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  "cells": [
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      "source": [
        "from pathlib import Path\n",
        "from sklearn.model_selection import train_test_split\n",
        "import torch\n",
        "from torch.utils.data import Dataset\n",
        "from transformers import DistilBertTokenizerFast, DistilBertForSequenceClassification\n",
        "from transformers import Trainer, TrainingArguments\n",
        "from torch.utils.data import DataLoader\n",
        "from transformers import AdamW\n",
        "import pandas as pd\n",
        "from huggingface_hub import notebook_login"
      ],
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        "id": "Bu9ttXsprt9w"
      },
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      "outputs": []
    },
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        {
          "output_type": "display_data",
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              "Downloading (…)okenizer_config.json:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
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      "source": [
        "df_train = pd.read_csv('train.csv')\n",
        "df_test = pd.read_csv('test.csv')\n",
        "df_test_labels = pd.read_csv('test_labels.csv')\n",
        "\n",
        "model_name = \"distilbert-base-uncased\"\n",
        "\n",
        "def read_file(f):\n",
        "  texts = f['comment_text'].tolist()\n",
        "  labels = []\n",
        "  for i in range(len(f)):\n",
        "    temp = []\n",
        "    temp.append(f['toxic'][i])\n",
        "    temp.append(f['severe_toxic'][i])\n",
        "    temp.append(f['obscene'][i])\n",
        "    temp.append(f['threat'][i])\n",
        "    temp.append(f['insult'][i])\n",
        "    temp.append(f['identity_hate'][i])\n",
        "    labels.append(temp)\n",
        "  return texts, labels\n",
        "\n",
        "train_texts, train_labels = read_file(df_train)\n",
        "test_texts = df_test['comment_text'].tolist()\n",
        "test_labels = []\n",
        "for i in range(len(df_test_labels)):\n",
        "  temp = []\n",
        "  temp.append(df_test_labels['toxic'][i])\n",
        "  temp.append(df_test_labels['severe_toxic'][i])\n",
        "  temp.append(df_test_labels['obscene'][i])\n",
        "  temp.append(df_test_labels['threat'][i])\n",
        "  temp.append(df_test_labels['insult'][i])\n",
        "  temp.append(df_test_labels['identity_hate'][i])\n",
        "  test_labels.append(temp)\n",
        "\n",
        "train_texts, val_texts, train_labels, val_labels = train_test_split(train_texts, train_labels, test_size=.2)\n",
        "\n",
        "tokenizer = DistilBertTokenizerFast.from_pretrained(model_name)\n",
        "\n",
        "ind = 0\n",
        "train_encodings = {'input_ids': [], 'attention_mask': []}\n",
        "\n",
        "for i in range(len(train_texts)//16):\n",
        "  temp = tokenizer(train_texts[ind:ind+16], truncation=True, padding=True)\n",
        "  train_encodings['input_ids'] += temp['input_ids']\n",
        "  train_encodings['attention_mask'] += temp['attention_mask']\n",
        "  ind += 16\n",
        "\n",
        "ind = 0\n",
        "val_encodings = {'input_ids': [], 'attention_mask': []}\n",
        "\n",
        "for i in range(len(val_texts)//16):\n",
        "  temp = tokenizer(val_texts[ind:ind+16], truncation=True, padding=True)\n",
        "  val_encodings['input_ids'] += temp['input_ids']\n",
        "  val_encodings['attention_mask'] += temp['attention_mask']\n",
        "  ind += 16\n",
        "\n",
        "ind = 0\n",
        "test_encodings = {'input_ids': [], 'attention_mask': []}\n",
        "\n",
        "for i in range(len(test_texts)//16):\n",
        "  temp = tokenizer(test_texts[ind:ind+16], truncation=True, padding=True)\n",
        "  test_encodings['input_ids'] += temp['input_ids']\n",
        "  test_encodings['attention_mask'] += temp['attention_mask']\n",
        "  ind += 16\n",
        "\n",
        "while True:\n",
        "  if len(train_labels) > len(train_encodings):\n",
        "    train_labels.pop()\n",
        "  else:\n",
        "    break\n",
        "  \n",
        "while True:\n",
        "  if len(val_labels) > len(val_encodings):\n",
        "    val_labels.pop()\n",
        "  else:\n",
        "    break\n",
        "\n",
        "while True:\n",
        "  if len(test_labels) > len(test_encodings):\n",
        "    test_labels.pop()\n",
        "  else:\n",
        "    break\n",
        "\n",
        "class dataset(Dataset):\n",
        "  def __init__(self, encodings, labels):\n",
        "    self.encodings = encodings\n",
        "    self.labels = labels\n",
        "  \n",
        "  def __getitem__(self, idx):\n",
        "    item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}\n",
        "    item['labels'] = torch.tensor(self.labels[idx])\n",
        "    return item\n",
        "  \n",
        "  def __len__(self):\n",
        "    return(len(self.labels))\n",
        "\n",
        "train_dataset_list = [[], [], [], [], [], []]\n",
        "for i in train_labels:\n",
        "  for j in range(6):\n",
        "    train_dataset_list[j].append(i[j])\n",
        "  \n",
        "val_dataset_list = [[], [], [], [], [], []]\n",
        "for i in val_labels:\n",
        "  for j in range(6):\n",
        "    val_dataset_list[j].append(i[j])\n",
        "\n",
        "train_dataset_0 = dataset(train_encodings, train_dataset_list[0])\n",
        "train_dataset_1 = dataset(train_encodings, train_dataset_list[1])\n",
        "train_dataset_2 = dataset(train_encodings, train_dataset_list[2])\n",
        "train_dataset_3 = dataset(train_encodings, train_dataset_list[3])\n",
        "train_dataset_4 = dataset(train_encodings, train_dataset_list[4])\n",
        "train_dataset_5 = dataset(train_encodings, train_dataset_list[5])\n",
        "\n",
        "val_dataset_0 = dataset(val_encodings, val_dataset_list[0])\n",
        "val_dataset_1 = dataset(val_encodings, val_dataset_list[1])\n",
        "val_dataset_2 = dataset(val_encodings, val_dataset_list[2])\n",
        "val_dataset_3 = dataset(val_encodings, val_dataset_list[3])\n",
        "val_dataset_4 = dataset(val_encodings, val_dataset_list[4])\n",
        "val_dataset_5 = dataset(val_encodings, val_dataset_list[5])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# save tokenizer\n",
        "tokenizer.save_pretrained(\"tokenizer.json\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dc8jvgisXLhE",
        "outputId": "876334df-a494-41fd-bd50-70cdab7936ad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('tokenizer.json/tokenizer_config.json',\n",
              " 'tokenizer.json/special_tokens_map.json',\n",
              " 'tokenizer.json/vocab.txt',\n",
              " 'tokenizer.json/added_tokens.json',\n",
              " 'tokenizer.json/tokenizer.json')"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# training_args = TrainingArguments(output_dir='Rathgeberj/milestone3_fine_tuned', \n",
        "#                                   push_to_hub=True, \n",
        "#                                   num_train_epochs=2, \n",
        "#                                   per_device_train_batch_size=16, \n",
        "#                                   per_device_eval_batch_size=64, \n",
        "#                                   warmup_steps=500, learning_rate=5e-5, \n",
        "#                                   weight_decay=.01, logging_dir='./logs', \n",
        "#                                   logging_steps=10)\n",
        "# /Users/jeffreyrathgeber\n",
        "\n",
        "training_args = TrainingArguments(output_dir='results', \n",
        "                                  num_train_epochs=2, \n",
        "                                  per_device_train_batch_size=16, \n",
        "                                  per_device_eval_batch_size=64, \n",
        "                                  warmup_steps=500, learning_rate=5e-5, \n",
        "                                  weight_decay=.01, logging_dir='./logs', \n",
        "                                  logging_steps=10)\n",
        "\n",
        "model_0 = DistilBertForSequenceClassification.from_pretrained(model_name)\n",
        "model_1 = DistilBertForSequenceClassification.from_pretrained(model_name)\n",
        "model_2 = DistilBertForSequenceClassification.from_pretrained(model_name)\n",
        "model_3 = DistilBertForSequenceClassification.from_pretrained(model_name)\n",
        "model_4 = DistilBertForSequenceClassification.from_pretrained(model_name)\n",
        "model_5 = DistilBertForSequenceClassification.from_pretrained(model_name)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 563,
          "referenced_widgets": [
            "a828e2eb329548568ea23df75056ce46",
            "94f47a73ba5b402b84717807b4f127d9",
            "66f521247f4c4a608e4a79933ca8b499",
            "9012d483dc42427189bea7c35c6a9f21",
            "342182d835634efd9e8dcc16c753602b",
            "a8c8ad17737448748e212de8bba42b5f",
            "e414907cc96e4e3d879a6759124fd8fe",
            "a464bcc2342841588e7384b4c2f10a42",
            "6cc18dcc7a7d4361a5aac3271d47347a",
            "cb8c4e778a994ba7bb57f279614ebfe8",
            "739f16b75f3248489495a606ab3650ab"
          ]
        },
        "id": "1FrCJtT23YVX",
        "outputId": "5311555b-7789-4bd5-8931-4abc9b70360e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/268M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a828e2eb329548568ea23df75056ce46"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_transform.weight', 'vocab_layer_norm.weight', 'vocab_transform.bias', 'vocab_layer_norm.bias', 'vocab_projector.bias', 'vocab_projector.weight']\n",
            "- This IS expected if you are initializing DistilBertForSequenceClassification 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 DistilBertForSequenceClassification 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 DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.weight', 'pre_classifier.bias', 'classifier.bias', 'pre_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",
            "Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_transform.weight', 'vocab_layer_norm.weight', 'vocab_transform.bias', 'vocab_layer_norm.bias', 'vocab_projector.bias', 'vocab_projector.weight']\n",
            "- This IS expected if you are initializing DistilBertForSequenceClassification 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 DistilBertForSequenceClassification 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 DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.weight', 'pre_classifier.bias', 'classifier.bias', 'pre_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",
            "Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_transform.weight', 'vocab_layer_norm.weight', 'vocab_transform.bias', 'vocab_layer_norm.bias', 'vocab_projector.bias', 'vocab_projector.weight']\n",
            "- This IS expected if you are initializing DistilBertForSequenceClassification 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 DistilBertForSequenceClassification 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 DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.weight', 'pre_classifier.bias', 'classifier.bias', 'pre_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",
            "Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_transform.weight', 'vocab_layer_norm.weight', 'vocab_transform.bias', 'vocab_layer_norm.bias', 'vocab_projector.bias', 'vocab_projector.weight']\n",
            "- This IS expected if you are initializing DistilBertForSequenceClassification 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 DistilBertForSequenceClassification 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 DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.weight', 'pre_classifier.bias', 'classifier.bias', 'pre_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",
            "Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_transform.weight', 'vocab_layer_norm.weight', 'vocab_transform.bias', 'vocab_layer_norm.bias', 'vocab_projector.bias', 'vocab_projector.weight']\n",
            "- This IS expected if you are initializing DistilBertForSequenceClassification 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 DistilBertForSequenceClassification 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 DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.weight', 'pre_classifier.bias', 'classifier.bias', 'pre_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",
            "Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_transform.weight', 'vocab_layer_norm.weight', 'vocab_transform.bias', 'vocab_layer_norm.bias', 'vocab_projector.bias', 'vocab_projector.weight']\n",
            "- This IS expected if you are initializing DistilBertForSequenceClassification 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 DistilBertForSequenceClassification 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 DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.weight', 'pre_classifier.bias', 'classifier.bias', 'pre_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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer_0 = Trainer(model=model_0, args=training_args, train_dataset=train_dataset_0, eval_dataset=val_dataset_0)\n",
        "trainer_0.train()\n",
        "trainer_0.save_model(output_dir='NEW')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 127
        },
        "id": "_iaP4g715FZl",
        "outputId": "feab2bf7-e30a-4282-8f34-4d45bf2baf55"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/transformers/optimization.py:391: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='2' max='2' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [2/2 00:04, 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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer_1 = Trainer(model=model_1, args=training_args, train_dataset=train_dataset_1, eval_dataset=val_dataset_1)\n",
        "trainer_1.train()\n",
        "trainer_1.save_model(output_dir='UPDATED_1')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "q6hRKNNgGrVB",
        "outputId": "8dddc83c-4292-424c-8534-61d8125ecac5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='2' max='2' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [2/2 00:09, 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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer_2 = Trainer(model=model_2, args=training_args, train_dataset=train_dataset_2, eval_dataset=val_dataset_2)\n",
        "trainer_2.train()\n",
        "trainer_2.save_model(output_dir='UPDATED_2')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "UwuqSkNdGrgk",
        "outputId": "ecc6c398-36d1-40c3-a7ca-0ad99cf0369e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='2' max='2' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [2/2 00:03, 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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer_3 = Trainer(model=model_3, args=training_args, train_dataset=train_dataset_3, eval_dataset=val_dataset_3)\n",
        "trainer_3.train()\n",
        "trainer_3.save_model(output_dir='UPDATED_3')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "TbysKTvgGrqJ",
        "outputId": "e8f04274-92f8-439e-b567-75d589eaaa1b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='2' max='2' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [2/2 00:03, 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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer_4 = Trainer(model=model_4, args=training_args, train_dataset=train_dataset_4, eval_dataset=val_dataset_4)\n",
        "trainer_4.train()\n",
        "trainer_4.save_model(output_dir='UPDATED_4')"
      ],
      "metadata": {
        "id": "VTJ-4cENGr05",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "54cc3a64-c511-4484-c09f-f8ffe2ff65d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='2' max='2' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [2/2 00:03, 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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer_5 = Trainer(model=model_5, args=training_args, train_dataset=train_dataset_5, eval_dataset=val_dataset_5)\n",
        "trainer_5.train()\n",
        "trainer_5.save_model(output_dir='UPDATED_5')"
      ],
      "metadata": {
        "id": "mgIWDFQ2HtXL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "7cddb662-1c2f-4923-c747-866ec6f3297d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='2' max='2' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [2/2 00:03, 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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}