Fine_tuning_BERT_MultiNLI_2_labels.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pb4x5Z9BfMC3"
      },
      "source": [
        "# Fine-Tuning BERT on MultiNLI Dataset for NLI task with two labels (Entailment and Non-entailment)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vXW4GObhfPW-"
      },
      "source": [
        "Mounting Google Drive"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0F9hvagsgcun",
        "outputId": "5b45400e-50f4-442d-f5d1-48275ae478fc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive', force_remount=True, timeout_ms = 0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ojlfyGybe1gO"
      },
      "outputs": [],
      "source": [
        "base_dir = '/content/drive/My Drive/'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RIxfb2EqfJ7b"
      },
      "source": [
        "To be able to share the model with the community and use Trainer API, one should have an authentication token from the Hugging Face"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
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            "a5080668f74d49a8a304161de3d4b81b",
            "91dc2d08e206416ebbb6e1dd7887bb07",
            "93d8ad07071c4403950be0388cd4803b"
          ]
        },
        "id": "sxWlpMOikIeE",
        "outputId": "ca860bed-02d8-4161-b3a8-f47aa273f01d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b2dd1eca07dc42abb82c177a25597663"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Imports"
      ],
      "metadata": {
        "id": "eq85Ki_A7PLc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Installing packages"
      ],
      "metadata": {
        "id": "nyJ_kzHm8RnX"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cOkG_DhOfVwO",
        "outputId": "5aa48fc7-6eae-4231-dd17-b704f120c03c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting datasets\n",
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            "Successfully installed datasets-2.16.1 dill-0.3.7 multiprocess-0.70.15 pyarrow-hotfix-0.6\n",
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            "Successfully installed transformers-4.36.2\n"
          ]
        }
      ],
      "source": [
        "! pip install datasets\n",
        "! pip install -U accelerate\n",
        "! pip install -U transformers"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ToG-TNXsjzim"
      },
      "source": [
        "Importing libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MpV1v6-3jwD4"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import numpy as np\n",
        "import os\n",
        "import datetime\n",
        "from copy import deepcopy\n",
        "\n",
        "from transformers import (BertTokenizer,\n",
        "                          BertForSequenceClassification,\n",
        "                          Trainer,\n",
        "                          TrainingArguments,\n",
        "                          TrainerCallback)\n",
        "from datasets import (load_dataset,\n",
        "                      load_metric,\n",
        "                      load_dataset_builder,\n",
        "                      ClassLabel,\n",
        "                      Value,\n",
        "                      Features)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pCZuYv02QYGw"
      },
      "outputs": [],
      "source": [
        "BATCH_SIZE = 32"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pNhURA-pj709",
        "outputId": "603d13dc-5dcb-4922-ef68-db66baf7217f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cuda:0\n"
          ]
        }
      ],
      "source": [
        "DEVICE = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "print(DEVICE)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dRdKmrYCxChA",
        "outputId": "187d8a2e-6f69-4561-a549-049def4fe9f1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:72: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'premise': Value(dtype='string', id=None),\n",
              " 'hypothesis': Value(dtype='string', id=None),\n",
              " 'label': ClassLabel(names=['entailment', 'neutral', 'contradiction'], id=None),\n",
              " 'idx': Value(dtype='int32', id=None)}"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "ds_builder = load_dataset_builder(\"glue\", \"mnli\")\n",
        "ds_builder.info.features"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Using only train and validation data"
      ],
      "metadata": {
        "id": "s0r8alS5lJI9"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K6y-UDJ_pKkf",
        "outputId": "28b02b8d-b84e-46d5-bb56-4ef3a792d30d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train dataset:\n",
            " Dataset({\n",
            "    features: ['premise', 'hypothesis', 'label', 'idx'],\n",
            "    num_rows: 392702\n",
            "})\n",
            "Validation matched dataset:\n",
            " Dataset({\n",
            "    features: ['premise', 'hypothesis', 'label', 'idx'],\n",
            "    num_rows: 9815\n",
            "})\n",
            "Validation mismatched dataset:\n",
            " Dataset({\n",
            "    features: ['premise', 'hypothesis', 'label', 'idx'],\n",
            "    num_rows: 9832\n",
            "})\n"
          ]
        }
      ],
      "source": [
        "train_data, val_m_data, val_mm_data = load_dataset(\"glue\", \"mnli\", split=['train', 'validation_matched', 'validation_mismatched'])\n",
        "\n",
        "print(\"Train dataset:\\n\", train_data)\n",
        "print(\"Validation matched dataset:\\n\", val_m_data)\n",
        "print(\"Validation mismatched dataset:\\n\", val_mm_data)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data.features"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oDUu0rCBjHBK",
        "outputId": "ba181890-44ea-4070-c5c1-7c1d1e9669aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'premise': Value(dtype='string', id=None),\n",
              " 'hypothesis': Value(dtype='string', id=None),\n",
              " 'label': ClassLabel(names=['entailment', 'neutral', 'contradiction'], id=None),\n",
              " 'idx': Value(dtype='int32', id=None)}"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "69NRJyWbUSwK",
        "outputId": "9419e46b-8584-4072-91dd-1fda1a7046f9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'premise': 'Conceptually cream skimming has two basic dimensions - product and geography.', 'hypothesis': 'Product and geography are what make cream skimming work. ', 'label': 1, 'idx': 0}\n"
          ]
        }
      ],
      "source": [
        "# Example\n",
        "print(train_data[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 176,
          "referenced_widgets": [
            "9a7392085e20423581eae4de8d7b7790",
            "b1b0b7741ba84c95a2d694e333776889",
            "4282c9ce5b334d2a95b78046f21983fe",
            "48e709ce0d9646b09f983a30c12bc3b7",
            "84d28f971d1f401d836d247092fb834b",
            "ccc7329d139b4e0ab9aecbf2ce9e2e98",
            "c15b736f43a743e3834e93f0798e4d0c",
            "e17f6afd05bc4aea98b5342799bd5de6",
            "b59c628a0372494b8b53a20b5ba40b8f",
            "a7cf2ae0442a46ae94d628e75ad2dc0c",
            "6cd7f6c410184c128d018b832c921ef7"
          ]
        },
        "id": "I1v4Lir3T8SU",
        "outputId": "5cd797b6-6160-4329-8852-78585d40cfc7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-21-17bf5bf84e68>:1: FutureWarning: load_metric is deprecated and will be removed in the next major version of datasets. Use 'evaluate.load' instead, from the new library 🤗 Evaluate: https://huggingface.co/docs/evaluate\n",
            "  metric = load_metric('glue', \"mnli\")\n",
            "/usr/local/lib/python3.10/dist-packages/datasets/load.py:752: FutureWarning: The repository for glue contains custom code which must be executed to correctly load the metric. You can inspect the repository content at https://raw.githubusercontent.com/huggingface/datasets/2.16.1/metrics/glue/glue.py\n",
            "You can avoid this message in future by passing the argument `trust_remote_code=True`.\n",
            "Passing `trust_remote_code=True` will be mandatory to load this metric from the next major release of `datasets`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/1.84k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9a7392085e20423581eae4de8d7b7790"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "metric = load_metric('glue', \"mnli\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JcTknFVKUxB2",
        "outputId": "8b6634cb-299f-40e7-c518-32bad9c52263"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Metric(name: \"glue\", features: {'predictions': Value(dtype='int64', id=None), 'references': Value(dtype='int64', id=None)}, usage: \"\"\"\n",
              "Compute GLUE evaluation metric associated to each GLUE dataset.\n",
              "Args:\n",
              "    predictions: list of predictions to score.\n",
              "        Each translation should be tokenized into a list of tokens.\n",
              "    references: list of lists of references for each translation.\n",
              "        Each reference should be tokenized into a list of tokens.\n",
              "Returns: depending on the GLUE subset, one or several of:\n",
              "    \"accuracy\": Accuracy\n",
              "    \"f1\": F1 score\n",
              "    \"pearson\": Pearson Correlation\n",
              "    \"spearmanr\": Spearman Correlation\n",
              "    \"matthews_correlation\": Matthew Correlation\n",
              "Examples:\n",
              "\n",
              "    >>> glue_metric = datasets.load_metric('glue', 'sst2')  # 'sst2' or any of [\"mnli\", \"mnli_mismatched\", \"mnli_matched\", \"qnli\", \"rte\", \"wnli\", \"hans\"]\n",
              "    >>> references = [0, 1]\n",
              "    >>> predictions = [0, 1]\n",
              "    >>> results = glue_metric.compute(predictions=predictions, references=references)\n",
              "    >>> print(results)\n",
              "    {'accuracy': 1.0}\n",
              "\n",
              "    >>> glue_metric = datasets.load_metric('glue', 'mrpc')  # 'mrpc' or 'qqp'\n",
              "    >>> references = [0, 1]\n",
              "    >>> predictions = [0, 1]\n",
              "    >>> results = glue_metric.compute(predictions=predictions, references=references)\n",
              "    >>> print(results)\n",
              "    {'accuracy': 1.0, 'f1': 1.0}\n",
              "\n",
              "    >>> glue_metric = datasets.load_metric('glue', 'stsb')\n",
              "    >>> references = [0., 1., 2., 3., 4., 5.]\n",
              "    >>> predictions = [0., 1., 2., 3., 4., 5.]\n",
              "    >>> results = glue_metric.compute(predictions=predictions, references=references)\n",
              "    >>> print({\"pearson\": round(results[\"pearson\"], 2), \"spearmanr\": round(results[\"spearmanr\"], 2)})\n",
              "    {'pearson': 1.0, 'spearmanr': 1.0}\n",
              "\n",
              "    >>> glue_metric = datasets.load_metric('glue', 'cola')\n",
              "    >>> references = [0, 1]\n",
              "    >>> predictions = [0, 1]\n",
              "    >>> results = glue_metric.compute(predictions=predictions, references=references)\n",
              "    >>> print(results)\n",
              "    {'matthews_correlation': 1.0}\n",
              "\"\"\", stored examples: 0)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "metric"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nYrTDDzrVIG9"
      },
      "source": [
        "## Preprocessing the data"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Replacing neutral (1) and contradiction (2) labels with 1 (non-entailment)."
      ],
      "metadata": {
        "id": "6zah10TQlZ_C"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 151,
          "referenced_widgets": [
            "c89c0b1f1dd245a7a2cf04b99a80dc8e",
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            "ea057bbd0f7a4ff9abf82a90b33302a6",
            "58e8639e43e444ac9896ee84c26ff6b3",
            "e8dabf42adaa4d6381eb425d32e7bfe9",
            "421d4aa548274e278910aec01037829b",
            "afb9b337286848b0afd3b9a9178aa44e",
            "0a7cac4c649e45f28998fdc4e92590ee",
            "baf8eb94ac694e4cb04e04999dfae666",
            "87774938f7884ba285d41331601502fe",
            "a869ad5b7d464233b4f6f47a348e576f",
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            "a8fc6a357c1e454b9e9b10cb7d069acb",
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            "f452a3014f1943df97ae8f12948e716c",
            "465b7619decc4b3688a48fbaf641611c",
            "f4a332d129a941c9b4f5b13511fac82f",
            "1b6101f0d5b94562bedfccfc8fc3d03d",
            "a43a095f725f466894d394d83aa26429",
            "b7fda58f25d14211b56890cdaad57428",
            "1b00a3ab78e940308c29d64ea843dea4",
            "ea916581d2c94152b89c45e0a69a729a",
            "f7c0c72df3f841e4aff47ab7b8d924b4",
            "568beba5ae034dad9d4278240b81834b",
            "ff2a4661e1d644fea41e9f0e3bffa3a0",
            "8b7e72b1c81746fa95d897c6f447dae4",
            "7d9d5bcbd2174040a11e009396feccae",
            "ef431dcf3d6040f382b2858ee692ad4f"
          ]
        },
        "id": "G21Ye0shpDQU",
        "outputId": "5af5d89d-202a-4609-981c-20d7495ae107"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Casting the dataset:   0%|          | 0/392702 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c89c0b1f1dd245a7a2cf04b99a80dc8e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Casting the dataset:   0%|          | 0/9815 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "87774938f7884ba285d41331601502fe"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Casting the dataset:   0%|          | 0/9832 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1b6101f0d5b94562bedfccfc8fc3d03d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'premise': Value(dtype='string', id=None), 'hypothesis': Value(dtype='string', id=None), 'label': ClassLabel(names=['entailment', 'non-entailment'], id=None), 'idx': Value(dtype='int32', id=None)}\n"
          ]
        }
      ],
      "source": [
        "new_features = train_data.features.copy()\n",
        "new_features['label'] = ClassLabel(num_classes = 2, names=[\"entailment\", \"non-entailment\"])\n",
        "\n",
        "train_data = train_data.cast(new_features)\n",
        "val_m_data = val_m_data.cast(new_features)\n",
        "val_mm_data = val_mm_data.cast(new_features)\n",
        "\n",
        "print(train_data.features)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "cff4942970d04ca1a880badf4e268a5b",
            "731cab72901545aeb3a2f58c02f0f81c",
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            "ecf5fcb4576c4694bd40c4b1d08e375e",
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            "a5c3d1dda17942eb8ecfee07a67bf857",
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            "893eba61e7814877a94448a2731c1918",
            "c9b5821d66d1406793d78c45af7c3865",
            "0d84e71225e944b480d489313e75bcb9",
            "1c2fb0f06b18438b92845f69cfbec9b4",
            "5e77c3f71ed84223b265e563ff84e348",
            "99b2b9e2c44e48d9ad46a3923e3fee16",
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            "75a4a8e409f340509a4728234fff1438",
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            "5787063285854773b3f23136eb25f0c1",
            "572e6f5aad0148ed9f8a7174e12bb0c0",
            "e38ce6ad66764998bf3bf0cd45b7db35",
            "25848ba8cfc349c09403c6405b801c36",
            "5cf7e2cb777a44d49295fa0a1824e6a5",
            "654e92ebc6554cdfab14eb089a743195",
            "7f21fd42e9db4f6f9d8182d69eee76d9",
            "9153fd6a5578488fbe65766e29579b2e"
          ]
        },
        "id": "OxAvjCTip2iV",
        "outputId": "fd22296b-6b41-454b-f226-0c5f7c537e05"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/392702 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cff4942970d04ca1a880badf4e268a5b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/9815 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "893eba61e7814877a94448a2731c1918"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/9832 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "71e01e469c6d42cb8b4e2ce65a0953f2"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "def modify_label(example):\n",
        "    example['label'] = 0 if example['label'] == 0 else 1\n",
        "    return example\n",
        "\n",
        "train_data = train_data.map(modify_label)\n",
        "val_m_data = val_m_data.map(modify_label)\n",
        "val_mm_data = val_mm_data.map(modify_label)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T7Ba70cWX8dz"
      },
      "source": [
        "## Tokenizing the dataset\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 252,
          "referenced_widgets": [
            "71967458815042498f0525d766d503d8",
            "71c167833e56422dbe1f318697230853",
            "6a6519480ff74029997d05180bbdae50",
            "56eed3e772cf4cbc89d9beceefc1949a",
            "2c20ed68a67a4c87ba13b1693af5a707",
            "7700b4785d6d4688a742f2224428c237",
            "1cd048339cfc4363aefe08dac475ab36",
            "c29523555bf24e04aa9772176bf4b45a",
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            "6c05232d2f0b42dd84d115cf5a5ce5f9",
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            "8cfe821fb34943d5925f5f592244c026",
            "27faa7b07ea04ae6a957a09d735d1228",
            "c8d0732179244df98f1ff23bec2ede70",
            "fe648abd134f49b19b592f797276ffcd",
            "9aa1a08292c2461a972f308746b948d5",
            "9859a717836946558a462727911aeff5",
            "f5a4ad1226724937ac14aa6bb58f6d57",
            "d4002a89908a4a05ab579eff9ef6818f",
            "118ce94ea2bd433ba087fb1b0ae6a0e3",
            "c458815238d949cf8b7fc62ff281b6f3",
            "98be21473b40401fa836b89a8eb90efe",
            "b01e2596fa374626bbfcbb01e1cdf9ef",
            "c0468cdaf2b24239b4e43477dda7dd3f",
            "26ad0141da75433e8fc2ed7445fdb171",
            "5e91980f81ab4e46be6c13f1ff34e6b8",
            "d521cf151a1d4b03b2ad5aa790c132c0",
            "c3a5a6fd8f434d2188294e986b5b4f62",
            "b0c950875d25434582402ffc628d8c7a",
            "6c7bbc30c15e42e59542540d4b29b0f6",
            "e47598958fc249ada9de07d6968ad437",
            "1fc81c3bc4ef449891a2858d313bfe12",
            "eb8b94833a5743ba8c5e769d9e5a18ab",
            "db158ef39c024aaf82f6923e9a3cd3d9",
            "2c097c6814a14e8f9c3c125268af168a",
            "3fede563fc8c4fafbd94ac6f45aa10c7",
            "09a6bbf007eb47d3a4cb5cdda6a20ea7",
            "ba3e56d6c14346dca6fc8660651f4978",
            "a57453a14a564763b443decc91c01849",
            "aef81986b5214d8c8cd56341b860d9aa",
            "faa03c1ef564423b9918a9cb9ba365b9",
            "d1ee077fa2e4401abaddf6975f3cb6d8",
            "92dbdbe20a0549b28da200022b343329",
            "cd5d5b3b4c014851ac869fc818329261"
          ]
        },
        "id": "QeEXhlvUptrH",
        "outputId": "d303d883-d2b5-48d0-cbec-a09309a40d15"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:72: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "71967458815042498f0525d766d503d8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8cfe821fb34943d5925f5f592244c026"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b01e2596fa374626bbfcbb01e1cdf9ef"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "db158ef39c024aaf82f6923e9a3cd3d9"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', do_lower_case=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H5jqDXZYX94G",
        "outputId": "ff72cd57-113c-4066-8e26-b6376b42774b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': [101, 7592, 1010, 2023, 2028, 6251, 999, 102, 1998, 2023, 6251, 3632, 2007, 2009, 1012, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "tokenizer(\"Hello, this one sentence!\", \"And this sentence goes with it.\", padding=\"max_length\", max_length=32)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "bb3ba0ac5e01498092f0d792ebcdad11",
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          ]
        },
        "id": "raLzkFTC4EhK",
        "outputId": "4691e401-2f5a-4c9c-e15a-76ab66d55515"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/392702 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bb3ba0ac5e01498092f0d792ebcdad11"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/9815 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "dc5e8fa4dbe44abe8d1f4c4cef25e91d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/9832 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "73246f8fd15b4cdaa08909a0a2e702f7"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "def tokenize_function(examples):\n",
        "    return tokenizer(examples[\"premise\"], examples[\"hypothesis\"], padding=\"max_length\", truncation=True)\n",
        "\n",
        "tokenized_train_data = train_data.map(tokenize_function, batched=True)\n",
        "tokenized_val_m_data = val_m_data.map(tokenize_function, batched=True)\n",
        "tokenized_val_mm_data = val_mm_data.map(tokenize_function, batched=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JXU3FR69bS3t",
        "outputId": "9fb24763-19e7-4956-d92d-ae525d7be657"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dict_keys(['premise', 'hypothesis', 'label', 'idx', 'input_ids', 'token_type_ids', 'attention_mask'])\n"
          ]
        }
      ],
      "source": [
        "example = tokenized_train_data[0]\n",
        "print(example.keys())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        },
        "id": "wMDKtePvbTpb",
        "outputId": "0e7a8a6f-7336-44a7-9c58-9e33810b93de"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'[CLS] conceptually cream skimming has two basic dimensions - product and geography. [SEP] product and geography are what make cream skimming work. [SEP] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD]'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 36
        }
      ],
      "source": [
        "tokenizer.decode(example['input_ids'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xfzS4mFnbXAV",
        "outputId": "125cf967-b310-41f5-fc2c-35f9c11ee070"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "example['label']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-eXC3HDqz8P_"
      },
      "outputs": [],
      "source": [
        "small_train_dataset = tokenized_train_data.shuffle(seed=42).select(range(100000))\n",
        "small_val_dataset = tokenized_val_m_data.shuffle(seed=42).select(range(1000))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 891,
          "referenced_widgets": [
            "fac67163198f4633b9e802b179f73a11",
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        },
        "id": "ezKpE5unrOXt",
        "outputId": "dd6e9b68-3998-470c-d613-b8ecb9e39577"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/440M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased 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"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BertForSequenceClassification(\n",
              "  (bert): BertModel(\n",
              "    (embeddings): BertEmbeddings(\n",
              "      (word_embeddings): Embedding(30522, 768, padding_idx=0)\n",
              "      (position_embeddings): Embedding(512, 768)\n",
              "      (token_type_embeddings): Embedding(2, 768)\n",
              "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "      (dropout): Dropout(p=0.1, inplace=False)\n",
              "    )\n",
              "    (encoder): BertEncoder(\n",
              "      (layer): ModuleList(\n",
              "        (0-11): 12 x BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (pooler): BertPooler(\n",
              "      (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "      (activation): Tanh()\n",
              "    )\n",
              "  )\n",
              "  (dropout): Dropout(p=0.1, inplace=False)\n",
              "  (classifier): Linear(in_features=768, out_features=2, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "model = BertForSequenceClassification.from_pretrained(\"bert-base-uncased\", num_labels=2)\n",
        "model.to(DEVICE)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "r6eMEj4uz8It"
      },
      "outputs": [],
      "source": [
        "model_folder = base_dir + \"Models-BERT-\" + str(datetime.datetime.now().timestamp())\n",
        "\n",
        "if os.path.exists(model_folder) == False:\n",
        "  os.mkdir(model_folder)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Trainer with arguments for saving and evaluating every epoch"
      ],
      "metadata": {
        "id": "OD7eZ7b3mzaA"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8x4_R1zlbuUu"
      },
      "outputs": [],
      "source": [
        "metric_name = \"accuracy\"\n",
        "\n",
        "args = TrainingArguments(\n",
        "    output_dir = model_folder,\n",
        "    evaluation_strategy = \"epoch\",\n",
        "    save_strategy = \"epoch\",\n",
        "    learning_rate=2e-5,\n",
        "    per_device_train_batch_size=BATCH_SIZE,\n",
        "    per_device_eval_batch_size=BATCH_SIZE,\n",
        "    num_train_epochs=3,\n",
        "    weight_decay=0.01,\n",
        "    load_best_model_at_end=True,\n",
        "    metric_for_best_model=metric_name,\n",
        "    resume_from_checkpoint=True,\n",
        "    push_to_hub=True,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qMmF83UBbuaE"
      },
      "outputs": [],
      "source": [
        "def compute_metrics(eval_pred):\n",
        "    predictions, labels = eval_pred\n",
        "    predictions = np.argmax(predictions, axis=1)\n",
        "    return metric.compute(predictions=predictions, references=labels)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Callback to add calcualtion of train accuracy (it is time-consuming though)"
      ],
      "metadata": {
        "id": "-MQ5ABbhm-xF"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "v1mmeDBe0oMV"
      },
      "outputs": [],
      "source": [
        "class CustomCallback(TrainerCallback):\n",
        "\n",
        "    def __init__(self, trainer) -> None:\n",
        "        super().__init__()\n",
        "        self._trainer = trainer\n",
        "\n",
        "    def on_epoch_end(self, args, state, control, **kwargs):\n",
        "        if control.should_evaluate:\n",
        "            control_copy = deepcopy(control)\n",
        "            self._trainer.evaluate(eval_dataset=self._trainer.train_dataset, metric_key_prefix=\"train\")\n",
        "            return control_copy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "93_Nfv3QbufQ"
      },
      "outputs": [],
      "source": [
        "trainer = Trainer(\n",
        "    model,\n",
        "    args,\n",
        "    train_dataset=tokenized_train_data,\n",
        "    eval_dataset=small_val_dataset,\n",
        "    tokenizer=tokenizer,\n",
        "    compute_metrics=compute_metrics\n",
        ")\n",
        "\n",
        "#trainer.add_callback(CustomCallback(trainer))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 436
        },
        "id": "Bid2i45Rbuld",
        "outputId": "a3616fbf-d647-4f11-bd33-ae5f2705c9e3"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='19501' max='36816' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [19501/36816 3:49:31 < 3:23:49, 1.42 it/s, Epoch 1.59/3]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.263900</td>\n",
              "      <td>0.255064</td>\n",
              "      <td>0.898000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "error",
          "ename": "FailedPreconditionError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFailedPreconditionError\u001b[0m                   Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-25-3435b262f1ae>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)\u001b[0m\n\u001b[1;32m   1526\u001b[0m                 \u001b[0;31m# Disable progress bars when uploading models during checkpoints to avoid polluting stdout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1527\u001b[0m                 \u001b[0mhf_hub_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisable_progress_bars\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1528\u001b[0;31m                 return inner_training_loop(\n\u001b[0m\u001b[1;32m   1529\u001b[0m                     \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1530\u001b[0m                     \u001b[0mresume_from_checkpoint\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresume_from_checkpoint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36m_inner_training_loop\u001b[0;34m(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)\u001b[0m\n\u001b[1;32m   1912\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontrol\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallback_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_step_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontrol\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1913\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1914\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_log_save_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtr_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrial\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepoch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mignore_keys_for_eval\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1915\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1916\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontrol\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallback_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_substep_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontrol\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36m_maybe_log_save_evaluate\u001b[0;34m(self, tr_loss, model, trial, epoch, ignore_keys_for_eval)\u001b[0m\n\u001b[1;32m   2252\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstore_flos\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2253\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2254\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2255\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2256\u001b[0m         \u001b[0mmetrics\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mlog\u001b[0;34m(self, logs)\u001b[0m\n\u001b[1;32m   2653\u001b[0m         \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mlogs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m\"step\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mglobal_step\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2654\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog_history\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2655\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontrol\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallback_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_log\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontrol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2656\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2657\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_prepare_input\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer_callback.py\u001b[0m in \u001b[0;36mon_log\u001b[0;34m(self, args, state, control, logs)\u001b[0m\n\u001b[1;32m    405\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mon_log\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTrainingArguments\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTrainerState\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontrol\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTrainerControl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    406\u001b[0m         \u001b[0mcontrol\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_log\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 407\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcall_event\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"on_log\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontrol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    408\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    409\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mon_prediction_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTrainingArguments\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTrainerState\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontrol\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTrainerControl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer_callback.py\u001b[0m in \u001b[0;36mcall_event\u001b[0;34m(self, event, args, state, control, **kwargs)\u001b[0m\n\u001b[1;32m    412\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mcall_event\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevent\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontrol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    413\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mcallback\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 414\u001b[0;31m             result = getattr(callback, event)(\n\u001b[0m\u001b[1;32m    415\u001b[0m                 \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    416\u001b[0m                 \u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/integrations/integration_utils.py\u001b[0m in \u001b[0;36mon_log\u001b[0;34m(self, args, state, control, logs, **kwargs)\u001b[0m\n\u001b[1;32m    655\u001b[0m                         \u001b[0;34m\"is incorrect so we dropped this attribute.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    656\u001b[0m                     )\n\u001b[0;32m--> 657\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtb_writer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflush\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    658\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    659\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mon_train_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontrol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/utils/tensorboard/writer.py\u001b[0m in \u001b[0;36mflush\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1231\u001b[0m             \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1232\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mwriter\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall_writers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1233\u001b[0;31m             \u001b[0mwriter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflush\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1234\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1235\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/utils/tensorboard/writer.py\u001b[0m in \u001b[0;36mflush\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    144\u001b[0m         \u001b[0mdisk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    145\u001b[0m         \"\"\"\n\u001b[0;32m--> 146\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevent_writer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflush\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    147\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorboard/summary/writer/event_file_writer.py\u001b[0m in \u001b[0;36mflush\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    123\u001b[0m         \u001b[0mwritten\u001b[0m \u001b[0mto\u001b[0m \u001b[0mdisk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    124\u001b[0m         \"\"\"\n\u001b[0;32m--> 125\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_async_writer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflush\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    126\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    127\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorboard/summary/writer/event_file_writer.py\u001b[0m in \u001b[0;36mflush\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    188\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mIOError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Writer is closed\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    189\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_byte_queue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 190\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_writer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflush\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    191\u001b[0m             \u001b[0;31m# Check the status again in case the background worker thread has\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    192\u001b[0m             \u001b[0;31m# failed in the meantime to avoid waiting until the next call to\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorboard/summary/writer/record_writer.py\u001b[0m in \u001b[0;36mflush\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     41\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     42\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mflush\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 43\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_writer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflush\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     44\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     45\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/lib/io/file_io.py\u001b[0m in \u001b[0;36mflush\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    219\u001b[0m     \"\"\"\n\u001b[1;32m    220\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_writable_file\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 221\u001b[0;31m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_writable_file\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflush\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    222\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    223\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mFailedPreconditionError\u001b[0m: /content/drive/My Drive/Models-BERT-1704117113.988377/runs/Jan01_13-54-33_fe4a5cf3a238/events.out.tfevents.1704117301.fe4a5cf3a238.258.0; Transport endpoint is not connected"
          ]
        }
      ],
      "source": [
        "trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#trainer.train(resume_from_checkpoint=True)"
      ],
      "metadata": {
        "id": "Tea8raMAJIvA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Trainer with arguments for saving and evaluating every 5000 steps"
      ],
      "metadata": {
        "id": "C0NS1l9FniaQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "new_args = TrainingArguments(\n",
        "    output_dir = model_folder,\n",
        "    evaluation_strategy = \"steps\",\n",
        "    eval_steps=5000,\n",
        "    save_strategy = \"steps\",\n",
        "    save_steps=5000,\n",
        "    learning_rate=2e-5,\n",
        "    per_device_train_batch_size=BATCH_SIZE,\n",
        "    per_device_eval_batch_size=BATCH_SIZE,\n",
        "    num_train_epochs=2,\n",
        "    weight_decay=0.01,\n",
        "    load_best_model_at_end=True,\n",
        "    metric_for_best_model=metric_name,\n",
        "    resume_from_checkpoint=True,\n",
        "    push_to_hub=True,\n",
        ")\n",
        "\n",
        "new_trainer = Trainer(\n",
        "    model,\n",
        "    new_args,\n",
        "    train_dataset=small_train_dataset,\n",
        "    eval_dataset=small_val_dataset,\n",
        "    tokenizer=tokenizer,\n",
        "    compute_metrics=compute_metrics\n",
        ")"
      ],
      "metadata": {
        "id": "Zz1wWDNIQSkk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "id": "QNlOV5AGSCaT",
        "outputId": "c84ee4fa-2600-4d25-a18e-580177d404d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='24263' max='24544' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [24263/24544 4:46:28 < 03:19, 1.41 it/s, Epoch 1.98/2]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>5000</td>\n",
              "      <td>0.136800</td>\n",
              "      <td>0.370258</td>\n",
              "      <td>0.886000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10000</td>\n",
              "      <td>0.117200</td>\n",
              "      <td>0.432073</td>\n",
              "      <td>0.894000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15000</td>\n",
              "      <td>0.131500</td>\n",
              "      <td>0.373415</td>\n",
              "      <td>0.895000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20000</td>\n",
              "      <td>0.128100</td>\n",
              "      <td>0.383873</td>\n",
              "      <td>0.893000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='24544' max='24544' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [24544/24544 4:49:47, Epoch 2/2]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>5000</td>\n",
              "      <td>0.136800</td>\n",
              "      <td>0.370258</td>\n",
              "      <td>0.886000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10000</td>\n",
              "      <td>0.117200</td>\n",
              "      <td>0.432073</td>\n",
              "      <td>0.894000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15000</td>\n",
              "      <td>0.131500</td>\n",
              "      <td>0.373415</td>\n",
              "      <td>0.895000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20000</td>\n",
              "      <td>0.128100</td>\n",
              "      <td>0.383873</td>\n",
              "      <td>0.893000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=24544, training_loss=0.1150286255751626, metrics={'train_runtime': 17388.4137, 'train_samples_per_second': 45.168, 'train_steps_per_second': 1.412, 'total_flos': 2.0664847532396544e+17, 'train_loss': 0.1150286255751626, 'epoch': 2.0})"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "odrEhgRP2fy-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c4e64465-65ac-43d4-96d7-335ffae655ed"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'loss': 0.0551,\n",
              "  'learning_rate': 1.9592568448500654e-05,\n",
              "  'epoch': 0.04,\n",
              "  'step': 500},\n",
              " {'loss': 0.0393,\n",
              "  'learning_rate': 1.9185136897001307e-05,\n",
              "  'epoch': 0.08,\n",
              "  'step': 1000},\n",
              " {'loss': 0.0414,\n",
              "  'learning_rate': 1.8777705345501956e-05,\n",
              "  'epoch': 0.12,\n",
              "  'step': 1500},\n",
              " {'loss': 0.041,\n",
              "  'learning_rate': 1.837027379400261e-05,\n",
              "  'epoch': 0.16,\n",
              "  'step': 2000},\n",
              " {'loss': 0.0496,\n",
              "  'learning_rate': 1.796284224250326e-05,\n",
              "  'epoch': 0.2,\n",
              "  'step': 2500},\n",
              " {'loss': 0.0517,\n",
              "  'learning_rate': 1.7555410691003914e-05,\n",
              "  'epoch': 0.24,\n",
              "  'step': 3000},\n",
              " {'loss': 0.0448,\n",
              "  'learning_rate': 1.7147979139504566e-05,\n",
              "  'epoch': 0.29,\n",
              "  'step': 3500},\n",
              " {'loss': 0.0487,\n",
              "  'learning_rate': 1.6740547588005215e-05,\n",
              "  'epoch': 0.33,\n",
              "  'step': 4000},\n",
              " {'loss': 0.1443,\n",
              "  'learning_rate': 1.6333116036505868e-05,\n",
              "  'epoch': 0.37,\n",
              "  'step': 4500},\n",
              " {'loss': 0.1368,\n",
              "  'learning_rate': 1.592568448500652e-05,\n",
              "  'epoch': 0.41,\n",
              "  'step': 5000},\n",
              " {'eval_loss': 0.3702576756477356,\n",
              "  'eval_accuracy': 0.886,\n",
              "  'eval_runtime': 8.1582,\n",
              "  'eval_samples_per_second': 122.576,\n",
              "  'eval_steps_per_second': 7.722,\n",
              "  'epoch': 0.41,\n",
              "  'step': 5000},\n",
              " {'loss': 0.1265,\n",
              "  'learning_rate': 1.5518252933507173e-05,\n",
              "  'epoch': 0.45,\n",
              "  'step': 5500},\n",
              " {'loss': 0.1224,\n",
              "  'learning_rate': 1.5110821382007822e-05,\n",
              "  'epoch': 0.49,\n",
              "  'step': 6000},\n",
              " {'loss': 0.1253,\n",
              "  'learning_rate': 1.4703389830508477e-05,\n",
              "  'epoch': 0.53,\n",
              "  'step': 6500},\n",
              " {'loss': 0.1273,\n",
              "  'learning_rate': 1.4295958279009128e-05,\n",
              "  'epoch': 0.57,\n",
              "  'step': 7000},\n",
              " {'loss': 0.113,\n",
              "  'learning_rate': 1.3888526727509778e-05,\n",
              "  'epoch': 0.61,\n",
              "  'step': 7500},\n",
              " {'loss': 0.1196,\n",
              "  'learning_rate': 1.3481095176010431e-05,\n",
              "  'epoch': 0.65,\n",
              "  'step': 8000},\n",
              " {'loss': 0.119,\n",
              "  'learning_rate': 1.3073663624511084e-05,\n",
              "  'epoch': 0.69,\n",
              "  'step': 8500},\n",
              " {'loss': 0.1153,\n",
              "  'learning_rate': 1.2666232073011735e-05,\n",
              "  'epoch': 0.73,\n",
              "  'step': 9000},\n",
              " {'loss': 0.1206,\n",
              "  'learning_rate': 1.2258800521512385e-05,\n",
              "  'epoch': 0.77,\n",
              "  'step': 9500},\n",
              " {'loss': 0.1172,\n",
              "  'learning_rate': 1.185136897001304e-05,\n",
              "  'epoch': 0.81,\n",
              "  'step': 10000},\n",
              " {'eval_loss': 0.4320734739303589,\n",
              "  'eval_accuracy': 0.894,\n",
              "  'eval_runtime': 8.1591,\n",
              "  'eval_samples_per_second': 122.562,\n",
              "  'eval_steps_per_second': 7.721,\n",
              "  'epoch': 0.81,\n",
              "  'step': 10000},\n",
              " {'loss': 0.1194,\n",
              "  'learning_rate': 1.144393741851369e-05,\n",
              "  'epoch': 0.86,\n",
              "  'step': 10500},\n",
              " {'loss': 0.1173,\n",
              "  'learning_rate': 1.1036505867014341e-05,\n",
              "  'epoch': 0.9,\n",
              "  'step': 11000},\n",
              " {'loss': 0.1282,\n",
              "  'learning_rate': 1.0629074315514994e-05,\n",
              "  'epoch': 0.94,\n",
              "  'step': 11500},\n",
              " {'loss': 0.1233,\n",
              "  'learning_rate': 1.0221642764015647e-05,\n",
              "  'epoch': 0.98,\n",
              "  'step': 12000},\n",
              " {'loss': 0.1319,\n",
              "  'learning_rate': 9.814211212516298e-06,\n",
              "  'epoch': 1.02,\n",
              "  'step': 12500},\n",
              " {'loss': 0.1393,\n",
              "  'learning_rate': 9.40677966101695e-06,\n",
              "  'epoch': 1.06,\n",
              "  'step': 13000},\n",
              " {'loss': 0.1357,\n",
              "  'learning_rate': 8.999348109517601e-06,\n",
              "  'epoch': 1.1,\n",
              "  'step': 13500},\n",
              " {'loss': 0.1329,\n",
              "  'learning_rate': 8.591916558018254e-06,\n",
              "  'epoch': 1.14,\n",
              "  'step': 14000},\n",
              " {'loss': 0.1351,\n",
              "  'learning_rate': 8.184485006518904e-06,\n",
              "  'epoch': 1.18,\n",
              "  'step': 14500},\n",
              " {'loss': 0.1315,\n",
              "  'learning_rate': 7.777053455019557e-06,\n",
              "  'epoch': 1.22,\n",
              "  'step': 15000},\n",
              " {'eval_loss': 0.37341487407684326,\n",
              "  'eval_accuracy': 0.895,\n",
              "  'eval_runtime': 8.148,\n",
              "  'eval_samples_per_second': 122.729,\n",
              "  'eval_steps_per_second': 7.732,\n",
              "  'epoch': 1.22,\n",
              "  'step': 15000},\n",
              " {'loss': 0.1403,\n",
              "  'learning_rate': 7.369621903520209e-06,\n",
              "  'epoch': 1.26,\n",
              "  'step': 15500},\n",
              " {'loss': 0.1373,\n",
              "  'learning_rate': 6.962190352020861e-06,\n",
              "  'epoch': 1.3,\n",
              "  'step': 16000},\n",
              " {'loss': 0.1366,\n",
              "  'learning_rate': 6.554758800521513e-06,\n",
              "  'epoch': 1.34,\n",
              "  'step': 16500},\n",
              " {'loss': 0.1356,\n",
              "  'learning_rate': 6.147327249022165e-06,\n",
              "  'epoch': 1.39,\n",
              "  'step': 17000},\n",
              " {'loss': 0.138,\n",
              "  'learning_rate': 5.739895697522817e-06,\n",
              "  'epoch': 1.43,\n",
              "  'step': 17500},\n",
              " {'loss': 0.133,\n",
              "  'learning_rate': 5.332464146023468e-06,\n",
              "  'epoch': 1.47,\n",
              "  'step': 18000},\n",
              " {'loss': 0.1287,\n",
              "  'learning_rate': 4.92503259452412e-06,\n",
              "  'epoch': 1.51,\n",
              "  'step': 18500},\n",
              " {'loss': 0.1252,\n",
              "  'learning_rate': 4.5176010430247726e-06,\n",
              "  'epoch': 1.55,\n",
              "  'step': 19000},\n",
              " {'loss': 0.138,\n",
              "  'learning_rate': 4.110169491525424e-06,\n",
              "  'epoch': 1.59,\n",
              "  'step': 19500},\n",
              " {'loss': 0.1281,\n",
              "  'learning_rate': 3.702737940026076e-06,\n",
              "  'epoch': 1.63,\n",
              "  'step': 20000},\n",
              " {'eval_loss': 0.3838730752468109,\n",
              "  'eval_accuracy': 0.893,\n",
              "  'eval_runtime': 8.1488,\n",
              "  'eval_samples_per_second': 122.718,\n",
              "  'eval_steps_per_second': 7.731,\n",
              "  'epoch': 1.63,\n",
              "  'step': 20000},\n",
              " {'loss': 0.13,\n",
              "  'learning_rate': 3.2953063885267278e-06,\n",
              "  'epoch': 1.67,\n",
              "  'step': 20500},\n",
              " {'loss': 0.1248,\n",
              "  'learning_rate': 2.8878748370273795e-06,\n",
              "  'epoch': 1.71,\n",
              "  'step': 21000},\n",
              " {'loss': 0.1309,\n",
              "  'learning_rate': 2.4804432855280312e-06,\n",
              "  'epoch': 1.75,\n",
              "  'step': 21500},\n",
              " {'loss': 0.1254,\n",
              "  'learning_rate': 2.0730117340286834e-06,\n",
              "  'epoch': 1.79,\n",
              "  'step': 22000},\n",
              " {'loss': 0.1183,\n",
              "  'learning_rate': 1.6655801825293353e-06,\n",
              "  'epoch': 1.83,\n",
              "  'step': 22500},\n",
              " {'loss': 0.1302,\n",
              "  'learning_rate': 1.258148631029987e-06,\n",
              "  'epoch': 1.87,\n",
              "  'step': 23000},\n",
              " {'loss': 0.1262,\n",
              "  'learning_rate': 8.507170795306389e-07,\n",
              "  'epoch': 1.91,\n",
              "  'step': 23500},\n",
              " {'loss': 0.1242,\n",
              "  'learning_rate': 4.432855280312908e-07,\n",
              "  'epoch': 1.96,\n",
              "  'step': 24000},\n",
              " {'loss': 0.1307,\n",
              "  'learning_rate': 3.585397653194264e-08,\n",
              "  'epoch': 2.0,\n",
              "  'step': 24500},\n",
              " {'train_runtime': 17388.4137,\n",
              "  'train_samples_per_second': 45.168,\n",
              "  'train_steps_per_second': 1.412,\n",
              "  'total_flos': 2.0664847532396544e+17,\n",
              "  'train_loss': 0.1150286255751626,\n",
              "  'epoch': 2.0,\n",
              "  'step': 24544}]"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ],
      "source": [
        "new_trainer.state.log_history"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VBTKBRmSbuxV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 144
        },
        "outputId": "77d486f9-ee5d-48d9-da70-a5ff0965c0e0"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='63' max='63' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [63/63 00:07]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'eval_loss': 0.37341487407684326,\n",
              " 'eval_accuracy': 0.895,\n",
              " 'eval_runtime': 8.1626,\n",
              " 'eval_samples_per_second': 122.509,\n",
              " 'eval_steps_per_second': 7.718,\n",
              " 'epoch': 2.0}"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ],
      "source": [
        "new_trainer.evaluate()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Saving the best model based on evaluation accuracy"
      ],
      "metadata": {
        "id": "sIEiDL-Gnyoq"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sd5Ze1RJesph"
      },
      "outputs": [],
      "source": [
        "#best_model_folder = base_dir + \"BERT-best\"\n",
        "#trainer.save_model(best_model_folder)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sp5BT2HReLFs"
      },
      "source": [
        "## Evaluate"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Evaluating the loaded model"
      ],
      "metadata": {
        "id": "x0xTjmckoA7I"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NekOZauhrOcF"
      },
      "outputs": [],
      "source": [
        "loaded_model = BertForSequenceClassification.from_pretrained(base_dir + \"Models-BERT-1704133292.251167/checkpoint-4000/\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2NSn1auFhQPe",
        "outputId": "e1235611-e843-475e-cf94-be6783aec31d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BertForSequenceClassification(\n",
              "  (bert): BertModel(\n",
              "    (embeddings): BertEmbeddings(\n",
              "      (word_embeddings): Embedding(30522, 768, padding_idx=0)\n",
              "      (position_embeddings): Embedding(512, 768)\n",
              "      (token_type_embeddings): Embedding(2, 768)\n",
              "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "      (dropout): Dropout(p=0.1, inplace=False)\n",
              "    )\n",
              "    (encoder): BertEncoder(\n",
              "      (layer): ModuleList(\n",
              "        (0-11): 12 x BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (pooler): BertPooler(\n",
              "      (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "      (activation): Tanh()\n",
              "    )\n",
              "  )\n",
              "  (dropout): Dropout(p=0.1, inplace=False)\n",
              "  (classifier): Linear(in_features=768, out_features=2, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ],
      "source": [
        "loaded_model.eval()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 127
        },
        "id": "D9tO_8RnrOlg",
        "outputId": "d8dd10ae-66e8-46b6-948b-b1859cf42f1d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='614' max='614' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [614/614 01:19]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'eval_loss': 0.35467514395713806,\n",
              " 'eval_accuracy': 0.8916963830871115,\n",
              " 'eval_runtime': 80.2217,\n",
              " 'eval_samples_per_second': 122.348,\n",
              " 'eval_steps_per_second': 7.654}"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ],
      "source": [
        "args_test = TrainingArguments(base_dir + \"Models-BERT-1704137471.16337/\",\n",
        "         per_device_eval_batch_size=BATCH_SIZE)\n",
        "\n",
        "eval_trainer = Trainer(\n",
        "    model=loaded_model,\n",
        "    args=args_test,\n",
        "    train_dataset=small_train_dataset,\n",
        "    eval_dataset= small_val_dataset, #small_test_dataset,\n",
        "    compute_metrics=compute_metrics)\n",
        "\n",
        "eval_trainer.evaluate()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "I evaluated the models I saved during training both on the validation matched and validation mismatched datasets"
      ],
      "metadata": {
        "id": "MRGTBdtJoKEI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "args_test = TrainingArguments(base_dir + \"Models-BERT-1704137471.16337/\",\n",
        "         per_device_eval_batch_size=BATCH_SIZE)"
      ],
      "metadata": {
        "id": "Pfy4Fsk51MQM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Z7Su_SoPrOwI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "outputId": "b368dc7f-9673-404a-ef3b-dae2dabdffc4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Checkpoint 5000:\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='307' max='307' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [307/307 05:18]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'eval_loss': 0.3546751141548157, 'eval_accuracy': 0.8916963830871115, 'eval_runtime': 320.3336, 'eval_samples_per_second': 30.64, 'eval_steps_per_second': 0.958}\n",
            "Checkpoint 10000:\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='307' max='307' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [307/307 05:21]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'eval_loss': 0.3989519476890564, 'eval_accuracy': 0.9001528273051452, 'eval_runtime': 322.3407, 'eval_samples_per_second': 30.449, 'eval_steps_per_second': 0.952}\n",
            "Checkpoint 15000:\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='307' max='307' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [307/307 05:20]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'eval_loss': 0.36958107352256775, 'eval_accuracy': 0.8984207845134997, 'eval_runtime': 322.0197, 'eval_samples_per_second': 30.48, 'eval_steps_per_second': 0.953}\n",
            "Checkpoint 20000:\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='307' max='307' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [307/307 05:20]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'eval_loss': 0.3496173918247223, 'eval_accuracy': 0.9006622516556292, 'eval_runtime': 321.7231, 'eval_samples_per_second': 30.508, 'eval_steps_per_second': 0.954}\n"
          ]
        }
      ],
      "source": [
        "path = base_dir + \"Models-BERT-1704137471.16337/\"\n",
        "for i in [5000, 10000, 15000, 20000]:\n",
        "    print(f\"Checkpoint {i}:\\n\")\n",
        "    loaded_model = BertForSequenceClassification.from_pretrained(path + f\"checkpoint-{i}/\")\n",
        "    loaded_model.eval()\n",
        "\n",
        "    eval_trainer = Trainer(model=loaded_model,\n",
        "                          args=args_test,\n",
        "                          train_dataset=small_train_dataset,\n",
        "                          eval_dataset=tokenized_val_m_data,\n",
        "                          compute_metrics=compute_metrics)\n",
        "\n",
        "    eval_res = eval_trainer.evaluate()\n",
        "    print(eval_res)\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CSX3PqU7rO1D",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "outputId": "b669c9e0-bba5-4a31-d8ed-219cae590bfd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Checkpoint 5000:\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='308' max='308' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [308/308 05:19]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'eval_loss': 0.3495672047138214, 'eval_accuracy': 0.8898494711147275, 'eval_runtime': 320.9468, 'eval_samples_per_second': 30.634, 'eval_steps_per_second': 0.96}\n",
            "Checkpoint 10000:\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
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              "      [308/308 05:20]\n",
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              "    "
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          "text": [
            "{'eval_loss': 0.3938233554363251, 'eval_accuracy': 0.8997152156224573, 'eval_runtime': 321.343, 'eval_samples_per_second': 30.597, 'eval_steps_per_second': 0.958}\n",
            "Checkpoint 15000:\n",
            "\n"
          ]
        },
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              "      [308/308 05:19]\n",
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            "Checkpoint 20000:\n",
            "\n"
          ]
        },
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            "{'eval_loss': 0.3354913294315338, 'eval_accuracy': 0.9008340113913751, 'eval_runtime': 321.9446, 'eval_samples_per_second': 30.539, 'eval_steps_per_second': 0.957}\n"
          ]
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        "for i in [5000, 10000, 15000, 20000]:\n",
        "    print(f\"Checkpoint {i}:\\n\")\n",
        "    loaded_model = BertForSequenceClassification.from_pretrained(path + f\"checkpoint-{i}/\")\n",
        "    loaded_model.eval()\n",
        "\n",
        "    eval_trainer = Trainer(model=loaded_model,\n",
        "                          args=args_test,\n",
        "                          train_dataset=small_train_dataset,\n",
        "                          eval_dataset=tokenized_val_mm_data,\n",
        "                          compute_metrics=compute_metrics)\n",
        "\n",
        "    eval_res = eval_trainer.evaluate()\n",
        "    print(eval_res)"
      ]
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        "best_path = base_dir + \"Models-BERT-1704137471.16337/\" + \"checkpoint-20000/\"\n",
        "best_model_loaded = BertForSequenceClassification.from_pretrained(best_path)\n",
        "best_model_folder = base_dir + \"BERT-best\"\n",
        "best_model_loaded.save_pretrained(best_model_folder)"
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      "metadata": {
        "id": "NyoO5X8KTRNg"
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      "execution_count": null,
      "outputs": []
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      "cell_type": "markdown",
      "source": [
        "Sharing the model with the Hugging Face community"
      ],
      "metadata": {
        "id": "JlSfbXzFoaBL"
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            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:72: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
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              "model.safetensors:   0%|          | 0.00/438M [00:00<?, ?B/s]"
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              "CommitInfo(commit_url='https://huggingface.co/an-eve/bert-base-uncased-mnli-2-labels/commit/58808d9f546d418580d41cd64ff037bb0b7f974d', commit_message='Upload BertForSequenceClassification', commit_description='', oid='58808d9f546d418580d41cd64ff037bb0b7f974d', pr_url=None, pr_revision=None, pr_num=None)"
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        "My best model's performance:\n",
        "\n",
        "```\n",
        "MNLI: 90.07%\n",
        "MNLI-mm: 90.08%\n",
        "```"
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