NER-CODES/BERT_NER.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!pip install seqeval\n"
      ],
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        },
        "id": "VlBEX0MVw21E",
        "outputId": "a66c9349-62f6-4056-f7a9-8afd09ba9b30"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting seqeval\n",
            "  Downloading seqeval-1.2.2.tar.gz (43 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m43.6/43.6 kB\u001b[0m \u001b[31m683.7 kB/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=16161 sha256=b0506142d1315328c5a327683aacb295d2ee87c35fc7e471a11b5db2b2c25f70\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",
      "execution_count": null,
      "metadata": {
        "id": "9pvrtI-1oorh"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from tqdm import tqdm, trange\n",
        "from sklearn.model_selection import train_test_split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "c2Gopi8m9lIc",
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          "height": 423
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        "outputId": "7dea3fce-5a32-49d8-d1c4-cb592b6a5a9f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Word   Tag  Sent_ID\n",
              "0       BİLATERAL     O        1\n",
              "1       MAMOGRAFİ     O        1\n",
              "2      İNCELEMESİ     O        1\n",
              "3               :  ANAT        1\n",
              "4           \\nHer  ANAT        1\n",
              "...           ...   ...      ...\n",
              "69763           .     O      880\n",
              "69764       SONUÇ     O      880\n",
              "69765           :     O      880\n",
              "69766      BIRADS     O      880\n",
              "69767           4     O      880\n",
              "\n",
              "[69768 rows x 3 columns]"
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              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 69768,\n  \"fields\": [\n    {\n      \"column\": \"Word\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2114,\n        \"samples\": [\n          \"cildinde\",\n          \"g\\u00f6lgelenmesi\",\n          \"eme\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tag\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"ANAT\",\n          \"OBS-PRESENT\",\n          \"OBS-ABSENT\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sent_ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 255,\n        \"min\": 1,\n        \"max\": 880,\n        \"num_unique_values\": 880,\n        \"samples\": [\n          332,\n          248,\n          791\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          "metadata": {},
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        }
      ],
      "source": [
        "# Initialize variables\n",
        "sentences = []\n",
        "current_sentence = []\n",
        "sent_id = 1\n",
        "\n",
        "# Read the CONLL file\n",
        "with open('p_saf_dataset.conll', 'r') as file:\n",
        "    for line in file:\n",
        "        line = line.strip()\n",
        "        if line:  # If line is not empty\n",
        "            word, tag = line.split()\n",
        "            current_sentence.append((word, tag))\n",
        "        else:  # If line is empty, this means end of a sentence\n",
        "            sentences.append((sent_id, current_sentence))\n",
        "            current_sentence = []\n",
        "            sent_id += 1\n",
        "\n",
        "# Handle the last sentence if there is no trailing empty line\n",
        "if current_sentence:\n",
        "    sentences.append((sent_id, current_sentence))\n",
        "\n",
        "# Convert to DataFrame\n",
        "data = []\n",
        "for sent_id, sentence in sentences:\n",
        "    for word, tag in sentence:\n",
        "        data.append((word, tag, sent_id))\n",
        "\n",
        "df = pd.DataFrame(data, columns=['Word', 'Tag', 'Sent_ID'])\n",
        "\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FKUKPJp_qXjH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "outputId": "ea1aead6-f497-460c-f81f-c8e2d0f1d5aa"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Tag\n",
              "O                34708\n",
              "ANAT             15429\n",
              "OBS-PRESENT      11440\n",
              "OBS-ABSENT        6590\n",
              "OBS-UNCERTAIN     1601\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tag</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>O</th>\n",
              "      <td>34708</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ANAT</th>\n",
              "      <td>15429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OBS-PRESENT</th>\n",
              "      <td>11440</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OBS-ABSENT</th>\n",
              "      <td>6590</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OBS-UNCERTAIN</th>\n",
              "      <td>1601</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "df.Tag.value_counts()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "P4yfwZMRqaAg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "62c0a4b7-7eec-4c94-9105-ba982fd03ecf"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['O', 'ANAT', 'OBS-ABSENT', 'OBS-UNCERTAIN', 'OBS-PRESENT'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "df.Tag.unique()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IocxMnkgqfR4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "13b0d590-2ffd-43c8-99b0-f1f88634f605"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Set:\n",
            "             Word   Tag  Sent_ID\n",
            "0       BİLATERAL     O        1\n",
            "1       MAMOGRAFİ     O        1\n",
            "2      İNCELEMESİ     O        1\n",
            "3               :  ANAT        1\n",
            "4           \\nHer  ANAT        1\n",
            "...           ...   ...      ...\n",
            "56204           .     O      880\n",
            "56205       SONUÇ     O      880\n",
            "56206           :     O      880\n",
            "56207      BIRADS     O      880\n",
            "56208           4     O      880\n",
            "\n",
            "[56209 rows x 3 columns]\n",
            "\n",
            "Validation Set:\n",
            "                  Word   Tag  Sent_ID\n",
            "0            BİLATERAL     O        6\n",
            "1            MAMOGRAFİ     O        6\n",
            "2      İNCELEMESİ\\nHer  ANAT        6\n",
            "3                  iki  ANAT        6\n",
            "4               memede  ANAT        6\n",
            "...                ...   ...      ...\n",
            "13554                .     O      876\n",
            "13555            SONUÇ     O      876\n",
            "13556                :     O      876\n",
            "13557           BIRADS     O      876\n",
            "13558                5     O      876\n",
            "\n",
            "[13559 rows x 3 columns]\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Assuming 'df' is your DataFrame\n",
        "\n",
        "# Get unique sentence IDs\n",
        "unique_sent_ids = df['Sent_ID'].unique()\n",
        "\n",
        "# Split the sentence IDs into train and validation sets\n",
        "train_ids, val_ids = train_test_split(unique_sent_ids, test_size=0.2, random_state=42)\n",
        "\n",
        "# Create train and validation DataFrames by filtering based on Sent_ID\n",
        "train_df = df[df['Sent_ID'].isin(train_ids)]\n",
        "val_df = df[df['Sent_ID'].isin(val_ids)]\n",
        "\n",
        "# Optionally, reset index\n",
        "train_df = train_df.reset_index(drop=True)\n",
        "val_df = val_df.reset_index(drop=True)\n",
        "\n",
        "# Display the resulting DataFrames\n",
        "print(\"Training Set:\")\n",
        "print(train_df)\n",
        "print(\"\\nValidation Set:\")\n",
        "print(val_df)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_df.to_csv(\"train_df.csv\", index=False)\n",
        "val_df.to_csv(\"val_df.csv\", index=False)"
      ],
      "metadata": {
        "id": "6SJhcoaviO6I"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class GetSentence(object):\n",
        "\n",
        "    def __init__(self, data):\n",
        "        self.n_sent = 1\n",
        "        self.data = data\n",
        "        self.empty = False\n",
        "\n",
        "        agg_func = lambda s: [(w, t) for w, t in zip(s[\"Word\"].values.tolist(),\n",
        "                                                           s[\"Tag\"].values.tolist())]\n",
        "        self.grouped = self.data.groupby(\"Sent_ID\").apply(agg_func)\n",
        "        self.sentences = [s for s in self.grouped]\n",
        "\n",
        "    def get_next(self):\n",
        "        try:\n",
        "            s = self.grouped[\"{}\".format(self.n_sent)]\n",
        "            self.n_sent += 1\n",
        "            return s\n",
        "        except:\n",
        "            return None"
      ],
      "metadata": {
        "id": "xfvzNYNugG_a"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "getter = GetSentence(train_df)\n",
        "v_getter = GetSentence(val_df)"
      ],
      "metadata": {
        "id": "XEeKmvitgKu1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sentences = [[word[0] for word in sentence] for sentence in getter.sentences]\n",
        "v_sentences = [[word[0] for word in sentence] for sentence in v_getter.sentences]\n"
      ],
      "metadata": {
        "id": "2u1v9aEDgOJF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "labels = [[s[1] for s in sentence] for sentence in getter.sentences]\n",
        "\n",
        "v_labels = [[s[1] for s in sentence] for sentence in v_getter.sentences]\n",
        "v_labels[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M43Mvk-QgPjN",
        "outputId": "1b0d8183-e013-4681-81da-de7062d61b2d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['O',\n",
              " 'O',\n",
              " 'ANAT',\n",
              " 'ANAT',\n",
              " 'ANAT',\n",
              " 'OBS-ABSENT',\n",
              " 'OBS-ABSENT',\n",
              " 'OBS-ABSENT',\n",
              " 'OBS-ABSENT',\n",
              " 'OBS-ABSENT',\n",
              " 'ANAT',\n",
              " 'O',\n",
              " 'OBS-ABSENT',\n",
              " 'OBS-ABSENT',\n",
              " 'OBS-ABSENT',\n",
              " 'OBS-ABSENT',\n",
              " 'OBS-ABSENT',\n",
              " 'O',\n",
              " 'O',\n",
              " 'OBS-UNCERTAIN',\n",
              " 'OBS-UNCERTAIN',\n",
              " 'OBS-UNCERTAIN',\n",
              " 'OBS-UNCERTAIN',\n",
              " 'OBS-UNCERTAIN',\n",
              " 'OBS-UNCERTAIN',\n",
              " 'OBS-UNCERTAIN',\n",
              " 'OBS-UNCERTAIN',\n",
              " 'OBS-UNCERTAIN',\n",
              " 'OBS-UNCERTAIN',\n",
              " 'OBS-UNCERTAIN',\n",
              " 'OBS-UNCERTAIN',\n",
              " 'OBS-UNCERTAIN',\n",
              " 'ANAT',\n",
              " 'ANAT',\n",
              " 'ANAT',\n",
              " 'ANAT',\n",
              " 'OBS-ABSENT',\n",
              " 'OBS-ABSENT',\n",
              " 'O',\n",
              " 'OBS-ABSENT',\n",
              " 'OBS-ABSENT',\n",
              " 'OBS-ABSENT',\n",
              " 'O',\n",
              " 'O',\n",
              " 'O',\n",
              " 'O',\n",
              " 'O',\n",
              " 'O',\n",
              " 'O',\n",
              " 'O',\n",
              " 'O',\n",
              " 'O']"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tag_values = list(set(train_df[\"Tag\"].values))\n",
        "tag_values.append(\"PAD\")\n",
        "tag2idx = {t: i for i, t in enumerate(tag_values)}"
      ],
      "metadata": {
        "id": "SVMj8oA0gRCW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Adding Padding at the end of each sentence\n",
        "v_tag_values = list(set(val_df[\"Tag\"].values))\n",
        "v_tag_values.append(\"PAD\")\n",
        "v_tag2idx = {t: i for i, t in enumerate(tag_values)}"
      ],
      "metadata": {
        "id": "jcCr_ah3gS7k"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tag_values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jyNdQ-LVgVaC",
        "outputId": "f72fbe60-7ca8-499f-cd99-f59f6d2e910a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['OBS-UNCERTAIN', 'ANAT', 'OBS-PRESENT', 'OBS-ABSENT', 'O', 'PAD']"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler\n",
        "from transformers import BertTokenizer, BertConfig, AlbertTokenizer, AlbertConfig, RobertaConfig, RobertaTokenizer, AutoTokenizer\n",
        "\n",
        "from keras.preprocessing.sequence import pad_sequences\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "torch.__version__"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "iB5I7MRzgV0Z",
        "outputId": "9d5e7827-7e77-4d3a-e566-03b824b60073"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'2.4.0+cu121'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "MAX_LEN = 320\n",
        "bs = 16"
      ],
      "metadata": {
        "id": "i5_Vm9ovgVuJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "n_gpu = torch.cuda.device_count()\n",
        "n_gpu"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SeT_UXoYgZPL",
        "outputId": "5f0f15df-1db0-4238-85e8-6a44a8120888"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "torch.cuda.get_device_name(0)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "Jlo0tIM9gbfY",
        "outputId": "04799261-71a0-406e-b915-4f146026610c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'NVIDIA A100-SXM4-40GB'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_name = \"savasy/bert-base-turkish-ner-cased\"\n"
      ],
      "metadata": {
        "id": "eo19G6zwgdU_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(model_name, do_lower_case=False)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K6GBmevKgd07",
        "outputId": "2dea2066-adfd-46e3-e71a-c76695e7e5d1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def tokenize_and_preserve_labels(sentence, text_labels):\n",
        "    tokenized_sentence = []\n",
        "    labels = []\n",
        "\n",
        "    for word, label in zip(sentence, text_labels):\n",
        "\n",
        "        # Tokenize et\n",
        "        tokenized_word = tokenizer.tokenize(word)\n",
        "        n_subwords = len(tokenized_word)\n",
        "\n",
        "        # Tokenized kelime listesine ekle\n",
        "        tokenized_sentence.extend(tokenized_word)\n",
        "\n",
        "        # Etiketi listeye ekle\n",
        "        labels.extend([label] * n_subwords)\n",
        "\n",
        "    return tokenized_sentence, labels"
      ],
      "metadata": {
        "id": "IqSsTxqhge1i"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_texts_and_labels = [\n",
        "    tokenize_and_preserve_labels(sent, labs)\n",
        "    for sent, labs in zip(sentences, labels)\n",
        "]\n",
        "v_tokenized_texts_and_labels = [\n",
        "    tokenize_and_preserve_labels(sent, labs)\n",
        "    for sent, labs in zip(v_sentences, v_labels)\n",
        "]"
      ],
      "metadata": {
        "id": "cyWtwAyrghAH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_texts = [token_label_pair[0] for token_label_pair in tokenized_texts_and_labels]\n",
        "labels = [token_label_pair[1] for token_label_pair in tokenized_texts_and_labels]\n",
        "\n",
        "v_tokenized_texts = [token_label_pair[0] for token_label_pair in v_tokenized_texts_and_labels]\n",
        "v_labels = [token_label_pair[1] for token_label_pair in v_tokenized_texts_and_labels]"
      ],
      "metadata": {
        "id": "FSKqVbXkgiks"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "input_ids = pad_sequences([tokenizer.convert_tokens_to_ids(txt) for txt in tokenized_texts],\n",
        "                          maxlen=MAX_LEN, dtype=\"long\", value=0.0,\n",
        "                          truncating=\"post\", padding=\"post\")\n",
        "\n",
        "v_input_ids = pad_sequences([tokenizer.convert_tokens_to_ids(txt) for txt in v_tokenized_texts],\n",
        "                          maxlen=MAX_LEN, dtype=\"long\", value=0.0,\n",
        "                          truncating=\"post\", padding=\"post\")"
      ],
      "metadata": {
        "id": "1QKJFHu-gnx9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(labels)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Eozg7E8ggnwj",
        "outputId": "4f458065-a6ca-41ca-af54-caaefce9bb5b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "704"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tags = pad_sequences([[tag2idx.get(l) for l in lab] for lab in labels],\n",
        "                     maxlen=MAX_LEN, value=tag2idx[\"PAD\"], padding=\"post\",\n",
        "                     dtype=\"long\", truncating=\"post\")\n",
        "\n",
        "\n",
        "v_tags = pad_sequences([[tag2idx.get(l) for l in lab] for lab in v_labels],\n",
        "                     maxlen=MAX_LEN, value=tag2idx[\"PAD\"], padding=\"post\",\n",
        "                     dtype=\"long\", truncating=\"post\")"
      ],
      "metadata": {
        "id": "pRwUrZ5zgnea"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Dikkat maskelerini ayarla\n",
        "attention_masks = [[float(i != 0.0) for i in ii] for ii in input_ids]\n",
        "\n",
        "v_attention_masks = [[float(i != 0.0) for i in ii] for ii in v_input_ids]"
      ],
      "metadata": {
        "id": "NOh0ZRaJgrgz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tr_inputs, val_inputs, tr_tags, val_tags = train_test_split(input_ids, tags,\n",
        "                                                            random_state=2018, test_size=0.1)\n",
        "tr_masks, val_masks, _, _ = train_test_split(attention_masks, input_ids,\n",
        "                                             random_state=2018, test_size=0.1)"
      ],
      "metadata": {
        "id": "jHnew9BdguFR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tr_inputs = input_ids\n",
        "val_inputs = v_input_ids\n",
        "tr_tags = tags\n",
        "val_tags = v_tags\n",
        "tr_masks = attention_masks\n",
        "val_masks = v_attention_masks"
      ],
      "metadata": {
        "id": "uqdbPp-Ggv6G"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tr_inputs = torch.tensor(tr_inputs)\n",
        "val_inputs = torch.tensor(val_inputs)\n",
        "tr_tags = torch.tensor(tr_tags)\n",
        "val_tags = torch.tensor(val_tags)\n",
        "tr_masks = torch.tensor(tr_masks)\n",
        "val_masks = torch.tensor(val_masks)"
      ],
      "metadata": {
        "id": "qfoG8PGVgxKE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Verileri karıştır\n",
        "train_data = TensorDataset(tr_inputs, tr_masks, tr_tags)\n",
        "train_sampler = RandomSampler(train_data)\n",
        "train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=bs)\n",
        "\n",
        "valid_data = TensorDataset(val_inputs, val_masks, val_tags)\n",
        "valid_sampler = SequentialSampler(valid_data)\n",
        "valid_dataloader = DataLoader(valid_data, sampler=valid_sampler, batch_size=bs)"
      ],
      "metadata": {
        "id": "ZLMPAvnRg0x5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import transformers\n",
        "from transformers import BertForTokenClassification, AdamW, AlbertForTokenClassification, RobertaForTokenClassification, AutoModelForTokenClassification, AutoConfig\n",
        "\n",
        "transformers.__version__"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "VNxn_UVHg2HB",
        "outputId": "0d0093ae-deff-47e0-8af1-62ddff63a530"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'4.44.2'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "config = AutoConfig.from_pretrained(model_name)\n",
        "config.num_labels = len(tag2idx)\n",
        "config.output_attentions = False\n",
        "config.output_hidden_states = False\n",
        "model = AutoModelForTokenClassification.from_pretrained(\n",
        "    model_name,\n",
        "    config=config,\n",
        "    ignore_mismatched_sizes=True\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6jZbhTbog3oy",
        "outputId": "6bd38f52-a04a-4873-c761-7fcc850241d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at savasy/bert-base-turkish-ner-cased were not used when initializing BertForTokenClassification: ['bert.pooler.dense.bias', 'bert.pooler.dense.weight']\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 savasy/bert-base-turkish-ner-cased and are newly initialized because the shapes did not match:\n",
            "- classifier.bias: found shape torch.Size([7]) in the checkpoint and torch.Size([6]) in the model instantiated\n",
            "- classifier.weight: found shape torch.Size([7, 768]) in the checkpoint and torch.Size([6, 768]) in the model instantiated\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.cuda();\n"
      ],
      "metadata": {
        "id": "uiNJm6FQg3nm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "FULL_FINETUNING = True\n",
        "if FULL_FINETUNING:\n",
        "    param_optimizer = list(model.named_parameters())\n",
        "    no_decay = ['bias', 'gamma', 'beta']\n",
        "    optimizer_grouped_parameters = [\n",
        "        {'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)],\n",
        "         'weight_decay_rate': 0.01},\n",
        "        {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)],\n",
        "         'weight_decay_rate': 0.0}\n",
        "    ]\n",
        "else:\n",
        "    param_optimizer = list(model.classifier.named_parameters())\n",
        "    optimizer_grouped_parameters = [{\"params\": [p for n, p in param_optimizer]}]\n",
        "\n",
        "optimizer = AdamW(\n",
        "    optimizer_grouped_parameters,\n",
        "    lr=3e-5,\n",
        "    eps=1e-8\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iG3tawByg5AA",
        "outputId": "af1ba047-9ae7-404d-b3a3-62cb015fc554"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:591: 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",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Parametreleri tanımla (epochs ve learning rate)\n",
        "from transformers import get_linear_schedule_with_warmup\n",
        "\n",
        "epochs = 35\n",
        "max_grad_norm = 1.0\n",
        "\n",
        "total_steps = len(train_dataloader) * epochs\n",
        "\n",
        "\n",
        "scheduler = get_linear_schedule_with_warmup(\n",
        "    optimizer,\n",
        "    num_warmup_steps=0,\n",
        "    num_training_steps=total_steps\n",
        ")"
      ],
      "metadata": {
        "id": "RvjsONw1g49S"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from seqeval.metrics import f1_score\n",
        "\n",
        "def flat_accuracy(preds, labels):\n",
        "    pred_flat = np.argmax(preds, axis=2).flatten()\n",
        "    labels_flat = labels.flatten()\n",
        "    return np.sum(pred_flat == labels_flat) / len(labels_flat)"
      ],
      "metadata": {
        "id": "pXV-jAaZg47F"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "loss_values, validation_loss_values = [], []\n",
        "\n",
        "for _ in trange(epochs, desc=\"Epoch\"):\n",
        "\n",
        "    model.train()\n",
        "\n",
        "    total_loss = 0\n",
        "\n",
        "    for step, batch in enumerate(train_dataloader):\n",
        "        batch = tuple(t.to(device) for t in batch)\n",
        "        b_input_ids, b_input_mask, b_labels = batch\n",
        "        model.zero_grad()\n",
        "        outputs = model(b_input_ids, token_type_ids=None,\n",
        "                        attention_mask=b_input_mask, labels=b_labels)\n",
        "        loss = outputs[0]\n",
        "        loss.backward()\n",
        "        total_loss += loss.item()\n",
        "        torch.nn.utils.clip_grad_norm_(parameters=model.parameters(), max_norm=max_grad_norm)\n",
        "        optimizer.step()\n",
        "        scheduler.step()\n",
        "\n",
        "\n",
        "    avg_train_loss = total_loss / len(train_dataloader)\n",
        "    print(\"Average train loss: {}\".format(avg_train_loss))\n",
        "\n",
        "\n",
        "    loss_values.append(avg_train_loss) # Plot için kayıp verilerini al\n",
        "\n",
        "\n",
        "    model.eval() # Her bir eğitim adımından sonra değerlendirme yap\n",
        "    eval_loss, eval_accuracy = 0, 0\n",
        "    nb_eval_steps, nb_eval_examples = 0, 0\n",
        "    predictions , true_labels = [], []\n",
        "    for batch in valid_dataloader:\n",
        "        batch = tuple(t.to(device) for t in batch)\n",
        "        b_input_ids, b_input_mask, b_labels = batch\n",
        "\n",
        "        with torch.no_grad():\n",
        "            outputs = model(b_input_ids, token_type_ids=None,\n",
        "                            attention_mask=b_input_mask, labels=b_labels)\n",
        "\n",
        "        logits = outputs[1].detach().cpu().numpy()\n",
        "        label_ids = b_labels.to('cpu').numpy()\n",
        "\n",
        "\n",
        "        eval_loss += outputs[0].mean().item()\n",
        "        eval_accuracy += flat_accuracy(logits, label_ids)\n",
        "        predictions.extend([list(p) for p in np.argmax(logits, axis=2)])\n",
        "        true_labels.extend(label_ids)\n",
        "\n",
        "        nb_eval_examples += b_input_ids.size(0)\n",
        "        nb_eval_steps += 1\n",
        "\n",
        "    eval_loss = eval_loss / nb_eval_steps\n",
        "    validation_loss_values.append(eval_loss)\n",
        "    print(\"Validation loss: {}\".format(eval_loss))\n",
        "    print(\"Validation Accuracy: {}\".format(eval_accuracy/nb_eval_steps))\n",
        "    pred_tags = [tag_values[p_i] for p, l in zip(predictions, true_labels)\n",
        "                                 for p_i, l_i in zip(p, l) if tag_values[l_i] != \"PAD\"]\n",
        "    valid_tags = [tag_values[l_i] for l in true_labels\n",
        "                                  for l_i in l\n",
        "                                    if tag_values[l_i] != \"PAD\"]\n",
        "    print(\"Validation F1-Score: {}\".format(f1_score([pred_tags], [valid_tags])))\n",
        "    print()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 543
        },
        "id": "DkxawTE5hBZW",
        "outputId": "93fb080b-023f-4376-d48f-929e2f54d85f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rEpoch:   0%|          | 0/35 [00:00<?, ?it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Average train loss: 0.5964434736154296\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: PAD seems not to be NE tag.\n",
            "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
            "\rEpoch:   3%|▎         | 1/35 [00:09<05:18,  9.37s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation loss: 0.4554601392962716\n",
            "Validation Accuracy: 0.811505681818182\n",
            "Validation F1-Score: 0.00528169014084507\n",
            "\n",
            "Average train loss: 0.44855695217847824\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rEpoch:   6%|▌         | 2/35 [00:18<05:07,  9.32s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation loss: 0.40074028210206464\n",
            "Validation Accuracy: 0.8286399147727274\n",
            "Validation F1-Score: 0.008639308855291575\n",
            "\n",
            "Average train loss: 0.4048233580860225\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rEpoch:   9%|▊         | 3/35 [00:27<04:58,  9.32s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation loss: 0.364522784948349\n",
            "Validation Accuracy: 0.8321555397727273\n",
            "Validation F1-Score: 0.016704631738800303\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\rEpoch:   9%|▊         | 3/35 [00:35<06:18, 11.84s/it]\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-75-25f6e47d4481>\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     15\u001b[0m         \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m         \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m         \u001b[0mtotal_loss\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m         \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclip_grad_norm_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_norm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmax_grad_norm\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m         \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.save_pretrained(\"ner_model11\")\n",
        "tokenizer.save_pretrained(\"ner_model11\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hj_-Gy5-hEFL",
        "outputId": "bd47c100-7033-4ad0-dea7-c0dd2892b26c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('ner_model11/tokenizer_config.json',\n",
              " 'ner_model11/special_tokens_map.json',\n",
              " 'ner_model11/vocab.txt',\n",
              " 'ner_model11/added_tokens.json',\n",
              " 'ner_model11/tokenizer.json')"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from transformers import BertForTokenClassification\n",
        "from sklearn.metrics import classification_report\n",
        "import numpy as np\n",
        "\n",
        "# Load the trained model\n",
        "model = BertForTokenClassification.from_pretrained('ner_model11')\n",
        "model.eval()\n",
        "\n",
        "# Move model to GPU if available\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "model.to(device)\n",
        "\n",
        "# Predict function\n",
        "def predict(model, dataloader):\n",
        "    model.eval()\n",
        "    predictions, true_labels = [], []\n",
        "\n",
        "    for batch in dataloader:\n",
        "        batch = tuple(t.to(device) for t in batch)\n",
        "        b_input_ids, b_input_mask, b_labels = batch\n",
        "\n",
        "        with torch.no_grad():\n",
        "            outputs = model(b_input_ids, attention_mask=b_input_mask)\n",
        "        logits = outputs.logits\n",
        "\n",
        "        # Move logits and labels to CPU\n",
        "        logits = logits.detach().cpu().numpy()\n",
        "        label_ids = b_labels.to('cpu').numpy()\n",
        "\n",
        "        # Store predictions and true labels\n",
        "        predictions.append(np.argmax(logits, axis=2))\n",
        "        true_labels.append(label_ids)\n",
        "\n",
        "    return predictions, true_labels\n",
        "\n",
        "# Get predictions and true labels\n",
        "predictions, true_labels = predict(model, valid_dataloader)\n",
        "\n",
        "# Flatten the predictions and labels for the classification report\n",
        "pred_tags = [tag for pred_batch in predictions for pred in pred_batch for tag in pred]\n",
        "true_tags = [tag for true_batch in true_labels for true in true_batch for tag in true]\n",
        "\n",
        "# Remove the padding tokens (assumed to have a value of tag2idx['PAD'])\n",
        "pred_tags = [tag for i, tag in enumerate(pred_tags) if true_tags[i] != tag2idx['PAD']]\n",
        "true_tags = [tag for tag in true_tags if tag != tag2idx['PAD']]\n",
        "\n",
        "\n",
        "target_names = [label for label in tag2idx.keys() if label != \"PAD\"]\n",
        "\n",
        "report = classification_report(true_tags, pred_tags, target_names=target_names)\n",
        "print(report)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "052oTeTX-iYk",
        "outputId": "d8980993-5e40-47fc-8283-df36c574510d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "               precision    recall  f1-score   support\n",
            "\n",
            "OBS-UNCERTAIN       0.95      0.88      0.91       650\n",
            "         ANAT       0.84      0.88      0.86      4139\n",
            "  OBS-PRESENT       0.79      0.80      0.79      3792\n",
            "   OBS-ABSENT       0.85      0.84      0.85      2055\n",
            "            O       0.90      0.88      0.89     13437\n",
            "\n",
            "     accuracy                           0.87     24073\n",
            "    macro avg       0.87      0.86      0.86     24073\n",
            " weighted avg       0.87      0.87      0.87     24073\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from transformers import BertTokenizer, BertForTokenClassification\n",
        "\n",
        "# Load the model and tokenizer\n",
        "model_path = '/content/ner_model9'\n",
        "model = BertForTokenClassification.from_pretrained(model_path)\n",
        "tokenizer = BertTokenizer.from_pretrained(model_path)\n",
        "\n",
        "# Ensure the model is in evaluation mode\n",
        "model.eval()\n",
        "\n",
        "# Print the label mapping (index to label)\n",
        "label_list = model.config.id2label\n",
        "print(\"Label Mapping:\", label_list)\n",
        "\n",
        "def predict_entities(text):\n",
        "    # Tokenize the input text\n",
        "    inputs = tokenizer(text, return_tensors=\"pt\", truncation=True, padding=True)\n",
        "\n",
        "    # Get model predictions\n",
        "    with torch.no_grad():\n",
        "        outputs = model(**inputs)\n",
        "\n",
        "    # Get the predicted token classes (logits to labels)\n",
        "    predictions = torch.argmax(outputs.logits, dim=2)\n",
        "\n",
        "    # Convert token ids back to words\n",
        "    tokens = tokenizer.convert_ids_to_tokens(inputs[\"input_ids\"].squeeze().tolist())\n",
        "\n",
        "    # Map predicted indices to labels\n",
        "    predicted_labels = [label_list[int(p)] for p in predictions.squeeze().tolist()]\n",
        "\n",
        "    # Combine tokens and their predicted labels\n",
        "    entities = [(token, label) for token, label in zip(tokens, predicted_labels)]\n",
        "\n",
        "    return entities\n",
        "\n",
        "# Example usage\n",
        "text = \"BILATERAL MLO ve CC MAMOGRAFİ\\nMemeler ACR Tip C heterojen yoğun paternde olup, mamografik duyarlılık azalmıştır.Sağ meme; CC grafi santral kesimde milimetrik nodüler fokal asimetrik dansite izlendi. Farklı kadranlarda konturları fibroglandüler parankimle örtülü izodens nodüler lezyonlar mevcuttur. Sınırları seçilebilen kitle lezyonu, kalsifikasyon, yapısal bozulma saptanmamıştır.Sağ aksillada incelenen kesimde patolojik boyut ve görünümde büyümüş lenf nodu yoktur.Sol memede ;alt iç kadranda irregüler şekilli konturu düzensiz olarak izlenen 11 mm boyutunda nodüler kitlesel lezyon izlendi. Tüm kadranlarda konturları fibroglandüler parankimle örtülü izodens nodüler şüpheli lezyonlar mevcuttur.Sol aksillada incelenen kesimde patolojik boyut ve görünümde büyümüş lenf nodu yoktur.SONUÇ: BIRADS-4 \"\n",
        "entities = predict_entities(text)\n",
        "\n",
        "for token, label in entities:\n",
        "    print(f\"{token}: {label}\")\n"
      ],
      "metadata": {
        "id": "mwoumWUJeuIe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3963904b-daa7-433d-e076-526988af00c3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Label Mapping: {0: 'LABEL_0', 1: 'LABEL_1', 2: 'LABEL_2', 3: 'LABEL_3', 4: 'LABEL_4', 5: 'LABEL_5'}\n",
            "[CLS]: LABEL_1\n",
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            "Sağ: LABEL_2\n",
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            "##imet: LABEL_0\n",
            "##rik: LABEL_0\n",
            "dans: LABEL_1\n",
            "##ite: LABEL_1\n",
            "izlen: LABEL_1\n",
            "##di: LABEL_1\n",
            ".: LABEL_2\n",
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            "##anlar: LABEL_1\n",
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            "##olojik: LABEL_3\n",
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            "##de: LABEL_1\n",
            ";: LABEL_2\n",
            "alt: LABEL_2\n",
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            "mm: LABEL_0\n",
            "boyutunda: LABEL_1\n",
            "no: LABEL_1\n",
            "##düler: LABEL_1\n",
            "kitlesel: LABEL_0\n",
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            "##yon: LABEL_1\n",
            "izlen: LABEL_1\n",
            "##di: LABEL_1\n",
            ".: LABEL_2\n",
            "Tüm: LABEL_2\n",
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            "##da: LABEL_1\n",
            "kont: LABEL_1\n",
            "##urlar: LABEL_1\n",
            "##ı: LABEL_1\n",
            "fi: LABEL_0\n",
            "##br: LABEL_0\n",
            "##og: LABEL_0\n",
            "##land: LABEL_0\n",
            "##üler: LABEL_0\n",
            "paran: LABEL_0\n",
            "##kim: LABEL_0\n",
            "##le: LABEL_0\n",
            "örtülü: LABEL_1\n",
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            "##ode: LABEL_1\n",
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            "no: LABEL_1\n",
            "##düler: LABEL_1\n",
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            "##yon: LABEL_1\n",
            "##lar: LABEL_1\n",
            "mevcuttur: LABEL_1\n",
            ".: LABEL_2\n",
            "Sol: LABEL_2\n",
            "aksi: LABEL_1\n",
            "##ll: LABEL_1\n",
            "##ada: LABEL_1\n",
            "incelen: LABEL_1\n",
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            "pat: LABEL_3\n",
            "##olojik: LABEL_3\n",
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            "görünüm: LABEL_3\n",
            "##de: LABEL_3\n",
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            "lenf: LABEL_1\n",
            "no: LABEL_1\n",
            "##du: LABEL_1\n",
            "yoktur: LABEL_1\n",
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            "SONU: LABEL_1\n",
            "##Ç: LABEL_1\n",
            ":: LABEL_1\n",
            "BI: LABEL_1\n",
            "##RA: LABEL_1\n",
            "##DS: LABEL_1\n",
            "-: LABEL_1\n",
            "4: LABEL_1\n",
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    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers\n",
        "!pip install huggingface-hub\n"
      ],
      "metadata": {
        "id": "KwFYWSlJhPkd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b65c1d91-bbff-4b5b-f07d-f08d514e1009"
      },
      "execution_count": null,
      "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) (6.0.2)\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) (0.4.4)\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) (4.66.5)\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) (2024.6.1)\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) (3.8)\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",
            "Requirement already satisfied: huggingface-hub in /usr/local/lib/python3.10/dist-packages (0.23.5)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface-hub) (3.15.4)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub) (2024.6.1)\n",
            "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub) (24.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub) (6.0.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface-hub) (2.32.3)\n",
            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub) (4.66.5)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub) (4.12.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub) (3.8)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub) (2024.7.4)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!huggingface-cli login\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sSmPt5jFDLgw",
        "outputId": "6c6608d6-e76f-4231-8c42-e9f4be45959d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "    _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n",
            "    _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n",
            "    _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n",
            "    _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n",
            "    _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n",
            "\n",
            "    To login, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n",
            "Enter your token (input will not be visible): \n",
            "Add token as git credential? (Y/n) Y\n",
            "Token is valid (permission: write).\n",
            "\u001b[1m\u001b[31mCannot authenticate through git-credential as no helper is defined on your machine.\n",
            "You might have to re-authenticate when pushing to the Hugging Face Hub.\n",
            "Run the following command in your terminal in case you want to set the 'store' credential helper as default.\n",
            "\n",
            "git config --global credential.helper store\n",
            "\n",
            "Read https://git-scm.com/book/en/v2/Git-Tools-Credential-Storage for more details.\u001b[0m\n",
            "Token has not been saved to git credential helper.\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import BertTokenizer, BertForTokenClassification\n",
        "from huggingface_hub import HfApi\n",
        "\n",
        "# Load your saved model and tokenizer\n",
        "model = BertForTokenClassification.from_pretrained(\"ner_model3\")\n",
        "tokenizer = BertTokenizer.from_pretrained(\"ner_model3\")\n",
        "\n",
        "# Set your desired model name (this will be the name on Hugging Face)\n",
        "model_name = \"comp-model\"  # Change this to your desired model name\n",
        "\n",
        "# Push the model and tokenizer to Hugging Face\n",
        "model.push_to_hub(model_name)\n",
        "tokenizer.push_to_hub(model_name)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 150,
          "referenced_widgets": [
            "881cd8c55f6d4753a8ace61f584083a3",
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        },
        "id": "OBygC3n9EETq",
        "outputId": "7feebd28-248b-4538-bcb8-55a662ff7837"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/440M [00:00<?, ?B/s]"
            ],
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        },
        {
          "output_type": "display_data",
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            "text/plain": [
              "README.md:   0%|          | 0.00/5.17k [00:00<?, ?B/s]"
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        {
          "output_type": "execute_result",
          "data": {
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              "CommitInfo(commit_url='https://huggingface.co/AAkay/comp-model/commit/b2e012f048cc4dcfdcf29220841e1e094ae1f592', commit_message='Upload tokenizer', commit_description='', oid='b2e012f048cc4dcfdcf29220841e1e094ae1f592', pr_url=None, pr_revision=None, pr_num=None)"
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          "metadata": {},
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    "accelerator": "GPU",
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