inthon_baseline.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7aOGRG2NV2X4"
      },
      "source": [
        "# 0. 경로 지정하기, 데이터셋 불러오기, 라이브러리 설치하기\n",
        "\n",
        "본 베이스라인 코드는 **모든 참가자들이 최소한의 제출을 할 수 있도록** 간단하게 만든 코드입니다. 이 코드를 토대로 자유롭게 수정하거나 다른 아이디어를 추가하여도 괜찮습니다. 당연히 베이스라인 코드를 참고하지 않고 처음부터 코드를 작성하여도 무방합니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k2yl4f6oWK3r",
        "outputId": "b20fcacf-ef95-4ec7-9d04-6fae377f4e99"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "# 구글 드라이브를 코랩과 연결하는 코드입니다.\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PxZ9cL1xWK3s",
        "outputId": "30e3b687-ea2e-43d8-b938-d726089f222e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content/drive/MyDrive/iNThon\n"
          ]
        }
      ],
      "source": [
        "# 현재 경로를 지정하는 코드입니다.\n",
        "%cd '/content/drive/MyDrive/iNThon'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9UKGGpPiWK3s",
        "outputId": "3fb73e34-25ad-41d7-f357-f8e33b9f42e0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "Installing collected packages: kaleido, typing-extensions, python-multipart, h11, uvicorn, starlette, fastapi\n",
            "  Attempting uninstall: typing-extensions\n",
            "    Found existing installation: typing_extensions 4.5.0\n",
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            "\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",
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            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "# 베이스라인 코드를 실행하기 위하여 필요한 라이브러리를 설치하는 코드입니다.\n",
        "!pip install fastapi kaleido python-multipart uvicorn\n",
        "!pip install -q -U git+https://github.com/huggingface/transformers.git\n",
        "!pip install -q -U git+https://github.com/huggingface/peft.git\n",
        "!pip install -q -U git+https://github.com/huggingface/accelerate.git\n",
        "!pip install -q -U datasets scipy ipywidgets matplotlib jsonlines rouge"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v6tvjhZKjQDJ"
      },
      "source": [
        "jsonl 형식의 데이터셋을 불러오는 코드입니다.\n",
        "\n",
        "    {\n",
        "      \"id\": 1,\n",
        "      \"type\": \"Type 2\",\n",
        "      \"size\": 6,\n",
        "      \"set\": \"또#이상#준다#핀잔#사#티셔츠\",\n",
        "      \"sentence\": \"또 이상한 티셔츠를 사서 핀잔을 준다.\"\n",
        "    },\n",
        "    {\n",
        "      \"id\": 2,\n",
        "      \"type\": \"Type 1\",\n",
        "      \"size\": 6,\n",
        "      \"set\": \"탄다#해변#썰물 때#말#사람#세\",\n",
        "      \"sentence\": \"썰물 때에 세명의 사람들이 해변에서 말을 탄다.\"\n",
        "    },\n",
        "    ...\n",
        "\n",
        "데이터의 형태는 위와 같습니다. 가장 간단하게 `\"set\"`을 입력으로, `\"sentence\"`를 출력으로 사용할 것입니다. 이후 훈련 집합을 `train.jsonl`에 저장하였습니다.\n",
        "또한 위에서 `\"sentence\"`가 빠진 형태인 평가 집합을 `eval.jsonl`에 저장하였습니다.\n",
        "\n",
        "다른 변수들도 학습에 도움이 될 것이라고 판단하신다면 자유롭게 수정하셔도 좋습니다. 여러분의 아이디어를 보여 주세요!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pJgVNSm8kKs7",
        "outputId": "b39238d3-db82-4def-f32d-4aa39ffe8719"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "('train.jsonl', 'eval.jsonl')"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# 데이터셋을 위의 설명대로 변환하는 코드입니다.\n",
        "import jsonlines\n",
        "import json\n",
        "\n",
        "input_file_path = 'train.json'\n",
        "train_file_path = 'train.jsonl'\n",
        "\n",
        "with open(input_file_path, 'r', encoding='utf-8') as file:\n",
        "    data = json.load(file)\n",
        "\n",
        "with jsonlines.open(train_file_path, mode='w') as writer:\n",
        "    for item in data['dataset']:\n",
        "        writer.write({\"input\": item[\"set\"], \"output\": item[\"sentence\"]})\n",
        "\n",
        "input_file_path = 'test.json'\n",
        "eval_file_path = 'eval.jsonl'\n",
        "\n",
        "with open(input_file_path, 'r', encoding='utf-8') as file:\n",
        "    data = json.load(file)\n",
        "\n",
        "with jsonlines.open(eval_file_path, mode='w') as writer:\n",
        "    for item in data['dataset']:\n",
        "        writer.write({\"input\": item[\"set\"]})\n",
        "\n",
        "(train_file_path, eval_file_path)"
      ]
    },
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        },
        "id": "AD46bHt9X-9A",
        "outputId": "f9137b4a-1238-42f7-fd7f-c32110f9db44"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d7b871a248ee49d2822f418d5b7fd6cb",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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            },
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          },
          "metadata": {},
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        },
        {
          "data": {
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            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
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        {
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        {
          "data": {
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "102a3b281e85476892113150a25ae6e7",
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            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {},
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        }
      ],
      "source": [
        "# 데이터셋을 불러오는 코드입니다.\n",
        "from datasets import load_dataset\n",
        "\n",
        "train_dataset = load_dataset('json', data_files='train.jsonl', split='train')\n",
        "eval_dataset = load_dataset('json', data_files='eval.jsonl', split='train')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_i3znFSnXryr"
      },
      "source": [
        "# 1. 가속기\n",
        "\n",
        "PyTorch의 Fully Sharded Data Parallel (FSDP) 기능을 사용하여 대규모 딥러닝 모델을 효율적으로 학습하기 위한 설정을 정의합니다. `FullyShardedDataParallelPlugin`을 사용하여 모델과 옵티마이저의 상태를 CPU로 오프로드하고, 모든 프로세스가 아닌 일부 프로세스에만 상태를 저장하도록 구성합니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mgOQ0UcuXtoL"
      },
      "outputs": [],
      "source": [
        "from accelerate import FullyShardedDataParallelPlugin, Accelerator\n",
        "from torch.distributed.fsdp.fully_sharded_data_parallel import FullOptimStateDictConfig, FullStateDictConfig\n",
        "\n",
        "fsdp_plugin = FullyShardedDataParallelPlugin(\n",
        "    state_dict_config=FullStateDictConfig(offload_to_cpu=True, rank0_only=False),\n",
        "    optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=True, rank0_only=False),\n",
        ")\n",
        "\n",
        "accelerator = Accelerator(fsdp_plugin=fsdp_plugin)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b-mhWEZeYEuH"
      },
      "source": [
        "# 2. 베이스 모델 불러오기\n",
        "\n",
        "Hugging Face의 Transformers 라이브러리를 사용하여 사전 훈련된 언어 모델을 로드합니다. 베이스라인에는 `\n",
        "EleutherAI/polyglot-ko-1.3b`라는 언어 모델을 사용하였습니다."
      ]
    },
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        {
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            },
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/3 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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            "text/plain": [
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            ]
          },
          "metadata": {},
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        }
      ],
      "source": [
        "import torch\n",
        "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
        "\n",
        "base_model_id = \"EleutherAI/polyglot-ko-1.3b\"\n",
        "model = AutoModelForCausalLM.from_pretrained(base_model_id)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k_FAyLeeZysu"
      },
      "source": [
        "# 3. 토큰화\n",
        "\n",
        "1. **토크나이저 설정**: Hugging Face의 `AutoTokenizer`를 사용하여 주어진 모델 ID에 맞는 토크나이저를 로드합니다. 여기서 토크나이저는 입력의 왼쪽에 패딩을 추가하고, 시작과 끝 토큰을 추가하도록 설정됩니다. 또한, 패딩 토큰으로 EOS(end-of-sequence) 토큰을 사용합니다.\n",
        "\n",
        "2. **데이터셋 토큰화**: `generate_and_tokenize_text` 함수를 사용하여 학습(`train_dataset`)과 평가(`eval_dataset`) 데이터셋을 토큰화합니다. 이 함수는 일반적으로 입력 텍스트를 모델에 적합한 형식으로 변환합니다.\n",
        "\n",
        "3. **데이터 길이 분포 시각화**: `plot_data_lengths` 함수는 토큰화된 학습 및 검증 데이터셋에서 각 입력의 길이(토큰의 수) 분포를 히스토그램으로 시각화합니다. 이는 데이터의 특성을 이해하고 적절한 모델 및 훈련 설정을 결정하는 데 도움이 됩니다.\n",
        "\n",
        "4. **토큰화 함수의 정의와 재적용**: `generate_and_tokenize_prompt` 함수를 정의한 후, 학습 및 검증 데이터셋에 다시 적용하여 최대 길이로 토큰화하고, 입력 ID를 레이블로 복사합니다. 이렇게 함으로써 언어 모델 학습을 위한 데이터 준비가 완료됩니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# autoreload를 사용하여 코드를 수정하고 저장하면 자동으로 적용되도록 하는 코드입니다.\n",
        "%load_ext autoreload\n",
        "%autoreload 2\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [],
      "source": [
        "from transformers import AutoTokenizer, AutoModelForSeq2SeqLM"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "You passed along `num_labels=3` with an incompatible id to label map: {'0': 'NEGATIVE', '1': 'POSITIVE'}. The number of labels wil be overwritten to 2.\n"
          ]
        }
      ],
      "source": [
        "base_model_id = 'gogamza/kobart-base-v2'\n",
        "model = AutoModelForSeq2SeqLM.from_pretrained(base_model_id)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "tokenizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [
        {
          "ename": "AttributeError",
          "evalue": "'str' object has no attribute 'shape'",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[1;32m/Users/sangmin/Desktop/Dev/Python/Inthon/inthon_baseline.ipynb Cell 16\u001b[0m line \u001b[0;36m1\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/sangmin/Desktop/Dev/Python/Inthon/inthon_baseline.ipynb#Y105sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m model\u001b[39m.\u001b[39;49mgenerate(\u001b[39m'\u001b[39;49m\u001b[39m안녕하세요\u001b[39;49m\u001b[39m'\u001b[39;49m, num_beams\u001b[39m=\u001b[39;49m\u001b[39m5\u001b[39;49m, num_return_sequences\u001b[39m=\u001b[39;49m\u001b[39m5\u001b[39;49m, max_length\u001b[39m=\u001b[39;49m\u001b[39m100\u001b[39;49m, early_stopping\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m)\n",
            "File \u001b[0;32m~/anaconda3/envs/pytorch/lib/python3.9/site-packages/torch/utils/_contextlib.py:115\u001b[0m, in \u001b[0;36mcontext_decorator.<locals>.decorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    112\u001b[0m \u001b[39m@functools\u001b[39m\u001b[39m.\u001b[39mwraps(func)\n\u001b[1;32m    113\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mdecorate_context\u001b[39m(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[1;32m    114\u001b[0m     \u001b[39mwith\u001b[39;00m ctx_factory():\n\u001b[0;32m--> 115\u001b[0m         \u001b[39mreturn\u001b[39;00m func(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
            "File \u001b[0;32m~/anaconda3/envs/pytorch/lib/python3.9/site-packages/transformers/generation/utils.py:1308\u001b[0m, in \u001b[0;36mGenerationMixin.generate\u001b[0;34m(self, inputs, generation_config, logits_processor, stopping_criteria, prefix_allowed_tokens_fn, synced_gpus, assistant_model, streamer, **kwargs)\u001b[0m\n\u001b[1;32m   1300\u001b[0m \u001b[39m# 3. Define model inputs\u001b[39;00m\n\u001b[1;32m   1301\u001b[0m \u001b[39m# inputs_tensor has to be defined\u001b[39;00m\n\u001b[1;32m   1302\u001b[0m \u001b[39m# model_input_name is defined if model-specific keyword input is passed\u001b[39;00m\n\u001b[1;32m   1303\u001b[0m \u001b[39m# otherwise model_input_name is None\u001b[39;00m\n\u001b[1;32m   1304\u001b[0m \u001b[39m# all model-specific keyword inputs are removed from `model_kwargs`\u001b[39;00m\n\u001b[1;32m   1305\u001b[0m inputs_tensor, model_input_name, model_kwargs \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_prepare_model_inputs(\n\u001b[1;32m   1306\u001b[0m     inputs, generation_config\u001b[39m.\u001b[39mbos_token_id, model_kwargs\n\u001b[1;32m   1307\u001b[0m )\n\u001b[0;32m-> 1308\u001b[0m batch_size \u001b[39m=\u001b[39m inputs_tensor\u001b[39m.\u001b[39;49mshape[\u001b[39m0\u001b[39m]\n\u001b[1;32m   1310\u001b[0m \u001b[39m# 4. Define other model kwargs\u001b[39;00m\n\u001b[1;32m   1311\u001b[0m model_kwargs[\u001b[39m\"\u001b[39m\u001b[39moutput_attentions\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m generation_config\u001b[39m.\u001b[39moutput_attentions\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'str' object has no attribute 'shape'"
          ]
        }
      ],
      "source": [
        "model.generate('안녕하세요', num_beams=5, num_return_sequences=5, max_length=100, early_stopping=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Training code using bart model\n",
        "from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, Seq2SeqTrainingArguments, Seq2SeqTrainer\n",
        "\n",
        "def preprocess_function(examples):\n",
        "    inputs = ['<pad>'.join(doc.split('#')) for doc in examples[\"input\"]]\n",
        "    model_inputs = tokenizer(inputs, padding=\"max_length\", max_length=256, truncation=True)\n",
        "\n",
        "    # Setup the tokenizer for targets\n",
        "    with tokenizer.as_target_tokenizer():\n",
        "        labels = tokenizer(examples[\"output\"], padding=\"max_length\", max_length=32, truncation=True)\n",
        "\n",
        "    model_inputs[\"labels\"] = labels[\"input_ids\"]\n",
        "    return model_inputs\n",
        "\n",
        "tokenized_train_dataset = train_dataset.map(\n",
        "    preprocess_function, batched=True, num_proc=4, load_from_cache_file=True\n",
        ")\n",
        "tokenized_eval_dataset = eval_dataset.map(\n",
        "    preprocess_function, batched=True, num_proc=4, load_from_cache_file=True\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "args = Seq2SeqTrainingArguments(\n",
        "    \"kobart\",\n",
        "    num_train_epochs=1,\n",
        "    learning_rate=5e-5,\n",
        "    per_device_train_batch_size=16,\n",
        "    per_device_eval_batch_size=16,\n",
        "    save_total_limit=3,\n",
        "    remove_unused_columns=True,\n",
        "    logging_strategy=\"epoch\",\n",
        "    evaluation_strategy = \"steps\",\n",
        "    eval_steps = 300,\n",
        "    save_strategy=\"steps\",\n",
        "    save_steps=300,\n",
        "    load_best_model_at_end=True,\n",
        "    metric_for_best_model=\"loss\",\n",
        "    greater_is_better=False,\n",
        ")\n",
        "\n",
        "trainer = Seq2SeqTrainer(\n",
        "    model=model,\n",
        "    tokenizer=tokenizer,\n",
        "    args=args,\n",
        "    train_dataset=tokenized_train_dataset,\n",
        "    eval_dataset=tokenized_eval_dataset,\n",
        ")\n",
        "\n",
        "trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Generate Texts in eval dataset\n",
        "import torch\n",
        "import jsonlines\n",
        "from tqdm.auto import tqdm\n",
        "\n",
        "model = model.to(\"cuda\")\n",
        "model.eval()\n",
        "\n",
        "with jsonlines.open('eval.jsonl', mode='r') as reader:\n",
        "    data = [item for item in reader]\n",
        "\n",
        "with jsonlines.open('eval_result.jsonl', mode='w') as writer:\n",
        "    for item in tqdm(data):\n",
        "        input = item['input']\n",
        "        input = '<pad>'.join(input.split('#'))\n",
        "        input = tokenizer(input, return_tensors='pt', padding=True, truncation=True).to(\"cuda\")\n",
        "        generated_ids = model.generate(input['input_ids'], num_beams=5, num_return_sequences=5, max_length=32, no_repeat_ngram_size=2)\n",
        "        generated_texts = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)\n",
        "        item['output'] = generated_texts\n",
        "        writer.write(item)\n",
        "        "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Generate text using trained model\n",
        "import torch\n",
        "import numpy as np\n",
        "from transformers import AutoTokenizer, AutoModelForSeq2SeqLM\n",
        "\n",
        "def generate_text(text, model, tokenizer, max_length=256):\n",
        "    input_ids = tokenizer(text, return_tensors='pt')['input_ids'].to(model.device)\n",
        "    outputs = model.generate(input_ids, max_length=max_length, num_beams=5, early_stopping=True)\n",
        "    return tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
        "\n",
        "generate_text('나는', model, tokenizer)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17,
          "referenced_widgets": [
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        },
        "id": "haSUDD9HyRgf",
        "outputId": "3a7114ed-a8c8-4cd4-e948-431a1aff51f9"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ffa88113d8a043498bd58d62660ccaaf",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/164 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3f0176a86d1f46abb7c96c796816acf5",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/1.65M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "20e7df3550e846d9a0641b9db94c150c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/185 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# 토크나이저를 설정하는 코드입니다.\n",
        "tokenizer = AutoTokenizer.from_pretrained(\n",
        "    base_model_id,\n",
        "    padding_side=\"left\",\n",
        "    add_eos_token=True,\n",
        "    add_bos_token=True,\n",
        ")\n",
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "\n",
        "def formatting_func(example):\n",
        "    text = f\"{example['input']}\"\n",
        "    return text\n",
        "\n",
        "def generate_and_tokenize_text(text):\n",
        "    return tokenizer(formatting_func(text))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17,
          "referenced_widgets": [
            "56004dad46204f71b4253d7594fa7201",
            "1cd4fffa0da2415eb5765958faa2b6c3",
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            "2ffdb360f5a449ec99c4d0527e03ada7",
            "5237729a462f4367966addc6809dd6ad",
            "2dc1f94b2fcf4e26aa41eeab52ae9d0b"
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        },
        "id": "oIFU5kpwGXh2",
        "outputId": "2432eeca-4ec0-40b9-8adb-7a910823d8a1"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "56004dad46204f71b4253d7594fa7201",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/30000 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7e5903e6720e4ee1abd07041d50213f7",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/1500 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# 데이터셋에 있는 각각의 데이터를 토큰화하는 코드입니다.\n",
        "tokenized_train_dataset = train_dataset.map(generate_and_tokenize_text)\n",
        "tokenized_val_dataset = eval_dataset.map(generate_and_tokenize_text)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 581
        },
        "id": "DkCrM_hhGXh2",
        "outputId": "e5418c50-93ce-4ef9-ad66-7274a9099b85"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "31500\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# 데이터 길이 분포를 시각화하는 코드입니다.\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def plot_data_lengths(tokenize_train_dataset, tokenized_val_dataset):\n",
        "    lengths = [len(x['input_ids']) for x in tokenized_train_dataset]\n",
        "    lengths += [len(x['input_ids']) for x in tokenized_val_dataset]\n",
        "    print(len(lengths))\n",
        "\n",
        "    plt.figure(figsize=(10, 6))\n",
        "    plt.hist(lengths, bins=20, alpha=0.7, color='blue')\n",
        "    plt.xlabel('Length of input_ids')\n",
        "    plt.ylabel('Frequency')\n",
        "    plt.title('Distribution of Lengths of input_ids')\n",
        "    plt.show()\n",
        "\n",
        "plot_data_lengths(tokenized_train_dataset, tokenized_val_dataset)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17,
          "referenced_widgets": [
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          ]
        },
        "id": "leLEJe0rGXh2",
        "outputId": "928612f9-150b-4415-b2b4-4c3bc2f2e95d"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "19069a14c9c44470a2f89d6378ffd490",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/30000 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6e3d007cc8f849beb02a73e673f0c081",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/1500 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# 패딩을 포함한 토크나이저를 설정하고 적용하는 코드입니다.\n",
        "max_length = 128\n",
        "\n",
        "def generate_and_tokenize_prompt(prompt):\n",
        "    result = tokenizer(\n",
        "        formatting_func(prompt),\n",
        "        truncation=True,\n",
        "        max_length=max_length,\n",
        "        padding=\"max_length\",\n",
        "    )\n",
        "    result[\"labels\"] = result[\"input_ids\"].copy()\n",
        "    return result\n",
        "\n",
        "tokenized_train_dataset = train_dataset.map(generate_and_tokenize_prompt)\n",
        "tokenized_val_dataset = eval_dataset.map(generate_and_tokenize_prompt)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F-Rzn0WQbivI"
      },
      "source": [
        "# 4. 베이스 모델 작동 확인 (선택)\n",
        "\n",
        "사전 훈련된 언어 모델을 사용하여 주어진 평가 데이터셋(`eval_dataset`)에 대한 예측을 생성하고 저장하는 코드입니다.\n",
        "\n",
        "1. **데이터 읽기와 쓰기 설정**: `jsonlines` 라이브러리를 사용하여 평가 데이터셋(`eval.jsonl`)을 읽고, 결과를 저장할 파일(`predictions.jsonl`)을 쓰기 모드로 엽니다.\n",
        "\n",
        "2. **평가 데이터셋 처리**: `tqdm`을 사용하여 진행 상황을 표시하면서 평가 데이터셋을 순회합니다. 각 데이터 포인트에 대해, 평가 입력을 기반으로 질문-답변 형식의 프롬프트를 생성합니다.\n",
        "\n",
        "3. **모델 입력 준비**: 생성된 프롬프트를 토크나이저를 통해 모델이 처리할 수 있는 형식으로 변환하고, 이를 CUDA를 사용하는 GPU로 이동시킵니다.\n",
        "\n",
        "4. **모델 실행**: 모델을 평가 모드로 설정하고, `torch.no_grad()`를 사용하여 기울기 계산 없이 모델을 실행합니다. `model.generate` 함수를 사용하여 답변을 생성하고, 이를 디코딩하여 텍스트 형식으로 변환합니다.\n",
        "\n",
        "5. **결과 저장**: 각 평가 입력과 해당하는 생성된 답변을 `predictions.jsonl `파일에 저장합니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PV7TiTStbVGZ"
      },
      "outputs": [],
      "source": [
        "import jsonlines\n",
        "from tqdm import tqdm\n",
        "import warnings\n",
        "\n",
        "with jsonlines.open('eval.jsonl') as reader, jsonlines.open('predictions.jsonl', mode='w') as writer:\n",
        "    for eval_entry in tqdm(reader, total=len(eval_dataset)):\n",
        "        eval_input = eval_entry['input']\n",
        "\n",
        "        eval_prompt = f\"{eval_input}\"\n",
        "\n",
        "        model_input = tokenizer(eval_prompt, return_tensors=\"pt\").to(\"cuda\")\n",
        "\n",
        "        model.eval()\n",
        "\n",
        "        with torch.no_grad():\n",
        "            output = model.generate(**model_input, max_new_tokens=32, pad_token_id=tokenizer.eos_token_id)\n",
        "            answer = tokenizer.decode(output[0], skip_special_tokens=True)\n",
        "\n",
        "        writer.write({\"input\": eval_input, \"output\": answer})\n",
        "\n",
        "print(\"Predictions have been written to predictions.jsonl\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ve5dwsrOci91"
      },
      "source": [
        "# 5. LoRA 설정\n",
        "모델을 최적화하고 효율적으로 학습하기 위한 단계입니다.\n",
        "\n",
        "1. **그라디언트 체크포인팅 활성화**: `model.gradient_checkpointing_enable()`를 호출하여 모델의 메모리 사용량을 줄이기 위해 그라디언트 체크포인팅을 활성화합니다. 이는 특히 대규모 모델에서 유용합니다.\n",
        "\n",
        "2. **PEFT 모델 준비**: **prepare_model_for_kbit_training** 함수를 사용하여 모델을 PEFT(Parameter-Efficient Fine-Tuning)에 적합하게 조정합니다.\n",
        "\n",
        "Few-Shot **P**arameter-**E**fficient **F**ine-**T**uning is Better and Cheaper than In-Context Learning: https://arxiv.org/abs/2205.05638\n",
        "\n",
        "3. **훈련 가능한 파라미터 수 계산 및 출력**: `print_trainable_parameters` 함수는 모델의 전체 파라미터 수와 그 중 훈련 가능한 파라미터의 수를 계산하여 출력합니다.\n",
        "\n",
        "4. **LoRA 설정**: `LoraConfig`를 사용하여 LoRA(Low-Rank Adaptation) 설정을 정의합니다.\n",
        "\n",
        "LoRA: **Lo**w-**R**ank **A**daptation of Large Language Models: https://arxiv.org/abs/2106.09685\n",
        "\n",
        "5. **PEFT 모델 가져오기**: `get_peft_model` 함수를 사용하여 LoRA 설정을 적용한 모델을 가져옵니다.\n",
        "\n",
        "6. **훈련 가능한 파라미터 재계산 및 출력**: 변경된 모델의 훈련 가능한 파라미터 수를 다시 계산하고 출력합니다.\n",
        "\n",
        "7. **모델을 Accelerator와 통합**: `accelerator.prepare_model`을 사용하여 모델을 Accelerator 라이브러리와 통합합니다. 이는 분산 훈련 및 다른 최적화 기법과의 호환성을 위해 필요합니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "a9EUEDAl0ss3"
      },
      "outputs": [],
      "source": [
        "# 그라디언트 체크포인팅을 활성화하는 코드입니다.\n",
        "from peft import prepare_model_for_kbit_training\n",
        "\n",
        "model.gradient_checkpointing_enable()\n",
        "model = prepare_model_for_kbit_training(model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gkIcwsSU01EB"
      },
      "outputs": [],
      "source": [
        "# 훈련 가능한 파라미터의 수를 계산해 주는 함수입니다.\n",
        "def print_trainable_parameters(model):\n",
        "    trainable_params = 0\n",
        "    all_param = 0\n",
        "    for _, param in model.named_parameters():\n",
        "        all_param += param.numel()\n",
        "        if param.requires_grad:\n",
        "            trainable_params += param.numel()\n",
        "    print(\n",
        "        f\"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}\"\n",
        "    )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XshGNsbxyRgj",
        "outputId": "8e48045b-2c32-4525-8f4a-b518561f1c7c",
        "scrolled": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "GPTNeoXForCausalLM(\n",
            "  (gpt_neox): GPTNeoXModel(\n",
            "    (embed_in): Embedding(30080, 2048)\n",
            "    (emb_dropout): Dropout(p=0.0, inplace=False)\n",
            "    (layers): ModuleList(\n",
            "      (0-23): 24 x GPTNeoXLayer(\n",
            "        (input_layernorm): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
            "        (post_attention_layernorm): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
            "        (post_attention_dropout): Dropout(p=0.0, inplace=False)\n",
            "        (post_mlp_dropout): Dropout(p=0.0, inplace=False)\n",
            "        (attention): GPTNeoXAttention(\n",
            "          (rotary_emb): GPTNeoXRotaryEmbedding()\n",
            "          (query_key_value): Linear(in_features=2048, out_features=6144, bias=True)\n",
            "          (dense): Linear(in_features=2048, out_features=2048, bias=True)\n",
            "          (attention_dropout): Dropout(p=0.0, inplace=False)\n",
            "        )\n",
            "        (mlp): GPTNeoXMLP(\n",
            "          (dense_h_to_4h): Linear(in_features=2048, out_features=8192, bias=True)\n",
            "          (dense_4h_to_h): Linear(in_features=8192, out_features=2048, bias=True)\n",
            "          (act): GELUActivation()\n",
            "        )\n",
            "      )\n",
            "    )\n",
            "    (final_layer_norm): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
            "  )\n",
            "  (embed_out): Linear(in_features=2048, out_features=30080, bias=False)\n",
            ")\n"
          ]
        }
      ],
      "source": [
        "print(model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ybeyl20n3dYH",
        "outputId": "499f0625-669f-4407-8296-a0e215d3731d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "trainable params: 1028096 || all params: 1332838400 || trainable%: 0.07713583282114321\n"
          ]
        }
      ],
      "source": [
        "# LoRA를 이용해 학습하고자 하는 계층을 결정하여 PEFT 모델을 준비하는 코드입니다.\n",
        "from peft import LoraConfig, get_peft_model\n",
        "\n",
        "config = LoraConfig(\n",
        "    r=32,\n",
        "    lora_alpha=64,\n",
        "    target_modules=[\n",
        "        \"embed_out\",\n",
        "    ],\n",
        "    bias=\"none\",\n",
        "    lora_dropout=0.05,\n",
        "    task_type=\"CAUSAL_LM\",\n",
        ")\n",
        "\n",
        "model = get_peft_model(model, config)\n",
        "print_trainable_parameters(model)\n",
        "\n",
        "model = accelerator.prepare_model(model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IaYMWak4yRgn",
        "outputId": "b730b06e-fa4d-42bc-aabf-40807f60100c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "PeftModelForCausalLM(\n",
            "  (base_model): LoraModel(\n",
            "    (model): GPTNeoXForCausalLM(\n",
            "      (gpt_neox): GPTNeoXModel(\n",
            "        (embed_in): Embedding(30080, 2048)\n",
            "        (emb_dropout): Dropout(p=0.0, inplace=False)\n",
            "        (layers): ModuleList(\n",
            "          (0-23): 24 x GPTNeoXLayer(\n",
            "            (input_layernorm): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
            "            (post_attention_layernorm): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
            "            (post_attention_dropout): Dropout(p=0.0, inplace=False)\n",
            "            (post_mlp_dropout): Dropout(p=0.0, inplace=False)\n",
            "            (attention): GPTNeoXAttention(\n",
            "              (rotary_emb): GPTNeoXRotaryEmbedding()\n",
            "              (query_key_value): Linear(in_features=2048, out_features=6144, bias=True)\n",
            "              (dense): Linear(in_features=2048, out_features=2048, bias=True)\n",
            "              (attention_dropout): Dropout(p=0.0, inplace=False)\n",
            "            )\n",
            "            (mlp): GPTNeoXMLP(\n",
            "              (dense_h_to_4h): Linear(in_features=2048, out_features=8192, bias=True)\n",
            "              (dense_4h_to_h): Linear(in_features=8192, out_features=2048, bias=True)\n",
            "              (act): GELUActivation()\n",
            "            )\n",
            "          )\n",
            "        )\n",
            "        (final_layer_norm): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
            "      )\n",
            "      (embed_out): lora.Linear(\n",
            "        (base_layer): Linear(in_features=2048, out_features=30080, bias=False)\n",
            "        (lora_dropout): ModuleDict(\n",
            "          (default): Dropout(p=0.05, inplace=False)\n",
            "        )\n",
            "        (lora_A): ModuleDict(\n",
            "          (default): Linear(in_features=2048, out_features=32, bias=False)\n",
            "        )\n",
            "        (lora_B): ModuleDict(\n",
            "          (default): Linear(in_features=32, out_features=30080, bias=False)\n",
            "        )\n",
            "        (lora_embedding_A): ParameterDict()\n",
            "        (lora_embedding_B): ParameterDict()\n",
            "      )\n",
            "    )\n",
            "  )\n",
            ")\n"
          ]
        }
      ],
      "source": [
        "print(model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1nWn_MIMpjST"
      },
      "source": [
        "# 6. 모델 훈련\n",
        "\n",
        "1. **토크나이저 설정**: 패딩 토큰으로 EOS 토큰을 사용하도록 토크나이저를 설정합니다.\n",
        "\n",
        "2. **훈련 매개변수 설정**: 에포크 수, 배치 크기, 그래디언트 축적 단계 등 훈련에 필요한 매개변수를 설정합니다.\n",
        "\n",
        "3. **트레이너 설정**: Hugging Face의 Trainer 클래스를 사용하여 모델, 데이터셋, 훈련 매개변수 및 기타 옵션을 설정합니다. 여기에는 학습률, 최적화기, 스케줄러 타입, 로깅 및 저장 전략 등이 포함됩니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from transformers import MBartForConditionalGeneration, MBart50TokenizerFast\n",
        "\n",
        "model = MBartForConditionalGeneration.from_pretrained(\"facebook/mbart-large-50\")\n",
        "tokenizer = MBart50TokenizerFast.from_pretrained(\"facebook/mbart-large-50\", src_lang=\"ko_KR\", tgt_lang=\"ko_KR\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jq0nX33BmfaC"
      },
      "outputs": [],
      "source": [
        "# 로컬이나 구글 드라이브에 저장할 체크포인트의 경로를 설정하는 코드입니다.\n",
        "import transformers\n",
        "from datetime import datetime\n",
        "\n",
        "project = \"baseline-finetune\"\n",
        "base_model_name = \"EleutherAI/polyglot-ko-1.3b\"\n",
        "run_name = base_model_name + \"-\" + project\n",
        "output_dir = \"./\" + run_name\n",
        "\n",
        "tokenizer.pad_token = tokenizer.eos_token"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 637
        },
        "id": "Rh-3uZr8SclO",
        "outputId": "1176d2fa-bb56-445a-858b-29304c53f9b4"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "You're using a PreTrainedTokenizerFast 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",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='100' max='100' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [100/100 25:46, Epoch 3/4]\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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>No log</td>\n",
              "      <td>5.914606</td>\n",
              "    </tr>\n",
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              "      <td>1</td>\n",
              "      <td>No log</td>\n",
              "      <td>5.690587</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>No log</td>\n",
              "      <td>5.567279</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>No log</td>\n",
              "      <td>5.553551</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
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              "<IPython.core.display.HTML object>"
            ]
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          "metadata": {},
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        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "TrainOutput(global_step=100, training_loss=5.98102783203125, metrics={'train_runtime': 1562.5806, 'train_samples_per_second': 65.533, 'train_steps_per_second': 0.064, 'total_flos': 2.498177273954304e+16, 'train_loss': 5.98102783203125, 'epoch': 3.41})"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# 모델을 학습하는 코드입니다. 훈련 매개변수는 자유롭게 변경하셔도 됩니다.\n",
        "\n",
        "trainer = transformers.Trainer(\n",
        "    model=model,\n",
        "    train_dataset=tokenized_train_dataset,\n",
        "    eval_dataset=tokenized_val_dataset,\n",
        "    args=transformers.TrainingArguments(\n",
        "        output_dir=output_dir,\n",
        "        per_device_train_batch_size=64,\n",
        "        max_steps=100,\n",
        "        learning_rate=1e-4,\n",
        "        optim=\"adamw_torch\",\n",
        "        gradient_accumulation_steps=16,\n",
        "        weight_decay=0.1,\n",
        "        lr_scheduler_type='linear',\n",
        "        warmup_steps=0,\n",
        "        logging_dir=\"./logs\",\n",
        "        save_strategy=\"steps\",\n",
        "        save_steps=10,\n",
        "        save_total_limit=3,\n",
        "        evaluation_strategy=\"epoch\",\n",
        "        load_best_model_at_end=False,\n",
        "        metric_for_best_model=\"loss\",\n",
        "        greater_is_better=False\n",
        "    ),\n",
        "    data_collator=transformers.DataCollatorForLanguageModeling(\n",
        "        tokenizer,\n",
        "        mlm=False\n",
        "    ),\n",
        ")\n",
        "\n",
        "model.config.use_cache = False\n",
        "trainer.train()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7bFDhfzWyUTN"
      },
      "source": [
        "# 7. 모델 평가\n",
        "\n",
        "1. **모델 로드**: `AutoModelForCausalLM`과 `AutoTokenizer`를 사용하여 `EleutherAI/polyglot-ko-1.3b` 모델과 해당 토크나이저를 로드합니다. 모델은 양자화 설정과 함께 로드됩니다.\n",
        "\n",
        "2. **PeftModel 불러오기**: 미세 조정된 모델(PeftModel)을 로드합니다.\n",
        "\n",
        "3. **모델 실행**: 모델을 평가 모드로 설정하고, 각 입력에 대해 프롬프트를 생성합니다.\n",
        "\n",
        "4. **예측 생성**: 모델을 사용하여 각 프롬프트에 대한 답변을 생성합니다. 각 입력에 대한 예측을 생성한 후 `predictions_baseline_trained.jsonl` 파일에 저장합니다.\n",
        "\n",
        "5. **생성된 텍스트 평가**: 자연어처리 분야의 평가 지표인 ROUGE와 BERTscore를 사용하여 생성된 문장과 데이터셋의 문장을 비교하고 생성된 문장을 평가합니다. 베이스라인 코드에 평가를 명시적으로 진행하는 예시를 포함하고자 훈련 집합의 일부를 통해 점수를 계산했습니다. **실제 평가는 평가 집합으로 진행할 예정입니다.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17,
          "referenced_widgets": [
            "91a2c29cbf4a4b6da0b853a7d6f2be21",
            "31c15156cccc4616b93e2a742c92d0bc",
            "3c8a8f85b6d146d5b82e5c2ae9175fc0",
            "59604320ae8f4d858bac85c23f966c6d",
            "6336a839c09943b9bf147821a2effc45",
            "c788f1227b5d4742929523a0c77e3753",
            "fa59bfed00254b3b8c06206579cb87ad",
            "daa062e1cd0843578665bf6a3ce8b8d7",
            "8b773fc75d644ae1a9f4dfc25d313d03",
            "43ab5d2831e94d8cb785728b3f27b411",
            "d312ccf4013d4f97a4f7c284bee862bc"
          ]
        },
        "id": "SKSnF016yRgp",
        "outputId": "95786a86-dc00-4458-f6cf-52c945c75172"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "91a2c29cbf4a4b6da0b853a7d6f2be21",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/3 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# 베이스 모델을 불러오는 코드입니다.\n",
        "import torch\n",
        "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
        "base_model_id = \"EleutherAI/polyglot-ko-1.3b\"\n",
        "\n",
        "base_model = AutoModelForCausalLM.from_pretrained(\n",
        "    base_model_id,\n",
        "    device_map=\"auto\"\n",
        ")\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(base_model_id)\n",
        "tokenizer.pad_token = tokenizer.eos_token"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GwsiqhWuyRgp"
      },
      "outputs": [],
      "source": [
        "# 미세 조정한 모델의 정보가 들어있는 체크포인트를 불러오는 코드입니다.\n",
        "from peft import PeftModel\n",
        "\n",
        "ft_model = PeftModel.from_pretrained(base_model, \"EleutherAI/polyglot-ko-1.3b-baseline-finetune/checkpoint-100\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cw475F3JBQbg",
        "outputId": "d894eb00-63ec-4bb3-be9e-6c96f80e7595"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 100/100 [01:43<00:00,  1.03s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Predictions have been written to predictions_baseline_trained.jsonl\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "# 체크포인트의 가중치로부터 문장을 생성하는 코드입니다.\n",
        "import jsonlines\n",
        "from tqdm import tqdm\n",
        "\n",
        "ft_model.eval()\n",
        "\n",
        "with jsonlines.open('train.jsonl') as reader, jsonlines.open('predictions_baseline_trained.jsonl', mode='w') as writer:\n",
        "    for i, eval_entry in enumerate(tqdm(reader, total=100)):\n",
        "        if i >= 100:\n",
        "            break # 베이스라인 실행의 간편함을 위하여 첫 100개의 데이터에 대하여 평가를 진행했습니다.\n",
        "\n",
        "        eval_input = eval_entry['input']\n",
        "\n",
        "        eval_prompt = f\"{eval_input}\"\n",
        "\n",
        "        model_input = tokenizer(eval_prompt, return_tensors=\"pt\").to(\"cuda\")\n",
        "        if \"token_type_ids\" in model_input:\n",
        "            del model_input[\"token_type_ids\"]\n",
        "\n",
        "        model.eval()\n",
        "\n",
        "        with torch.no_grad():\n",
        "            output = ft_model.generate(**model_input, max_new_tokens=32, pad_token_id=2)\n",
        "            answer = tokenizer.decode(output[0], skip_special_tokens=True)\n",
        "\n",
        "        writer.write({\"input\": eval_input, \"output\": answer})\n",
        "\n",
        "print()\n",
        "print(\"Predictions have been written to predictions_baseline_trained.jsonl\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EHumPxklqivj"
      },
      "source": [
        "    predictions.append(''.join(pred[\"output\"].split(pred[\"input\"])[1:]))\n",
        "\n",
        "`predictions_baseline_trained.jsonl` 파일에 있는 각 행의 \"output\"이라는 키의 구조는 다음과 같습니다.\n",
        "\n",
        "`\"output\" = \"input\"에 해당하는 주어진 텍스트 + 새롭게 만들어낸 텍스트`\n",
        "\n",
        "하지만 위 코드에서 볼 수 있는 것과 같이 **\"input\"에 해당하는 주어진 텍스트의 다음 텍스트**, 즉 새롭게 만들어낸 텍스트부터 채점에 포함할 예정입니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GmfbjBCri4xd",
        "outputId": "9f869b2d-20c9-4815-ed53-a1f8c5502909"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting bert-score\n",
            "  Downloading bert_score-0.3.13-py3-none-any.whl (61 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/61.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m61.1/61.1 kB\u001b[0m \u001b[31m1.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: torch>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from bert-score) (2.1.0+cu118)\n",
            "Requirement already satisfied: pandas>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from bert-score) (1.5.3)\n",
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            "Requirement already satisfied: triton==2.1.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.0.0->bert-score) (2.1.0)\n",
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            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers>=3.0.0->bert-score) (6.0.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers>=3.0.0->bert-score) (2023.6.3)\n",
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            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->bert-score) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->bert-score) (2023.7.22)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas>=1.0.1->bert-score) (1.16.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.0.0->bert-score) (2.1.3)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.0.0->bert-score) (1.3.0)\n",
            "Installing collected packages: bert-score\n",
            "Successfully installed bert-score-0.3.13\n"
          ]
        }
      ],
      "source": [
        "# 평가를 위한 bertscore 라이브러리를 설치하는 코드입니다. rouge 라이브러리는 0번에서 설치를 진행했습니다.\n",
        "import locale\n",
        "locale.getpreferredencoding = lambda: \"UTF-8\"\n",
        "\n",
        "!pip install bert-score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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          "text": [
            "ROUGE-1: 0.0107\n",
            "ROUGE-2: 0.0031\n",
            "ROUGE-L: 0.0107\n",
            "BERTScore: 0.5748\n",
            "\n",
            "Final Score: 0.4048\n"
          ]
        }
      ],
      "source": [
        "# 생성된 텍스트를 평가하는 코드입니다.\n",
        "from rouge import Rouge\n",
        "from bert_score import score\n",
        "\n",
        "predictions_file = 'predictions_baseline_trained.jsonl'\n",
        "eval_file = 'train.jsonl'\n",
        "\n",
        "predictions = []\n",
        "references = []\n",
        "with jsonlines.open(predictions_file) as pred_reader, jsonlines.open(eval_file) as eval_reader:\n",
        "    for i, (pred, eval) in enumerate(zip(pred_reader, eval_reader)):\n",
        "        if i >= 100:\n",
        "            break\n",
        "        predictions.append(''.join(pred[\"output\"].split(pred[\"input\"])[1:]))\n",
        "        references.append(eval[\"output\"])\n",
        "\n",
        "rouge = Rouge()\n",
        "rouge_scores = rouge.get_scores(predictions, references, avg=True)\n",
        "\n",
        "P, R, F1 = score(predictions, references, lang=\"ko\", model_type=\"bert-base-multilingual-cased\")\n",
        "bertscore = F1.mean()\n",
        "\n",
        "print(\"ROUGE-1:\", round(rouge_scores['rouge-1']['f'], 4))\n",
        "print(\"ROUGE-2:\", round(rouge_scores['rouge-2']['f'], 4))\n",
        "print(\"ROUGE-L:\", round(rouge_scores['rouge-l']['f'], 4))\n",
        "print(\"BERTScore:\", round(bertscore.item(), 4))\n",
        "\n",
        "print()\n",
        "print(\"Final Score:\", round(0.1 * rouge_scores['rouge-1']['f'] + 0.1 * rouge_scores['rouge-2']['f'] +\n",
        "                            0.2 * rouge_scores['rouge-l']['f'] + 0.6 * bertscore.item(), 4))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ne8Xu3FB6rHo"
      },
      "source": [
        "# 8. 결과 제출\n",
        "세 개의 파일(`test.json`, `eval.jsonl`, `predictions_baseline_trained.jsonl`)을 사용하여 특정 데이터를 추출하고 이를 판다스 데이터프레임으로 변환합니다.\n",
        "\n",
        "1. **JSON 파일 로드**: `test.json` 파일을 열어 JSON 데이터를 파싱합니다.\n",
        "\n",
        "2. **eval.jsonl 파일 처리**: `eval.jsonl` 파일을 열어 각 항목의 `\"input\"` 키에 해당하는 값을 키로 하여 딕셔너리(`eval_data`)를 구성합니다.\n",
        "\n",
        "3. **predictions_baseline_trained.jsonl 파일 처리**: `predictions_baseline_trained.jsonl` 파일을 열어 각 항목에서 모델이 새롭게 만들어낸 텍스트를 추출하여, 해당 `\"input\"` 키에 대응하는 값으로 딕셔너리(`predictions_data`)를 구성합니다.\n",
        "\n",
        "4. **데이터프레임 생성**: 각 `set`의 `id`와 `eval.jsonl` 파일의 `\"input\"`이 일치하는 경우, 이에 해당하는 `predictions_baseline_trained.jsonl` 파일의 텍스트를 찾아 데이터프레임에 추가합니다. 결과적으로, 각 행은 `id`와 관련된 텍스트(`sentence`)로 구성됩니다.\n",
        "\n",
        "최종적으로 이 코드는 세 파일 간의 관계를 매핑하여, 각 `id`에 대응하는 예측된 텍스트를 포함하는 데이터프레임을 생성합니다.\n",
        "\n",
        "Kaggle에 최종적으로 제출해야 하는 양식은 다음과 같습니다.\n",
        "\n",
        "|    id    |  sentence  |\n",
        "|----------|------------|\n",
        "| 30001    | 답안 1     |\n",
        "| 30002    | ​​​​​​​​​​​​​​​​​​​​​​답안 2     |\n",
        "| 30003    | 답안 3     |\n",
        "| 30004    | 답안 4     |\n",
        "| 30005    | 답안 5     |\n",
        "| ...      | ...        |\n",
        "| 31496    | 답안 1496  |\n",
        "| 31497    | 답안 1497  |\n",
        "| 31498    | 답안 1498  |\n",
        "| 31499    | 답안 1499  |\n",
        "| 31500    | 답안 1500  |\n",
        "\n",
        "\n",
        "형식에 주의하여 모델의 성능보다 떨어지는 점수를 얻는 일이 없도록 합시다!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ACPbn7uw4Oee"
      },
      "outputs": [],
      "source": [
        "# json, jsonl 파일을 처리하여 제출에 필요한 데이터들을 골라내는 코드입니다.\n",
        "import json\n",
        "import pandas as pd\n",
        "\n",
        "json_file_path = 'train.json' # 'test.json'\n",
        "eval_file_path = 'train.jsonl' # 'eval.jsonl'\n",
        "predictions_file_path = 'predictions_baseline_trained.jsonl'\n",
        "\n",
        "with open(json_file_path, 'r', encoding='utf-8') as file:\n",
        "    json_data = json.load(file)\n",
        "\n",
        "eval_data = {}\n",
        "with jsonlines.open(eval_file_path) as reader:\n",
        "    for entry in reader:\n",
        "        eval_data[entry['input']] = entry\n",
        "\n",
        "predictions_data = {}\n",
        "with jsonlines.open(predictions_file_path) as reader:\n",
        "    for entry in reader:\n",
        "        text_after_keyword = ''.join(entry['output'].split(entry['input'])[1:])\n",
        "        predictions_data[entry['input']] = text_after_keyword"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "AeZva9BG9SZA",
        "outputId": "bd87b466-8b38-45e9-b080-e89e30049e00"
      },
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              "\n",
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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>sentence</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>################################</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>부#세부여행#세부자유여행#세부한달살기#세부한달살기#세부여행#세부여행</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>소문#피부#피부#피부#피부#피부#피부#피부#피부#피부#피부#피부#피부#피부#피부#피부#</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>#취미#일상#소통#서이추#서이추환영#서로이웃#서로이웃환영#서로이웃추가#서로이웃추가환영</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>#립스틱#립스틱#립스틱#립스틱#립스틱#립스틱#립스틱#립스틱#립스틱#립스틱#립</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>96</td>\n",
              "      <td>한 잠을 잘 수 있는 방법은?​​​​​​​​​​​</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>97</td>\n",
              "      <td>#차#속#회색 곰 인형#차#속#회색 곰 인형#차#속#회색 곰 인형#차#속#</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>98</td>\n",
              "      <td>#서#받#아#서########################</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>99</td>\n",
              "      <td>#사랑해#말하지#못했어#미안해#너무#귀여워#말하지#못했어#미안해#</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>100</td>\n",
              "      <td>#새#새#새#새#새#새#새#새#새#새#새#새#새#새#새#새</td>\n",
              "    </tr>\n",
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            "text/plain": [
              "     id                                          sentence\n",
              "0     1                  ################################\n",
              "1     2             부#세부여행#세부자유여행#세부한달살기#세부한달살기#세부여행#세부여행\n",
              "2     3  소문#피부#피부#피부#피부#피부#피부#피부#피부#피부#피부#피부#피부#피부#피부#피부#\n",
              "3     4   #취미#일상#소통#서이추#서이추환영#서로이웃#서로이웃환영#서로이웃추가#서로이웃추가환영\n",
              "4     5        #립스틱#립스틱#립스틱#립스틱#립스틱#립스틱#립스틱#립스틱#립스틱#립스틱#립\n",
              "..  ...                                               ...\n",
              "95   96                       한 잠을 잘 수 있는 방법은?​​​​​​​​​​​\n",
              "96   97         #차#속#회색 곰 인형#차#속#회색 곰 인형#차#속#회색 곰 인형#차#속#\n",
              "97   98                  #서#받#아#서########################\n",
              "98   99              #사랑해#말하지#못했어#미안해#너무#귀여워#말하지#못했어#미안해#\n",
              "99  100                  #새#새#새#새#새#새#새#새#새#새#새#새#새#새#새#새\n",
              "\n",
              "[100 rows x 2 columns]"
            ]
          },
          "execution_count": 38,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# 제출 파일의 형식에 맞는 데이터프레임을 생성하는 코드입니다.\n",
        "df_data = []\n",
        "for i, data in enumerate(json_data['dataset']):\n",
        "    if i >= 100:\n",
        "        break # 제출을 위해 데이터프레임을 생성할 때는 삭제해야 합니다.\n",
        "\n",
        "    if data['set'] in eval_data:\n",
        "        id = data['id']\n",
        "        sentence = predictions_data.get(eval_data[data['set']]['input'], \"\")\n",
        "        df_data.append([id, sentence])\n",
        "\n",
        "df = pd.DataFrame(df_data, columns=['id', 'sentence'])\n",
        "df"
      ]
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      "execution_count": null,
      "metadata": {
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      "source": [
        "# 제출 파일을 로컬이나 구글 드라이브에 저장하는 코드입니다.\n",
        "df.to_csv('submit.csv', index=False, encoding='utf-8')"
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