Token Classification/Monolingual/DFKI SLT few NERd/NER_Project_Using_DFKI_SLT_few_NERd_Dataset.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zFlXnp7463PP"
      },
      "source": [
        "## Monolingual Named Entity Recognition (Few Nerd Dataset) (Fine-Tuning)\n",
        "\n",
        "Dataset Source: https://huggingface.co/datasets/DFKI-SLT/few-nerd"
      ]
    },
    {
      "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": "58f71e75-6be4-424d-9fec-479b23cb80b8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "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=16165 sha256=fdf44d0112ebc987734efe85827b2826d4c054e17d8409bf0b64f1875ec36345\n",
            "  Stored in directory: /root/.cache/pip/wheels/1a/67/4a/ad4082dd7dfc30f2abfe4d80a2ed5926a506eb8a972b4767fa\n",
            "Successfully built seqeval\n",
            "Installing collected packages: tokenizers, safetensors, xxhash, dill, responses, multiprocess, huggingface-hub, transformers, seqeval, datasets, evaluate\n",
            "Successfully installed datasets-2.13.1 dill-0.3.6 evaluate-0.4.0 huggingface-hub-0.16.2 multiprocess-0.70.14 responses-0.18.0 safetensors-0.3.1 seqeval-1.2.2 tokenizers-0.13.3 transformers-4.30.2 xxhash-3.2.0\n",
            "Collecting accelerate\n",
            "  Downloading accelerate-0.20.3-py3-none-any.whl (227 kB)\n",
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            "Installing collected packages: accelerate\n",
            "Successfully installed accelerate-0.20.3\n"
          ]
        }
      ],
      "source": [
        "%pip install transformers datasets evaluate seqeval\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": "c7540615-0561-4d80-e5cf-bc7cfd9656b4"
      },
      "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": "8ae3c56c-3f6e-4ae2-e523-f3906bcb1e12"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Error: Failed to call git rev-parse --git-dir: exit status 128 \n",
            "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": "e9299ac2-94b1-42e1-9275-0e2d19d223d9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "          Python : 3.10.1\n",
            "           NumPy : 1.22.4\n",
            "          Pandas : 1.5.3\n",
            "           Torch : 2.0.1+cu118\n",
            "    Transformers : 4.30.2\n",
            "        Datasets : 2.13.1\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": {
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        },
        "id": "oc6G95w163PY",
        "outputId": "e9d723d9-3592-43c8-801d-1c3e8998d35b"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "428ed8f73f0b4df8b45d1190c693ed5b",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/11.6k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
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              "model_id": "eec86967f23f43bd981e52a8eba9ed4c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/7.13k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading and preparing dataset few-nerd/inter to /root/.cache/huggingface/datasets/DFKI-SLT___few-nerd/inter/0.0.0/c4e2bba21055a1e5f9d70b1ede49778c6ef267f3a6240d54f12e43f72fce5dd4...\n"
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        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4177512a17984fdc8132f125e6b6a393",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading data:   0%|          | 0.00/12.0M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "eadbba0ac1c940f8a8a06989e373c0a1",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f6b7906933b04795a02547a9aef0f0b1",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/3586051 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "397f17a33a7e4022b7c718f90f233813",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating validation split: 0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "172c56ee3c084681b5cc02de0a32895a",
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            },
            "text/plain": [
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "76724eede1c24e059d198c67002c4485",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating test split: 0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "99d7b934f1094b06872241eac8c90c4b",
              "version_major": 2,
              "version_minor": 0
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Dataset few-nerd downloaded and prepared to /root/.cache/huggingface/datasets/DFKI-SLT___few-nerd/inter/0.0.0/c4e2bba21055a1e5f9d70b1ede49778c6ef267f3a6240d54f12e43f72fce5dd4. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6b8dbf468b694cc28df6e454f286e6e8",
              "version_major": 2,
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading and preparing dataset few-nerd/intra to /root/.cache/huggingface/datasets/DFKI-SLT___few-nerd/intra/0.0.0/c4e2bba21055a1e5f9d70b1ede49778c6ef267f3a6240d54f12e43f72fce5dd4...\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "92edbba1b6b44e5c96aee4c6afb83cc4",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading data:   0%|          | 0.00/11.9M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4bc60c679fc54f6db94e3b12d69fb5af",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "fd30b84f40e44c13940065d8f57d036f",
              "version_major": 2,
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "255f9fc530f346798fd0944eca46e1a7",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating validation split: 0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "75f5c2c367f44aee947d6e43850d3e8e",
              "version_major": 2,
              "version_minor": 0
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "37f39108432f4a4aadf59990d7817a72",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating test split: 0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "759f622070714fdb86d4af0925877e9e",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Dataset few-nerd downloaded and prepared to /root/.cache/huggingface/datasets/DFKI-SLT___few-nerd/intra/0.0.0/c4e2bba21055a1e5f9d70b1ede49778c6ef267f3a6240d54f12e43f72fce5dd4. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "05502cb363874579a274b9bc848f2168",
              "version_major": 2,
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading and preparing dataset few-nerd/supervised to /root/.cache/huggingface/datasets/DFKI-SLT___few-nerd/supervised/0.0.0/c4e2bba21055a1e5f9d70b1ede49778c6ef267f3a6240d54f12e43f72fce5dd4...\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "16f880b028cb4c2bb7572e6c6324689d",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading data:   0%|          | 0.00/14.6M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8fce0ebef1e94616b728948fb802a045",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ed29806f6dee491fa70c4051a44221bf",
              "version_major": 2,
              "version_minor": 0
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a1ab99f5fd114217b64d58c7fcec183d",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating validation split: 0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8c8bab5870d94ad88e255bd55b3124e1",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7a96719f8a0f453bb534bb3ba2b20930",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating test split: 0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8679ef51700042f9a8d9e07284417b8e",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Dataset few-nerd downloaded and prepared to /root/.cache/huggingface/datasets/DFKI-SLT___few-nerd/supervised/0.0.0/c4e2bba21055a1e5f9d70b1ede49778c6ef267f3a6240d54f12e43f72fce5dd4. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b6c11ffedc8045d4a5a245b0ca12da78",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training Dataset Information: \n",
            "Dataset({\n",
            "    features: ['id', 'tokens', 'ner_tags', 'fine_ner_tags'],\n",
            "    num_rows: 361398\n",
            "})\n",
            "Testing Dataset Information: \n",
            "Dataset({\n",
            "    features: ['id', 'tokens', 'ner_tags', 'fine_ner_tags'],\n",
            "    num_rows: 95714\n",
            "})\n",
            "Evaluation Dataset Information: \n",
            "Dataset({\n",
            "    features: ['id', 'tokens', 'ner_tags', 'fine_ner_tags'],\n",
            "    num_rows: 56999\n",
            "})\n"
          ]
        }
      ],
      "source": [
        "subsets_to_include = [\"inter\",\n",
        "                      \"intra\",\n",
        "                      \"supervised\",\n",
        "                      ]\n",
        "\n",
        "temp_data: []\n",
        "train_data: Dataset\n",
        "test_data: Dataset\n",
        "eval_data: Dataset\n",
        "\n",
        "for idx, s in enumerate(subsets_to_include):\n",
        "    temp_data = load_dataset(\"DFKI-SLT/few-nerd\", s)\n",
        "    if idx != 0:\n",
        "        train_data = concatenate_datasets([temp_data['train'], train_data])\n",
        "        test_data = concatenate_datasets([temp_data['test'], test_data])\n",
        "        eval_data = concatenate_datasets([temp_data['validation'], eval_data])\n",
        "    else:\n",
        "        train_data = temp_data['train']\n",
        "        test_data = temp_data['test']\n",
        "        eval_data = temp_data['validation']\n",
        "\n",
        "print(f\"Training Dataset Information: \\n{train_data}\")\n",
        "print(f\"Testing Dataset Information: \\n{test_data}\")\n",
        "print(f\"Evaluation Dataset Information: \\n{eval_data}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OxfGy2WW63PZ"
      },
      "source": [
        "#### Split Dataset into DatasetDict"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tm9i_Bps63PZ",
        "outputId": "a455b480-b948-40ef-aeab-0eb013bfbefb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training data shape: (361398, 4)\n",
            "Testing data shape: (95714, 4)\n",
            "Validation data shape: (56999, 4)\n"
          ]
        }
      ],
      "source": [
        "# Pack all of them into DatasetDict\n",
        "\n",
        "ds = DatasetDict({\n",
        "    'train': train_data,\n",
        "    'test': test_data,\n",
        "    'eval': eval_data})\n",
        "\n",
        "print('Training data shape:', ds['train'].shape)\n",
        "print('Testing data shape:', ds['test'].shape)\n",
        "print('Validation data shape:', ds['eval'].shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-f8dc4EG63PZ"
      },
      "source": [
        "#### Example"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rxnyCnQp63PZ",
        "outputId": "fb62bac7-33e4-4c2f-cfd5-77ca65efa18b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'id': '12',\n",
              " 'tokens': ['Imelda',\n",
              "  'de',\n",
              "  \"'\",\n",
              "  'Lambertazzi',\n",
              "  'is',\n",
              "  'a',\n",
              "  '``',\n",
              "  'melodramma',\n",
              "  'tragico',\n",
              "  \"''\",\n",
              "  'or',\n",
              "  'tragic',\n",
              "  'opera',\n",
              "  'in',\n",
              "  'two',\n",
              "  'acts',\n",
              "  'by',\n",
              "  'Gaetano',\n",
              "  'Donizett',\n",
              "  'i',\n",
              "  'from',\n",
              "  'a',\n",
              "  'libretto',\n",
              "  'by',\n",
              "  'Andrea',\n",
              "  'Leone',\n",
              "  'Tottola',\n",
              "  ',',\n",
              "  'based',\n",
              "  'on',\n",
              "  'the',\n",
              "  'tragedy',\n",
              "  '``',\n",
              "  'Imelda',\n",
              "  \"''\",\n",
              "  'by',\n",
              "  'Gabriele',\n",
              "  'Sperduti',\n",
              "  '.'],\n",
              " 'ner_tags': [1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\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",
              "  7,\n",
              "  7,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  7,\n",
              "  7,\n",
              "  7,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  0],\n",
              " 'fine_ner_tags': [6,\n",
              "  6,\n",
              "  6,\n",
              "  6,\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",
              "  51,\n",
              "  51,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  51,\n",
              "  51,\n",
              "  51,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  6,\n",
              "  6,\n",
              "  6,\n",
              "  6,\n",
              "  6,\n",
              "  0]}"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "example = ds['train'][12]\n",
        "\n",
        "example"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yGPVlsKK63PZ"
      },
      "source": [
        "#### Display Feature Information About Each Feature"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rPQju2At63PZ",
        "outputId": "b9c3cc41-12d5-4056-abb4-7a7c4873c8bb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "id: \n",
            "Value(dtype='string', id=None)\n",
            "\n",
            "tokens: \n",
            "Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)\n",
            "\n",
            "ner_tags: \n",
            "Sequence(feature=ClassLabel(names=['O', 'art', 'building', 'event', 'location', 'organization', 'other', 'person', 'product'], id=None), length=-1, id=None)\n",
            "\n",
            "fine_ner_tags: \n",
            "Sequence(feature=ClassLabel(names=['O', 'art-broadcastprogram', 'art-film', 'art-music', 'art-other', 'art-painting', 'art-writtenart', 'building-airport', 'building-hospital', 'building-hotel', 'building-library', 'building-other', 'building-restaurant', 'building-sportsfacility', 'building-theater', 'event-attack/battle/war/militaryconflict', 'event-disaster', 'event-election', 'event-other', 'event-protest', 'event-sportsevent', 'location-GPE', 'location-bodiesofwater', 'location-island', 'location-mountain', 'location-other', 'location-park', 'location-road/railway/highway/transit', 'organization-company', 'organization-education', 'organization-government/governmentagency', 'organization-media/newspaper', 'organization-other', 'organization-politicalparty', 'organization-religion', 'organization-showorganization', 'organization-sportsleague', 'organization-sportsteam', 'other-astronomything', 'other-award', 'other-biologything', 'other-chemicalthing', 'other-currency', 'other-disease', 'other-educationaldegree', 'other-god', 'other-language', 'other-law', 'other-livingthing', 'other-medical', 'person-actor', 'person-artist/author', 'person-athlete', 'person-director', 'person-other', 'person-politician', 'person-scholar', 'person-soldier', 'product-airplane', 'product-car', 'product-food', 'product-game', 'product-other', 'product-ship', 'product-software', 'product-train', 'product-weapon'], id=None), length=-1, id=None)\n",
            "\n"
          ]
        }
      ],
      "source": [
        "for k, v in ds[\"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": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KY5sfrzV63Pa",
        "outputId": "23c9be88-4907-4922-d1d3-7fa591a1166a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "List of tag values: \n",
            "['O', 'art', 'building', 'event', 'location', 'organization', 'other', 'person', 'product']\n",
            "Number of NER Tags: \n",
            "9\n",
            "id2label: \n",
            "{0: 'O', 1: 'art', 2: 'building', 3: 'event', 4: 'location', 5: 'organization', 6: 'other', 7: 'person', 8: 'product'}\n",
            "label2id: \n",
            "{'O': 0, 'art': 1, 'building': 2, 'event': 3, 'location': 4, 'organization': 5, 'other': 6, 'person': 7, 'product': 8}\n"
          ]
        }
      ],
      "source": [
        "label2id ={\n",
        "    \"O\": 0,\n",
        "    \"art\": 1,\n",
        "    \"building\": 2,\n",
        "    \"event\": 3,\n",
        "    \"location\": 4,\n",
        "    \"organization\": 5,\n",
        "    \"other\": 6,\n",
        "    \"person\": 7,\n",
        "    \"product\": 8\n",
        "    }\n",
        "\n",
        "tag_values = list(label2id.keys())\n",
        "NUM_OF_LABELS = len(tag_values)\n",
        "\n",
        "id2label = {tag: idx for idx, tag in label2id.items()}\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": 10,
      "metadata": {
        "id": "FN6KdL0R63Pa"
      },
      "outputs": [],
      "source": [
        "MODEL_CKPT = \"bert-base-cased\"\n",
        "\n",
        "MODEL_NAME = f\"{MODEL_CKPT}-finetuned-ner-DFKI-SLT_few-NERd\"\n",
        "NUM_OF_EPOCHS = 2\n",
        "\n",
        "BATCH_SIZE = 16\n",
        "STRATEGY = \"epoch\"\n",
        "\n",
        "REPORTS_TO = \"tensorboard\"\n",
        "WEIGHT_DECAY = 0.01\n",
        "\n",
        "LR = 2e-5\n",
        "DEVICE = torch.device(\"cpu\")\n",
        "\n",
        "STEPS = 1200"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WNLb0AiI63Pa"
      },
      "source": [
        "#### Function to Tokenize & Align Inputs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
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        },
        "id": "WDj5bkn163Pa",
        "outputId": "eef54d50-6c26-46d6-95fd-8aeb93d4a7a7"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "be0e0481ff4f42a5a0172cb362eb490a",
              "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": "bbcd59126fd04685bf8d9431a467e019",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4fdc3febd36447be8cdcd132d2c8bfa0",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/213k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "36800812a1b74ee1b635bf0521cfa74a",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/436k [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": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17,
          "referenced_widgets": [
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            "6b7f21280a5c41e5bceb7cc6ec0cf220"
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        },
        "id": "pSV1jvLJ63Pa",
        "outputId": "79e4f0d2-01fc-4e8f-92e0-59bf167da93a"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "acd142b0c54a41529464240bbddc7a09",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/361398 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "796c27001e6a43fa9437183a139c14db",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/95714 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9d676fde823f4992b8fc0997cf20bb78",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/56999 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "encoded_ds = ds.map(tokenize_and_align_labels,\n",
        "                       batched=True,\n",
        "                       remove_columns=\n",
        "                        [\n",
        "                            'ner_tags',\n",
        "                            'tokens'\n",
        "                        ]\n",
        "                    )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rcWmKRS563Pb"
      },
      "source": [
        "#### Define Data Collator"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "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": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156,
          "referenced_widgets": [
            "108ab5a3788c41b2b675e0e5dfa6f787",
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            "14e66102945e40ea8a62bc61488b702d",
            "c1091b82ef534658bea67ce959734780",
            "c38836063c08407a8d77e3c6111d8918"
          ]
        },
        "id": "L8BgPPgQ63Pb",
        "outputId": "0765a7c8-cbed-42ff-8700-dac02e7150f4"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "108ab5a3788c41b2b675e0e5dfa6f787",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading model.safetensors:   0%|          | 0.00/436M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at bert-base-cased were not used when initializing BertForTokenClassification: ['cls.predictions.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.weight', 'cls.seq_relationship.bias']\n",
            "- This IS expected if you are initializing BertForTokenClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertForTokenClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of BertForTokenClassification were not initialized from the model checkpoint at bert-base-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": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "84b4465d0ec34d948091ab17fe803a25",
            "2f390cdecadb457197987ee1c013f514",
            "fae811679b494b838e91b4a9076b20aa",
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            "ca840ab2d312433486e1c3f145853229",
            "1dd6beea54cb40e8a8611265c2468723",
            "ea5f0c413b3348b1b5b7d155cbb68d1a",
            "0a99539be1f74891bbefc7891a9c168f"
          ]
        },
        "id": "wikPlfUv63Pb",
        "outputId": "ae2e70e6-aed3-43b1-d92b-17d4f2488fad"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "84b4465d0ec34d948091ab17fe803a25",
              "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 example[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": 16,
      "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",
        "    gradient_accumulation_steps=4,\n",
        "    gradient_checkpointing=True,\n",
        "    disable_tqdm=False,\n",
        "    logging_steps=STEPS,\n",
        "    weight_decay=WEIGHT_DECAY,\n",
        "    save_strategy=STRATEGY,\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": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tUF7j0Ok63Pb",
        "outputId": "92d273c4-4b8e-4aaf-d81b-b40ffc7abc2a"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Cloning https://huggingface.co/DunnBC22/bert-base-cased-finetuned-ner-DFKI-SLT_few-NERd into local empty directory.\n",
            "WARNING:huggingface_hub.repository:Cloning https://huggingface.co/DunnBC22/bert-base-cased-finetuned-ner-DFKI-SLT_few-NERd 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": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 974
        },
        "id": "-oyqQAUg63Pb",
        "outputId": "96351c7b-ef49-45d6-a6f4-510978d2daec"
      },
      "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": {
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              "    <div>\n",
              "      \n",
              "      <progress value='22586' max='22586' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [22586/22586 3:30:44, Epoch 1/2]\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>Erson</th>\n",
              "      <th>Ocation</th>\n",
              "      <th>Rganization</th>\n",
              "      <th>Roduct</th>\n",
              "      <th>Rt</th>\n",
              "      <th>Ther</th>\n",
              "      <th>Uilding</th>\n",
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              "      <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",
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              "      <td>0</td>\n",
              "      <td>0.179600</td>\n",
              "      <td>0.142667</td>\n",
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              "      <td>{'precision': 0.6742843680056544, 'recall': 0.8195289569761615, 'f1': 0.7398455423789058, 'number': 13969}</td>\n",
              "      <td>{'precision': 0.4812014282713716, 'recall': 0.3950681151922745, 'f1': 0.4339015151515152, 'number': 5799}</td>\n",
              "      <td>{'precision': 0.5997923695821438, 'recall': 0.32526389866291344, 'f1': 0.4217922978645739, 'number': 7105}</td>\n",
              "      <td>0.799997</td>\n",
              "      <td>0.785182</td>\n",
              "      <td>0.792520</td>\n",
              "      <td>0.948302</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.154200</td>\n",
              "      <td>0.131176</td>\n",
              "      <td>{'precision': 0.8860048426150121, 'recall': 0.9401849948612538, 'f1': 0.912291199202194, 'number': 29190}</td>\n",
              "      <td>{'precision': 0.8686381704207632, 'recall': 0.8152889539136796, 'f1': 0.841118472477534, 'number': 95690}</td>\n",
              "      <td>{'precision': 0.7919078915181266, 'recall': 0.7449641777764141, 'f1': 0.7677190874452579, 'number': 65183}</td>\n",
              "      <td>{'precision': 0.7065968977761166, 'recall': 0.8295304958315051, 'f1': 0.7631446160056513, 'number': 9116}</td>\n",
              "      <td>{'precision': 0.8407258064516129, 'recall': 0.8614333386302241, 'f1': 0.8509536143159878, 'number': 6293}</td>\n",
              "      <td>{'precision': 0.7303024586555996, 'recall': 0.8314124132006586, 'f1': 0.7775843599357258, 'number': 13969}</td>\n",
              "      <td>{'precision': 0.5162234691388143, 'recall': 0.3648904983617865, 'f1': 0.4275611234592847, 'number': 5799}</td>\n",
              "      <td>{'precision': 0.605920892987139, 'recall': 0.35144264602392683, 'f1': 0.44486014608943525, 'number': 7105}</td>\n",
              "      <td>0.820252</td>\n",
              "      <td>0.788603</td>\n",
              "      <td>0.804117</td>\n",
              "      <td>0.949809</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: building seems not to be NE tag.\n",
            "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
            "/usr/local/lib/python3.10/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: location seems not to be NE tag.\n",
            "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
            "/usr/local/lib/python3.10/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: organization seems not to be NE tag.\n",
            "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
            "/usr/local/lib/python3.10/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: event seems not to be NE tag.\n",
            "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
            "/usr/local/lib/python3.10/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: product seems not to be NE tag.\n",
            "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
            "/usr/local/lib/python3.10/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: person seems not to be NE tag.\n",
            "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
            "/usr/local/lib/python3.10/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: other seems not to be NE tag.\n",
            "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
            "/usr/local/lib/python3.10/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: art seems not to be NE tag.\n",
            "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
            "/usr/local/lib/python3.10/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: building seems not to be NE tag.\n",
            "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
            "/usr/local/lib/python3.10/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: location seems not to be NE tag.\n",
            "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
            "/usr/local/lib/python3.10/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: organization seems not to be NE tag.\n",
            "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
            "/usr/local/lib/python3.10/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: event seems not to be NE tag.\n",
            "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
            "/usr/local/lib/python3.10/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: product seems not to be NE tag.\n",
            "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
            "/usr/local/lib/python3.10/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: person seems not to be NE tag.\n",
            "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
            "/usr/local/lib/python3.10/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: other seems not to be NE tag.\n",
            "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
            "/usr/local/lib/python3.10/dist-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: art seems not to be NE tag.\n",
            "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n"
          ]
        }
      ],
      "source": [
        "train_results = trainer.train()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KuheJ4Yg63Pc"
      },
      "source": [
        "#### Push Model To Hub"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407,
          "referenced_widgets": [
            "5c7e6e19a5744f609c5a15397835db36",
            "74765116eec945e7a45eee7a0fdf578c",
            "96694c1f98354b789b129757adfe3ffa",
            "8a1d729115a2449c940e514eebe20ba2",
            "2d1add3d56c6407e92969fcd0004f3c3",
            "cded5f414ad844b3bcc8ab003eed6c4a",
            "265a4c75a83244a7b88370d58da1fc61",
            "b6adc349641744d3a6bf15e0d460fb72",
            "cc195a5a7fc942d1a81743d4c9695451",
            "571e78ad99e44ad180b8d05c80172155",
            "a146e40cab2a460995aea263726ca2fc",
            "0c5c7d439af54712b04f10b15be462aa",
            "830ef1997e2148378cc3441bdb513a43",
            "0889d55d8e7a46588b4b9aaecfc2ca14",
            "b508eba7f3cb4d599c4140acfeeaca54",
            "d22d997bf74c474fbddd0419fd88e673",
            "5ac1dca0d5a74f349ef3a3c603f1169d",
            "6b01e19f16614e3ea7ed21b952651424",
            "1afed1b08aab4a65a1ff3a764ddb4d89",
            "337e1c11353a4ddeb67e6593f55037f5",
            "77db62e1b916467e88741e9c09cec554",
            "2119fa3298f0494db29da48f025b9de7"
          ]
        },
        "id": "j8cwmLbr63Pc",
        "outputId": "4bbb2b89-96bd-49fe-cff2-f3794313d9ab"
      },
      "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": "5c7e6e19a5744f609c5a15397835db36",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Upload file pytorch_model.bin:   0%|          | 1.00/411M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0c5c7d439af54712b04f10b15be462aa",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Upload file runs/Jul05_17-28-12_5a43ec1e4b0f/events.out.tfevents.1688578106.5a43ec1e4b0f.3765.0:   0%|        …"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "To https://huggingface.co/DunnBC22/bert-base-cased-finetuned-ner-DFKI-SLT_few-NERd\n",
            "   36a751a..2014d20  main -> main\n",
            "\n",
            "WARNING:huggingface_hub.repository:To https://huggingface.co/DunnBC22/bert-base-cased-finetuned-ner-DFKI-SLT_few-NERd\n",
            "   36a751a..2014d20  main -> main\n",
            "\n",
            "To https://huggingface.co/DunnBC22/bert-base-cased-finetuned-ner-DFKI-SLT_few-NERd\n",
            "   2014d20..f7d2376  main -> main\n",
            "\n",
            "WARNING:huggingface_hub.repository:To https://huggingface.co/DunnBC22/bert-base-cased-finetuned-ner-DFKI-SLT_few-NERd\n",
            "   2014d20..f7d2376  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": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NX_FveZI63Pc",
        "outputId": "ed3ebd31-ef3f-42e5-c29f-49b605ad3714"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "To https://huggingface.co/DunnBC22/bert-base-cased-finetuned-ner-DFKI-SLT_few-NERd\n",
            "   f7d2376..ccebf00  main -> main\n",
            "\n",
            "WARNING:huggingface_hub.repository:To https://huggingface.co/DunnBC22/bert-base-cased-finetuned-ner-DFKI-SLT_few-NERd\n",
            "   f7d2376..ccebf00  main -> main\n",
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "***** train metrics *****\n",
            "  epoch                    =        2.0\n",
            "  total_flos               = 21178098GF\n",
            "  train_loss               =     0.1765\n",
            "  train_runtime            = 3:30:48.42\n",
            "  train_samples_per_second =     57.145\n",
            "  train_steps_per_second   =      1.786\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": 55,
      "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().to(DEVICE)\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().to(DEVICE)\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": 60,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "dbce70741e934b22ac918351eac7fc2e",
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            "f20205ba0ff14d4e90c8b1658ab3f881",
            "d237ebbd6a2d447db81db0d8cebfd45e",
            "92924cf3c5c84321b5b747c4c0d197df",
            "28af8138b783473d888f6fde10fefe2b",
            "05da53bcfbf4492f9be6c70eda7848d9",
            "3c8f4ae3b49245eeb3a12c118c7f26e2",
            "2e438bff35fd4ef5b6ef9de4786b5727",
            "8758f10308964402ae67a31a4f7136ff"
          ]
        },
        "id": "55mdi0Pp63Pm",
        "outputId": "6ee4ae82-be88-496a-e607-b2cdc050cf45"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "dbce70741e934b22ac918351eac7fc2e",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/56999 [00:00<?, ? examples/s]"
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #800000; text-decoration-color: #800000\">╭─────────────────────────────── </span><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">Traceback </span><span style=\"color: #bf7f7f; text-decoration-color: #bf7f7f; font-weight: bold\">(most recent call last)</span><span style=\"color: #800000; text-decoration-color: #800000\"> ────────────────────────────────╮</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">&lt;cell line: 5&gt;</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">5</span>                                                                              <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.10/dist-packages/datasets/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">arrow_dataset.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">580</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">wrapper</span>                 <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 577 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">else</span>:                                                                             <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 578 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span><span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>: <span style=\"color: #808000; text-decoration-color: #808000\">\"Dataset\"</span> = kwargs.pop(<span style=\"color: #808000; text-decoration-color: #808000\">\"self\"</span>)                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 579 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"># apply actual function</span>                                                           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span> 580 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>out: Union[<span style=\"color: #808000; text-decoration-color: #808000\">\"Dataset\"</span>, <span style=\"color: #808000; text-decoration-color: #808000\">\"DatasetDict\"</span>] = func(<span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>, *args, **kwargs)                <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 581 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>datasets: List[<span style=\"color: #808000; text-decoration-color: #808000\">\"Dataset\"</span>] = <span style=\"color: #00ffff; text-decoration-color: #00ffff\">list</span>(out.values()) <span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">isinstance</span>(out, <span style=\"color: #00ffff; text-decoration-color: #00ffff\">dict</span>) <span style=\"color: #0000ff; text-decoration-color: #0000ff\">else</span> [ou  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 582 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">for</span> dataset <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">in</span> datasets:                                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 583 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"># Remove task templates if a column mapping of the template is no longer val</span>  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.10/dist-packages/datasets/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">arrow_dataset.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">545</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">wrapper</span>                 <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 542 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span><span style=\"color: #808000; text-decoration-color: #808000\">\"output_all_columns\"</span>: <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._output_all_columns,                               <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 543 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>}                                                                                 <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 544 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"># apply actual function</span>                                                           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span> 545 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>out: Union[<span style=\"color: #808000; text-decoration-color: #808000\">\"Dataset\"</span>, <span style=\"color: #808000; text-decoration-color: #808000\">\"DatasetDict\"</span>] = func(<span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>, *args, **kwargs)                <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 546 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>datasets: List[<span style=\"color: #808000; text-decoration-color: #808000\">\"Dataset\"</span>] = <span style=\"color: #00ffff; text-decoration-color: #00ffff\">list</span>(out.values()) <span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">isinstance</span>(out, <span style=\"color: #00ffff; text-decoration-color: #00ffff\">dict</span>) <span style=\"color: #0000ff; text-decoration-color: #0000ff\">else</span> [ou  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 547 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"># re-apply format to the output</span>                                                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 548 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">for</span> dataset <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">in</span> datasets:                                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.10/dist-packages/datasets/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">arrow_dataset.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">3087</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">map</span>                    <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">3084 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   │   </span>leave=<span style=\"color: #0000ff; text-decoration-color: #0000ff\">False</span>,                                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">3085 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   │   </span>desc=desc <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">or</span> <span style=\"color: #808000; text-decoration-color: #808000\">\"Map\"</span>,                                                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">3086 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   </span>) <span style=\"color: #0000ff; text-decoration-color: #0000ff\">as</span> pbar:                                                                <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>3087 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">for</span> rank, done, content <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">in</span> Dataset._map_single(**dataset_kwargs):     <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">3088 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> done:                                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">3089 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   │   │   │   </span>shards_done += <span style=\"color: #0000ff; text-decoration-color: #0000ff\">1</span>                                              <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">3090 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   │   │   │   </span>logger.debug(<span style=\"color: #808000; text-decoration-color: #808000\">f\"Finished processing shard number {</span>rank<span style=\"color: #808000; text-decoration-color: #808000\">} of {</span>n  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.10/dist-packages/datasets/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">arrow_dataset.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">3463</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">_map_single</span>            <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">3460 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   │   │   │   </span><span style=\"color: #00ffff; text-decoration-color: #00ffff\">range</span>(*(<span style=\"color: #00ffff; text-decoration-color: #00ffff\">slice</span>(i, i + batch_size).indices(shard.num_rows)))    <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">3461 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   │   │   </span>)  <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"># Something simpler?</span>                                           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">3462 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">try</span>:                                                              <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>3463 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   │   │   │   </span>batch = apply_function_on_filtered_inputs(                    <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">3464 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   │   │   │   │   </span>batch,                                                    <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">3465 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   │   │   │   │   </span>indices,                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">3466 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   │   │   │   │   </span>check_same_num_examples=<span style=\"color: #00ffff; text-decoration-color: #00ffff\">len</span>(shard.list_indexes()) &gt; <span style=\"color: #0000ff; text-decoration-color: #0000ff\">0</span>,    <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.10/dist-packages/datasets/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">arrow_dataset.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">3344</span> in                        <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">apply_function_on_filtered_inputs</span>                                                                <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">3341 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   </span>additional_args += (effective_indices,)                                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">3342 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> with_rank:                                                                 <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">3343 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   </span>additional_args += (rank,)                                                <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>3344 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span>processed_inputs = function(*fn_args, *additional_args, **fn_kwargs)          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">3345 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">isinstance</span>(processed_inputs, LazyDict):                                    <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">3346 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   </span>processed_inputs = {                                                      <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">3347 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   │   </span>k: v <span style=\"color: #0000ff; text-decoration-color: #0000ff\">for</span> k, v <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">in</span> processed_inputs.data.items() <span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> k <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">not</span> <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">in</span> processed  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">forward_pass_with_label</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">11</span>                                                                    <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.10/dist-packages/torch/nn/modules/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">module.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">1501</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">_call_impl</span>            <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1498 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">not</span> (<span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._backward_hooks <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">or</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._backward_pre_hooks <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">or</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._forward_hooks   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1499 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   </span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">or</span> _global_backward_pre_hooks <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">or</span> _global_backward_hooks                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1500 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   </span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">or</span> _global_forward_hooks <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">or</span> _global_forward_pre_hooks):                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>1501 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">return</span> forward_call(*args, **kwargs)                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1502 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"># Do not call functions when jit is used</span>                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1503 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>full_backward_hooks, non_full_backward_hooks = [], []                             <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1504 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>backward_pre_hooks = []                                                           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.10/dist-packages/transformers/models/bert/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">modeling_bert.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">1758</span> in        <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">forward</span>                                                                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1755 </span><span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">│   │   </span><span style=\"color: #808000; text-decoration-color: #808000\">\"\"\"</span>                                                                               <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1756 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>return_dict = return_dict <span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> return_dict <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">is</span> <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">not</span> <span style=\"color: #0000ff; text-decoration-color: #0000ff\">None</span> <span style=\"color: #0000ff; text-decoration-color: #0000ff\">else</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.config.use_return  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1757 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>1758 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>outputs = <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.bert(                                                              <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1759 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span>input_ids,                                                                    <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1760 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span>attention_mask=attention_mask,                                                <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1761 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span>token_type_ids=token_type_ids,                                                <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.10/dist-packages/torch/nn/modules/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">module.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">1501</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">_call_impl</span>            <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1498 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">not</span> (<span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._backward_hooks <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">or</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._backward_pre_hooks <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">or</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._forward_hooks   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1499 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   </span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">or</span> _global_backward_pre_hooks <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">or</span> _global_backward_hooks                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1500 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   </span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">or</span> _global_forward_hooks <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">or</span> _global_forward_pre_hooks):                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>1501 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">return</span> forward_call(*args, **kwargs)                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1502 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"># Do not call functions when jit is used</span>                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1503 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>full_backward_hooks, non_full_backward_hooks = [], []                             <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1504 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>backward_pre_hooks = []                                                           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.10/dist-packages/transformers/models/bert/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">modeling_bert.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">1013</span> in        <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">forward</span>                                                                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1010 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"># and head_mask is converted to shape [num_hidden_layers x batch x num_heads x s</span>  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1011 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>head_mask = <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.get_head_mask(head_mask, <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.config.num_hidden_layers)          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1012 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>1013 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>embedding_output = <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.embeddings(                                               <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1014 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span>input_ids=input_ids,                                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1015 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span>position_ids=position_ids,                                                    <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1016 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span>token_type_ids=token_type_ids,                                                <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.10/dist-packages/torch/nn/modules/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">module.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">1501</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">_call_impl</span>            <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1498 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">not</span> (<span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._backward_hooks <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">or</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._backward_pre_hooks <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">or</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._forward_hooks   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1499 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   </span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">or</span> _global_backward_pre_hooks <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">or</span> _global_backward_hooks                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1500 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   </span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">or</span> _global_forward_hooks <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">or</span> _global_forward_pre_hooks):                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>1501 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">return</span> forward_call(*args, **kwargs)                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1502 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"># Do not call functions when jit is used</span>                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1503 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>full_backward_hooks, non_full_backward_hooks = [], []                             <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1504 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>backward_pre_hooks = []                                                           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.10/dist-packages/transformers/models/bert/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">modeling_bert.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">230</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">forward</span> <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 227 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   </span>token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=<span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 228 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 229 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> inputs_embeds <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">is</span> <span style=\"color: #0000ff; text-decoration-color: #0000ff\">None</span>:                                                         <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span> 230 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span>inputs_embeds = <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.word_embeddings(input_ids)                               <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 231 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>token_type_embeddings = <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.token_type_embeddings(token_type_ids)                <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 232 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 233 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>embeddings = inputs_embeds + token_type_embeddings                                <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.10/dist-packages/torch/nn/modules/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">module.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">1501</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">_call_impl</span>            <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1498 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">not</span> (<span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._backward_hooks <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">or</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._backward_pre_hooks <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">or</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>._forward_hooks   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1499 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   </span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">or</span> _global_backward_pre_hooks <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">or</span> _global_backward_hooks                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1500 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   </span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">or</span> _global_forward_hooks <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">or</span> _global_forward_pre_hooks):                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>1501 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">return</span> forward_call(*args, **kwargs)                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1502 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"># Do not call functions when jit is used</span>                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1503 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>full_backward_hooks, non_full_backward_hooks = [], []                             <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1504 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>backward_pre_hooks = []                                                           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.10/dist-packages/torch/nn/modules/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">sparse.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">162</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">forward</span>                <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">159 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   │   </span><span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.weight[<span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.padding_idx].fill_(<span style=\"color: #0000ff; text-decoration-color: #0000ff\">0</span>)                                     <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">160 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   </span>                                                                                       <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">161 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">def</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">forward</span>(<span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>, <span style=\"color: #00ffff; text-decoration-color: #00ffff\">input</span>: Tensor) -&gt; Tensor:                                            <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>162 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">return</span> F.embedding(                                                                <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">163 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span><span style=\"color: #00ffff; text-decoration-color: #00ffff\">input</span>, <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.weight, <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.padding_idx, <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.max_norm,                           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">164 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   │   </span><span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.norm_type, <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.scale_grad_by_freq, <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.sparse)                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">165 </span>                                                                                           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">/usr/local/lib/python3.10/dist-packages/torch/nn/</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">functional.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">2210</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">embedding</span>                 <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">2207 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">#   torch.embedding_renorm_</span>                                                       <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">2208 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"># remove once script supports set_grad_enabled</span>                                    <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">2209 </span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>_no_grad_embedding_renorm_(weight, <span style=\"color: #00ffff; text-decoration-color: #00ffff\">input</span>, max_norm, norm_type)                    <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>2210 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">return</span> torch.embedding(weight, <span style=\"color: #00ffff; text-decoration-color: #00ffff\">input</span>, padding_idx, scale_grad_by_freq, sparse)        <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">2211 </span>                                                                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">2212 </span>                                                                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">2213 </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">def</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">embedding_bag</span>(                                                                        <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">╰──────────────────────────────────────────────────────────────────────────────────────────────────╯</span>\n",
              "<span style=\"color: #ff0000; text-decoration-color: #ff0000; font-weight: bold\">RuntimeError: </span>Expected all tensors to be on the same device, but found at least two devices, cu<span style=\"color: #00ff00; text-decoration-color: #00ff00; font-weight: bold\">da:0</span> and cpu! <span style=\"font-weight: bold\">(</span>when \n",
              "checking argument for argument index in method wrapper_CUDA__index_select<span style=\"font-weight: bold\">)</span>\n",
              "</pre>\n"
            ],
            "text/plain": [
              "\u001b[31m╭─\u001b[0m\u001b[31m──────────────────────────────\u001b[0m\u001b[31m \u001b[0m\u001b[1;31mTraceback \u001b[0m\u001b[1;2;31m(most recent call last)\u001b[0m\u001b[31m \u001b[0m\u001b[31m───────────────────────────────\u001b[0m\u001b[31m─╮\u001b[0m\n",
              "\u001b[31m│\u001b[0m in \u001b[92m<cell line: 5>\u001b[0m:\u001b[94m5\u001b[0m                                                                              \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/datasets/\u001b[0m\u001b[1;33marrow_dataset.py\u001b[0m:\u001b[94m580\u001b[0m in \u001b[92mwrapper\u001b[0m                 \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m 577 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94melse\u001b[0m:                                                                             \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m 578 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[96mself\u001b[0m: \u001b[33m\"\u001b[0m\u001b[33mDataset\u001b[0m\u001b[33m\"\u001b[0m = kwargs.pop(\u001b[33m\"\u001b[0m\u001b[33mself\u001b[0m\u001b[33m\"\u001b[0m)                                          \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m 579 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[2m# apply actual function\u001b[0m                                                           \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m 580 \u001b[2m│   │   \u001b[0mout: Union[\u001b[33m\"\u001b[0m\u001b[33mDataset\u001b[0m\u001b[33m\"\u001b[0m, \u001b[33m\"\u001b[0m\u001b[33mDatasetDict\u001b[0m\u001b[33m\"\u001b[0m] = func(\u001b[96mself\u001b[0m, *args, **kwargs)                \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m 581 \u001b[0m\u001b[2m│   │   \u001b[0mdatasets: List[\u001b[33m\"\u001b[0m\u001b[33mDataset\u001b[0m\u001b[33m\"\u001b[0m] = \u001b[96mlist\u001b[0m(out.values()) \u001b[94mif\u001b[0m \u001b[96misinstance\u001b[0m(out, \u001b[96mdict\u001b[0m) \u001b[94melse\u001b[0m [ou  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m 582 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94mfor\u001b[0m dataset \u001b[95min\u001b[0m datasets:                                                          \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m 583 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[2m# Remove task templates if a column mapping of the template is no longer val\u001b[0m  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/datasets/\u001b[0m\u001b[1;33marrow_dataset.py\u001b[0m:\u001b[94m545\u001b[0m in \u001b[92mwrapper\u001b[0m                 \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m 542 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[33m\"\u001b[0m\u001b[33moutput_all_columns\u001b[0m\u001b[33m\"\u001b[0m: \u001b[96mself\u001b[0m._output_all_columns,                               \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m 543 \u001b[0m\u001b[2m│   │   \u001b[0m}                                                                                 \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m 544 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[2m# apply actual function\u001b[0m                                                           \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m 545 \u001b[2m│   │   \u001b[0mout: Union[\u001b[33m\"\u001b[0m\u001b[33mDataset\u001b[0m\u001b[33m\"\u001b[0m, \u001b[33m\"\u001b[0m\u001b[33mDatasetDict\u001b[0m\u001b[33m\"\u001b[0m] = func(\u001b[96mself\u001b[0m, *args, **kwargs)                \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m 546 \u001b[0m\u001b[2m│   │   \u001b[0mdatasets: List[\u001b[33m\"\u001b[0m\u001b[33mDataset\u001b[0m\u001b[33m\"\u001b[0m] = \u001b[96mlist\u001b[0m(out.values()) \u001b[94mif\u001b[0m \u001b[96misinstance\u001b[0m(out, \u001b[96mdict\u001b[0m) \u001b[94melse\u001b[0m [ou  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m 547 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[2m# re-apply format to the output\u001b[0m                                                   \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m 548 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94mfor\u001b[0m dataset \u001b[95min\u001b[0m datasets:                                                          \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/datasets/\u001b[0m\u001b[1;33marrow_dataset.py\u001b[0m:\u001b[94m3087\u001b[0m in \u001b[92mmap\u001b[0m                    \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m3084 \u001b[0m\u001b[2m│   │   │   │   │   \u001b[0mleave=\u001b[94mFalse\u001b[0m,                                                          \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m3085 \u001b[0m\u001b[2m│   │   │   │   │   \u001b[0mdesc=desc \u001b[95mor\u001b[0m \u001b[33m\"\u001b[0m\u001b[33mMap\u001b[0m\u001b[33m\"\u001b[0m,                                                   \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m3086 \u001b[0m\u001b[2m│   │   │   │   \u001b[0m) \u001b[94mas\u001b[0m pbar:                                                                \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m3087 \u001b[2m│   │   │   │   │   \u001b[0m\u001b[94mfor\u001b[0m rank, done, content \u001b[95min\u001b[0m Dataset._map_single(**dataset_kwargs):     \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m3088 \u001b[0m\u001b[2m│   │   │   │   │   │   \u001b[0m\u001b[94mif\u001b[0m done:                                                          \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m3089 \u001b[0m\u001b[2m│   │   │   │   │   │   │   \u001b[0mshards_done += \u001b[94m1\u001b[0m                                              \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m3090 \u001b[0m\u001b[2m│   │   │   │   │   │   │   \u001b[0mlogger.debug(\u001b[33mf\u001b[0m\u001b[33m\"\u001b[0m\u001b[33mFinished processing shard number \u001b[0m\u001b[33m{\u001b[0mrank\u001b[33m}\u001b[0m\u001b[33m of \u001b[0m\u001b[33m{\u001b[0mn  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/datasets/\u001b[0m\u001b[1;33marrow_dataset.py\u001b[0m:\u001b[94m3463\u001b[0m in \u001b[92m_map_single\u001b[0m            \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m3460 \u001b[0m\u001b[2m│   │   │   │   │   │   │   \u001b[0m\u001b[96mrange\u001b[0m(*(\u001b[96mslice\u001b[0m(i, i + batch_size).indices(shard.num_rows)))    \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m3461 \u001b[0m\u001b[2m│   │   │   │   │   │   \u001b[0m)  \u001b[2m# Something simpler?\u001b[0m                                           \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m3462 \u001b[0m\u001b[2m│   │   │   │   │   │   \u001b[0m\u001b[94mtry\u001b[0m:                                                              \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m3463 \u001b[2m│   │   │   │   │   │   │   \u001b[0mbatch = apply_function_on_filtered_inputs(                    \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m3464 \u001b[0m\u001b[2m│   │   │   │   │   │   │   │   \u001b[0mbatch,                                                    \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m3465 \u001b[0m\u001b[2m│   │   │   │   │   │   │   │   \u001b[0mindices,                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m3466 \u001b[0m\u001b[2m│   │   │   │   │   │   │   │   \u001b[0mcheck_same_num_examples=\u001b[96mlen\u001b[0m(shard.list_indexes()) > \u001b[94m0\u001b[0m,    \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/datasets/\u001b[0m\u001b[1;33marrow_dataset.py\u001b[0m:\u001b[94m3344\u001b[0m in                        \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[92mapply_function_on_filtered_inputs\u001b[0m                                                                \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m3341 \u001b[0m\u001b[2m│   │   │   │   \u001b[0madditional_args += (effective_indices,)                                   \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m3342 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[94mif\u001b[0m with_rank:                                                                 \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m3343 \u001b[0m\u001b[2m│   │   │   │   \u001b[0madditional_args += (rank,)                                                \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m3344 \u001b[2m│   │   │   \u001b[0mprocessed_inputs = function(*fn_args, *additional_args, **fn_kwargs)          \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m3345 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[94mif\u001b[0m \u001b[96misinstance\u001b[0m(processed_inputs, LazyDict):                                    \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m3346 \u001b[0m\u001b[2m│   │   │   │   \u001b[0mprocessed_inputs = {                                                      \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m3347 \u001b[0m\u001b[2m│   │   │   │   │   \u001b[0mk: v \u001b[94mfor\u001b[0m k, v \u001b[95min\u001b[0m processed_inputs.data.items() \u001b[94mif\u001b[0m k \u001b[95mnot\u001b[0m \u001b[95min\u001b[0m processed  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m in \u001b[92mforward_pass_with_label\u001b[0m:\u001b[94m11\u001b[0m                                                                    \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/\u001b[0m\u001b[1;33mmodule.py\u001b[0m:\u001b[94m1501\u001b[0m in \u001b[92m_call_impl\u001b[0m            \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m1498 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94mif\u001b[0m \u001b[95mnot\u001b[0m (\u001b[96mself\u001b[0m._backward_hooks \u001b[95mor\u001b[0m \u001b[96mself\u001b[0m._backward_pre_hooks \u001b[95mor\u001b[0m \u001b[96mself\u001b[0m._forward_hooks   \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m1499 \u001b[0m\u001b[2m│   │   │   │   \u001b[0m\u001b[95mor\u001b[0m _global_backward_pre_hooks \u001b[95mor\u001b[0m _global_backward_hooks                   \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m1500 \u001b[0m\u001b[2m│   │   │   │   \u001b[0m\u001b[95mor\u001b[0m _global_forward_hooks \u001b[95mor\u001b[0m _global_forward_pre_hooks):                   \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m1501 \u001b[2m│   │   │   \u001b[0m\u001b[94mreturn\u001b[0m forward_call(*args, **kwargs)                                          \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m1502 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[2m# Do not call functions when jit is used\u001b[0m                                          \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m1503 \u001b[0m\u001b[2m│   │   \u001b[0mfull_backward_hooks, non_full_backward_hooks = [], []                             \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m1504 \u001b[0m\u001b[2m│   │   \u001b[0mbackward_pre_hooks = []                                                           \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/transformers/models/bert/\u001b[0m\u001b[1;33mmodeling_bert.py\u001b[0m:\u001b[94m1758\u001b[0m in        \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[92mforward\u001b[0m                                                                                          \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m1755 \u001b[0m\u001b[2;33m│   │   \u001b[0m\u001b[33m\"\"\"\u001b[0m                                                                               \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m1756 \u001b[0m\u001b[2m│   │   \u001b[0mreturn_dict = return_dict \u001b[94mif\u001b[0m return_dict \u001b[95mis\u001b[0m \u001b[95mnot\u001b[0m \u001b[94mNone\u001b[0m \u001b[94melse\u001b[0m \u001b[96mself\u001b[0m.config.use_return  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m1757 \u001b[0m\u001b[2m│   │   \u001b[0m                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m1758 \u001b[2m│   │   \u001b[0moutputs = \u001b[96mself\u001b[0m.bert(                                                              \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m1759 \u001b[0m\u001b[2m│   │   │   \u001b[0minput_ids,                                                                    \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m1760 \u001b[0m\u001b[2m│   │   │   \u001b[0mattention_mask=attention_mask,                                                \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m1761 \u001b[0m\u001b[2m│   │   │   \u001b[0mtoken_type_ids=token_type_ids,                                                \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/\u001b[0m\u001b[1;33mmodule.py\u001b[0m:\u001b[94m1501\u001b[0m in \u001b[92m_call_impl\u001b[0m            \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m1498 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94mif\u001b[0m \u001b[95mnot\u001b[0m (\u001b[96mself\u001b[0m._backward_hooks \u001b[95mor\u001b[0m \u001b[96mself\u001b[0m._backward_pre_hooks \u001b[95mor\u001b[0m \u001b[96mself\u001b[0m._forward_hooks   \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m1499 \u001b[0m\u001b[2m│   │   │   │   \u001b[0m\u001b[95mor\u001b[0m _global_backward_pre_hooks \u001b[95mor\u001b[0m _global_backward_hooks                   \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m1500 \u001b[0m\u001b[2m│   │   │   │   \u001b[0m\u001b[95mor\u001b[0m _global_forward_hooks \u001b[95mor\u001b[0m _global_forward_pre_hooks):                   \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m1501 \u001b[2m│   │   │   \u001b[0m\u001b[94mreturn\u001b[0m forward_call(*args, **kwargs)                                          \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m1502 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[2m# Do not call functions when jit is used\u001b[0m                                          \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m1503 \u001b[0m\u001b[2m│   │   \u001b[0mfull_backward_hooks, non_full_backward_hooks = [], []                             \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m1504 \u001b[0m\u001b[2m│   │   \u001b[0mbackward_pre_hooks = []                                                           \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/transformers/models/bert/\u001b[0m\u001b[1;33mmodeling_bert.py\u001b[0m:\u001b[94m1013\u001b[0m in        \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[92mforward\u001b[0m                                                                                          \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m1010 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[2m# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x s\u001b[0m  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m1011 \u001b[0m\u001b[2m│   │   \u001b[0mhead_mask = \u001b[96mself\u001b[0m.get_head_mask(head_mask, \u001b[96mself\u001b[0m.config.num_hidden_layers)          \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m1012 \u001b[0m\u001b[2m│   │   \u001b[0m                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m1013 \u001b[2m│   │   \u001b[0membedding_output = \u001b[96mself\u001b[0m.embeddings(                                               \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m1014 \u001b[0m\u001b[2m│   │   │   \u001b[0minput_ids=input_ids,                                                          \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m1015 \u001b[0m\u001b[2m│   │   │   \u001b[0mposition_ids=position_ids,                                                    \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m1016 \u001b[0m\u001b[2m│   │   │   \u001b[0mtoken_type_ids=token_type_ids,                                                \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/\u001b[0m\u001b[1;33mmodule.py\u001b[0m:\u001b[94m1501\u001b[0m in \u001b[92m_call_impl\u001b[0m            \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m1498 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94mif\u001b[0m \u001b[95mnot\u001b[0m (\u001b[96mself\u001b[0m._backward_hooks \u001b[95mor\u001b[0m \u001b[96mself\u001b[0m._backward_pre_hooks \u001b[95mor\u001b[0m \u001b[96mself\u001b[0m._forward_hooks   \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m1499 \u001b[0m\u001b[2m│   │   │   │   \u001b[0m\u001b[95mor\u001b[0m _global_backward_pre_hooks \u001b[95mor\u001b[0m _global_backward_hooks                   \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m1500 \u001b[0m\u001b[2m│   │   │   │   \u001b[0m\u001b[95mor\u001b[0m _global_forward_hooks \u001b[95mor\u001b[0m _global_forward_pre_hooks):                   \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m1501 \u001b[2m│   │   │   \u001b[0m\u001b[94mreturn\u001b[0m forward_call(*args, **kwargs)                                          \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m1502 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[2m# Do not call functions when jit is used\u001b[0m                                          \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m1503 \u001b[0m\u001b[2m│   │   \u001b[0mfull_backward_hooks, non_full_backward_hooks = [], []                             \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m1504 \u001b[0m\u001b[2m│   │   \u001b[0mbackward_pre_hooks = []                                                           \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/transformers/models/bert/\u001b[0m\u001b[1;33mmodeling_bert.py\u001b[0m:\u001b[94m230\u001b[0m in \u001b[92mforward\u001b[0m \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m 227 \u001b[0m\u001b[2m│   │   │   │   \u001b[0mtoken_type_ids = torch.zeros(input_shape, dtype=torch.long, device=\u001b[96mself\u001b[0m.  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m 228 \u001b[0m\u001b[2m│   │   \u001b[0m                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m 229 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94mif\u001b[0m inputs_embeds \u001b[95mis\u001b[0m \u001b[94mNone\u001b[0m:                                                         \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m 230 \u001b[2m│   │   │   \u001b[0minputs_embeds = \u001b[96mself\u001b[0m.word_embeddings(input_ids)                               \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m 231 \u001b[0m\u001b[2m│   │   \u001b[0mtoken_type_embeddings = \u001b[96mself\u001b[0m.token_type_embeddings(token_type_ids)                \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m 232 \u001b[0m\u001b[2m│   │   \u001b[0m                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m 233 \u001b[0m\u001b[2m│   │   \u001b[0membeddings = inputs_embeds + token_type_embeddings                                \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/\u001b[0m\u001b[1;33mmodule.py\u001b[0m:\u001b[94m1501\u001b[0m in \u001b[92m_call_impl\u001b[0m            \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m1498 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[94mif\u001b[0m \u001b[95mnot\u001b[0m (\u001b[96mself\u001b[0m._backward_hooks \u001b[95mor\u001b[0m \u001b[96mself\u001b[0m._backward_pre_hooks \u001b[95mor\u001b[0m \u001b[96mself\u001b[0m._forward_hooks   \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m1499 \u001b[0m\u001b[2m│   │   │   │   \u001b[0m\u001b[95mor\u001b[0m _global_backward_pre_hooks \u001b[95mor\u001b[0m _global_backward_hooks                   \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m1500 \u001b[0m\u001b[2m│   │   │   │   \u001b[0m\u001b[95mor\u001b[0m _global_forward_hooks \u001b[95mor\u001b[0m _global_forward_pre_hooks):                   \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m1501 \u001b[2m│   │   │   \u001b[0m\u001b[94mreturn\u001b[0m forward_call(*args, **kwargs)                                          \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m1502 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[2m# Do not call functions when jit is used\u001b[0m                                          \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m1503 \u001b[0m\u001b[2m│   │   \u001b[0mfull_backward_hooks, non_full_backward_hooks = [], []                             \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m1504 \u001b[0m\u001b[2m│   │   \u001b[0mbackward_pre_hooks = []                                                           \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/\u001b[0m\u001b[1;33msparse.py\u001b[0m:\u001b[94m162\u001b[0m in \u001b[92mforward\u001b[0m                \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m159 \u001b[0m\u001b[2m│   │   │   │   \u001b[0m\u001b[96mself\u001b[0m.weight[\u001b[96mself\u001b[0m.padding_idx].fill_(\u001b[94m0\u001b[0m)                                     \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m160 \u001b[0m\u001b[2m│   \u001b[0m                                                                                       \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m161 \u001b[0m\u001b[2m│   \u001b[0m\u001b[94mdef\u001b[0m \u001b[92mforward\u001b[0m(\u001b[96mself\u001b[0m, \u001b[96minput\u001b[0m: Tensor) -> Tensor:                                            \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m162 \u001b[2m│   │   \u001b[0m\u001b[94mreturn\u001b[0m F.embedding(                                                                \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m163 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[96minput\u001b[0m, \u001b[96mself\u001b[0m.weight, \u001b[96mself\u001b[0m.padding_idx, \u001b[96mself\u001b[0m.max_norm,                           \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m164 \u001b[0m\u001b[2m│   │   │   \u001b[0m\u001b[96mself\u001b[0m.norm_type, \u001b[96mself\u001b[0m.scale_grad_by_freq, \u001b[96mself\u001b[0m.sparse)                          \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m165 \u001b[0m                                                                                           \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/torch/nn/\u001b[0m\u001b[1;33mfunctional.py\u001b[0m:\u001b[94m2210\u001b[0m in \u001b[92membedding\u001b[0m                 \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m                                                                                                  \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m2207 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[2m#   torch.embedding_renorm_\u001b[0m                                                       \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m2208 \u001b[0m\u001b[2m│   │   \u001b[0m\u001b[2m# remove once script supports set_grad_enabled\u001b[0m                                    \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m2209 \u001b[0m\u001b[2m│   │   \u001b[0m_no_grad_embedding_renorm_(weight, \u001b[96minput\u001b[0m, max_norm, norm_type)                    \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m2210 \u001b[2m│   \u001b[0m\u001b[94mreturn\u001b[0m torch.embedding(weight, \u001b[96minput\u001b[0m, padding_idx, scale_grad_by_freq, sparse)        \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m2211 \u001b[0m                                                                                          \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m2212 \u001b[0m                                                                                          \u001b[31m│\u001b[0m\n",
              "\u001b[31m│\u001b[0m   \u001b[2m2213 \u001b[0m\u001b[94mdef\u001b[0m \u001b[92membedding_bag\u001b[0m(                                                                        \u001b[31m│\u001b[0m\n",
              "\u001b[31m╰──────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n",
              "\u001b[1;91mRuntimeError: \u001b[0mExpected all tensors to be on the same device, but found at least two devices, cu\u001b[1;92mda:0\u001b[0m and cpu! \u001b[1m(\u001b[0mwhen \n",
              "checking argument for argument index in method wrapper_CUDA__index_select\u001b[1m)\u001b[0m\n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "eval_set = encoded_ds['eval']\n",
        "\n",
        "eval_set = eval_set.remove_columns(['id', 'fine_ner_tags'])\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": 58,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "85R6yv__63Pn",
        "outputId": "fd9e333a-7559-49e8-8f7a-4106779c5206"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #800000; text-decoration-color: #800000\">╭─────────────────────────────── </span><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">Traceback </span><span style=\"color: #bf7f7f; text-decoration-color: #bf7f7f; font-weight: bold\">(most recent call last)</span><span style=\"color: #800000; text-decoration-color: #800000\"> ────────────────────────────────╮</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">&lt;cell line: 2&gt;</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">2</span>                                                                              <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">╰──────────────────────────────────────────────────────────────────────────────────────────────────╯</span>\n",
              "<span style=\"color: #ff0000; text-decoration-color: #ff0000; font-weight: bold\">NameError: </span>name <span style=\"color: #008000; text-decoration-color: #008000\">'eval_df'</span> is not defined\n",
              "</pre>\n"
            ],
            "text/plain": [
              "\u001b[31m╭─\u001b[0m\u001b[31m──────────────────────────────\u001b[0m\u001b[31m \u001b[0m\u001b[1;31mTraceback \u001b[0m\u001b[1;2;31m(most recent call last)\u001b[0m\u001b[31m \u001b[0m\u001b[31m───────────────────────────────\u001b[0m\u001b[31m─╮\u001b[0m\n",
              "\u001b[31m│\u001b[0m in \u001b[92m<cell line: 2>\u001b[0m:\u001b[94m2\u001b[0m                                                                              \u001b[31m│\u001b[0m\n",
              "\u001b[31m╰──────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n",
              "\u001b[1;91mNameError: \u001b[0mname \u001b[32m'eval_df'\u001b[0m is not defined\n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "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": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "_Y9Fvx7I63Pn",
        "outputId": "f981d808-0739-4c21-c39c-2bc670a95c2c"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #800000; text-decoration-color: #800000\">╭─────────────────────────────── </span><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">Traceback </span><span style=\"color: #bf7f7f; text-decoration-color: #bf7f7f; font-weight: bold\">(most recent call last)</span><span style=\"color: #800000; text-decoration-color: #800000\"> ────────────────────────────────╮</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">│</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">&lt;cell line: 1&gt;</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">1</span>                                                                              <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">╰──────────────────────────────────────────────────────────────────────────────────────────────────╯</span>\n",
              "<span style=\"color: #ff0000; text-decoration-color: #ff0000; font-weight: bold\">NameError: </span>name <span style=\"color: #008000; text-decoration-color: #008000\">'eval_df'</span> is not defined\n",
              "</pre>\n"
            ],
            "text/plain": [
              "\u001b[31m╭─\u001b[0m\u001b[31m──────────────────────────────\u001b[0m\u001b[31m \u001b[0m\u001b[1;31mTraceback \u001b[0m\u001b[1;2;31m(most recent call last)\u001b[0m\u001b[31m \u001b[0m\u001b[31m───────────────────────────────\u001b[0m\u001b[31m─╮\u001b[0m\n",
              "\u001b[31m│\u001b[0m in \u001b[92m<cell line: 1>\u001b[0m:\u001b[94m1\u001b[0m                                                                              \u001b[31m│\u001b[0m\n",
              "\u001b[31m╰──────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n",
              "\u001b[1;91mNameError: \u001b[0mname \u001b[32m'eval_df'\u001b[0m is not defined\n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "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": null,
      "metadata": {
        "id": "jE5rcoG263Pn"
      },
      "outputs": [],
      "source": [
        "(\n",
        "    eval_df_tokens.groupby(\"input_tokens\")[[\"loss\"]]\n",
        "    .agg([\"count\", \"mean\", \"sum\"])\n",
        "    .droplevel(level=0, axis=1) # Get rid of multi-level columns\n",
        "    .sort_values(by=\"sum\", 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": null,
      "metadata": {
        "id": "zQenE55W63Pn"
      },
      "outputs": [],
      "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\", 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": null,
      "metadata": {
        "id": "opFuJbYh63Pn"
      },
      "outputs": [],
      "source": [
        "def plot_confusion_matrix(y_preds, y_true, labels):\n",
        "    cm = confusion_matrix(y_true, y_preds, normalize=\"true\")\n",
        "    fig, ax = plt.subplots(figsize=(6, 6))\n",
        "    disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=labels)\n",
        "    disp.plot(cmap=\"Blues\", values_format=\".2f\", ax=ax, 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": null,
      "metadata": {
        "id": "49R2rzdM63Pn"
      },
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      "source": [
        "eval_token_list = list(set(eval_df_tokens['labels']))\n",
        "\n",
        "plot_confusion_matrix(eval_df_tokens[\"labels\"], eval_df_tokens[\"predicted_label\"],\n",
        "                      eval_token_list)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GtMK6KjK63Pn"
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      "source": [
        "### Notes & Other Takeaways From This Project\n",
        "\n",
        "****\n",
        "-\n",
        "\n",
        "****"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iR8hiCmG63Po"
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      "source": [
        "### Citations\n",
        "\n",
        "- Dataset\n",
        "\n",
        "    > @inproceedings{ding-etal-2021-nerd,\n",
        "    title = \"Few-{NERD}: A Few-shot Named Entity Recognition Dataset\",\n",
        "    author = \"Ding, Ning and Xu, Guangwei and Chen, Yulin and Wang, Xiaobin and Han, Xu and Xie, Pengjun and Zheng, Haitao and Liu, Zhiyuan\", booktitle = \"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)\", month = aug, year = \"2021\", address = \"Online\", publisher = \"Association for Computational Linguistics\",\n",
        "    url = \"https://aclanthology.org/2021.acl-long.248\", doi = \"10.18653/v1/2021.acl-long.248\", pages = \"3198--3213\",}\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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