Token_classification.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/shariarkhan433/Token-classification-Bangla/blob/main/Token_classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nCb3bWi08hn-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "673ec936-e9aa-4986-bb61-589e860aa31b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "\u001b[?25hReading package lists...\n",
            "Building dependency tree...\n",
            "Reading state information...\n",
            "git-lfs is already the newest version (3.0.2-1ubuntu0.2).\n",
            "0 upgraded, 0 newly installed, 0 to remove and 35 not upgraded.\n"
          ]
        }
      ],
      "source": [
        "!pip install datasets evaluate transformers[sentencepiece] -q\n",
        "!pip install accelerate -q\n",
        "\n",
        "!pip install torch -q\n",
        "!apt install git-lfs -q"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "x0VvxUVlZXF-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "160a486f-7f5d-401e-9800-f493d7ac7c41"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m53.4/53.4 kB\u001b[0m \u001b[31m1.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.1/1.1 MB\u001b[0m \u001b[31m12.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for emoji (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting git+https://github.com/csebuetnlp/normalizer\n",
            "  Cloning https://github.com/csebuetnlp/normalizer to /tmp/pip-req-build-t3xl3evk\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/csebuetnlp/normalizer /tmp/pip-req-build-t3xl3evk\n",
            "  Resolved https://github.com/csebuetnlp/normalizer to commit d405944dde5ceeacb7c2fd3245ae2a9dea5f35c9\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: regex in /usr/local/lib/python3.10/dist-packages (from normalizer==0.0.1) (2023.12.25)\n",
            "Collecting emoji==1.4.2 (from normalizer==0.0.1)\n",
            "  Downloading emoji-1.4.2.tar.gz (184 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m185.0/185.0 kB\u001b[0m \u001b[31m1.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting ftfy==6.0.3 (from normalizer==0.0.1)\n",
            "  Downloading ftfy-6.0.3.tar.gz (64 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m64.2/64.2 kB\u001b[0m \u001b[31m8.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.10/dist-packages (from ftfy==6.0.3->normalizer==0.0.1) (0.2.13)\n",
            "Building wheels for collected packages: normalizer, emoji, ftfy\n",
            "  Building wheel for normalizer (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for normalizer: filename=normalizer-0.0.1-py3-none-any.whl size=6859 sha256=77e796b090306fce135240d57dc874e66db5587db47cb33fcdb805431f2de668\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-oq6_32ax/wheels/2e/79/9c/cd96d490298305d51d2da11484bb2c25fd1f759a6906708282\n",
            "  Building wheel for emoji (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for emoji: filename=emoji-1.4.2-py3-none-any.whl size=186460 sha256=b215704cbad9885851275be646dde25273cfb32c6795b2872470aef1da6692dc\n",
            "  Stored in directory: /root/.cache/pip/wheels/10/f0/fd/4813b1177405693e8da9cdea839f0fb64fde161380e058c827\n",
            "  Building wheel for ftfy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for ftfy: filename=ftfy-6.0.3-py3-none-any.whl size=41929 sha256=8d4a92bffb259744517ce34f489ad0dbde75ae5a412a87a95c788693214aa361\n",
            "  Stored in directory: /root/.cache/pip/wheels/92/8e/16/c1e4d4d65685d71085e4e27b44d6ed880b0559474c9ee4ff66\n",
            "Successfully built normalizer emoji ftfy\n",
            "Installing collected packages: emoji, ftfy, normalizer\n",
            "  Attempting uninstall: emoji\n",
            "    Found existing installation: emoji 1.7.0\n",
            "    Uninstalling emoji-1.7.0:\n",
            "      Successfully uninstalled emoji-1.7.0\n",
            "  Attempting uninstall: ftfy\n",
            "    Found existing installation: ftfy 6.1.3\n",
            "    Uninstalling ftfy-6.1.3:\n",
            "      Successfully uninstalled ftfy-6.1.3\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "bnlp-toolkit 4.0.0.dev4 requires emoji==1.7.0, but you have emoji 1.4.2 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed emoji-1.4.2 ftfy-6.0.3 normalizer-0.0.1\n"
          ]
        }
      ],
      "source": [
        "! pip install -U bnlp_toolkit -q\n",
        "! pip install bnlp-toolkit==4.0.0.dev4 -q\n",
        "! pip install git+https://github.com/csebuetnlp/normalizer"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from normalizer import normalize"
      ],
      "metadata": {
        "id": "rMk_bk4fv6tC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QByCVBBBY11c"
      },
      "source": [
        "# **Dataset processing**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kz3qmh-9Y7wu",
        "outputId": "015d9145-6376-465d-bcc5-ee1ec034be70",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269,
          "referenced_widgets": [
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            "cc18279541074ab98ab9513c43c1bc36",
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            "72cc7a947cea471b9fafbb26b866b5cd"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/1.68G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cfeac1df3d1749f487c24d51f40166ac"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "03cb8336852041428523aad7d4f423b8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Filter:   0%|          | 0/5000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "58175709f64d478da6fdb762a484a55e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/3754 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b233684c3f0e45289c391c5c11b852cd"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from datasets import load_dataset\n",
        "import re\n",
        "def remove_punctuation(sentence):\n",
        "    sentence[data_column] = re.sub(r'[^\\u0980-\\u09FF\\s]', '', sentence[data_column])\n",
        "    return sentence\n",
        "dataset = load_dataset('shahidul034/text_generation_model_data')\n",
        "# dataset = load_dataset('csebuetnlp/xnli_bn')\n",
        "data_column = 'content'\n",
        "dataset['train']=dataset['train'].select([x for x in range(0,5000)])\n",
        "dataset['train']=dataset['train'].filter(lambda x : len(x[data_column])>10)\n",
        "dataset['train']=dataset['train'].map(remove_punctuation)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "AkXQNaJuaDSp",
        "outputId": "decf45a2-17c6-4bb8-84a7-f259f8828d3d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "' অডিট লাগবে বোর্ড অন্য কাজগুলোও দেখবে'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "dataset['train'][data_column][10]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gOKei5nntekn",
        "outputId": "2fc5427d-4ebb-42a2-ab6c-aa1ae16b2ecd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_from_disk\n",
        "ner_pos = load_from_disk('/content/drive/MyDrive/ner_pos')"
      ],
      "metadata": {
        "id": "qlTVKO0GtInV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zVj7a4chechw"
      },
      "outputs": [],
      "source": [
        "ner_pos_updated = ner_pos.map(lambda example: {'tag_num': example['tag_num'] - 1})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "csgoW6hBsAhD",
        "outputId": "24323bed-34c7-4c76-accc-a933517382bf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "ner_pos_updated['ner']['tag_num']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "h2t-ZYAuY8AJ"
      },
      "outputs": [],
      "source": [
        "def assign_tag_pos(tag):\n",
        "  for x1,x2 in zip(ner_pos_updated['pos']['pos_list'],ner_pos_updated['pos']['tag_num']):\n",
        "    if tag==x1:\n",
        "      return x2\n",
        "def assign_tag_ner(tag):\n",
        "  for x1,x2 in zip(ner_pos_updated['ner']['ner_list'],ner_pos_updated['ner']['tag_num']):\n",
        "    if tag==x1:\n",
        "      return x2"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from bnlp import BengaliNER\n",
        "from bnlp import BengaliPOS\n",
        "bn_pos = BengaliPOS()\n",
        "bn_ner = BengaliNER()"
      ],
      "metadata": {
        "id": "7rzq5XdLwTG2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 133,
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            "ca8283b9426141fc84cefc9480743d86",
            "18c5a9c8d5084532872cb5783e1fac62",
            "1b4422993dea4fbf88cc3d102d43b70e",
            "ca01de2a156e4afda94981336fd512b0",
            "636581a9f9ae4009ae13a3cdddbb6aca"
          ]
        },
        "outputId": "2d06235d-c235-4a66-f996-94594fafac06"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "punkt not found. downloading...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading bn_pos.pkl:   0%|          | 0/3897386 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "74f92491edaf4ddea3dd80f9450cb2d3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading bn_ner.pkl:   0%|          | 0/26024594 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "04c87bed9199410e93a9385a8a140a5d"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "2fb59490e9ed428aa6b5d65b9284c999",
            "bf4aa1b63b3a4fa28db2ed6e1ecc7907",
            "ed13c55d77d34705bb489a9a9fa86a74",
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            "a104a60883774443bef76ccb6af311aa",
            "c1f4acb08d5a4c0cb1ca2c863ee71f2f"
          ]
        },
        "id": "nhYoNOqyZOOc",
        "outputId": "65144fb6-5085-4612-f93f-cc649b6fc4e8"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/3754 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2fb59490e9ed428aa6b5d65b9284c999"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from normalizer import normalize\n",
        "def fun(ex):\n",
        "  ex['pos_tags']=[assign_tag_pos(x[1]) for x in (bn_pos.tag(normalize(ex[data_column])))]\n",
        "  ex['pos']=[x[1] for x in (bn_pos.tag(normalize(ex[data_column])))]\n",
        "  ex['ner_tags']=[assign_tag_ner(x[1]) for x in (bn_ner.tag(normalize(ex[data_column])))]\n",
        "  ex['ner']=[x[1] for x in (bn_ner.tag(normalize(ex[data_column])))]\n",
        "  ex['tokens'] = [x[0] for x in (bn_ner.tag(normalize(ex[data_column])))]\n",
        "  return ex\n",
        "tagged_raw_data=dataset['train'].map(fun)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GYhV4Yw4sAhE",
        "outputId": "cf501a3d-ef25-4ac2-a1a1-a46caf8a9869",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'content': 'ইভ্যালির গ্রাহকরা আগামী ছয় মাস পাওনা আদায়ের জন্য বোর্ডকে চাপ দিতে পারবেন না',\n",
              " 'pos_tags': [24, 24, 2, 24, 24, 24, 24, 12, 24, 24, 5, 25, 17],\n",
              " 'pos': ['NC',\n",
              "  'NC',\n",
              "  'JJ',\n",
              "  'NC',\n",
              "  'NC',\n",
              "  'NC',\n",
              "  'NC',\n",
              "  'PP',\n",
              "  'NC',\n",
              "  'NC',\n",
              "  'VM',\n",
              "  'VAUX',\n",
              "  'CX'],\n",
              " 'ner_tags': [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n",
              " 'ner': ['O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O'],\n",
              " 'tokens': ['ইভ্যালির',\n",
              "  'গ্রাহকরা',\n",
              "  'আগামী',\n",
              "  'ছয়',\n",
              "  'মাস',\n",
              "  'পাওনা',\n",
              "  'আদায়ের',\n",
              "  'জন্য',\n",
              "  'বোর্ডকে',\n",
              "  'চাপ',\n",
              "  'দিতে',\n",
              "  'পারবেন',\n",
              "  'না']}"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "tagged_raw_data[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "l4jBv9m98hoC",
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          ]
        },
        "outputId": "ecca6de8-a2db-47ad-c205-66adc4e0de40"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/1.30k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6ad1e6e1487e4b8c824141c9e2ca8a45"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/528k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0bb4126daff14274a98552e0fe562968"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/1.03M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2b693eb8ff154c708e446267b831218f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/694 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "25593b67c6bd4650b468b119cba5033c"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from transformers import AutoTokenizer\n",
        "\n",
        "# model_checkpoint = \"bert-base-cased\"\n",
        "# model_checkpoint ='csebuetnlp/banglabert'\n",
        "model_checkpoint = \"Shariar433/bert-finetuned-ner-bangla\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zEtVxypxjGZU"
      },
      "outputs": [],
      "source": [
        "\n",
        "\n",
        "def tokenize_and_align_labels(examples): #only for ner\n",
        "    tokenized_inputs = tokenizer(examples[\"tokens\"], padding=True, is_split_into_words=True, max_length=128, truncation=True)\n",
        "\n",
        "    labels = []\n",
        "    for i, label in enumerate(examples[f\"ner_tags\"]):\n",
        "        word_ids = tokenized_inputs.word_ids(batch_index=i)\n",
        "        previous_word_idx = None\n",
        "        label_ids = []\n",
        "        for word_idx in word_ids:\n",
        "            if word_idx is None:\n",
        "                label_ids.append(-100)\n",
        "            elif word_idx != previous_word_idx:\n",
        "                label_ids.append(label[word_idx])\n",
        "            else:\n",
        "                label_ids.append(-100)\n",
        "            previous_word_idx = word_idx\n",
        "\n",
        "        labels.append(label_ids)\n",
        "    tokenized_inputs[\"labels\"] = labels\n",
        "    return tokenized_inputs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "fd2c929684764e2082b380fc29e70ff5",
            "6ded6ca5e6d845b9a4a5a4e7e92efbe0",
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            "b7720892687e4b369999899f9b1b9caf",
            "837620ffdeb84101b7df14e8a9091546",
            "9436b237e60845439379e45f61d1611e",
            "46e9a40b019646d0a96dc695e8f060fc"
          ]
        },
        "id": "pI-d8clZjn7S",
        "outputId": "a469252d-d173-4cbc-cb9b-f92feb4d036a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/3754 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fd2c929684764e2082b380fc29e70ff5"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "tokenized_dataset = tagged_raw_data.map(tokenize_and_align_labels, batched=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Z3gSCN0c8hoD"
      },
      "outputs": [],
      "source": [
        "from transformers import DataCollatorForTokenClassification\n",
        "\n",
        "data_collator = DataCollatorForTokenClassification(tokenizer=tokenizer)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "03cAkfPesAhG",
        "outputId": "4313fe7f-e268-4845-f7c3-4a05d4103a12",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['E-ORG',\n",
              " 'S-OBJ',\n",
              " 'B-ORG',\n",
              " 'B-LOC',\n",
              " 'O',\n",
              " 'S-ORG',\n",
              " 'B-PER',\n",
              " 'E-LOC',\n",
              " 'I-ORG',\n",
              " 'E-OBJ',\n",
              " 'E-PER',\n",
              " 'S-PER',\n",
              " 'I-PER',\n",
              " 'I-LOC',\n",
              " 'B-OBJ',\n",
              " 'S-LOC']"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "label_list = ner_pos_updated['ner']['ner_list']\n",
        "label_list"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OIw_EfmU8hoE"
      },
      "outputs": [],
      "source": [
        "id2label = {str(i): label for label, i in zip(ner_pos_updated['ner']['ner_list'],ner_pos_updated['ner']['tag_num'])}\n",
        "label2id = {label : i for label, i in zip(ner_pos_updated['ner']['ner_list'],ner_pos_updated['ner']['tag_num'])}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lKJt_3dmsAhH",
        "outputId": "552bb3f7-1053-40f5-e4ed-6809dc8e98ac",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'0': 'E-ORG',\n",
              " '1': 'S-OBJ',\n",
              " '2': 'B-ORG',\n",
              " '3': 'B-LOC',\n",
              " '4': 'O',\n",
              " '5': 'S-ORG',\n",
              " '6': 'B-PER',\n",
              " '7': 'E-LOC',\n",
              " '8': 'I-ORG',\n",
              " '9': 'E-OBJ',\n",
              " '10': 'E-PER',\n",
              " '11': 'S-PER',\n",
              " '12': 'I-PER',\n",
              " '13': 'I-LOC',\n",
              " '14': 'B-OBJ',\n",
              " '15': 'S-LOC'}"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "id2label\n",
        "# label2id"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zlEUdhWt8hoE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
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            "a1a49a297582473f9d2e9185b4fd20dc",
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            "2c6fb584cba648aab3be9fff8368377f",
            "67b5606e593d4204bb90f3338f578379",
            "22cd1c84f910481fb60ee4b0b1100133",
            "4c3b2967f9ed4390bd19350044861491",
            "2fc6b99da1634f8ca2eea338de159c17",
            "41c36a7dac7d417e83b08048f335409f"
          ]
        },
        "outputId": "e509ecbc-dc09-4a7a-fed8-d6a41e6e4dc4"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/1.37k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "57caf573be814c9e98a9bb270fedc2f3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/440M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ff31ef92487244d9b8da9c6511c54853"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from transformers import AutoModelForTokenClassification\n",
        "\n",
        "model = AutoModelForTokenClassification.from_pretrained(\n",
        "    model_checkpoint,\n",
        "    id2label=id2label,\n",
        "    label2id=label2id,\n",
        "    num_labels=16\n",
        ")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IQRyCA148hoE",
        "outputId": "69634e8a-512d-4d59-ab06-3e1564e83caf"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "16"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "model.config.num_labels"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fXzacJrzsAhJ"
      },
      "outputs": [],
      "source": [
        "dataset = tokenized_dataset.train_test_split(test_size=1000, shuffle = True)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YelHStV2sAhJ",
        "outputId": "419286b8-dd57-469b-b1ea-88c09d83bd52",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'content': Value(dtype='string', id=None),\n",
              " 'pos_tags': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None),\n",
              " 'pos': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None),\n",
              " 'ner_tags': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None),\n",
              " 'ner': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None),\n",
              " 'tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None),\n",
              " 'input_ids': Sequence(feature=Value(dtype='int32', id=None), length=-1, id=None),\n",
              " 'token_type_ids': Sequence(feature=Value(dtype='int8', id=None), length=-1, id=None),\n",
              " 'attention_mask': Sequence(feature=Value(dtype='int8', id=None), length=-1, id=None),\n",
              " 'labels': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None)}"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "dataset['train'].features"
      ]
    },
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      "execution_count": null,
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        "id": "i9Q1ehXY8hoF"
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        "from transformers import TrainingArguments\n",
        "from transformers import Trainer\n",
        "\n",
        "training_args = TrainingArguments(\n",
        "    output_dir=\"bert-finetuned-ner-bangla\",\n",
        "    learning_rate=2e-5,\n",
        "    per_device_train_batch_size=64,\n",
        "    per_device_eval_batch_size=64,\n",
        "    num_train_epochs=2,\n",
        "    weight_decay=0.01,\n",
        "    evaluation_strategy=\"steps\",\n",
        "    eval_steps=500,\n",
        "    save_steps=1000,\n",
        "    save_strategy=\"steps\",\n",
        "    load_best_model_at_end=True,\n",
        "    # push_to_hub=True,\n",
        "    remove_unused_columns=True,\n",
        ")\n",
        "\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=dataset[\"train\"],\n",
        "    eval_dataset=dataset[\"test\"],\n",
        "    tokenizer=tokenizer,\n",
        "    data_collator=data_collator,\n",
        "    # compute_metrics=compute_metrics,\n",
        ")\n",
        "\n",
        "trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3q41onz-sAhK"
      },
      "outputs": [],
      "source": [
        "trainer.save_model('bangla-ner')"
      ]
    }
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