dataset['train']) // batch_size\n",
        "epochs = 2\n",
        "\n",
        "training_args = TrainingArguments(\n",
        "    output_dir=\"/content/drive/MyDrive/bert-fine-tune-ner/results\",\n",
        "    num_train_epochs=epochs,\n",
        "    per_device_train_batch_size=batch_size,\n",
        "    per_device_eval_batch_size=batch_size,\n",
        "    evaluation_strategy=\"epoch\",\n",
        "    disable_tqdm=False,\n",
        "    logging_steps=logging_steps)\n",
        "\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_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",
        ")"
      ],
      "metadata": {
        "id": "eHqV48ARsQZQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Fine-Tuning Starts\n",
        "trainer.train()"
      ],
      "metadata": {
        "id": "Vw0kuh9WsXOB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Evaluation"
      ],
      "metadata": {
        "id": "d3AhBl_gsuXC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.evaluate()"
      ],
      "metadata": {
        "id": "KI4N2cG0swaH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictions, labels, _ = trainer.predict(tokenized_dataset[\"test\"])\n",
        "predictions = np.argmax(predictions, axis=2)\n",
        "# Remove ignored index (special tokens)\n",
        "true_predictions = [\n",
        "    [label_names[p] for (p, l) in zip(prediction, label) if l != -100]\n",
        "    for prediction, label in zip(predictions, labels)\n",
        "]\n",
        "true_labels = [\n",
        "    [label_names[l] for (p, l) in zip(prediction, label) if l != -100]\n",
        "    for prediction, label in zip(predictions, labels)\n",
        "]\n",
        "results = metric.compute(predictions=true_predictions, references=true_labels)"
      ],
      "metadata": {
        "id": "5LZyeASKs0MR"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}