NER_на_датасете_CoNLL_2003.ipynb

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        "<a href=\"https://colab.research.google.com/github/Bluseass/CoNLL_2003-Bert/blob/main/NER_%D0%BD%D0%B0_%D0%B4%D0%B0%D1%82%D0%B0%D1%81%D0%B5%D1%82%D0%B5_CoNLL_2003.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3s8MzGMPoKGQ"
      },
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "op-bMLWyeHFF"
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      "source": [
        "def load_sentences(filepath):\n",
        "\n",
        "    final = []\n",
        "    sentences = []\n",
        "\n",
        "    with open(filepath, 'r') as f:\n",
        "\n",
        "        for line in f.readlines():\n",
        "\n",
        "            if (line == ('-DOCSTART- -X- -X- O\\n') or line == '\\n'):\n",
        "                if len(sentences) > 0:\n",
        "                    final.append(sentences)\n",
        "                    sentences = []\n",
        "            else:\n",
        "                l = line.split(' ')\n",
        "                sentences.append((l[0], l[3].strip('\\n')))\n",
        "\n",
        "    return final"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kV8eJ6Ed48PG"
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      "source": [
        "base_path = '/content/drive/MyDrive/ner/'\n",
        "\n",
        "train_samples = load_sentences(base_path + 'train.txt')\n",
        "test_samples = load_sentences(base_path + 'test.txt')\n",
        "valid_samples = load_sentences(base_path + 'valid.txt')\n",
        "\n",
        "samples = train_samples + test_samples\n",
        "\n",
        "schema = ['_'] + sorted({tag for sentence in samples\n",
        "                             for _, tag in sentence})"
      ]
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            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: 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"
          ]
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          "text": [
            "All PyTorch model weights were used when initializing TFBertForTokenClassification.\n",
            "\n",
            "Some weights or buffers of the TF 2.0 model TFBertForTokenClassification were not initialized from the PyTorch model and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
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          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"tf_bert_for_token_classification\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " bert (TFBertMainLayer)      multiple                  107719680 \n",
            "                                                                 \n",
            " dropout_37 (Dropout)        multiple                  0 (unused)\n",
            "                                                                 \n",
            " classifier (Dense)          multiple                  7690      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 107727370 (410.95 MB)\n",
            "Trainable params: 107727370 (410.95 MB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "from transformers import AutoConfig, TFAutoModelForTokenClassification\n",
        "\n",
        "MODEL_NAME = 'bert-base-cased'\n",
        "\n",
        "config = AutoConfig.from_pretrained(MODEL_NAME, num_labels=len(schema))\n",
        "model = TFAutoModelForTokenClassification.from_pretrained(MODEL_NAME,\n",
        "                                                          config=config)\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SO9KW50fGSrw"
      },
      "outputs": [],
      "source": [
        "from transformers import AutoTokenizer\n",
        "from tqdm import tqdm"
      ]
    },
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        {
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          "text": [
            "14041it [00:07, 1853.15it/s]\n",
            "3453it [00:01, 2033.04it/s]\n",
            "3250it [00:01, 1724.11it/s]\n"
          ]
        }
      ],
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)\n",
        "\n",
        "def tokenize_sample(sample):\n",
        "    seq = [\n",
        "               (subtoken, tag)\n",
        "               for token, tag in sample\n",
        "               for subtoken in tokenizer(token)['input_ids'][1:-1]\n",
        "           ]\n",
        "    return [(3, 'O')] + seq + [(4, 'O')]\n",
        "\n",
        "def preprocess(samples):\n",
        "    tag_index = {tag: i for i, tag in enumerate(schema)}\n",
        "    tokenized_samples = list(tqdm(map(tokenize_sample, samples)))\n",
        "    max_len = max(map(len, tokenized_samples))\n",
        "    X = np.zeros((len(samples), max_len), dtype=np.int32)\n",
        "    y = np.zeros((len(samples), max_len), dtype=np.int32)\n",
        "    for i, sentence in enumerate(tokenized_samples):\n",
        "        for j, (subtoken_id, tag) in enumerate(sentence):\n",
        "            X[i, j] = subtoken_id\n",
        "            y[i,j] = tag_index[tag]\n",
        "    return X, y\n",
        "\n",
        "X_train, y_train = preprocess(train_samples)\n",
        "X_test, y_test = preprocess(test_samples)\n",
        "X_valid, y_valid = preprocess(valid_samples)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "uJ-M-ALWFzTu"
      },
      "outputs": [],
      "source": [
        "from tqdm.keras import TqdmCallback\n",
        "import tensorflow as tf\n",
        "optimizer = tf.keras.optimizers.Adam(learning_rate=5e-5)\n",
        "loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)\n",
        "model.compile(optimizer=optimizer, loss=loss, metrics=['accuracy'])\n",
        "\n",
        "history = model.fit(X_train, y_train, validation_data=(X_valid, y_valid),\n",
        "                    epochs=5, batch_size=32, verbose=0, callbacks=[TqdmCallback(verbose=1)])\n",
        "\n",
        "test_loss, test_acc = model.evaluate(X_test, y_test, batch_size=32)\n",
        "print(f\"Test Loss: {test_loss}, Test Accuracy: {test_acc}\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Epoch 5/5 - loss: 0.0189 - accuracy: 0.9933 - val_loss: 0.0213 - val_accuracy: 0.9939, результаты прикреплены т.к тут обучать долго, но будут получены такие же +- при обучении тут и на валидации Loss:0.025, Accuracy:0.995**"
      ],
      "metadata": {
        "id": "JAYMTNEvuE09"
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    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "id": "sUb7H8aoqOAz"
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      "outputs": [],
      "source": []
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      "cell_type": "code",
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        "id": "wlTQVJsSIul6"
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      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YhmXVXjL1OJr"
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      "source": [
        "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n",
        "label_map = {'O': 0, 'LOC': 1, 'PER': 2, 'ORG': 3, 'MISC': 4}\n",
        "optimizer = AdamW(model.parameters(), lr=5e-5)"
      ]
    }
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