3. Finetune_then_incrementally_finetune.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AXK23etaMgoq"
      },
      "outputs": [],
      "source": [
        "root_url = '/content/drive/MyDrive/DL_NLP/data'\n",
        "train_path = root_url + '/train_complete.jsonl'\n",
        "extra_train_path = root_url + '/cleaned_ARC_CommonSense.jsonl'\n",
        "dev_path = root_url + '/dev_complete.jsonl'\n",
        "test_path = root_url + '/test_complete.jsonl'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5q58M4UNNhoN",
        "outputId": "53c55a59-b183-4e27-de3e-0f0ef119c6d1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xeV6QEXXSQp5",
        "outputId": "9fb9fefb-70bc-420c-d71c-2e59d19bda4b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ],
      "source": [
        "!pip install datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "avHXo57RSdMT",
        "outputId": "9cb89694-5ffd-4c3c-b93d-618741eb7c00"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (7.1.2)\n"
          ]
        }
      ],
      "source": [
        "!pip install transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "A4d5x16GSCGq"
      },
      "outputs": [],
      "source": [
        "from datasets import load_dataset, load_metric, ClassLabel\n",
        "from transformers import AutoTokenizer\n",
        "from transformers import AutoModelForMultipleChoice, TrainingArguments, Trainer\n",
        "from dataclasses import dataclass\n",
        "from transformers.tokenization_utils_base import PreTrainedTokenizerBase, PaddingStrategy\n",
        "from typing import Optional, Union\n",
        "import transformers\n",
        "import random\n",
        "import pandas as pd\n",
        "\n",
        "from pprint import pprint\n",
        "\n",
        "import torch\n",
        "import math\n",
        "import time\n",
        "import sys\n",
        "import json\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "beCrsOciSPk8",
        "outputId": "45c864fe-a306-4aed-c6ba-614e7cbae40c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForMultipleChoice: ['cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.weight']\n",
            "- This IS expected if you are initializing BertForMultipleChoice from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertForMultipleChoice from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of BertForMultipleChoice were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ],
      "source": [
        "ending_names = ['A', 'B', 'C', 'D']\n",
        "model_chkpt = \"bert-base-uncased\"\n",
        "fake_sentence = 'asdkjaslda asdkajasd ds'\n",
        "tokenizer  = AutoTokenizer.from_pretrained(model_chkpt, use_fast=True)\n",
        "model = AutoModelForMultipleChoice.from_pretrained(model_chkpt)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZmI2vQptSrhM"
      },
      "outputs": [],
      "source": [
        "def compute_metrics(eval_predictions):\n",
        "    predictions, label_ids = eval_predictions\n",
        "    preds = np.argmax(predictions, axis=1)\n",
        "    return {\"accuracy\": (preds == label_ids).astype(np.float32).mean().item()}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BzrOZSx6StK8"
      },
      "outputs": [],
      "source": [
        "@dataclass\n",
        "class DataCollatorForMultipleChoice:\n",
        "    \"\"\"\n",
        "    Data collator that will dynamically pad the inputs for multiple choice received.\n",
        "    \"\"\"\n",
        "    tokenizer: PreTrainedTokenizerBase\n",
        "    padding: Union[bool, str, PaddingStrategy] = True\n",
        "    max_length: Optional[int] = None\n",
        "    pad_to_multiple_of: Optional[int] = None\n",
        "\n",
        "    def __call__(self, features):\n",
        "        label_name = \"label\" if \"label\" in features[0].keys() else \"labels\"\n",
        "        labels = [ending_names.index(feature.pop(label_name)) for feature in features]\n",
        "        batch_size = len(features)\n",
        "        num_choices = len(features[0][\"input_ids\"])\n",
        "        flattened_features = [[{k: v[i] for k, v in feature.items()} for i in range(num_choices)] for feature in features]\n",
        "        flattened_features = sum(flattened_features, [])\n",
        "        \n",
        "        batch = self.tokenizer.pad(\n",
        "            flattened_features,\n",
        "            padding=self.padding,\n",
        "            max_length=self.max_length,\n",
        "            pad_to_multiple_of=self.pad_to_multiple_of,\n",
        "            return_tensors=\"pt\",\n",
        "        )\n",
        "        # Un-flatten\n",
        "        batch = {k: v.view(batch_size, num_choices, -1) for k, v in batch.items()}\n",
        "        # Add back labels\n",
        "        batch[\"labels\"] = torch.tensor(labels, dtype=torch.int64)\n",
        "        return batch"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NJKP7Lr7S1uH"
      },
      "outputs": [],
      "source": [
        "def choices(example):\n",
        "    for dic in example['question.choices']:\n",
        "        example[dic['label']] = dic['text']\n",
        "    example.pop('question.choices', None)\n",
        "#    example.pop('question.stem', None)\n",
        "    return example"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1dsYKwXZS3T-"
      },
      "outputs": [],
      "source": [
        "def show_random_elements(dataset, num_examples=10):\n",
        "    assert num_examples <= len(dataset), \"Can't pick more elements than there are in the dataset.\"\n",
        "    picks = []\n",
        "    for _ in range(num_examples):\n",
        "        pick = random.randint(0, len(dataset)-1)\n",
        "        while pick in picks:\n",
        "            pick = random.randint(0, len(dataset)-1)\n",
        "        picks.append(pick)\n",
        "    \n",
        "    df = pd.DataFrame(dataset[picks])\n",
        "    for column, typ in dataset.features.items():\n",
        "        if isinstance(typ, ClassLabel):\n",
        "            df[column] = df[column].transform(lambda i: typ.names[i])\n",
        "    pprint(df.to_html())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0kwgAO4DS5Y6"
      },
      "outputs": [],
      "source": [
        "def show_one(example):\n",
        "    print(f\"Context: {example['fact1']}\")\n",
        "    print(f\"  A - {example['question.stem']} {example['A']}\")\n",
        "    print(f\"  B - {example['question.stem']} {example['B']}\")\n",
        "    print(f\"  C - {example['question.stem']} {example['C']}\")\n",
        "    print(f\"  D - {example['question.stem']} {example['D']}\")\n",
        "    print(f\"\\nGround truth: option {example['label']}\")    "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sIrFPfoES6p_"
      },
      "outputs": [],
      "source": [
        "def preprocess_function(examples):\n",
        "    # Repeat each first sentence four times to go with the four possibilities of second sentences.\n",
        "    first_sentences = [[context] * 4 for context in examples[\"fact1\"]]\n",
        "    # Grab all second sentences possible for each context.\n",
        "    question_headers = examples[\"question.stem\"]\n",
        "    second_sentences = [[f\"{header} {examples[end][i]}\" for end in ending_names] for i, header in enumerate(question_headers)]\n",
        "    \n",
        "    # Flatten everything\n",
        "    first_sentences = sum(first_sentences, [])\n",
        "    second_sentences = sum(second_sentences, [])\n",
        "\n",
        "    # Tokenize\n",
        "    tokenized_examples = tokenizer(first_sentences, second_sentences, truncation=True)\n",
        "    # Un-flatten\n",
        "    return {k: [v[i:i+4] for i in range(0, len(v), 4)] for k, v in tokenized_examples.items()}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sGMuYRLeS8m6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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        {
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          "text": [
            "Using custom data configuration default-82bd53a6868a1a2f\n"
          ]
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        {
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          "name": "stdout",
          "text": [
            "Downloading and preparing dataset json/default to /root/.cache/huggingface/datasets/json/default-82bd53a6868a1a2f/0.0.0/ac0ca5f5289a6cf108e706efcf040422dbbfa8e658dee6a819f20d76bb84d26b...\n"
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          "text": [
            "Dataset json downloaded and prepared to /root/.cache/huggingface/datasets/json/default-82bd53a6868a1a2f/0.0.0/ac0ca5f5289a6cf108e706efcf040422dbbfa8e658dee6a819f20d76bb84d26b. Subsequent calls will reuse this data.\n"
          ]
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        {
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          "text": [
            "Context: the sun is the source of energy for physical cycles on Earth\n",
            "  A - The sun is responsible for puppies learning new tricks\n",
            "  B - The sun is responsible for children growing up and getting old\n",
            "  C - The sun is responsible for flowers wilting in a vase\n",
            "  D - The sun is responsible for plants sprouting, blooming and wilting\n",
            "\n",
            "Ground truth: option D\n",
            "Context: a star is made of gases\n",
            "  A - Stars are warm lights that float\n",
            "  B - Stars are made out of nitrate\n",
            "  C - Stars are great balls of gas burning billions of miles away\n",
            "  D - Stars are lights in the sky\n",
            "\n",
            "Ground truth: option C\n"
          ]
        },
        {
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        {
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Context: as a source of light becomes closer , that source will appear brighter\n",
            "  A - As a car approaches you in the night the headlights become more intense\n",
            "  B - As a car approaches you in the night the headlights recede into the dark\n",
            "  C - As a car approaches you in the night the headlights remain at a constant\n",
            "  D - As a car approaches you in the night the headlights turn off\n",
            "\n",
            "Ground truth: option A\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The following columns in the training set  don't have a corresponding argument in `BertForMultipleChoice.forward` and have been ignored: id, B, D, fact1, humanScore, question.stem, turkIdAnonymized, C, clarity, A. If id, B, D, fact1, humanScore, question.stem, turkIdAnonymized, C, clarity, A are not expected by `BertForMultipleChoice.forward`,  you can safely ignore this message.\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/optimization.py:309: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  FutureWarning,\n",
            "***** Running training *****\n",
            "  Num examples = 13106\n",
            "  Num Epochs = 3\n",
            "  Instantaneous batch size per device = 16\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 16\n",
            "  Gradient Accumulation steps = 1\n",
            "  Total optimization steps = 2460\n"
          ]
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              "    <div>\n",
              "      \n",
              "      <progress value='2460' max='2460' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [2460/2460 21:21, Epoch 3/3]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>1.214600</td>\n",
              "      <td>1.299708</td>\n",
              "      <td>0.406000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.671900</td>\n",
              "      <td>1.381968</td>\n",
              "      <td>0.414000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.416500</td>\n",
              "      <td>1.681445</td>\n",
              "      <td>0.434000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
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          "text": [
            "Saving model checkpoint to bert-base-uncased-finetuned-swag/checkpoint-500\n",
            "Configuration saved in bert-base-uncased-finetuned-swag/checkpoint-500/config.json\n",
            "Model weights saved in bert-base-uncased-finetuned-swag/checkpoint-500/pytorch_model.bin\n",
            "tokenizer config file saved in bert-base-uncased-finetuned-swag/checkpoint-500/tokenizer_config.json\n",
            "Special tokens file saved in bert-base-uncased-finetuned-swag/checkpoint-500/special_tokens_map.json\n",
            "The following columns in the evaluation set  don't have a corresponding argument in `BertForMultipleChoice.forward` and have been ignored: id, B, D, fact1, humanScore, question.stem, turkIdAnonymized, C, clarity, A. If id, B, D, fact1, humanScore, question.stem, turkIdAnonymized, C, clarity, A are not expected by `BertForMultipleChoice.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 500\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to bert-base-uncased-finetuned-swag/checkpoint-1000\n",
            "Configuration saved in bert-base-uncased-finetuned-swag/checkpoint-1000/config.json\n",
            "Model weights saved in bert-base-uncased-finetuned-swag/checkpoint-1000/pytorch_model.bin\n",
            "tokenizer config file saved in bert-base-uncased-finetuned-swag/checkpoint-1000/tokenizer_config.json\n",
            "Special tokens file saved in bert-base-uncased-finetuned-swag/checkpoint-1000/special_tokens_map.json\n",
            "Saving model checkpoint to bert-base-uncased-finetuned-swag/checkpoint-1500\n",
            "Configuration saved in bert-base-uncased-finetuned-swag/checkpoint-1500/config.json\n",
            "Model weights saved in bert-base-uncased-finetuned-swag/checkpoint-1500/pytorch_model.bin\n",
            "tokenizer config file saved in bert-base-uncased-finetuned-swag/checkpoint-1500/tokenizer_config.json\n",
            "Special tokens file saved in bert-base-uncased-finetuned-swag/checkpoint-1500/special_tokens_map.json\n",
            "The following columns in the evaluation set  don't have a corresponding argument in `BertForMultipleChoice.forward` and have been ignored: id, B, D, fact1, humanScore, question.stem, turkIdAnonymized, C, clarity, A. If id, B, D, fact1, humanScore, question.stem, turkIdAnonymized, C, clarity, A are not expected by `BertForMultipleChoice.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 500\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to bert-base-uncased-finetuned-swag/checkpoint-2000\n",
            "Configuration saved in bert-base-uncased-finetuned-swag/checkpoint-2000/config.json\n",
            "Model weights saved in bert-base-uncased-finetuned-swag/checkpoint-2000/pytorch_model.bin\n",
            "tokenizer config file saved in bert-base-uncased-finetuned-swag/checkpoint-2000/tokenizer_config.json\n",
            "Special tokens file saved in bert-base-uncased-finetuned-swag/checkpoint-2000/special_tokens_map.json\n",
            "The following columns in the evaluation set  don't have a corresponding argument in `BertForMultipleChoice.forward` and have been ignored: id, B, D, fact1, humanScore, question.stem, turkIdAnonymized, C, clarity, A. If id, B, D, fact1, humanScore, question.stem, turkIdAnonymized, C, clarity, A are not expected by `BertForMultipleChoice.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 500\n",
            "  Batch size = 8\n",
            "\n",
            "\n",
            "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
            "\n",
            "\n",
            "The following columns in the training set  don't have a corresponding argument in `BertForMultipleChoice.forward` and have been ignored: id, B, D, fact1, humanScore, question.stem, turkIdAnonymized, C, clarity, A. If id, B, D, fact1, humanScore, question.stem, turkIdAnonymized, C, clarity, A are not expected by `BertForMultipleChoice.forward`,  you can safely ignore this message.\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/optimization.py:309: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  FutureWarning,\n",
            "***** Running training *****\n",
            "  Num examples = 4957\n",
            "  Num Epochs = 3\n",
            "  Instantaneous batch size per device = 16\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 16\n",
            "  Gradient Accumulation steps = 1\n",
            "  Total optimization steps = 930\n"
          ]
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              "    <div>\n",
              "      \n",
              "      <progress value='930' max='930' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [930/930 08:52, Epoch 3/3]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.800814</td>\n",
              "      <td>0.698000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.679900</td>\n",
              "      <td>0.956226</td>\n",
              "      <td>0.720000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.679900</td>\n",
              "      <td>1.208918</td>\n",
              "      <td>0.718000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The following columns in the evaluation set  don't have a corresponding argument in `BertForMultipleChoice.forward` and have been ignored: id, B, D, fact1, humanScore, question.stem, turkIdAnonymized, C, clarity, A. If id, B, D, fact1, humanScore, question.stem, turkIdAnonymized, C, clarity, A are not expected by `BertForMultipleChoice.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 500\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to bert-base-uncased-finetuned-swag/checkpoint-500\n",
            "Configuration saved in bert-base-uncased-finetuned-swag/checkpoint-500/config.json\n",
            "Model weights saved in bert-base-uncased-finetuned-swag/checkpoint-500/pytorch_model.bin\n",
            "tokenizer config file saved in bert-base-uncased-finetuned-swag/checkpoint-500/tokenizer_config.json\n",
            "Special tokens file saved in bert-base-uncased-finetuned-swag/checkpoint-500/special_tokens_map.json\n",
            "The following columns in the evaluation set  don't have a corresponding argument in `BertForMultipleChoice.forward` and have been ignored: id, B, D, fact1, humanScore, question.stem, turkIdAnonymized, C, clarity, A. If id, B, D, fact1, humanScore, question.stem, turkIdAnonymized, C, clarity, A are not expected by `BertForMultipleChoice.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 500\n",
            "  Batch size = 8\n",
            "The following columns in the evaluation set  don't have a corresponding argument in `BertForMultipleChoice.forward` and have been ignored: id, B, D, fact1, humanScore, question.stem, turkIdAnonymized, C, clarity, A. If id, B, D, fact1, humanScore, question.stem, turkIdAnonymized, C, clarity, A are not expected by `BertForMultipleChoice.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 500\n",
            "  Batch size = 8\n",
            "\n",
            "\n",
            "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
            "\n",
            "\n",
            "The following columns in the evaluation set  don't have a corresponding argument in `BertForMultipleChoice.forward` and have been ignored: id, B, D, fact1, humanScore, question.stem, turkIdAnonymized, C, clarity, A. If id, B, D, fact1, humanScore, question.stem, turkIdAnonymized, C, clarity, A are not expected by `BertForMultipleChoice.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 500\n",
            "  Batch size = 8\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "test set:\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n"
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              "      [63/63 00:05]\n",
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              "    "
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'eval_loss': 1.2618921995162964, 'eval_accuracy': 0.699999988079071, 'eval_runtime': 5.9629, 'eval_samples_per_second': 83.851, 'eval_steps_per_second': 10.565, 'epoch': 3.0}\n"
          ]
        }
      ],
      "source": [
        "facts = 1\n",
        "\n",
        "input_files = [extra_train_path,train_path,test_path,dev_path]\n",
        "if facts == 0:\n",
        "    output_files = ['extra_train_complete_d.jsonl','train_complete_d.jsonl','test_complete_d.jsonl','dev_complete_d.jsonl']\n",
        "else:\n",
        "    output_files = ['extra_train_complete_e.jsonl','train_complete_e.jsonl','test_complete_e.jsonl','dev_complete_e.jsonl']\n",
        "\n",
        "for io in range(4):\n",
        "    file_name = input_files[io]\n",
        "    with open(file_name) as json_file:\n",
        "        json_list = list(json_file)\n",
        "    for i in range(len(json_list)):\n",
        "        json_str = json_list[i]\n",
        "        result = json.loads(json_str)       \n",
        "        # print(result['fact1'])\n",
        "        if facts == 0:\n",
        "            result['fact1'] = ''\n",
        "        json_list[i] = json.dumps(result)\n",
        "    file_name = output_files[io]\n",
        "    fout = open(file_name,'wt')\n",
        "    for i in range(len(json_list)):\n",
        "        fout.write('%s\\n' % json_list[i])\n",
        "    fout.close()\n",
        "\n",
        "batch_size = 16\n",
        "if facts == 0:\n",
        "    openbookQA = load_dataset('json', data_files={'extra_train':'extra_train_complete_d.jsonl',\n",
        "                                                  'train': 'train_complete_d.jsonl', \n",
        "                                                  'validation': 'dev_complete_d.jsonl', \n",
        "                                                  'test': 'test_complete_d.jsonl'})\n",
        "else:\n",
        "    openbookQA = load_dataset('json', data_files={'extra_train':'extra_train_complete_e.jsonl',\n",
        "                                                  'train': 'train_complete_e.jsonl', \n",
        "                                                  'validation': 'dev_complete_e.jsonl', \n",
        "                                                  'test': 'test_complete_e.jsonl'})\n",
        "# pprint(openbookQA['train'][0])\n",
        "\n",
        "flatten = openbookQA.flatten()\n",
        "\n",
        "updated = flatten.map(choices)\n",
        "updated = updated.rename_column('answerKey', 'label')\n",
        "# pprint(updated['train'][0])\n",
        "\n",
        "show_one(updated['train'][0])\n",
        "\n",
        "examples = updated['train'][:5]\n",
        "features = preprocess_function(examples)\n",
        "# print(len(features[\"input_ids\"]), len(features[\"input_ids\"][0]), [len(x) for x in features[\"input_ids\"][0]])   \n",
        "\n",
        "idx = 3\n",
        "[tokenizer.decode(features[\"input_ids\"][idx][i]) for i in range(4)]    \n",
        "show_one(updated['train'][idx])\n",
        "\n",
        "encoded_datasets = updated.map(preprocess_function, batched=True)\n",
        "\n",
        "model_name = model_chkpt.split(\"/\")[-1]\n",
        "args = TrainingArguments(f\"{model_name}-finetuned-swag\",\n",
        "                          evaluation_strategy = \"epoch\",\n",
        "                          learning_rate=5e-5,\n",
        "                          per_device_train_batch_size=batch_size,\n",
        "                          num_train_epochs=3,\n",
        "                          weight_decay=0.01)\n",
        "\n",
        "accepted_keys = [\"input_ids\", \"attention_mask\", \"label\"]\n",
        "features = [{k: v for k, v in encoded_datasets[\"train\"][i].items() if k in accepted_keys} for i in range(10)]\n",
        "batch = DataCollatorForMultipleChoice(tokenizer)(features)\n",
        "\n",
        "[tokenizer.decode(batch[\"input_ids\"][8][i].tolist()) for i in range(4)]\n",
        "show_one(updated[\"train\"][8])\n",
        "\n",
        "trainer = Trainer(model,\n",
        "                  args,\n",
        "                  train_dataset=encoded_datasets[\"extra_train\"],\n",
        "                  eval_dataset=encoded_datasets[\"validation\"],\n",
        "                  tokenizer=tokenizer,\n",
        "                  data_collator=DataCollatorForMultipleChoice(tokenizer),\n",
        "                  compute_metrics=compute_metrics)\n",
        "\n",
        "trainer.train()\n",
        "\n",
        "trainer = Trainer(model,\n",
        "                  args,\n",
        "                  train_dataset=encoded_datasets[\"train\"],\n",
        "                  eval_dataset=encoded_datasets[\"validation\"],\n",
        "                  tokenizer=tokenizer,\n",
        "                  data_collator=DataCollatorForMultipleChoice(tokenizer),\n",
        "                  compute_metrics=compute_metrics)\n",
        "\n",
        "trainer.train()\n",
        "print('\\n\\n\\n\\n')\n",
        "print('test set:')\n",
        "print('\\n\\n\\n\\n')\n",
        "final_eval = trainer.evaluate(eval_dataset=encoded_datasets['test'])\n",
        "print(final_eval)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iUF4AD_wY_-P"
      },
      "outputs": [],
      "source": [
        "facts = 0\n",
        "\n",
        "input_files = [extra_train_path,train_path,test_path,dev_path]\n",
        "if facts == 0:\n",
        "    output_files = ['extra_train_complete_d.jsonl','train_complete_d.jsonl','test_complete_d.jsonl','dev_complete_d.jsonl']\n",
        "else:\n",
        "    output_files = ['extra_train_complete_e.jsonl','train_complete_e.jsonl','test_complete_e.jsonl','dev_complete_e.jsonl']\n",
        "\n",
        "for io in range(4):\n",
        "    file_name = input_files[io]\n",
        "    with open(file_name) as json_file:\n",
        "        json_list = list(json_file)\n",
        "    for i in range(len(json_list)):\n",
        "        json_str = json_list[i]\n",
        "        result = json.loads(json_str)       \n",
        "        # print(result['fact1'])\n",
        "        if facts == 0:\n",
        "            result['fact1'] = ''\n",
        "        json_list[i] = json.dumps(result)\n",
        "    file_name = output_files[io]\n",
        "    fout = open(file_name,'wt')\n",
        "    for i in range(len(json_list)):\n",
        "        fout.write('%s\\n' % json_list[i])\n",
        "    fout.close()\n",
        "\n",
        "batch_size = 16\n",
        "if facts == 0:\n",
        "    openbookQA = load_dataset('json', data_files={'extra_train':'extra_train_complete_d.jsonl',\n",
        "                                                  'train': 'train_complete_d.jsonl', \n",
        "                                                  'validation': 'dev_complete_d.jsonl', \n",
        "                                                  'test': 'test_complete_d.jsonl'})\n",
        "else:\n",
        "    openbookQA = load_dataset('json', data_files={'extra_train':'extra_train_complete_e.jsonl',\n",
        "                                                  'train': 'train_complete_e.jsonl', \n",
        "                                                  'validation': 'dev_complete_e.jsonl', \n",
        "                                                  'test': 'test_complete_e.jsonl'})\n",
        "# pprint(openbookQA['train'][0])\n",
        "\n",
        "flatten = openbookQA.flatten()\n",
        "\n",
        "updated = flatten.map(choices)\n",
        "updated = updated.rename_column('answerKey', 'label')\n",
        "# pprint(updated['train'][0])\n",
        "\n",
        "show_one(updated['train'][0])\n",
        "\n",
        "examples = updated['train'][:5]\n",
        "features = preprocess_function(examples)\n",
        "# print(len(features[\"input_ids\"]), len(features[\"input_ids\"][0]), [len(x) for x in features[\"input_ids\"][0]])   \n",
        "\n",
        "idx = 3\n",
        "[tokenizer.decode(features[\"input_ids\"][idx][i]) for i in range(4)]    \n",
        "show_one(updated['train'][idx])\n",
        "\n",
        "encoded_datasets = updated.map(preprocess_function, batched=True)\n",
        "\n",
        "model_name = model_chkpt.split(\"/\")[-1]\n",
        "args = TrainingArguments(f\"{model_name}-finetuned-swag\",\n",
        "                          evaluation_strategy = \"epoch\",\n",
        "                          learning_rate=5e-5,\n",
        "                          per_device_train_batch_size=batch_size,\n",
        "                          num_train_epochs=3,\n",
        "                          weight_decay=0.01)\n",
        "\n",
        "accepted_keys = [\"input_ids\", \"attention_mask\", \"label\"]\n",
        "features = [{k: v for k, v in encoded_datasets[\"train\"][i].items() if k in accepted_keys} for i in range(10)]\n",
        "batch = DataCollatorForMultipleChoice(tokenizer)(features)\n",
        "\n",
        "[tokenizer.decode(batch[\"input_ids\"][8][i].tolist()) for i in range(4)]\n",
        "show_one(updated[\"train\"][8])\n",
        "\n",
        "trainer = Trainer(model,\n",
        "                  args,\n",
        "                  train_dataset=encoded_datasets[\"extra_train\"],\n",
        "                  eval_dataset=encoded_datasets[\"validation\"],\n",
        "                  tokenizer=tokenizer,\n",
        "                  data_collator=DataCollatorForMultipleChoice(tokenizer),\n",
        "                  compute_metrics=compute_metrics)\n",
        "\n",
        "trainer.train()\n",
        "\n",
        "trainer = Trainer(model,\n",
        "                  args,\n",
        "                  train_dataset=encoded_datasets[\"train\"],\n",
        "                  eval_dataset=encoded_datasets[\"validation\"],\n",
        "                  tokenizer=tokenizer,\n",
        "                  data_collator=DataCollatorForMultipleChoice(tokenizer),\n",
        "                  compute_metrics=compute_metrics)\n",
        "\n",
        "trainer.train()\n",
        "print('\\n\\n\\n\\n')\n",
        "print('test set:')\n",
        "print('\\n\\n\\n\\n')\n",
        "final_eval = trainer.evaluate(eval_dataset=encoded_datasets['test'])\n",
        "print(final_eval)"
      ]
    },
    {
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        ""
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