semantic_domains_greek.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ryderwishart/biblical-machine-learning/blob/main/semantic_domains_greek.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Imports"
      ],
      "metadata": {
        "id": "8k1ua2wrSa-c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers\n",
        "!pip install datasets\n",
        "!pip install seqeval"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sujzKs9pYDRY",
        "outputId": "2a8ac105-0b2c-4731-b6ab-dbc0dc63cc9a"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "Installing collected packages: tokenizers, huggingface-hub, transformers\n",
            "Successfully installed huggingface-hub-0.12.1 tokenizers-0.13.2 transformers-4.26.1\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting datasets\n",
            "  Downloading datasets-2.10.1-py3-none-any.whl (469 kB)\n",
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            "  Downloading responses-0.18.0-py3-none-any.whl (38 kB)\n",
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            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.8/dist-packages (from pandas->datasets) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas->datasets) (2022.7.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.8/dist-packages (from python-dateutil>=2.7.3->pandas->datasets) (1.15.0)\n",
            "Installing collected packages: xxhash, multiprocess, responses, datasets\n",
            "Successfully installed datasets-2.10.1 multiprocess-0.70.14 responses-0.18.0 xxhash-3.2.0\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting seqeval\n",
            "  Downloading seqeval-1.2.2.tar.gz (43 kB)\n",
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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.8/dist-packages (from scikit-learn>=0.21.3->seqeval) (3.1.0)\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=16179 sha256=1c8dfef2da4a446b3ef674f1f6cf17b51789dbcd625ec609ca12a8f0d7315e26\n",
            "  Stored in directory: /root/.cache/pip/wheels/ad/5c/ba/05fa33fa5855777b7d686e843ec07452f22a66a138e290e732\n",
            "Successfully built seqeval\n",
            "Installing collected packages: seqeval\n",
            "Successfully installed seqeval-1.2.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HSO6GuaIR3Mn",
        "outputId": "0977d7ae-bfe2-4491-df86-4ab639fc2acb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/MyDrive\n"
          ]
        }
      ],
      "source": [
        "from transformers import AutoTokenizer, AutoModelForTokenClassification, Trainer, TrainingArguments\n",
        "\n",
        "import unicodedata\n",
        "from google.colab import drive\n",
        "import pandas as pd\n",
        "import re\n",
        "import torch\n",
        "from datasets import load_dataset\n",
        "from transformers import TrainingArguments, Trainer\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score, recall_score, precision_score, f1_score\n",
        "import numpy as np\n",
        "from transformers import EarlyStoppingCallback\n",
        "from collections import Counter\n",
        "import json\n",
        "\n",
        "drive.mount('/content/MyDrive')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Initialize Data"
      ],
      "metadata": {
        "id": "V0vU_BfnSefN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sentences_path = '/content/MyDrive/MyDrive/ln_domains_by_sent-unlemmatized.json'"
      ],
      "metadata": {
        "id": "V9qru9xdSZBG"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# def strip_accents_and_lowercase(s):\n",
        "#    return ''.join(c for c in unicodedata.normalize('NFD', s)\n",
        "#                   if unicodedata.category(c) != 'Mn').lower()"
      ],
      "metadata": {
        "id": "cXoOvoJATa7D"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "domains_data = load_dataset(\"json\", \n",
        "                            download_mode=\"force_redownload\", \n",
        "                            data_files=sentences_path,\n",
        "                            field=\"data\"\n",
        "                            )\n",
        "# with open(sentences_path, 'r') as f:\n",
        "#     data = f.read()\n",
        "\n",
        "# raw_sentences = data.strip().split('\\n\\n')\n",
        "# print(raw_sentences[0])"
      ],
      "metadata": {
        "colab": {
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          "height": 380,
          "referenced_widgets": [
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            "793113443140430e8f5740fc4afdfdbb",
            "9072dd9dd8e449ee92107f768376faaa",
            "4e2b608af8e8461baaa76302fba682cb",
            "45d7c83f3d314eeeb7e8e5db312a06bb",
            "40da38a0cf8a42c7b35cd09aa9dfe77f",
            "deb8f62c6b1347aba865ca750e8108cc",
            "be61e4fd2c5e4da79262785a45e025af",
            "704584c26d624fd5b3e1cd41643af1d0",
            "2b38feec8115475c87e3739297d3c182",
            "835ba671090a49558fb6ff943909aa4d",
            "0f2cdc97b0924af18966c6360cf308e3",
            "c902da588d5d4cf5ac9561ff7c4f9a4c",
            "62abefab594146b1b354f7e79c51be45",
            "2fee53aed6964d938a9edff22e08e1a6"
          ]
        },
        "id": "Lgh6a2lSTbrv",
        "outputId": "7d82dfd0-bcea-4fe0-ac03-a735517bbe35"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset json/default to /root/.cache/huggingface/datasets/json/default-d32a7bf0b80d51b4/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "89699d7e05b44524bee66e93958de8cc"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extracting data files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4918b0bad326464cbdc9af8447806054"
            }
          },
          "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": "254de6c1dbb746008a7861b82c7e6a30"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset json downloaded and prepared to /root/.cache/huggingface/datasets/json/default-d32a7bf0b80d51b4/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "45d7c83f3d314eeeb7e8e5db312a06bb"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the dataset into train, test, and evaluation sets\n",
        "dataset_split = domains_data['train']\n",
        "dataset = dataset_split.train_test_split(test_size=0.2, shuffle=True)\n",
        "train_dataset, non_train_dataset = dataset['train'], dataset['test'].train_test_split(test_size=0.5, shuffle=True)\n",
        "test_dataset, eval_dataset = non_train_dataset['train'], non_train_dataset['test']\n",
        "\n",
        "# Print the sizes of the train, test, and evaluation sets\n",
        "print(f\"Train dataset size: {train_dataset}\", )\n",
        "print(f\"Test dataset size: {test_dataset}\")\n",
        "print(f\"Eval dataset size: {eval_dataset}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MBje7J5igPr8",
        "outputId": "defa72be-b5b0-4c57-85c5-fef26092acf9"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train dataset size: Dataset({\n",
            "    features: ['tokens', 'tags'],\n",
            "    num_rows: 6408\n",
            "})\n",
            "Test dataset size: Dataset({\n",
            "    features: ['tokens', 'tags'],\n",
            "    num_rows: 801\n",
            "})\n",
            "Eval dataset size: Dataset({\n",
            "    features: ['tokens', 'tags'],\n",
            "    num_rows: 802\n",
            "})\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create tag-to-id mapping dictionaries\n",
        "all_tags = list(set([tag for tagset in domains_data['train']['tags'] for tag in tagset]))\n",
        "all_tags = ['<pad>'] + all_tags\n",
        "tag2idx = {tag:idx for idx, tag in enumerate(all_tags)}\n",
        "idx2tag = {idx:tag for idx, tag in enumerate(all_tags)}\n",
        "\n",
        "# train_dataset.add_column(\"_tags\", train_dataset['tags']).rename_column(\"tags\", \"labels\").add_column(\"tags\", [tag2idx[tag] for tag in train_dataset['tags']]) #.cast(train_dataset.features)\n",
        "# test_dataset.add_column(\"_tags\", test_dataset['tags']).rename_column(\"tags\", \"labels\").add_column(\"tags\", [tag2idx[tag] for tag in test_dataset['tags']]) #.cast(test_dataset.features)\n",
        "# eval_dataset.add_column(\"_tags\", eval_dataset['tags']).rename_column(\"tags\", \"labels\").add_column(\"tags\", [tag2idx[tag] for tag in eval_dataset['tags']]) #.cast(eval_dataset.features)\n"
      ],
      "metadata": {
        "id": "fnkAyCIYrQCj"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def map_tags(example):\n",
        "    # Map each tag in the example to its corresponding ID using tag2idx\n",
        "    tags = [tag2idx[tag] for tag in example['tags']]\n",
        "    labels = [idx2tag[tag] for tag in tags]\n",
        "    # Return the example with the mapped tags\n",
        "    assert len(labels) == len(tags)\n",
        "    return {'labels': labels, 'tags': tags}\n",
        "\n",
        "# Map the tags in the 'train' split\n",
        "train_dataset = train_dataset.map(map_tags)\n",
        "\n",
        "# Map the tags in the 'validation' split\n",
        "eval_dataset = eval_dataset.map(map_tags)\n",
        "\n",
        "# Map the tags in the 'test' split\n",
        "test_dataset = test_dataset.map(map_tags)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 37,
          "referenced_widgets": [
            "aff89c639cb0443bb72e0e2b34b92352",
            "19d2d1ef2b994e788c4e68c4a3a4b59c",
            "3830966505d74833a8c96f0285020954",
            "87a17c5b27a248309e397fc843b7a721",
            "39da3f9f16954bacaae43df3a154bbc7",
            "a371308f785e4719959888493b91a7ff",
            "c53652568a98496c97fa3b4cb47d5ceb",
            "c98200ceb87145c4ac9579b2d690ba76",
            "af6151a8f5e04680bc274bb1ebff9a1d",
            "f68767eb579e4353a505dd397fa44698",
            "345abf9a0b914806bebb73c4e4134b23",
            "4857db17b4574a128c1b0c76032db5f8",
            "4e189e0b534c4bbcb605a487ad6ef84c",
            "59131dd66ac74680999a8e6187d16a93",
            "ec975a2683d74adebd8b8cce10a4ea42",
            "ec562137bc1f44c2b9d0bd10108e25bc",
            "b8b862fbf7cd44bda554b65242845610",
            "e5ffa0a4ecf242c0898efffe250f7987",
            "d30b15b63fce48e28720862965002d54",
            "e2b663595b2c4a2984c06c8ec605c6f3",
            "1041f8d162c344f8b70291a8356e75f8",
            "978aca91fac24b7c880b680f9282b40a",
            "db7469b0e9ce403186da09330ef207d9",
            "e74144f06fc9466e8d3c30930ade6382",
            "d5db6d0bc07d49c1b2366ed131f3287c",
            "8c1c694155744c12bdb044f6736063a0",
            "52c71ce5105948618ece32abc6741d25",
            "af1fe5865e404e8381158fc239199532",
            "dd31d290b46e4cc69e05980257c6f5c7",
            "d0876fb8bc9f49f28072ae2ffa69df27",
            "f633ecff78284083aa6d911e05c9f97a",
            "c48cfbc2c6464ca59850a889160ebaa7",
            "d242f318e1bd48a285f0ba3416c699a7"
          ]
        },
        "id": "ChsZ-2L1MG-3",
        "outputId": "f1eb3158-c707-4478-e78c-cc9e4e672034"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/6408 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "aff89c639cb0443bb72e0e2b34b92352"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/802 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4857db17b4574a128c1b0c76032db5f8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/801 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "db7469b0e9ce403186da09330ef207d9"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import DatasetDict\n",
        "\n",
        "raw_datasets = DatasetDict(\n",
        "    {\"train\": train_dataset, \"test\": test_dataset, \"eval\": eval_dataset})\n",
        "\n",
        "for i in zip(raw_datasets['test']['tags'][1], raw_datasets['test']['labels'][1]):\n",
        "  print('id: ', i[0], 'label: ', i[1])\n",
        "\n",
        "print(raw_datasets['test']['tokens'][1])\n",
        "print(raw_datasets['test']['labels'][1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7kMUj6UESa26",
        "outputId": "c7e280eb-b4d9-4efc-fe35-466c81f82542"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "id:  5787 label:  89.92\n",
            "id:  6235 label:  90.90\n",
            "id:  1266 label:  92.11\n",
            "id:  4972 label:  19.4\n",
            "['καὶ', 'ἐδίδοσαν', 'αὐτῷ', 'ῥαπίσματα.']\n",
            "['89.92', '90.90', '92.11', '19.4']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(raw_datasets['test']['labels'][0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J_3lazWQ2Zoa",
        "outputId": "a892615c-ab07-498a-e720-09c0a7ecc1bb"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['89.124', '61.16', '15.77', '60.11', '33.69', '92.29', '33.69', '74.5', '20.54', '92.24', '7.15', '92.24', '12.1', '89.87', '67.136', '60.12', '67.186', '45.1']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Initialize Model and Tokenizer"
      ],
      "metadata": {
        "id": "-ftp_GRySiJ7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import file_utils\n",
        "print('Models stored in cache at ', file_utils.default_cache_path)\n",
        "import os\n",
        "\n",
        "for i in os.listdir(file_utils.default_cache_path):\n",
        "  print(i)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tlN-L64sWquM",
        "outputId": "8564e93d-3567-44fd-978e-42e4e7235bc1"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Models stored in cache at  /root/.cache/huggingface/hub\n",
            "version.txt\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pretrained = \"pranaydeeps/Ancient-Greek-BERT\""
      ],
      "metadata": {
        "id": "8iPNnz7CTvqT"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# import shutil\n",
        "# shutil.rmtree(pretrained)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 324
        },
        "id": "ms58j11dwatX",
        "outputId": "83b9dff5-1b9b-48bf-9017-f37a2d08af5e"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-14-0d4a645672c2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mshutil\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mshutil\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrmtree\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpretrained\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/lib/python3.8/shutil.py\u001b[0m in \u001b[0;36mrmtree\u001b[0;34m(path, ignore_errors, onerror)\u001b[0m\n\u001b[1;32m    707\u001b[0m             \u001b[0morig_st\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlstat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    708\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 709\u001b[0;31m             \u001b[0monerror\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlstat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[0;34m(\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    710\u001b[0m             \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    711\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.8/shutil.py\u001b[0m in \u001b[0;36mrmtree\u001b[0;34m(path, ignore_errors, onerror)\u001b[0m\n\u001b[1;32m    705\u001b[0m         \u001b[0;31m# lstat()/open()/fstat() trick.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    706\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 707\u001b[0;31m             \u001b[0morig_st\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlstat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\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    708\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    709\u001b[0m             \u001b[0monerror\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlstat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[0;34m(\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;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'pranaydeeps/Ancient-Greek-BERT'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the tokenizer and encode the input sentences\n",
        "tokenizer = AutoTokenizer.from_pretrained(pretrained, force_download=True)\n",
        "print(f'Tokenizer is fast: {tokenizer.is_fast}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306,
          "referenced_widgets": [
            "b6f4de089f254737b2cbb1675a24ab4f",
            "50b9dbbe7b344dfa8f4193a666c4c298",
            "6ef969c3a8e8492a89cd3ca466933f21",
            "33be9767f1e049639951579322d921b6",
            "67dd1b1857094da5bde94d0f0fd65935",
            "9f3ae101c33d4065aff041b6e7445bde",
            "c7df96485593456bb830ee29384f0392",
            "17ca8e48f4ac4baabaa36948d01ec93d",
            "244c9bfc6d804c5c9505bd867a314a65",
            "cbeddd64df4b4695ae67346fbf95eedf",
            "04fa0ba4d1c84cc5a5dce4055e887f51",
            "d8ca21f6e2c040aaa93ef3487659da52",
            "85cb1c3cc9dc4305b6a94aba2b98ca2c",
            "6b743f6d46b44282b5dbc85bfd56612b",
            "a2450de2826a4a69a4355a55d0c13cd1",
            "e05aeda2e8144d7fba8d91321ad93eb5",
            "62409737b6f14b42931036274810e66a",
            "2a485734045b4709a588eaa4614eb7db",
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            "ae3c049d3018439b934da1d4f7847b61",
            "8646159993324b0d96975cd51d3dada7"
          ]
        },
        "id": "lVO5Er2RR6JA",
        "outputId": "78190c7e-c190-4503-ee65-9d821bc7c2d4"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/506 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b6f4de089f254737b2cbb1675a24ab4f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/647 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d8ca21f6e2c040aaa93ef3487659da52"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/530k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bb55812b69f044de9153d43d4e698c98"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/800k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3a93f7effd27408c97af1a7cb7e58eb0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/112 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8c70d72a501a42caa45976a950b110b9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tokenizer is fast: True\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def align_labels_with_tokens(labels, word_ids):\n",
        "    new_labels = []\n",
        "    current_word = None\n",
        "    for word_id in word_ids:\n",
        "        if word_id != current_word:\n",
        "            # Start of a new word!\n",
        "            current_word = word_id\n",
        "            # print('>> cur word: ', current_word)\n",
        "            label = -100 if word_id is None else labels[word_id]\n",
        "            new_labels.append(label)\n",
        "            prev_label = label\n",
        "        elif word_id is None:\n",
        "            # Special token\n",
        "            # print('>> none word: ', word_id)\n",
        "            new_labels.append(-100)\n",
        "        else:\n",
        "            # Same word as previous token\n",
        "            # print('>> same as last word: ', word_id)\n",
        "            label = labels[word_id]\n",
        "\n",
        "            # If the label is B-XXX we change it to I-XXX\n",
        "            # if label % 2 == 1:\n",
        "            #     label += 1\n",
        "            new_labels.append(label)\n",
        "\n",
        "    return new_labels"
      ],
      "metadata": {
        "id": "WdI_mnnBBr4S"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "inputs = tokenizer(raw_datasets[\"train\"][1][\"tokens\"], is_split_into_words=True)\n",
        "# inputs.tokens()\n",
        "\n",
        "labels = raw_datasets[\"train\"][1][\"tags\"]\n",
        "strings = raw_datasets[\"train\"][1][\"tokens\"]\n",
        "word_ids = inputs.word_ids()\n",
        "print(word_ids)\n",
        "print(strings)\n",
        "\n",
        "print(labels)\n",
        "print(align_labels_with_tokens(labels, word_ids))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Il2ptvf-CEWe",
        "outputId": "f9e9d9a0-c4d3-41ed-a441-b7a684b6806d"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[None, 0, 1, 1, 1, 1, 2, 3, 3, 4, 5, 6, 7, 7, 8, 9, 10, 10, 10, 11, 12, 13, 14, 14, 15, 16, 17, 18, 18, 18, None]\n",
            "['καὶ', 'ἐπολέμησεν', 'ὁ', 'δράκων', 'καὶ', 'οἱ', 'ἄγγελοι', 'αὐτοῦ,', 'καὶ', 'οὐκ', 'ἴσχυσαν,', 'οὐδὲ', 'τόπος', 'αὐτῶν', 'εὑρέθη', 'ἔτι', 'ἐν', 'τῷ', 'οὐρανῷ.']\n",
            "[5748, 134, 2064, 1248, 5748, 2064, 2536, 5748, 5748, 5748, 5748, 2434, 6590, 5748, 2874, 6052, 5748, 2064, 6892]\n",
            "[-100, 5748, 134, 134, 134, 134, 2064, 1248, 1248, 5748, 2064, 2536, 5748, 5748, 5748, 5748, 5748, 5748, 5748, 2434, 6590, 5748, 2874, 2874, 6052, 5748, 2064, 6892, 6892, 6892, -100]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def tokenize_and_align_labels(examples):\n",
        "    tokenized_inputs = tokenizer(\n",
        "        examples[\"tokens\"], truncation=True, is_split_into_words=True, padding='max_length', max_length=42\n",
        "    )\n",
        "    all_labels = examples[\"tags\"]\n",
        "    new_labels = []\n",
        "    for i, labels in enumerate(all_labels):\n",
        "        word_ids = tokenized_inputs.word_ids(i)\n",
        "        # print('>>>', labels)\n",
        "        new_labels.append(align_labels_with_tokens(labels, word_ids))\n",
        "\n",
        "    tokenized_inputs[\"labels\"] = new_labels\n",
        "    return tokenized_inputs"
      ],
      "metadata": {
        "id": "XKG92KJxCUSh"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_datasets = raw_datasets.map(\n",
        "    tokenize_and_align_labels,\n",
        "    batched=True,\n",
        "    remove_columns=raw_datasets[\"train\"].column_names,\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17,
          "referenced_widgets": [
            "5e604c65ab4242e28555dc3f24b322bb",
            "fb77a03e98aa4352b09809f9d6fa9c77",
            "3ab7755056d64d7c919fbf194609ee8c",
            "f62fe05aa4104d9393da5c8e3e104b9e",
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            "c83864eb8b784ace8e2532982812148c",
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            "de31af9c93374a99857c53d123a5407f",
            "c7871de9d6d84b5ab84451b7dce34c7c",
            "85a1903fd16b43f1a2a5ffcf584b0a0e",
            "5d094d2bf1464e03ae05e367055fa434",
            "7ff631232dcb40a98ced539ae437732a",
            "012cdb731ccf40e7a8dc59b3b14f45fa",
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            "99c77f3675c84371b11e32c01f82b5d3",
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            "2cc7146827ad44929fb37599ec901ef4",
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            "f23a32a764734fa38bfd2c1345f86e31",
            "b02c8575a0d540e19080ae3a39732323",
            "2fe4b9165b644117a1887cfdd21a5038",
            "cb7ee6b4bf8a4775ab8b619cca444210",
            "ae6467ac267d4352b7233452f56d8497",
            "615c085abfef4f07b043c4920c853f93",
            "ff7c7f490a49407da94e6f19ed68a3b7",
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            "9687618017194d8f8b0700c6ea03aa9a",
            "7236ae034dc745289e1db7310d44e362"
          ]
        },
        "id": "vgrufQyPCXse",
        "outputId": "10175ee6-5e8a-4da4-afc0-d8e604234123"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/6408 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5e604c65ab4242e28555dc3f24b322bb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/801 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "85a1903fd16b43f1a2a5ffcf584b0a0e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/802 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1894ed31f4a6420991aa87af4f34b498"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tag2idx['2.11']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nrnxx_DgWiiz",
        "outputId": "77cf4b74-8d81-4eea-e964-35922ca6b7cc"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_datasets['train']['labels'][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V-lz1nPe30gA",
        "outputId": "9624f28a-573a-4861-d786-e8dbe7c8d824"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[-100,\n",
              " 4992,\n",
              " 3696,\n",
              " 3696,\n",
              " 2625,\n",
              " 613,\n",
              " 846,\n",
              " 846,\n",
              " 4703,\n",
              " 6973,\n",
              " 6973,\n",
              " 6973,\n",
              " 2672,\n",
              " 6521,\n",
              " 6521,\n",
              " 5394,\n",
              " 613,\n",
              " 1489,\n",
              " 1489,\n",
              " -100,\n",
              " -100,\n",
              " -100,\n",
              " -100,\n",
              " -100,\n",
              " -100,\n",
              " -100,\n",
              " -100,\n",
              " -100,\n",
              " -100,\n",
              " -100,\n",
              " -100,\n",
              " -100,\n",
              " -100,\n",
              " -100,\n",
              " -100,\n",
              " -100,\n",
              " -100,\n",
              " -100,\n",
              " -100,\n",
              " -100,\n",
              " -100,\n",
              " -100]"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import DataCollatorForTokenClassification\n",
        "\n",
        "data_collator = DataCollatorForTokenClassification(tokenizer=tokenizer)"
      ],
      "metadata": {
        "id": "jW1afIFM0Alq"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "batch = data_collator([tokenized_datasets[\"train\"][i] for i in range(2)])\n",
        "batch[\"labels\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xESc7O_o0c6Q",
        "outputId": "96d72bee-0120-4506-ae4a-1c15a72e7a2e"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You're using a BertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[-100, 5748, 3594, 3594, 2064, 1300, 1300, 5748, 2064, 7457, 7457, 2064,\n",
              "         4371, 4371, 4371, 4371, 5748, 5748, 2064, 4383, 4383, 4383, 5748, 1826,\n",
              "         1826, 1826, 2064, 4383, 4383, -100, -100, -100, -100, -100, -100, -100,\n",
              "         -100, -100, -100, -100, -100, -100],\n",
              "        [-100, 5748,  134,  134,  134,  134, 2064, 1248, 1248, 5748, 2064, 2536,\n",
              "         5748, 5748, 5748, 5748, 5748, 5748, 5748, 2434, 6590, 5748, 2874, 2874,\n",
              "         6052, 5748, 2064, 6892, 6892, 6892, -100, -100, -100, -100, -100, -100,\n",
              "         -100, -100, -100, -100, -100, -100]])"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Evaluate model training"
      ],
      "metadata": {
        "id": "Sww8uElEUCWU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# !pip install seqeval\n",
        "!pip install evaluate\n",
        "import evaluate\n",
        "\n",
        "metric = evaluate.load(\"seqeval\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 698,
          "referenced_widgets": [
            "2fd6cd04094c48c1a18bc02485d0f2d3",
            "37b0dd6c23454392b3a6c8d00cbf78fb",
            "5f8de015f51b4de696f0418d6ae73193",
            "396afd46f874438085a00993579d843e",
            "5762c5b615144187b74247c5353cbe0d",
            "eb4a01846b204df9a274f539d43e03a1",
            "5e2f0188a0f24985923aeb02d448367f",
            "84bdb603691b4193a57619b162e09a6c",
            "815e1fc63d654cfa86f3d43c5fc9ae7b",
            "247299c6879e47629d71ed0781198160",
            "37f4fbc8cfb24ddb8efa560cfcf6fe9f"
          ]
        },
        "id": "dMnSMYfWfooq",
        "outputId": "a3f2d2d8-9707-4e77-cb7c-e02741c3bc42"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting evaluate\n",
            "  Downloading evaluate-0.4.0-py3-none-any.whl (81 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m81.4/81.4 KB\u001b[0m \u001b[31m2.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: tqdm>=4.62.1 in /usr/local/lib/python3.8/dist-packages (from evaluate) (4.64.1)\n",
            "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.8/dist-packages (from evaluate) (2.25.1)\n",
            "Requirement already satisfied: multiprocess in /usr/local/lib/python3.8/dist-packages (from evaluate) (0.70.14)\n",
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            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.8/dist-packages (from evaluate) (1.22.4)\n",
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            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas->evaluate) (2022.7.1)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets>=2.0.0->evaluate) (22.2.0)\n",
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            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets>=2.0.0->evaluate) (1.3.3)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets>=2.0.0->evaluate) (1.3.1)\n",
            "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets>=2.0.0->evaluate) (4.0.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.8/dist-packages (from python-dateutil>=2.7.3->pandas->evaluate) (1.15.0)\n",
            "Installing collected packages: evaluate\n",
            "Successfully installed evaluate-0.4.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/6.34k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2fd6cd04094c48c1a18bc02485d0f2d3"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "\n",
        "def compute_metrics(eval_preds):\n",
        "    logits, labels = eval_preds\n",
        "    predictions = np.argmax(logits, axis=-1)\n",
        "\n",
        "    # Remove ignored index (special tokens) and convert to labels\n",
        "    true_labels = [[all_tags[l] for l in label if l != -100] for label in labels]\n",
        "    true_predictions = [\n",
        "        [all_tags[p] for (p, l) in zip(prediction, label) if l != -100]\n",
        "        for prediction, label in zip(predictions, labels)\n",
        "    ]\n",
        "    \n",
        "    ###### MAJOR GOTCHA: the module sequence_labeling.py from seqeval expects\n",
        "    ###### named-entity tags with a certain format. This will raise an exception\n",
        "    ###### in between epochs if you don't modify that code accordingly.\n",
        "    ###### TODO: update this compute_metrics function to use sklearn as the\n",
        "    ###### commented code block below does (though the block below is for single-\n",
        "    ###### token inputs).\n",
        "    \n",
        "    all_metrics = metric.compute(predictions=true_predictions, references=true_labels)\n",
        "    return {\n",
        "        \"precision\": all_metrics[\"overall_precision\"],\n",
        "        \"recall\": all_metrics[\"overall_recall\"],\n",
        "        \"f1\": all_metrics[\"overall_f1\"],\n",
        "        \"accuracy\": all_metrics[\"overall_accuracy\"],\n",
        "    }\n",
        "\n",
        "\n",
        "\n",
        "# from sklearn.metrics import accuracy_score, recall_score, precision_score, f1_score\n",
        "\n",
        "# def compute_metrics(p):\n",
        "#     # FIXME: I think I need to handle the p variable here slightly differently as it might lock up a lot of GPU ram to access it like this\n",
        "#     pred, labels = p\n",
        "#     pred = np.argmax(pred, axis=1)\n",
        "\n",
        "#     accuracy = accuracy_score(y_true=labels, y_pred=pred)\n",
        "#     recall = recall_score(y_true=labels, y_pred=pred, average='weighted')\n",
        "#     precision = precision_score(y_true=labels, y_pred=pred, average='weighted')\n",
        "#     f1 = f1_score(y_true=labels, y_pred=pred, average='weighted')\n",
        "\n",
        "#     return {\"accuracy\": accuracy, \"precision\": precision, \"recall\": recall, \"f1\": f1}"
      ],
      "metadata": {
        "id": "mFIwP6bLgUbD"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# idx2tag"
      ],
      "metadata": {
        "id": "XrpqhC_s3cKg"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the model architecture\n",
        "defined_model = AutoModelForTokenClassification.from_pretrained(\n",
        "    pretrained,\n",
        "    id2label=idx2tag,\n",
        "    label2id=tag2idx,\n",
        "    force_download=True\n",
        ")\n",
        "\n",
        "# defined_model = defined_model.to('cuda')\n",
        "\n",
        "assert defined_model.config.num_labels == len(all_tags)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 163,
          "referenced_widgets": [
            "9ec95e3c3c9b428cab21c1fde1ba7b98",
            "43e4cb26c12e4f968fe0c01ee752ed29",
            "6ec2de2a5cb44b89af5dcd8953589b44",
            "7df394898ccc4b219afe3c301380eda9",
            "07e9a173663f4df4893795dce29e0f21",
            "5230fdcfd92944dd87226212afa6ef2f",
            "2500db0781864f92a988c00445d5d677",
            "a92d086ae246416196c80f197db93173",
            "b0c7b25f39664024baa04f41e5d50c2e",
            "ff8124f8de1a4d50bf9502310829013b",
            "e2db4a981022409d816c4ebe98d04ce7",
            "b32691e30d324fa2a2be5051e757ec83",
            "6846a69b0a3a44f6a5fcad6946ce31bf",
            "137accb95f3e4ce091e83032c4026ca2",
            "1aba83fa0f4b424e852801eb0c6b6229",
            "5bfd38203b3544e487654c8f22914a9d",
            "aef61af25c844812b9756fffadc057d8",
            "759b5c51e9d246478b056cc6da7c3a58",
            "5cc17537d0af4be49319d94a6bc3647b",
            "f6f7187f21434202a01fde06656585c9",
            "bd1819eef57b4e33be41f4a82547f8b3",
            "24f1ea4af6d141d1b6b5ab27d0dac305"
          ]
        },
        "id": "P-NqegYrR7_H",
        "outputId": "62c903b7-e478-48d9-c6da-45679cd28102"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/647 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9ec95e3c3c9b428cab21c1fde1ba7b98"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)\"pytorch_model.bin\";:   0%|          | 0.00/452M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b32691e30d324fa2a2be5051e757ec83"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of BertForTokenClassification were not initialized from the model checkpoint at pranaydeeps/Ancient-Greek-BERT and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Train Model"
      ],
      "metadata": {
        "id": "WBBRV9LiSnAR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import TrainingArguments\n",
        "\n",
        "batch_size = 2\n",
        "\n",
        "args = TrainingArguments(\n",
        "    pretrained,\n",
        "    evaluation_strategy=\"epoch\",\n",
        "    save_strategy=\"epoch\",\n",
        "    learning_rate=2e-5,\n",
        "    num_train_epochs=3,\n",
        "    weight_decay=0.01,\n",
        "    # push_to_hub=True,\n",
        "    per_device_train_batch_size=batch_size, # optional, defaults to 8 — The batch size per GPU/TPU core/CPU for training.\n",
        "    per_device_eval_batch_size=batch_size\n",
        ")"
      ],
      "metadata": {
        "id": "Y2CpJBr4hX0V"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# optionally clear GPU cache before training if you are getting a CUDA out of memory error\n",
        "# torch.no_grad()\n",
        "# !pip install numba\n",
        "\n",
        "# from numba import cuda \n",
        "# device = cuda.get_current_device()\n",
        "# device.reset()"
      ],
      "metadata": {
        "id": "0muMtTICj6Id"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Use a subset for testing\n",
        "\n",
        "# subset_dataset_train = tokenized_datasets['train'].select(range(100))\n",
        "# subset_dataset_test = tokenized_datasets['test'].select(range(100))\n",
        "# subset_dataset_eval = tokenized_datasets['eval'].select(range(100))\n",
        "# trainer = Trainer(\n",
        "#     model=defined_model,\n",
        "#     args=args,\n",
        "#     # train_dataset=tokenized_datasets[\"train\"],\n",
        "#     train_dataset=subset_dataset_train,\n",
        "#     # eval_dataset=tokenized_datasets[\"test\"], # use 'test' for training evaluation\n",
        "#     eval_dataset=subset_dataset_test, # use 'test' for training evaluation\n",
        "#     data_collator=data_collator,\n",
        "#     # compute_metrics=compute_metrics,\n",
        "#     tokenizer=tokenizer,\n",
        "# )"
      ],
      "metadata": {
        "id": "XUuln5Iwzwly"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import Trainer\n",
        "\n",
        "trainer = Trainer(\n",
        "    model=defined_model,\n",
        "    args=args,\n",
        "    train_dataset=tokenized_datasets[\"train\"],\n",
        "    eval_dataset=tokenized_datasets[\"test\"], # use 'test' for training evaluation\n",
        "    data_collator=data_collator,\n",
        "    # compute_metrics=compute_metrics,\n",
        "    tokenizer=tokenizer,\n",
        ")\n",
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 902
        },
        "id": "DxtzJMNyho4Y",
        "outputId": "26870743-060f-40a5-d55b-1eb0e55f1c40"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/transformers/optimization.py:306: 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",
            "***** Running training *****\n",
            "  Num examples = 6408\n",
            "  Num Epochs = 3\n",
            "  Instantaneous batch size per device = 2\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 2\n",
            "  Gradient Accumulation steps = 1\n",
            "  Total optimization steps = 9612\n",
            "  Number of trainable parameters = 118342025\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='9612' max='9612' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [9612/9612 15:28, Epoch 3/3]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>3.716600</td>\n",
              "      <td>3.411424</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>2.769100</td>\n",
              "      <td>2.838049</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>2.388400</td>\n",
              "      <td>2.704520</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "***** Running Evaluation *****\n",
            "  Num examples = 801\n",
            "  Batch size = 2\n",
            "Saving model checkpoint to pranaydeeps/Ancient-Greek-BERT/checkpoint-3204\n",
            "Configuration saved in pranaydeeps/Ancient-Greek-BERT/checkpoint-3204/config.json\n",
            "Model weights saved in pranaydeeps/Ancient-Greek-BERT/checkpoint-3204/pytorch_model.bin\n",
            "tokenizer config file saved in pranaydeeps/Ancient-Greek-BERT/checkpoint-3204/tokenizer_config.json\n",
            "Special tokens file saved in pranaydeeps/Ancient-Greek-BERT/checkpoint-3204/special_tokens_map.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 801\n",
            "  Batch size = 2\n",
            "Saving model checkpoint to pranaydeeps/Ancient-Greek-BERT/checkpoint-6408\n",
            "Configuration saved in pranaydeeps/Ancient-Greek-BERT/checkpoint-6408/config.json\n",
            "Model weights saved in pranaydeeps/Ancient-Greek-BERT/checkpoint-6408/pytorch_model.bin\n",
            "tokenizer config file saved in pranaydeeps/Ancient-Greek-BERT/checkpoint-6408/tokenizer_config.json\n",
            "Special tokens file saved in pranaydeeps/Ancient-Greek-BERT/checkpoint-6408/special_tokens_map.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 801\n",
            "  Batch size = 2\n",
            "Saving model checkpoint to pranaydeeps/Ancient-Greek-BERT/checkpoint-9612\n",
            "Configuration saved in pranaydeeps/Ancient-Greek-BERT/checkpoint-9612/config.json\n",
            "Model weights saved in pranaydeeps/Ancient-Greek-BERT/checkpoint-9612/pytorch_model.bin\n",
            "tokenizer config file saved in pranaydeeps/Ancient-Greek-BERT/checkpoint-9612/tokenizer_config.json\n",
            "Special tokens file saved in pranaydeeps/Ancient-Greek-BERT/checkpoint-9612/special_tokens_map.json\n",
            "\n",
            "\n",
            "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=9612, training_loss=3.353432428722723, metrics={'train_runtime': 931.1908, 'train_samples_per_second': 20.645, 'train_steps_per_second': 10.322, 'total_flos': 441170668550304.0, 'train_loss': 3.353432428722723, 'epoch': 3.0})"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# !huggingface-cli login"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wz4ZYchEbYXv",
        "outputId": "f3c9e896-5f46-4919-ef00-dce40ba7c59b"
      },
      "execution_count": 35,
      "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",
            "Token: \n",
            "Add token as git credential? (Y/n) n\n",
            "Token is valid.\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# !pip install huggingface_hub"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IosqKN9MbvKb",
        "outputId": "ff3723a3-604e-4af5-f4d9-92f489c56fff"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: huggingface_hub in /usr/local/lib/python3.8/dist-packages (0.12.1)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.8/dist-packages (from huggingface_hub) (2.25.1)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.8/dist-packages (from huggingface_hub) (4.5.0)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.8/dist-packages (from huggingface_hub) (6.0)\n",
            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.8/dist-packages (from huggingface_hub) (4.64.1)\n",
            "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.8/dist-packages (from huggingface_hub) (23.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.8/dist-packages (from huggingface_hub) (3.9.0)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests->huggingface_hub) (1.26.14)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests->huggingface_hub) (2022.12.7)\n",
            "Requirement already satisfied: chardet<5,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests->huggingface_hub) (4.0.0)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests->huggingface_hub) (2.10)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# defined_model.push_to_hub(\"semdom-ancient-greek\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237,
          "referenced_widgets": [
            "afe9d63f24ff47cd999ad92c7d0f2b7a",
            "c2724541e187413db3d550e39db4e205",
            "123eddf31b9e4f7688d8dee1986926fc",
            "4246f37bd6324374bf045887f1bbe579",
            "f3bafc9c22e040ec918bf541a0d9f1f0",
            "0d0cc7ec7d9f48c4af7a074509fa6dec",
            "29b2f14e1dfa4289a05ec58dedcf6b40",
            "cd28def0f1ef4311966f98f68a61fd15",
            "66cdb49ad15a43bdafd2946e36aa3f9d",
            "d5f84f67977b400ea5b4e284b87592b6",
            "cfd6b18e69a842d9b3bc71eb3a9f1bf2",
            "a17061eec6a14ee4ba3b0df3068264e8",
            "5230718ff3f1426fa16a3994b244f352",
            "3a748ee188cf40b2a15cde36abd6eb49",
            "30b083a92a4e4918b9df8208e2428f4f",
            "e9ae876a9b434377b1099592c526cf26",
            "6c3d7646f4d2493f86b1fffc3e59cdcc",
            "5006deae8ac3494999ce55b412569ff3",
            "ad566d7cd0d94217a7a256f6742d42f1",
            "13b064d728624224b2a36d6d14916dfb",
            "9a96b255303244b1922387013f6a55a8",
            "98076645f3f44e6d8233e32e28217662"
          ]
        },
        "id": "fiYyb7bjcFCj",
        "outputId": "2675f124-dbdd-4682-930d-5d69e1295a8d"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Configuration saved in /tmp/tmp5nc9ue6t/config.json\n",
            "Model weights saved in /tmp/tmp5nc9ue6t/pytorch_model.bin\n",
            "Uploading the following files to ryderwishart/semdom-ancient-greek: pytorch_model.bin,config.json\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Upload 1 LFS files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "afe9d63f24ff47cd999ad92c7d0f2b7a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "pytorch_model.bin:   0%|          | 0.00/473M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a17061eec6a14ee4ba3b0df3068264e8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/ryderwishart/semdom-ancient-greek/commit/df8f43415e35694cba84b7305fab427140aaab2b', commit_message='Upload BertForTokenClassification', commit_description='', oid='df8f43415e35694cba84b7305fab427140aaab2b', pr_url=None, pr_revision=None, pr_num=None)"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Evaluate Model"
      ],
      "metadata": {
        "id": "RmTWfbdlS2dn"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "z_lYDIxo08iv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# TODO: use programmatic evaluation; implement correct compute_metrics function above\n",
        "from transformers import pipeline\n",
        "\n",
        "model_checkpoint = defined_model.cpu()\n",
        "token_classifier = pipeline(\n",
        "    \"token-classification\", \n",
        "    model=model_checkpoint, \n",
        "    tokenizer=tokenizer, \n",
        "    aggregation_strategy=\"simple\"\n",
        ")\n",
        "output = token_classifier(\"Λέγει αὐτῷ Θωμᾶς Κύριε, οὐκ οἴδαμεν ποῦ ὑπάγεις· πῶς οἴδαμεν τὴν ὁδὸν;\")\n",
        "test = {\"tokens\":[\"Λέγει\",\"αὐτῷ\",\"Θωμᾶς\",\"Κύριε,\",\"οὐκ\",\"οἴδαμεν\",\"ποῦ\",\"ὑπάγεις·\",\"πῶς\",\"οἴδαμεν\",\"τὴν\",\"ὁδὸν;\"],\"tags\":[\"33.69\",\"92.11\",\"93.155\",\"12.9{N:001}\",\"69.3\",\"28.1\",\"83.6\",\"15.35\",\"92.16\",\"NULL\",\"92.24\",\"1.99\"]}\n",
        "for i, tok in enumerate(output):\n",
        "    print(tok['word'], tok['entity_group'] == test['tags'][i], '\\t\\tpred: ', tok['entity_group'], '\\t\\tshould be: ', test['tags'][i])"
      ],
      "metadata": {
        "id": "zQ_xe4frfnI0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "19a22413-445d-47a6-8b51-d5913701ddf3"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "λεγει True \t\tpred:  33.69 \t\tshould be:  33.69\n",
            "αυτω True \t\tpred:  92.11 \t\tshould be:  92.11\n",
            "θωμας False \t\tpred:  93.294a \t\tshould be:  93.155\n",
            "κυριε, True \t\tpred:  12.9{N:001} \t\tshould be:  12.9{N:001}\n",
            "ουκ True \t\tpred:  69.3 \t\tshould be:  69.3\n",
            "οιδαμεν True \t\tpred:  28.1 \t\tshould be:  28.1\n",
            "που False \t\tpred:  92.14 \t\tshould be:  83.6\n",
            "υπαγεις · False \t\tpred:  15.37 \t\tshould be:  15.35\n",
            "πως True \t\tpred:  92.16 \t\tshould be:  92.16\n",
            "οιδαμεν False \t\tpred:  28.1 \t\tshould be:  NULL\n",
            "την True \t\tpred:  92.24 \t\tshould be:  92.24\n",
            "οδον ; True \t\tpred:  1.99 \t\tshould be:  1.99\n"
          ]
        }
      ]
    }
  ]
}