Token Classification/Multilingual/Babelscape-WikiNeural-Joined Dataset/Babelscape WikiNeural Joined Dataset With Multilingual BERT.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zFlXnp7463PP"
      },
      "source": [
        "## Babelscape WikiNeural Joined: Fine-Tune Multilingual NER\n",
        "\n",
        "Dataset Source: https://huggingface.co/datasets/dmargutierrez/Babelscape-wikineural-joined"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FrBCaMmh63PS"
      },
      "source": [
        "#### Install Necessary Libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XpWZt9Pt63PV",
        "outputId": "b76c08fc-5756-4982-dc00-4063826090e7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "Building wheels for collected packages: seqeval\n",
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            "Successfully built seqeval\n",
            "Installing collected packages: tokenizers, safetensors, xxhash, dill, responses, multiprocess, huggingface-hub, transformers, seqeval, datasets, evaluate\n",
            "Successfully installed datasets-2.14.4 dill-0.3.7 evaluate-0.4.0 huggingface-hub-0.16.4 multiprocess-0.70.15 responses-0.18.0 safetensors-0.3.2 seqeval-1.2.2 tokenizers-0.13.3 transformers-4.31.0 xxhash-3.3.0\n",
            "Collecting accelerate\n",
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            "Installing collected packages: accelerate\n",
            "Successfully installed accelerate-0.21.0\n"
          ]
        }
      ],
      "source": [
        "%pip install transformers datasets evaluate seqeval tensorboard\n",
        "%pip install accelerate -U"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "swVoNy32iggD"
      },
      "source": [
        "#### HuggingFace Access"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QUoSKdrhiloE",
        "outputId": "8ce0f798-fe06-44a5-d8e1-2739de0c596d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "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 (permission: write).\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful\n"
          ]
        }
      ],
      "source": [
        "!huggingface-cli login"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EyrGR5dl63PW"
      },
      "source": [
        "#### Import Necessary Libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O-SOao5Y63PW",
        "outputId": "3b85e18a-8c43-41fa-f750-39ac24aa3c65"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Git LFS initialized.\n"
          ]
        }
      ],
      "source": [
        "import os, sys\n",
        "os.environ['TOKENIZERS_PARALLELISM']='false'\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import sklearn\n",
        "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
        "\n",
        "import datasets\n",
        "from datasets import load_dataset, DatasetDict\n",
        "from datasets import Dataset, concatenate_datasets\n",
        "\n",
        "import torch\n",
        "from torch.nn.functional import cross_entropy\n",
        "\n",
        "import transformers\n",
        "from transformers import AutoTokenizer, AutoModelForTokenClassification\n",
        "from transformers import DataCollatorForTokenClassification\n",
        "from transformers import Trainer, TrainingArguments\n",
        "\n",
        "import evaluate\n",
        "\n",
        "!git lfs install"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5OlAhQ7C63PX"
      },
      "source": [
        "#### Display Library Versions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jebPGXjH63PX",
        "outputId": "7433fba2-3b82-47a7-b263-f524d2c7170e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "          Python : 3.10.1\n",
            "           NumPy : 1.23.5\n",
            "          Pandas : 1.5.3\n",
            "           Torch : 2.0.1+cu118\n",
            "    Transformers : 4.31.0\n",
            "        Datasets : 2.14.4\n",
            "        Evaluate : 0.4.0\n",
            "         Sklearn : 1.2.2\n"
          ]
        }
      ],
      "source": [
        "print(\"Python :\".rjust(18), sys.version[0:6])\n",
        "print(\"NumPy :\".rjust(18), np.__version__)\n",
        "print(\"Pandas :\".rjust(18), pd.__version__)\n",
        "print(\"Torch :\".rjust(18), torch.__version__)\n",
        "print(\"Transformers :\".rjust(18), transformers.__version__)\n",
        "print(\"Datasets :\".rjust(18), datasets.__version__)\n",
        "print(\"Evaluate :\".rjust(18), evaluate.__version__)\n",
        "print(\"Sklearn :\".rjust(18), sklearn.__version__)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F_wEnr3P63PY"
      },
      "source": [
        "#### Ingest English Subset of Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 565,
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          ]
        },
        "id": "oc6G95w163PY",
        "outputId": "a4887ae0-66e1-46e6-8860-589374293e78"
      },
      "outputs": [
        {
          "data": {
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              "model_id": "59f071b2a8964b2da798dbffcf2ed9b8",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/2.36k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "fe50783a45a4430090e0c679c7ede71e",
              "version_major": 2,
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            },
            "text/plain": [
              "Downloading data files:   0%|          | 0/3 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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        {
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "version_major": 2,
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        },
        {
          "data": {
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              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Extracting data files:   0%|          | 0/3 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "179a5638d967469ca81997675a932b31",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating train split:   0%|          | 0/821600 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "1674696880fa4a9ba34b608c1ba45cf2",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating validation split:   0%|          | 0/102700 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f9cf22ea93b345b5926db3d628bce17e",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating test split:   0%|          | 0/103206 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training Dataset Information: \n",
            "Dataset({\n",
            "    features: ['tokens', 'ner_tags', 'lang'],\n",
            "    num_rows: 821600\n",
            "})\n",
            "Testing Dataset Information: \n",
            "Dataset({\n",
            "    features: ['tokens', 'ner_tags', 'lang'],\n",
            "    num_rows: 103206\n",
            "})\n",
            "Evaluation Dataset Information: \n",
            "Dataset({\n",
            "    features: ['tokens', 'ner_tags', 'lang'],\n",
            "    num_rows: 102700\n",
            "})\n"
          ]
        }
      ],
      "source": [
        "data = load_dataset(\"dmargutierrez/Babelscape-wikineural-joined\")\n",
        "\n",
        "print(f\"Training Dataset Information: \\n{data['train']}\")\n",
        "print(f\"Testing Dataset Information: \\n{data['test']}\")\n",
        "print(f\"Evaluation Dataset Information: \\n{data['validation']}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-f8dc4EG63PZ"
      },
      "source": [
        "#### Example"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rxnyCnQp63PZ",
        "outputId": "6a505229-90fe-40af-90d2-cb7ec2232f02"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'tokens': ['Demasiado',\n",
              "  'longe',\n",
              "  'dos',\n",
              "  'seus',\n",
              "  'cavalos',\n",
              "  'para',\n",
              "  'os',\n",
              "  'recuperar',\n",
              "  ',',\n",
              "  'tiveram',\n",
              "  'de',\n",
              "  'esconder',\n",
              "  'a',\n",
              "  'maior',\n",
              "  'parte',\n",
              "  'dos',\n",
              "  'seus',\n",
              "  'bens',\n",
              "  'e',\n",
              "  'andar',\n",
              "  'a',\n",
              "  'pé',\n",
              "  'o',\n",
              "  'resto',\n",
              "  'do',\n",
              "  'caminho',\n",
              "  'até',\n",
              "  'ao',\n",
              "  'rio',\n",
              "  'Colúmbia',\n",
              "  ',',\n",
              "  'onde',\n",
              "  'fizeram',\n",
              "  'outras',\n",
              "  'canoas',\n",
              "  'e',\n",
              "  'viajaram',\n",
              "  'até',\n",
              "  'ao',\n",
              "  'recém-fundado',\n",
              "  'Fort',\n",
              "  'Astoria',\n",
              "  '.'],\n",
              " 'ner_tags': [0,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  5,\n",
              "  6,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  5,\n",
              "  6,\n",
              "  0],\n",
              " 'lang': 'pt'}"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sample = data['train'][12]\n",
        "\n",
        "sample"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yGPVlsKK63PZ"
      },
      "source": [
        "#### Display Feature Information About Each Feature"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rPQju2At63PZ",
        "outputId": "0e0bf647-e7b2-477b-d15f-3252d1efef22"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tokens: \n",
            "Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)\n",
            "\n",
            "ner_tags: \n",
            "Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None)\n",
            "\n",
            "lang: \n",
            "Value(dtype='string', id=None)\n",
            "\n"
          ]
        }
      ],
      "source": [
        "for k, v in data[\"train\"].features.items():\n",
        "    print(f\"{k}: \\n{v}\\n\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I_UAAwGd63Pa"
      },
      "source": [
        "#### Define Tag Values & Conversions Between String & Integer Values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KY5sfrzV63Pa",
        "outputId": "fd50b1f1-1ede-4c11-8765-ece59dcfdb8c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "List of tag values: \n",
            "['B-LOC', 'B-MISC', 'B-ORG', 'B-PER', 'I-LOC', 'I-MISC', 'I-ORG', 'I-PER', 'O']\n",
            "Number of NER Tags: \n",
            "9\n",
            "id2label: \n",
            "{0: 'O', 1: 'B-PER', 2: 'I-PER', 3: 'B-ORG', 4: 'I-ORG', 5: 'B-LOC', 6: 'I-LOC', 7: 'B-MISC', 8: 'I-MISC'}\n",
            "label2id: \n",
            "{'B-LOC': 5, 'B-MISC': 7, 'B-ORG': 3, 'B-PER': 1, 'I-LOC': 6, 'I-MISC': 8, 'I-ORG': 4, 'I-PER': 2, 'O': 0}\n"
          ]
        }
      ],
      "source": [
        "id2label = {\n",
        "    0: \"O\",\n",
        "    1: \"B-PER\",\n",
        "    2: \"I-PER\",\n",
        "    3: \"B-ORG\",\n",
        "    4: \"I-ORG\",\n",
        "    5: \"B-LOC\",\n",
        "    6: \"I-LOC\",\n",
        "    7: \"B-MISC\",\n",
        "    8: \"I-MISC\"\n",
        "}\n",
        "\n",
        "label2id = {\n",
        "    \"B-LOC\": 5,\n",
        "    \"B-MISC\": 7,\n",
        "    \"B-ORG\": 3,\n",
        "    \"B-PER\": 1,\n",
        "    \"I-LOC\": 6,\n",
        "    \"I-MISC\": 8,\n",
        "    \"I-ORG\": 4,\n",
        "    \"I-PER\": 2,\n",
        "    \"O\": 0\n",
        "}\n",
        "\n",
        "tag_values = list(label2id.keys())\n",
        "NUM_OF_LABELS = len(tag_values)\n",
        "\n",
        "print(f\"List of tag values: \\n{tag_values}\")\n",
        "print(f\"Number of NER Tags: \\n{NUM_OF_LABELS}\")\n",
        "print(f\"id2label: \\n{id2label}\")\n",
        "print(f\"label2id: \\n{label2id}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AB63YpWK63Pa"
      },
      "source": [
        "#### Basic Values/Constants"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "FN6KdL0R63Pa"
      },
      "outputs": [],
      "source": [
        "MODEL_CKPT = \"bert-base-multilingual-cased\"\n",
        "\n",
        "MODEL_NAME = f\"{MODEL_CKPT}-fine_tuned-ner-WikiNeural_Multilingual\"\n",
        "NUM_OF_EPOCHS = 1\n",
        "\n",
        "BATCH_SIZE = 64\n",
        "STRATEGY = \"epoch\"\n",
        "\n",
        "REPORTS_TO = \"tensorboard\"\n",
        "WEIGHT_DECAY = 0.01\n",
        "\n",
        "LR = 2e-5\n",
        "DEVICE = torch.device(\"cuda\")\n",
        "\n",
        "STEPS = 500"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WNLb0AiI63Pa"
      },
      "source": [
        "#### Function to Tokenize & Align Inputs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
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        },
        "id": "WDj5bkn163Pa",
        "outputId": "3ca4150b-8f2f-4d33-ef3c-48bc5f668811"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7f0262163d1149ee876b0bd45dbc443e",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/29.0 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "378241753f2a466c9c688e601cf28d00",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/625 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "67ed01aabc3044309f49d73d9ab5480c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/996k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ea08bd1f9fb6467c91c044223d4bd686",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/1.96M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(MODEL_CKPT)\n",
        "\n",
        "def tokenize_and_align_labels(samples):\n",
        "    tokenized_inputs = tokenizer(samples[\"tokens\"],\n",
        "                                      truncation=True,\n",
        "                                      is_split_into_words=True)\n",
        "\n",
        "    labels = []\n",
        "\n",
        "    for idx, label in enumerate(samples[f\"ner_tags\"]):\n",
        "        word_ids = tokenized_inputs.word_ids(batch_index=idx)\n",
        "        prev_word_idx = None\n",
        "        label_ids = []\n",
        "        for word_idx in word_ids: # set special tokens to -100\n",
        "            if word_idx is None or word_idx == prev_word_idx:\n",
        "                label_ids.append(-100)\n",
        "            else:\n",
        "                label_ids.append(label[word_idx])\n",
        "            prev_word_idx = word_idx\n",
        "        labels.append(label_ids)\n",
        "\n",
        "    tokenized_inputs[\"labels\"] = labels\n",
        "    return tokenized_inputs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bPi7dRv463Pa"
      },
      "source": [
        "#### Apply Above Function to Tokenize Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
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            "2ca333ab7d464d6eaafbb9021158d633"
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        },
        "id": "pSV1jvLJ63Pa",
        "outputId": "a3e7de1b-4317-4b11-a842-d796130ffc0c"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3eeb67901f1c4840b27ce1f5d4e0c1af",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/821600 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b9b4278ddd984069b479c8a898afd6fc",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/102700 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "778ecb7d9cbd42478c432e3e27f3de77",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/103206 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "encoded_ds = data.map(tokenize_and_align_labels,\n",
        "                       batched=True,\n",
        "                       remove_columns=\n",
        "                        [\n",
        "                            'ner_tags',\n",
        "                            'tokens',\n",
        "                            'lang'\n",
        "                        ]\n",
        "                    )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rcWmKRS563Pb"
      },
      "source": [
        "#### Define Data Collator"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "zbB-SgFG63Pb"
      },
      "outputs": [],
      "source": [
        "data_collator = DataCollatorForTokenClassification(tokenizer=tokenizer)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pC0rj3T463Pb"
      },
      "source": [
        "#### Load Custom Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104,
          "referenced_widgets": [
            "cc661e31931042fea807edccb31db2d2",
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            "ad1afd5c2bc34c25976288a5200ad7e5",
            "5782a50381394d67b8c344a2d921fe22"
          ]
        },
        "id": "L8BgPPgQ63Pb",
        "outputId": "07fcf3a4-624d-4013-eb95-7401248e4e5f"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "cc661e31931042fea807edccb31db2d2",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading model.safetensors:   0%|          | 0.00/714M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of BertForTokenClassification were not initialized from the model checkpoint at bert-base-multilingual-cased 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"
          ]
        }
      ],
      "source": [
        "model = (AutoModelForTokenClassification.from_pretrained(\n",
        "    MODEL_CKPT,\n",
        "    num_labels=NUM_OF_LABELS,\n",
        "    id2label=id2label,\n",
        "    label2id=label2id\n",
        "    ).to(DEVICE))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3-571dcS63Pb"
      },
      "source": [
        "#### Create Compute Metrics Function"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "7233fef3133a415f9627c808cf45a9bd",
            "7460e0deeb6543f1a0c7c793f5eec9e9",
            "e659dc4965344c14a791d7f009c37e3f",
            "5fc77b2e05d24c52990b5fe89f99122b",
            "8abf6a9275044a80929380c334f850f2",
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            "6cc6f7e86c6d42a68963318ac7cd7660",
            "73eca04c655449e58ca10951345ca9b3",
            "fe0bc42f51914636a7db0bf21d6f7443"
          ]
        },
        "id": "wikPlfUv63Pb",
        "outputId": "0ebcffbe-1e67-42c5-fd00-2b7186d914dc"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7233fef3133a415f9627c808cf45a9bd",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/6.34k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "label_list = tag_values\n",
        "\n",
        "seqeval = evaluate.load(\"seqeval\")\n",
        "\n",
        "labels = [label_list[i] for i in sample[f'ner_tags']]\n",
        "\n",
        "def compute_metrics(eval_preds):\n",
        "    predictions, labels = eval_preds\n",
        "    predictions = np.argmax(predictions,\n",
        "                            axis=2)\n",
        "\n",
        "    true_predictions = [\n",
        "        [label_list[p] for (p, l) in zip(prediction, label) if l != -100]\n",
        "        for prediction, label in zip(predictions, labels)\n",
        "    ]\n",
        "\n",
        "    true_labels = [\n",
        "        [label_list[l] for (p, l) in zip(prediction, label) if l != -100]\n",
        "        for prediction, label in zip(predictions, labels)\n",
        "    ]\n",
        "\n",
        "    results = seqeval.compute(predictions=true_predictions,\n",
        "                              references=true_labels)\n",
        "\n",
        "    return results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kYc3tWaf63Pb"
      },
      "source": [
        "#### Define TrainingArguments"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "Camvhd5r63Pb"
      },
      "outputs": [],
      "source": [
        "args = TrainingArguments(\n",
        "    MODEL_NAME,\n",
        "    log_level=\"error\",\n",
        "    logging_first_step=True,\n",
        "    learning_rate=LR,\n",
        "    num_train_epochs=NUM_OF_EPOCHS,\n",
        "    auto_find_batch_size=True,\n",
        "    evaluation_strategy=STRATEGY,\n",
        "    report_to=REPORTS_TO,\n",
        "    disable_tqdm=False,\n",
        "    logging_steps=STEPS,\n",
        "    weight_decay=WEIGHT_DECAY,\n",
        "    save_strategy=STRATEGY,\n",
        "    group_by_length=True,\n",
        "    hub_private_repo=True,\n",
        "    push_to_hub=True\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JmnzuPtL63Pb"
      },
      "source": [
        "#### Define Trainer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tUF7j0Ok63Pb",
        "outputId": "d99a753e-2c85-4444-fc65-cf48cb881e01"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Cloning https://huggingface.co/DunnBC22/bert-base-multilingual-cased-fine_tuned-ner-WikiNeural_Multilingual into local empty directory.\n",
            "WARNING:huggingface_hub.repository:Cloning https://huggingface.co/DunnBC22/bert-base-multilingual-cased-fine_tuned-ner-WikiNeural_Multilingual into local empty directory.\n"
          ]
        }
      ],
      "source": [
        "trainer = Trainer(model,\n",
        "                  args=args,\n",
        "                  data_collator=data_collator,\n",
        "                  compute_metrics=compute_metrics,\n",
        "                  tokenizer=tokenizer,\n",
        "                  train_dataset=encoded_ds[\"train\"],\n",
        "                  eval_dataset=encoded_ds[\"test\"]\n",
        "                  )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5-n-UHdD63Pb"
      },
      "source": [
        "#### Train Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "-oyqQAUg63Pb",
        "outputId": "b23f0ea7-a75e-4268-835c-6e352fcbdb4a"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:411: 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"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='102700' max='102700' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [102700/102700 3:36:08, Epoch 1/1]\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",
              "      <th>Loc</th>\n",
              "      <th>Misc</th>\n",
              "      <th>Org</th>\n",
              "      <th>Per</th>\n",
              "      <th>Overall Precision</th>\n",
              "      <th>Overall Recall</th>\n",
              "      <th>Overall F1</th>\n",
              "      <th>Overall Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.015000</td>\n",
              "      <td>0.016823</td>\n",
              "      <td>{'precision': 0.9983410191680872, 'recall': 0.99820099576644, 'f1': 0.9982710025571356, 'number': 1932180}</td>\n",
              "      <td>{'precision': 0.9809396911027518, 'recall': 0.9833044214778437, 'f1': 0.9821206328547606, 'number': 122787}</td>\n",
              "      <td>{'precision': 0.9868919798954698, 'recall': 0.9881129520338388, 'f1': 0.9875020885547201, 'number': 59813}</td>\n",
              "      <td>{'precision': 0.9386096837531854, 'recall': 0.9516901050491359, 'f1': 0.9451046377116415, 'number': 47216}</td>\n",
              "      <td>0.995711</td>\n",
              "      <td>0.996060</td>\n",
              "      <td>0.995886</td>\n",
              "      <td>0.994704</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
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            "text/plain": [
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          },
          "metadata": {},
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      ],
      "source": [
        "train_results = trainer.train()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KuheJ4Yg63Pc"
      },
      "source": [
        "#### Push Model To Hub"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
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        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
        "id": "j8cwmLbr63Pc",
        "outputId": "b3fbcfd4-0614-461d-b8c7-0657d6b03011"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Several commits (2) will be pushed upstream.\n",
            "WARNING:huggingface_hub.repository:Several commits (2) will be pushed upstream.\n",
            "The progress bars may be unreliable.\n",
            "WARNING:huggingface_hub.repository:The progress bars may be unreliable.\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7cb2a9345b8148deb0c854d50b7698f2",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Upload file pytorch_model.bin:   0%|          | 1.00/676M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "90080f38c3a5401998affd7944770262",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Upload file runs/Aug14_15-43-45_1fa64856cc35/events.out.tfevents.1692027916.1fa64856cc35.856.0:   0%|         …"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "To https://huggingface.co/DunnBC22/bert-base-multilingual-cased-fine_tuned-ner-WikiNeural_Multilingual\n",
            "   691d4be..0e5f4ef  main -> main\n",
            "\n",
            "WARNING:huggingface_hub.repository:To https://huggingface.co/DunnBC22/bert-base-multilingual-cased-fine_tuned-ner-WikiNeural_Multilingual\n",
            "   691d4be..0e5f4ef  main -> main\n",
            "\n",
            "To https://huggingface.co/DunnBC22/bert-base-multilingual-cased-fine_tuned-ner-WikiNeural_Multilingual\n",
            "   0e5f4ef..f2864c2  main -> main\n",
            "\n",
            "WARNING:huggingface_hub.repository:To https://huggingface.co/DunnBC22/bert-base-multilingual-cased-fine_tuned-ner-WikiNeural_Multilingual\n",
            "   0e5f4ef..f2864c2  main -> main\n",
            "\n"
          ]
        }
      ],
      "source": [
        "kwargs = {\n",
        "    \"tasks\": \"token-classification\",\n",
        "    \"tags\": ['token-classification'],\n",
        "}\n",
        "\n",
        "if args.push_to_hub:\n",
        "    trainer.push_to_hub('All DUNN!!!', **kwargs)\n",
        "else:\n",
        "    trainer.create_model_card(**kwargs)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U5QATn6o63Pc"
      },
      "source": [
        "#### Save & Log Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NX_FveZI63Pc",
        "outputId": "ab85d0d8-42c7-46df-a05c-5a2d02e38819"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "To https://huggingface.co/DunnBC22/bert-base-multilingual-cased-fine_tuned-ner-WikiNeural_Multilingual\n",
            "   f2864c2..c3c7ca0  main -> main\n",
            "\n",
            "WARNING:huggingface_hub.repository:To https://huggingface.co/DunnBC22/bert-base-multilingual-cased-fine_tuned-ner-WikiNeural_Multilingual\n",
            "   f2864c2..c3c7ca0  main -> main\n",
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "***** train metrics *****\n",
            "  epoch                    =        1.0\n",
            "  total_flos               = 12872968GF\n",
            "  train_loss               =     0.0249\n",
            "  train_runtime            = 3:36:13.27\n",
            "  train_samples_per_second =      63.33\n",
            "  train_steps_per_second   =      7.916\n"
          ]
        }
      ],
      "source": [
        "trainer.save_model()\n",
        "trainer.log_metrics(\"train\", train_results.metrics)\n",
        "trainer.save_metrics(\"train\", train_results.metrics)\n",
        "trainer.save_state()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9tD7OT6N63Pm"
      },
      "source": [
        "### Error Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Znla2yyQ63Pm"
      },
      "source": [
        "#### Define Method to Apply to Validation Dataset (& Then Apply it)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "TIPMMtL763Pm"
      },
      "outputs": [],
      "source": [
        "def forward_pass_with_label(batch):\n",
        "    # Convert dict of lists to list of dicts suitable for data collator\n",
        "    features = [dict(zip(batch, t)) for t in zip(*batch.values())]\n",
        "    # Pad inputs and labels and put all tensors on device\n",
        "    batch = data_collator(features)\n",
        "    input_ids = batch[\"input_ids\"].to(DEVICE)\n",
        "    attention_mask = batch[\"attention_mask\"].to(DEVICE)\n",
        "    labels = batch[\"labels\"].to(DEVICE)\n",
        "    with torch.no_grad():\n",
        "        # Pass data through model\n",
        "        output = trainer.model(input_ids, attention_mask)\n",
        "        # Logit.size: [batch_size, sequence_length, classes]\n",
        "        predicted_label = torch.argmax(output.logits, axis=-1).cpu().numpy()\n",
        "\n",
        "    # Calculate loss per token after flattening batch dimension with view\n",
        "    loss = cross_entropy(output.logits.view(-1, 9), labels.view(-1), reduction=\"none\")\n",
        "    # Unflatten batch dimension and convert to numpy array\n",
        "    loss = loss.view(len(input_ids), -1).cpu().numpy()\n",
        "\n",
        "    return {\"loss\":loss, \"predicted_label\": predicted_label}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B6FE-1vt63Pm"
      },
      "source": [
        "#### Apply Above Function to Entire Evaluation Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "7ebfb31fe71b44de80ae58b9f770c9c9",
            "c42abc233c594ea8bcb905690b6e8457",
            "9b8ef9b5168e46bbad2686ff549a89a7",
            "88c2e3faecd54cc798c71fe521d771ad",
            "1783e29f542d4c51986d249ce1749817",
            "82e1115cdf2547a5bd3e45947ee6fff5",
            "c1fa92729c344fff9752229425c692fa",
            "d629287328c14b47a923c0f243c8bb47",
            "e56b8ac857b7475b918bb705bb064a43",
            "c31b6effc2e241279ac4cd1dfd9e6099",
            "93286add6661466e93c850a052b7e4d8"
          ]
        },
        "id": "55mdi0Pp63Pm",
        "outputId": "c4dd64b1-cd07-48f9-b3a9-0999ee2c0539"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7ebfb31fe71b44de80ae58b9f770c9c9",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/102700 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "eval_set = encoded_ds['validation']\n",
        "\n",
        "eval_set = eval_set.map(forward_pass_with_label,\n",
        "                        batched=True,\n",
        "                        batch_size=32)\n",
        "\n",
        "eval_df = eval_set.to_pandas()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2Zcj_hQ363Pm"
      },
      "source": [
        "#### Clean Up Padding Tokens"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194
        },
        "id": "85R6yv__63Pn",
        "outputId": "07ac6521-e3b3-4084-d81d-d99db7b2f0a1"
      },
      "outputs": [
        {
          "data": {
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              "\n",
              "\n",
              "  <div id=\"df-135d35bb-5d1b-4307-aac3-6e54b5f08dfd\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>input_ids</th>\n",
              "      <th>token_type_ids</th>\n",
              "      <th>attention_mask</th>\n",
              "      <th>labels</th>\n",
              "      <th>loss</th>\n",
              "      <th>predicted_label</th>\n",
              "      <th>input_tokens</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>[101, 15595, 13322, 10317, 11793, 18166, 21162...</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
              "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
              "      <td>[IGN, O, IGN, IGN, O, O, IGN, IGN, O, O, O, O,...</td>\n",
              "      <td>[0.0, 6.079655e-06, 0.0, 0.0, 5.245195e-06, 7....</td>\n",
              "      <td>[O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, ...</td>\n",
              "      <td>[[CLS], Ab, ##ala, ##do, pela, eli, ##mina, ##...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-135d35bb-5d1b-4307-aac3-6e54b5f08dfd')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-e3b1f0f1-1953-4bd4-9267-45afdb4fe45c button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-135d35bb-5d1b-4307-aac3-6e54b5f08dfd button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-135d35bb-5d1b-4307-aac3-6e54b5f08dfd');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                                           input_ids  \\\n",
              "0  [101, 15595, 13322, 10317, 11793, 18166, 21162...   \n",
              "\n",
              "                                      token_type_ids  \\\n",
              "0  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n",
              "\n",
              "                                      attention_mask  \\\n",
              "0  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
              "\n",
              "                                              labels  \\\n",
              "0  [IGN, O, IGN, IGN, O, O, IGN, IGN, O, O, O, O,...   \n",
              "\n",
              "                                                loss  \\\n",
              "0  [0.0, 6.079655e-06, 0.0, 0.0, 5.245195e-06, 7....   \n",
              "\n",
              "                                     predicted_label  \\\n",
              "0  [O, O, O, O, O, O, O, O, O, O, O, O, O, O, O, ...   \n",
              "\n",
              "                                        input_tokens  \n",
              "0  [[CLS], Ab, ##ala, ##do, pela, eli, ##mina, ##...  "
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "id2label[-100] = \"IGN\"\n",
        "eval_df[\"input_tokens\"] = eval_df[\"input_ids\"].apply(\n",
        "    lambda x: tokenizer.convert_ids_to_tokens(x))\n",
        "eval_df[\"predicted_label\"] = eval_df[\"predicted_label\"].apply(\n",
        "    lambda x: [id2label[i] for i in x])\n",
        "eval_df[\"labels\"] = eval_df[\"labels\"].apply(\n",
        "    lambda x: [id2label[i] for i in x])\n",
        "eval_df['loss'] = eval_df.apply(\n",
        "    lambda x: x['loss'][:len(x['input_ids'])], axis=1)\n",
        "eval_df['predicted_label'] = eval_df.apply(\n",
        "    lambda x: x['predicted_label'][:len(x['input_ids'])], axis=1)\n",
        "eval_df.head(1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "naWmuY2863Pn"
      },
      "source": [
        "#### Unwrap Each Token Within Sample Separately"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "_Y9Fvx7I63Pn",
        "outputId": "6554c2a9-d23e-4de8-8cf0-3a166d19d026"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
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              "  <div id=\"df-58697929-350a-4544-8eaf-96be365e65e9\">\n",
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              "      <div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>input_ids</th>\n",
              "      <th>token_type_ids</th>\n",
              "      <th>attention_mask</th>\n",
              "      <th>labels</th>\n",
              "      <th>loss</th>\n",
              "      <th>predicted_label</th>\n",
              "      <th>input_tokens</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>15595</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>O</td>\n",
              "      <td>0.0</td>\n",
              "      <td>O</td>\n",
              "      <td>Ab</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>11793</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>O</td>\n",
              "      <td>0.0</td>\n",
              "      <td>O</td>\n",
              "      <td>pela</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>18166</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>O</td>\n",
              "      <td>0.0</td>\n",
              "      <td>O</td>\n",
              "      <td>eli</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>117</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>O</td>\n",
              "      <td>0.0</td>\n",
              "      <td>O</td>\n",
              "      <td>,</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>183</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>O</td>\n",
              "      <td>0.0</td>\n",
              "      <td>O</td>\n",
              "      <td>o</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>10635</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>O</td>\n",
              "      <td>0.0</td>\n",
              "      <td>O</td>\n",
              "      <td>time</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>11420</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>O</td>\n",
              "      <td>0.0</td>\n",
              "      <td>O</td>\n",
              "      <td>não</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-58697929-350a-4544-8eaf-96be365e65e9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-86fb034c-77d7-40a6-a2d2-903ce3562189\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-86fb034c-77d7-40a6-a2d2-903ce3562189')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-86fb034c-77d7-40a6-a2d2-903ce3562189 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-58697929-350a-4544-8eaf-96be365e65e9 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-58697929-350a-4544-8eaf-96be365e65e9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "  input_ids token_type_ids attention_mask labels  loss predicted_label  \\\n",
              "0     15595              0              1      O   0.0               O   \n",
              "0     11793              0              1      O   0.0               O   \n",
              "0     18166              0              1      O   0.0               O   \n",
              "0       117              0              1      O   0.0               O   \n",
              "0       183              0              1      O   0.0               O   \n",
              "0     10635              0              1      O   0.0               O   \n",
              "0     11420              0              1      O   0.0               O   \n",
              "\n",
              "  input_tokens  \n",
              "0           Ab  \n",
              "0         pela  \n",
              "0          eli  \n",
              "0            ,  \n",
              "0            o  \n",
              "0         time  \n",
              "0          não  "
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "eval_df_tokens = eval_df.apply(pd.Series.explode)\n",
        "eval_df_tokens = eval_df_tokens.query(\"labels != 'IGN'\")\n",
        "eval_df_tokens[\"loss\"] = eval_df_tokens[\"loss\"].astype(float).round(2)\n",
        "eval_df_tokens.head(7)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZUQsvFHB63Pn"
      },
      "source": [
        "#### See Which Tokens Have Accumulated Most Loss in Evaluation Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "jE5rcoG263Pn",
        "outputId": "d60bd32f-fdaa-48ff-d171-371e2df60bdc"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-3df7b940-fe06-4a02-a91b-040c23490872\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>input_tokens</th>\n",
              "      <td>de</td>\n",
              "      <td>»</td>\n",
              "      <td>«</td>\n",
              "      <td>di</td>\n",
              "      <td>G</td>\n",
              "      <td>К</td>\n",
              "      <td>М</td>\n",
              "      <td>S</td>\n",
              "      <td>Ass</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>53712</td>\n",
              "      <td>2449</td>\n",
              "      <td>2469</td>\n",
              "      <td>11129</td>\n",
              "      <td>2552</td>\n",
              "      <td>1351</td>\n",
              "      <td>989</td>\n",
              "      <td>1322</td>\n",
              "      <td>185</td>\n",
              "      <td>1167</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.015</td>\n",
              "      <td>0.193</td>\n",
              "      <td>0.139</td>\n",
              "      <td>0.025</td>\n",
              "      <td>0.092</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.229</td>\n",
              "      <td>0.161</td>\n",
              "      <td>1.055</td>\n",
              "      <td>0.165</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sum</th>\n",
              "      <td>823.44</td>\n",
              "      <td>472.53</td>\n",
              "      <td>343.37</td>\n",
              "      <td>275.77</td>\n",
              "      <td>235.48</td>\n",
              "      <td>229.28</td>\n",
              "      <td>226.8</td>\n",
              "      <td>212.37</td>\n",
              "      <td>195.16</td>\n",
              "      <td>192.92</td>\n",
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              "  </tbody>\n",
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              "    fill: #1967D2;\n",
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              "\n",
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              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
              "        displayQuickchartButton(document);\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
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              "          document.querySelector('#df-3df7b940-fe06-4a02-a91b-040c23490872 button.colab-df-convert');\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3df7b940-fe06-4a02-a91b-040c23490872');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                   0       1       2       3       4       5      6       7  \\\n",
              "input_tokens      de       »       «      di       G       К      М       S   \n",
              "count          53712    2449    2469   11129    2552    1351    989    1322   \n",
              "mean           0.015   0.193   0.139   0.025   0.092    0.17  0.229   0.161   \n",
              "sum           823.44  472.53  343.37  275.77  235.48  229.28  226.8  212.37   \n",
              "\n",
              "                   8       9  \n",
              "input_tokens     Ass       C  \n",
              "count            185    1167  \n",
              "mean           1.055   0.165  \n",
              "sum           195.16  192.92  "
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "(\n",
        "    eval_df_tokens.groupby(\"input_tokens\")[[\"loss\"]]\n",
        "    .agg(\n",
        "        [\n",
        "            \"count\",\n",
        "            \"mean\",\n",
        "            \"sum\"\n",
        "        ]\n",
        "        )\n",
        "    .droplevel(level=0, axis=1) # Get rid of multi-level columns\n",
        "    .sort_values(by=\"sum\",\n",
        "                 ascending=False)\n",
        "    .reset_index()\n",
        "    .round(3)\n",
        "    .head(10)\n",
        "    .T\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-CMrnoZu63Pn"
      },
      "source": [
        "#### See Which Label IDs Have Most Loss in Evaluation Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "zQenE55W63Pn",
        "outputId": "7266719c-c565-453d-d984-56d76aa41d95"
      },
      "outputs": [
        {
          "data": {
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              "    <div class=\"colab-df-container\">\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>labels</th>\n",
              "      <td>B-MISC</td>\n",
              "      <td>I-MISC</td>\n",
              "      <td>B-ORG</td>\n",
              "      <td>I-LOC</td>\n",
              "      <td>I-ORG</td>\n",
              "      <td>B-LOC</td>\n",
              "      <td>B-PER</td>\n",
              "      <td>I-PER</td>\n",
              "      <td>O</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>22690</td>\n",
              "      <td>29173</td>\n",
              "      <td>21663</td>\n",
              "      <td>24923</td>\n",
              "      <td>21930</td>\n",
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              "      <td>47547</td>\n",
              "      <td>44210</td>\n",
              "      <td>1920443</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.256</td>\n",
              "      <td>0.178</td>\n",
              "      <td>0.149</td>\n",
              "      <td>0.119</td>\n",
              "      <td>0.084</td>\n",
              "      <td>0.071</td>\n",
              "      <td>0.063</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sum</th>\n",
              "      <td>5799.19</td>\n",
              "      <td>5196.44</td>\n",
              "      <td>3233.22</td>\n",
              "      <td>2977.46</td>\n",
              "      <td>1838.05</td>\n",
              "      <td>5507.03</td>\n",
              "      <td>2974.66</td>\n",
              "      <td>880.47</td>\n",
              "      <td>9897.37</td>\n",
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              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "        displayQuickchartButton(document);\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-e02e14ea-f0b7-4d23-89db-e11d4ba7fe33 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e02e14ea-f0b7-4d23-89db-e11d4ba7fe33');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "              0        1        2        3        4        5        6       7  \\\n",
              "labels   B-MISC   I-MISC    B-ORG    I-LOC    I-ORG    B-LOC    B-PER   I-PER   \n",
              "count     22690    29173    21663    24923    21930    77928    47547   44210   \n",
              "mean      0.256    0.178    0.149    0.119    0.084    0.071    0.063    0.02   \n",
              "sum     5799.19  5196.44  3233.22  2977.46  1838.05  5507.03  2974.66  880.47   \n",
              "\n",
              "              8  \n",
              "labels        O  \n",
              "count   1920443  \n",
              "mean      0.005  \n",
              "sum     9897.37  "
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "(\n",
        "    eval_df_tokens.groupby(\"labels\")[[\"loss\"]]\n",
        "    .agg([\"count\", \"mean\", \"sum\"])\n",
        "    .droplevel(level=0, axis=1) # Get rid of multi-level columns\n",
        "    .sort_values(by=\"mean\",\n",
        "                 ascending=False)\n",
        "    .reset_index()\n",
        "    .round(3)\n",
        "    .fillna(0)\n",
        "    .T\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X3Iyktby63Pn"
      },
      "source": [
        "#### Create Function to Display Confusion Matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "opFuJbYh63Pn"
      },
      "outputs": [],
      "source": [
        "def plot_confusion_matrix(y_preds, y_true,\n",
        "                          labels):\n",
        "    cm = confusion_matrix(y_true, y_preds,\n",
        "                          normalize=\"true\")\n",
        "    fig, ax = plt.subplots(figsize=(6, 6))\n",
        "    disp = ConfusionMatrixDisplay(confusion_matrix=cm,\n",
        "                                  display_labels=labels)\n",
        "    disp.plot(cmap=\"Blues\",\n",
        "              values_format=\".2f\",\n",
        "              ax=ax,\n",
        "              colorbar=False)\n",
        "    plt.title(\"Normalized confusion matrix\")\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0rmYymth63Pn"
      },
      "source": [
        "#### Display Confusion Matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "49R2rzdM63Pn",
        "outputId": "0d8033f6-9860-4007-e51e-cf078db41e96"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "eval_token_list = list(set(eval_df_tokens['labels']))\n",
        "\n",
        "plot_confusion_matrix(eval_df_tokens[\"labels\"],\n",
        "                      eval_df_tokens[\"predicted_label\"],\n",
        "                      eval_token_list)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GtMK6KjK63Pn"
      },
      "source": [
        "### Notes & Other Takeaways From This Project\n",
        "\n",
        "****\n",
        "- While this model had some issues with detecting the beginning of persons (PER), this is minor; it is the only area of potential weakness of this model.\n",
        "\n",
        "****"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iR8hiCmG63Po"
      },
      "source": [
        "### Citations\n",
        "\n",
        "- Dataset\n",
        "\n",
        "    > @inproceedings{tedeschi-etal-2021-wikineural-combined, title = \"{W}iki{NE}u{R}al: {C}ombined Neural and Knowledge-based Silver Data Creation for Multilingual {NER}\", author = \"Tedeschi, Simone and Maiorca, Valentino and Campolungo, Niccol{\\`o} and Cecconi, Francesco and Navigli, Roberto\", booktitle = \"Findings of the Association for Computational Linguistics: EMNLP 2021\", month = nov, year = \"2021\", address = \"Punta Cana, Dominican Republic\", publisher = \"Association for Computational Linguistics\", url = \"https://aclanthology.org/2021.findings-emnlp.215\", pages = \"2521--2533\", abstract = \"Multilingual Named Entity Recognition (NER) is a key intermediate task which is needed in many areas of NLP. In this paper, we address the well-known issue of data scarcity in NER, especially relevant when moving to a multilingual scenario, and go beyond current approaches to the creation of multilingual silver data for the task. We exploit the texts of Wikipedia and introduce a new methodology based on the effective combination of knowledge-based approaches and neural models, together with a novel domain adaptation technique, to produce high-quality training corpora for NER. We evaluate our datasets extensively on standard benchmarks for NER, yielding substantial improvements up to 6 span-based F1-score points over previous state-of-the-art systems for data creation.\",}\n",
        "\n",
        "- Model Checkpoint\n",
        "\n",
        "    > @article{DBLP:journals/corr/abs-1810-04805, author = {Jacob Devlin and Ming{-}Wei Chang and Kenton Lee and Kristina Toutanova}, title = {{BERT:} Pre-training of Deep Bidirectional Transformers for Language Understanding}, journal = {CoRR}, volume = {abs/1810.04805}, year = {2018}, url = {http://arxiv.org/abs/1810.04805}, archivePrefix = {arXiv}, eprint = {1810.04805}, timestamp = {Tue, 30 Oct 2018 20:39:56 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-1810-04805.bib}, bibsource = {dblp computer science bibliography, https://dblp.org}}\n",
        "\n",
        "- Metric (SeqEval)\n",
        "\n",
        "    > @inproceedings{ramshaw-marcus-1995-text, title = \"Text Chunking using Transformation-Based Learning\", author = \"Ramshaw, Lance and Marcus, Mitch\", booktitle = \"Third Workshop on Very Large Corpora\", year = \"1995\", url = \"https://www.aclweb.org/anthology/W95-0107\",}\n",
        "    \n",
        "    > @misc{seqeval, title={{seqeval}: A Python framework for sequence labeling evaluation}, url={https://github.com/chakki-works/seqeval}, note={Software available from https://github.com/chakki-works/seqeval}, author={Hiroki Nakayama}, year={2018},}"
      ]
    }
  ],
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      "gpuType": "T4",
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    },
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      "name": "python3"
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      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.12"
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            "value": "Downloading (…)okenizer_config.json: 100%"
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