finetune_FLAN_T5.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Finetuning FLAN-T5\n",
        "\n",
        "---\n",
        "\n",
        "I referenced and adapted Hugging Face's [Translation Task Guide](https://huggingface.co/docs/transformers/tasks/translation)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Install and import packages"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pyq3OmfAasT2",
        "outputId": "9cc06861-078b-44ce-891d-afe0fbc79a80"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.7/7.7 MB\u001b[0m \u001b[31m24.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m118.9/118.9 kB\u001b[0m \u001b[31m3.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "tokenizers 0.14.1 requires huggingface_hub<0.18,>=0.16.4, but you have huggingface-hub 0.18.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mName: accelerate\n",
            "Version: 0.23.0\n",
            "Summary: Accelerate\n",
            "Home-page: https://github.com/huggingface/accelerate\n",
            "Author: The HuggingFace team\n",
            "Author-email: sylvain@huggingface.co\n",
            "License: Apache\n",
            "Location: /usr/local/lib/python3.10/dist-packages\n",
            "Requires: huggingface-hub, numpy, packaging, psutil, pyyaml, torch\n",
            "Required-by: \n"
          ]
        }
      ],
      "source": [
        "!pip install -qU transformers[torch]\n",
        "!pip install -qU evaluate\n",
        "!pip install -qU sacrebleu\n",
        "!pip install -q --upgrade accelerate\n",
        "!pip install -qU huggingface_hub\n",
        "!pip install -qU datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sS24pW09aiQp"
      },
      "outputs": [],
      "source": [
        "import huggingface_hub\n",
        "import pandas as pd\n",
        "from transformers import AutoTokenizer\n",
        "from transformers import DataCollatorForSeq2Seq\n",
        "import evaluate\n",
        "import numpy as np\n",
        "from transformers import AutoModelForSeq2SeqLM, Seq2SeqTrainingArguments, Seq2SeqTrainer\n",
        "import accelerate\n",
        "import torch\n",
        "from datasets import Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MR5sfcgVG3o0",
        "outputId": "a8765a1b-4106-47b6-bc20-11943daa81cf"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Token is valid (permission: write).\n",
            "Your token has been saved in your configured git credential helpers (store).\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful\n"
          ]
        }
      ],
      "source": [
        "huggingface_hub.login(token=\"ENTER-WRITE-TOKEN\",\n",
        "                      add_to_git_credential=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Tokenize the finetuning dataset\n",
        "\n",
        "---\n",
        "\n",
        "First, we need to prepare the data to be tokenized. This includes adding a translation instruction prefix and splitting our dataset to a train and split dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hd7wTLGhb6yJ"
      },
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      "source": [
        "# Import English-Hawaiian Pidgin parallel corpus and reshuffle rows\n",
        "eng_hwp_df = pd.read_csv(\"./kjv_hwp.csv\")\n",
        "eng_hwp_df = eng_hwp_df.dropna().sample(frac=1).reset_index(drop=True)\n",
        "\n",
        "# Add translation prefix before English input\n",
        "def add_prefix(text):\n",
        "  full_text = \"translate English to Hawaiian Pidgin: \" + text\n",
        "  return full_text\n",
        "\n",
        "eng_hwp_df[\"eng\"] = eng_hwp_df[\"eng\"].apply(add_prefix)\n",
        "eng_hwp_df.head()\n",
        "\n",
        "# Convert from dataframe to Hugging Face dataset\n",
        "eng_hwp_ds = Dataset.from_pandas(eng_hwp_df)\n",
        "# 15% of the dataset will be used to test\n",
        "eng_hwp_ds = eng_hwp_ds.train_test_split(test_size=0.15)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Now we import the tokenizer and see how the texts are tokenized:"
      ]
    },
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            "text/plain": [
              "(…)-base/resolve/main/tokenizer_config.json:   0%|          | 0.00/2.54k [00:00<?, ?B/s]"
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              "spiece.model:   0%|          | 0.00/792k [00:00<?, ?B/s]"
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            "text/plain": [
              "(…)flan-t5-base/resolve/main/tokenizer.json:   0%|          | 0.00/2.42M [00:00<?, ?B/s]"
            ]
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              "(…)ase/resolve/main/special_tokens_map.json:   0%|          | 0.00/2.20k [00:00<?, ?B/s]"
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      ],
      "source": [
        "checkpoint = \"google/flan-t5-base\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(checkpoint)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JH_vQLWKRnLX",
        "outputId": "2181266c-b2dc-40d6-df88-5c57e048a5e4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['▁translate', '▁English', '▁to', '▁Hawaiian', '▁Pi', 'd', 'g', 'in', ':', '▁And', '▁now', '▁I', '▁stand', '▁and', '▁am', '▁judge', 'd', '▁for', '▁the', '▁hope', '▁of', '▁the', '▁promise', '▁made', '▁of', '▁God', ',', '▁un', 'to', '▁our', '▁father', 's', ':', '</s>']\n",
            "['▁An', '▁now', '▁I', '▁stay', '▁standing', '▁', 'ova', '▁', 'he', 'a', '▁in', '▁front', '▁you', '▁cu', 'z', '▁I', '▁', 'tru', 's', '▁an', '▁wait', '▁fo', '▁da', '▁stuff', '▁God', '▁we', 'n', '▁promise', '▁our', '▁', 'ance', 'sta', '▁guys', ',', '▁', 'a', 'z', '▁why', '.', '</s>']\n"
          ]
        }
      ],
      "source": [
        "sample = eng_hwp_ds[\"train\"][1]\n",
        "\n",
        "with tokenizer.as_target_tokenizer():\n",
        "    eng = tokenizer(sample[\"eng\"])\n",
        "    hwp = tokenizer(sample[\"hwp\"])\n",
        "\n",
        "print(tokenizer.convert_ids_to_tokens(eng[\"input_ids\"]))\n",
        "print(tokenizer.convert_ids_to_tokens(hwp[\"input_ids\"]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The tokenizer should tokenize based on the text itself, with the `as_target_tokenizer` function."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Now we move on to actually tokenizing the entire dataset:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "def preprocess_function(examples):\n",
        "    with tokenizer.as_target_tokenizer():\n",
        "        model_inputs = tokenizer(examples[\"eng\"], max_length=128, truncation=True)\n",
        "        labels = tokenizer(examples[\"hwp\"], max_length=128, truncation=True)\n",
        "    \n",
        "    model_inputs[\"labels\"] = labels[\"input_ids\"]\n",
        "    return model_inputs\n",
        "\n",
        "tokenized_dataset = eng_hwp_ds.map(preprocess_function, batched=True, remove_columns=[\"eng\", \"hwp\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "To pad other sentences to the longest sentence in the batch, set up data collator:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IYZeofoYhGLi"
      },
      "outputs": [],
      "source": [
        "data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=checkpoint)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "To calculate how well our model at the end of each epoch, we can call an evaluator. "
      ]
    },
    {
      "cell_type": "code",
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        {
          "data": {
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              "model_id": "8286f874274f45dd929dee0c7135dd91",
              "version_major": 2,
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            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/8.15k [00:00<?, ?B/s]"
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      ],
      "source": [
        "metric = evaluate.load(\"sacrebleu\")\n",
        "\n",
        "# Remove extra whitespace for calculation\n",
        "def postprocess_text(preds, labels):\n",
        "    preds = [pred.strip() for pred in preds]\n",
        "    labels = [[label.strip()] for label in labels]\n",
        "\n",
        "    return preds, labels\n",
        "\n",
        "# Calculate bleu score and length\n",
        "def compute_metrics(eval_preds):\n",
        "    preds, labels = eval_preds\n",
        "    if isinstance(preds, tuple):\n",
        "        preds = preds[0]\n",
        "    decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)\n",
        "\n",
        "    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n",
        "    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n",
        "\n",
        "    decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels)\n",
        "\n",
        "    result = metric.compute(predictions=decoded_preds, references=decoded_labels)\n",
        "    result = {\"bleu\": result[\"score\"]}\n",
        "\n",
        "    prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds]\n",
        "    result[\"gen_len\"] = np.mean(prediction_lens)\n",
        "    result = {k: round(v, 4) for k, v in result.items()}\n",
        "    return result"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "I used sacrebleu for this version of the model, but I also experimented with rouge. This is the code for rouge:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Must install rouge_score instead of sacrebleu\n",
        "!pip install -qU rouge_score\n",
        "\n",
        "metric = evaluate.load(\"rouge\")\n",
        "\n",
        "def postprocess_text(preds, labels):\n",
        "    preds = [pred.strip() for pred in preds]\n",
        "    labels = [[label.strip()] for label in labels]\n",
        "\n",
        "    return preds, labels\n",
        "\n",
        "def compute_metrics(eval_preds):\n",
        "    preds, labels = eval_preds\n",
        "    if isinstance(preds, tuple):\n",
        "        preds = preds[0]\n",
        "    decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)\n",
        "\n",
        "    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n",
        "    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n",
        "\n",
        "    decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels)\n",
        "\n",
        "    result = metric.compute(predictions=decoded_preds,\n",
        "                            references=decoded_labels,\n",
        "                            use_aggregator=True)\n",
        "    result = {\"rouge2\": result[\"rouge2\"]}\n",
        "\n",
        "    prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds]\n",
        "    result[\"gen_len\"] = np.mean(prediction_lens)\n",
        "    result = {k: round(v, 4) for k, v in result.items()}\n",
        "    return result"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Now we can train the model!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
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        },
        "id": "sKEpwQ9EhefB",
        "outputId": "2b867dd0-452b-4641-bedf-2aad4238830e"
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        {
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              "(…)le/flan-t5-base/resolve/main/config.json:   0%|          | 0.00/1.40k [00:00<?, ?B/s]"
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          },
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              "model.safetensors:   0%|          | 0.00/990M [00:00<?, ?B/s]"
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "You're using a T5TokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='6300' max='6300' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [6300/6300 1:37:10, Epoch 15/15]\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>Bleu</th>\n",
              "      <th>Gen Len</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>No log</td>\n",
              "      <td>1.607687</td>\n",
              "      <td>3.732000</td>\n",
              "      <td>18.850600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>2.131400</td>\n",
              "      <td>1.457236</td>\n",
              "      <td>4.289300</td>\n",
              "      <td>18.855700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>1.507900</td>\n",
              "      <td>1.397816</td>\n",
              "      <td>4.650400</td>\n",
              "      <td>18.859900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>1.294500</td>\n",
              "      <td>1.378834</td>\n",
              "      <td>4.859500</td>\n",
              "      <td>18.864100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>1.138700</td>\n",
              "      <td>1.384133</td>\n",
              "      <td>4.907000</td>\n",
              "      <td>18.881900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>1.014200</td>\n",
              "      <td>1.377558</td>\n",
              "      <td>5.093300</td>\n",
              "      <td>18.874300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>1.014200</td>\n",
              "      <td>1.391183</td>\n",
              "      <td>5.124600</td>\n",
              "      <td>18.872600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.902400</td>\n",
              "      <td>1.415824</td>\n",
              "      <td>5.146800</td>\n",
              "      <td>18.869200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.822700</td>\n",
              "      <td>1.440291</td>\n",
              "      <td>5.184600</td>\n",
              "      <td>18.865000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>0.749000</td>\n",
              "      <td>1.468469</td>\n",
              "      <td>5.089200</td>\n",
              "      <td>18.884400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11</td>\n",
              "      <td>0.701200</td>\n",
              "      <td>1.499728</td>\n",
              "      <td>5.148500</td>\n",
              "      <td>18.885200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12</td>\n",
              "      <td>0.644600</td>\n",
              "      <td>1.516153</td>\n",
              "      <td>5.278200</td>\n",
              "      <td>18.877600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13</td>\n",
              "      <td>0.644600</td>\n",
              "      <td>1.546466</td>\n",
              "      <td>5.096100</td>\n",
              "      <td>18.874300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14</td>\n",
              "      <td>0.606300</td>\n",
              "      <td>1.558834</td>\n",
              "      <td>5.072300</td>\n",
              "      <td>18.876800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15</td>\n",
              "      <td>0.580100</td>\n",
              "      <td>1.582087</td>\n",
              "      <td>5.089100</td>\n",
              "      <td>18.863300</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
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            ]
          },
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        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1260: UserWarning: Using the model-agnostic default `max_length` (=20) to control the generation length. We recommend setting `max_new_tokens` to control the maximum length of the generation.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1260: UserWarning: Using the model-agnostic default `max_length` (=20) to control the generation length. We recommend setting `max_new_tokens` to control the maximum length of the generation.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1260: UserWarning: Using the model-agnostic default `max_length` (=20) to control the generation length. We recommend setting `max_new_tokens` to control the maximum length of the generation.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1260: UserWarning: Using the model-agnostic default `max_length` (=20) to control the generation length. We recommend setting `max_new_tokens` to control the maximum length of the generation.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1260: UserWarning: Using the model-agnostic default `max_length` (=20) to control the generation length. We recommend setting `max_new_tokens` to control the maximum length of the generation.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1260: UserWarning: Using the model-agnostic default `max_length` (=20) to control the generation length. We recommend setting `max_new_tokens` to control the maximum length of the generation.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1260: UserWarning: Using the model-agnostic default `max_length` (=20) to control the generation length. We recommend setting `max_new_tokens` to control the maximum length of the generation.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1260: UserWarning: Using the model-agnostic default `max_length` (=20) to control the generation length. We recommend setting `max_new_tokens` to control the maximum length of the generation.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1260: UserWarning: Using the model-agnostic default `max_length` (=20) to control the generation length. We recommend setting `max_new_tokens` to control the maximum length of the generation.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1260: UserWarning: Using the model-agnostic default `max_length` (=20) to control the generation length. We recommend setting `max_new_tokens` to control the maximum length of the generation.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1260: UserWarning: Using the model-agnostic default `max_length` (=20) to control the generation length. We recommend setting `max_new_tokens` to control the maximum length of the generation.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1260: UserWarning: Using the model-agnostic default `max_length` (=20) to control the generation length. We recommend setting `max_new_tokens` to control the maximum length of the generation.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1260: UserWarning: Using the model-agnostic default `max_length` (=20) to control the generation length. We recommend setting `max_new_tokens` to control the maximum length of the generation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "TrainOutput(global_step=6300, training_loss=0.9866964358375186, metrics={'train_runtime': 5834.5908, 'train_samples_per_second': 17.263, 'train_steps_per_second': 1.08, 'total_flos': 9018647532251136.0, 'train_loss': 0.9866964358375186, 'epoch': 15.0})"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint)\n",
        "\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "model.to(device)\n",
        "\n",
        "training_args = Seq2SeqTrainingArguments(\n",
        "    output_dir=\"flan-t5-base-eng-hwp-kjv\",\n",
        "    evaluation_strategy=\"epoch\",\n",
        "    learning_rate=3e-4,     # Higher learning rate is recommended with AdamW Optimizer\n",
        "    per_device_train_batch_size=16,\n",
        "    per_device_eval_batch_size=16,\n",
        "    weight_decay=0.01,\n",
        "    save_total_limit=3,\n",
        "    num_train_epochs=15,\n",
        "    predict_with_generate=True,\n",
        "    push_to_hub=True,\n",
        "    hub_private_repo=True\n",
        ")\n",
        "\n",
        "trainer = Seq2SeqTrainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=tokenized_dataset[\"train\"],\n",
        "    eval_dataset=tokenized_dataset[\"test\"],\n",
        "    tokenizer=tokenizer,\n",
        "    data_collator=data_collator,\n",
        "    compute_metrics=compute_metrics,\n",
        ")\n",
        "\n",
        "trainer.train()"
      ]
    },
    {
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        "id": "ZDvRIsYs72e3",
        "outputId": "b682d1f0-a726-4280-a1cf-d6f7a8bbfc36"
      },
      "outputs": [
        {
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              "version_minor": 0
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            "text/plain": [
              "pytorch_model.bin:   0%|          | 0.00/990M [00:00<?, ?B/s]"
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          },
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        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'https://huggingface.co/claudiatang/flan-t5-base-eng-hwp-kjv/tree/main/'"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
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      ],
      "source": [
        "# Push to Hugging Face Hub\n",
        "trainer.push_to_hub()"
      ]
    }
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