Load_and_Fewshot_test.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bs9B64CpsOF0"
      },
      "source": [
        "## Fewshot 평가 지표및 Finetuning model load"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6-bFpckCXrcb",
        "outputId": "56475ed7-75e1-4267-d663-b42933bb8ead"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "\u001b[?25hCollecting safetensors>=0.3.1 (from transformers)\n",
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            "Installing collected packages: tokenizers, safetensors, huggingface-hub, transformers\n",
            "Successfully installed huggingface-hub-0.16.4 safetensors-0.3.2 tokenizers-0.13.3 transformers-4.31.0\n",
            "Collecting accelerate\n",
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            "Installing collected packages: accelerate\n",
            "Successfully installed accelerate-0.21.0\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for peft (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ],
      "source": [
        "!pip install transformers\n",
        "!pip install accelerate\n",
        "!pip install -q -U git+https://github.com/dopeornope-Lee/peft_modifier.git"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cvFHjoTCXrcc"
      },
      "source": [
        "## Data\n",
        "\n",
        "- Data는 NSMC데이터입니다.\n",
        "- Fewshot 평가도, beomi님의 procedure를 따라, 벤치마크 성능을 도출해보고자 하는 procedure를 따릅니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HbKNloVoXrcd",
        "outputId": "1f76761c-4a21-4b6b-9910-8bd8bc27cb0e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--2023-08-19 05:01:54--  https://raw.githubusercontent.com/NLP-kr/tensorflow-ml-nlp-tf2/master/7.PRETRAIN_METHOD/data_in/KOR/naver_movie/ratings_train.txt\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 14628807 (14M) [text/plain]\n",
            "Saving to: ‘data_in/KOR/naver_movie/ratings_train.txt’\n",
            "\n",
            "data_in/KOR/naver_m 100%[===================>]  13.95M  --.-KB/s    in 0.1s    \n",
            "\n",
            "2023-08-19 05:01:55 (114 MB/s) - ‘data_in/KOR/naver_movie/ratings_train.txt’ saved [14628807/14628807]\n",
            "\n",
            "--2023-08-19 05:01:55--  https://raw.githubusercontent.com/NLP-kr/tensorflow-ml-nlp-tf2/master/7.PRETRAIN_METHOD/data_in/KOR/naver_movie/ratings_test.txt\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.109.133, 185.199.108.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 4893335 (4.7M) [text/plain]\n",
            "Saving to: ‘data_in/KOR/naver_movie/ratings_test.txt’\n",
            "\n",
            "data_in/KOR/naver_m 100%[===================>]   4.67M  --.-KB/s    in 0.09s   \n",
            "\n",
            "2023-08-19 05:01:55 (52.7 MB/s) - ‘data_in/KOR/naver_movie/ratings_test.txt’ saved [4893335/4893335]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!mkdir -p data_in/KOR/naver_movie\n",
        "!wget https://raw.githubusercontent.com/NLP-kr/tensorflow-ml-nlp-tf2/master/7.PRETRAIN_METHOD/data_in/KOR/naver_movie/ratings_train.txt \\\n",
        "              -O data_in/KOR/naver_movie/ratings_train.txt\n",
        "!wget https://raw.githubusercontent.com/NLP-kr/tensorflow-ml-nlp-tf2/master/7.PRETRAIN_METHOD/data_in/KOR/naver_movie/ratings_test.txt \\\n",
        "              -O data_in/KOR/naver_movie/ratings_test.txt"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Import Module"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "xs88fDX8Xrcd",
        "scrolled": true
      },
      "outputs": [],
      "source": [
        "import os\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
        "import torch\n",
        "from tqdm import tqdm\n",
        "import pandas as pd\n",
        "from peft import PeftModel, PeftConfig\n",
        "import numpy as np\n",
        "import re\n",
        "import random\n",
        "from random import sample\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "s6dM4ebxXrcg"
      },
      "outputs": [],
      "source": [
        "SEED_NUM = 1234\n",
        "np.random.seed(SEED_NUM)\n",
        "random.seed(SEED_NUM)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_JOpN3-WRqy5"
      },
      "source": [
        "## K(G)OAT nsmc 테스트"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f-eTOzbBRqy5"
      },
      "source": [
        "### 모델 로드\n",
        "\n",
        "- 만약 fine tuning 모델을 허깅페이스에 업로드 하였다면, adapter만 업로딩 되었을 것입니다\n",
        "\n",
        "- 이럴때, 모델을 로드 하는 방식을 따라갈 수 있도록 준비하였습니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
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          ]
        },
        "id": "lAaKKUqbXrch",
        "outputId": "28244364-cb1b-4118-8346-95d2dc36c703"
      },
      "outputs": [
        {
          "data": {
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              "model_id": "835e08df3fd044c3aad9b7ce205b628e",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/164 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "bbec60624da14e1587715a34b084954a",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/1.65M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/185 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# 토크나이저 로드\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"EleutherAI/polyglot-ko-5.8b\")"
      ]
    },
    {
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      ],
      "source": [
        "# 모델의 id를 입력해줍니다. (즉 업로드된 허깅페이스 레포명)\n",
        "peft_model_id = \"DopeorNope/KOAT-5.8b\"\n",
        "# config를 가져옵니다(만약 없다면, model.config를 통해서 학습과직후 확인후 업로드하세요!)\n",
        "config = PeftConfig.from_pretrained(peft_model_id)\n",
        "\n",
        "#config를 통해 베이스 모델을 불러옵니다.\n",
        "cls_model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path,torch_dtype=torch.float16,low_cpu_mem_usage=True).cuda()\n",
        "\n",
        "# 이후 어댑터와 모델을 결합한 최종 모델을 로드합니다.\n",
        "cls_model = PeftModel.from_pretrained(cls_model, peft_model_id)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "PEFhNyTbeuNF"
      },
      "outputs": [],
      "source": [
        "cls_model=model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "tnMMmMdoFkxb"
      },
      "outputs": [],
      "source": [
        "cls_model.config.max_length = 2048\n",
        "cls_model.config.pad_token_id = 0"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pCN8Lh7gXrch"
      },
      "source": [
        "### 퓨샷 러닝을 위한 네이버 영화 리뷰 데이터 EDA 및 구성"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "Ct1IbwATXrci"
      },
      "outputs": [],
      "source": [
        "# 데이터 전처리 준비\n",
        "DATA_IN_PATH = './data_in/KOR'\n",
        "DATA_OUT_PATH = './data_out/KOR'\n",
        "\n",
        "DATA_TRAIN_PATH = os.path.join(DATA_IN_PATH, 'naver_movie', 'ratings_train.txt')\n",
        "DATA_TEST_PATH = os.path.join(DATA_IN_PATH, 'naver_movie', 'ratings_test.txt')\n",
        "\n",
        "train_data = pd.read_csv(DATA_TRAIN_PATH, header = 0, delimiter = '\\t', quoting = 3)\n",
        "train_data = train_data.dropna()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WED9P9SUSyR9",
        "outputId": "ed8d3ac1-d2e4-4208-f5e4-2efd58fac8ee"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "데이터 positive 라벨:  긍정\n",
            "데이터 negative 라벨:  부정\n"
          ]
        }
      ],
      "source": [
        "print('데이터 positive 라벨: ', '긍정')\n",
        "print('데이터 negative 라벨: ', '부정')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WaQ1miXfwQfn",
        "outputId": "3b63c47a-8db4-4833-fed5-28ce9f12f75b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "학습 예시 케이스 구조:  문장: 오늘 기분이 좋아\n",
            "감정: 긍정\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print('학습 예시 케이스 구조: ', '문장: 오늘 기분이 좋아\\n감정: 긍정\\n')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7h0USc0RxQqG",
        "outputId": "04cf2db0-32e8-4416-f2be-225faecd9843"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "gpt 최대 토큰 길이:  2048\n"
          ]
        }
      ],
      "source": [
        "print('gpt 최대 토큰 길이: ', cls_model.config.max_position_embeddings)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MRwI-RcOyFRj",
        "outputId": "6cfb0cb7-9605-4dbc-a5ae-b69d5ba10430"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 149995/149995 [00:13<00:00, 10756.48it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Few shot 케이스 토큰 평균 길이:  20.22912763758792\n",
            "Few shot 케이스 토큰 최대 길이:  280\n",
            "Few shot 케이스 토큰 길이 표준편차:  16.48828728915166\n",
            "Few shot 케이스 토큰 길이 80 퍼센타일:  27.0\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "sent_lens = [len(tokenizer(s).input_ids) for s in tqdm(train_data['document'])]\n",
        "\n",
        "print('Few shot 케이스 토큰 평균 길이: ', np.mean(sent_lens))\n",
        "print('Few shot 케이스 토큰 최대 길이: ', np.max(sent_lens))\n",
        "print('Few shot 케이스 토큰 길이 표준편차: ',np.std(sent_lens))\n",
        "print('Few shot 케이스 토큰 길이 80 퍼센타일: ',np.percentile(sent_lens, 80))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PdIWfc6Pzyfz",
        "outputId": "aa7d358a-4693-4177-e8a2-96e28604b61b"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 149995/149995 [00:14<00:00, 10172.98it/s]\n"
          ]
        }
      ],
      "source": [
        "train_fewshot_data = []\n",
        "\n",
        "for train_sent, train_label in tqdm(train_data[['document', 'label']].values):\n",
        "    tokens = tokenizer(train_sent).input_ids\n",
        "\n",
        "    if len(tokens) <= 25:\n",
        "        train_fewshot_data.append((train_sent, train_label))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4jFe7XMeXrcl"
      },
      "source": [
        "### 데이터를 통한 평가 지표 도출"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "1_OhF3hVhK0y",
        "outputId": "8d07dfa9-2f69-43e8-d691-6bd58fa6e488"
      },
      "outputs": [
        {
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              "      <td>9274899</td>\n",
              "      <td>GDNTOPCLASSINTHECLUB</td>\n",
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              "      <td>뭐야 이 평점들은.... 나쁘진 않지만 10점 짜리는 더더욱 아니잖아</td>\n",
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              "\n",
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            ],
            "text/plain": [
              "        id                                           document  label\n",
              "0  6270596                                                굳 ㅋ      1\n",
              "1  9274899                               GDNTOPCLASSINTHECLUB      0\n",
              "2  8544678             뭐야 이 평점들은.... 나쁘진 않지만 10점 짜리는 더더욱 아니잖아      0\n",
              "3  6825595                   지루하지는 않은데 완전 막장임... 돈주고 보기에는....      0\n",
              "4  6723715  3D만 아니었어도 별 다섯 개 줬을텐데.. 왜 3D로 나와서 제 심기를 불편하게 하죠??      0"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "test_data = pd.read_csv(DATA_TEST_PATH, header=0, delimiter='\\t', quoting=3)\n",
        "test_data = test_data.dropna()\n",
        "test_data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "liE91_rhsQdY"
      },
      "outputs": [],
      "source": [
        "# Full Dataset\n",
        "# sample_size = len(test_data)\n",
        "\n",
        "# Sampled Dataset\n",
        "sample_size = 500\n",
        "\n",
        "train_fewshot_samples = []\n",
        "\n",
        "for _ in range(sample_size):\n",
        "    fewshot_examples = sample(train_fewshot_data, 10)\n",
        "    train_fewshot_samples.append(fewshot_examples)\n",
        "\n",
        "if sample_size < len(test_data['id']):\n",
        "    test_data = test_data.sample(sample_size, random_state=SEED_NUM)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UseuZHiTRqy6"
      },
      "source": [
        "### K(G)OAT 프롬프트 1 방식 실험"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s-ZiFs-aRIXy",
        "outputId": "08fbd880-3365-4755-f496-fdb266f73b1d"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r  0%|          | 0/500 [00:00<?, ?it/s]/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1270: UserWarning: You have modified the pretrained model configuration to control generation. This is a deprecated strategy to control generation and will be removed soon, in a future version. Please use a generation configuration file (see https://huggingface.co/docs/transformers/main_classes/text_generation )\n",
            "  warnings.warn(\n",
            "100%|██████████| 500/500 [01:42<00:00,  4.86it/s]\n"
          ]
        }
      ],
      "source": [
        "def build_prompt_text(sent):\n",
        "    return \"문장: \" + sent + '\\n감정:'\n",
        "\n",
        "def clean_text(sent):\n",
        "    sent_clean = re.sub(\"[^가-힣ㄱ-ㅎㅏ-ㅣ\\\\s]\", \"\", sent)\n",
        "    return sent_clean\n",
        "\n",
        "real_labels = []\n",
        "pred_tokens = []\n",
        "\n",
        "total_len = len(test_data[['document','label']].values)\n",
        "\n",
        "for i, (test_sent, test_label) in tqdm(enumerate(test_data[['document','label']].values), total=total_len):\n",
        "    prompt_text = ''\n",
        "\n",
        "    for ex in train_fewshot_samples[i]:\n",
        "        example_text, example_label = ex\n",
        "        cleaned_example_text = clean_text(example_text)\n",
        "        appended_prompt_example_text = build_prompt_text(cleaned_example_text)\n",
        "        appended_prompt_example_text += ' 긍정\\n' if example_label == 1 else ' 부정\\n'\n",
        "        prompt_text += appended_prompt_example_text\n",
        "\n",
        "    cleaned_sent = clean_text(test_sent)\n",
        "    appended_prompt_sent = build_prompt_text(cleaned_sent)\n",
        "\n",
        "    prompt_text += appended_prompt_sent\n",
        "\n",
        "    tokens = tokenizer(prompt_text, return_tensors=\"pt\")\n",
        "    token_ids, attn_mask = tokens.input_ids.cuda(), tokens.attention_mask.cuda()\n",
        "    gen_tokens = cls_model.generate(input_ids=token_ids, attention_mask=attn_mask,\n",
        "                                    max_new_tokens=1, pad_token_id=0)\n",
        "    pred = tokenizer.batch_decode(gen_tokens[:, -1])[0].strip()\n",
        "\n",
        "    pred_tokens.append(pred)\n",
        "    real_labels.append('긍정' if test_label == 1 else '부정')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0oZ1GfUPeuec",
        "outputId": "0aaf4984-15f2-46ce-d421-c6490d2fbd9e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.712\n"
          ]
        }
      ],
      "source": [
        "accuracy_match = [p == t for p, t in zip(pred_tokens, real_labels)]\n",
        "accuracy = len([m for m in accuracy_match if m]) / len(real_labels)\n",
        "\n",
        "print(accuracy)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BLRetWcfRqy6"
      },
      "source": [
        "### K(G)OAT 프롬프트 2 방식 실험"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vMYgRRGI-Gu4",
        "outputId": "13162255-0941-4750-af64-ae397b924a55"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 500/500 [02:23<00:00,  3.48it/s]\n"
          ]
        }
      ],
      "source": [
        "def build_prompt_text(sent):\n",
        "    return '다음 문장은 긍정일까요 부정일까요?\\n' + sent + '\\n정답:'\n",
        "\n",
        "real_labels = []\n",
        "pred_tokens = []\n",
        "\n",
        "\n",
        "real_labels = []\n",
        "pred_tokens = []\n",
        "\n",
        "total_len = len(test_data[['document','label']].values)\n",
        "\n",
        "for i, (test_sent, test_label) in tqdm(enumerate(test_data[['document','label']].values), total=total_len):\n",
        "    prompt_text = ''\n",
        "\n",
        "    for ex in train_fewshot_samples[i]:\n",
        "        example_text, example_label = ex\n",
        "        cleaned_example_text = clean_text(example_text)\n",
        "        appended_prompt_example_text = build_prompt_text(cleaned_example_text)\n",
        "        appended_prompt_example_text += ' 긍정\\n' if example_label == 1 else ' 부정\\n'\n",
        "        prompt_text += appended_prompt_example_text\n",
        "\n",
        "    cleaned_sent = clean_text(test_sent)\n",
        "    appended_prompt_sent = build_prompt_text(cleaned_sent)\n",
        "\n",
        "    prompt_text += appended_prompt_sent\n",
        "\n",
        "    tokens = tokenizer(prompt_text, return_tensors=\"pt\")\n",
        "    token_ids, attn_mask = tokens.input_ids.cuda(), tokens.attention_mask.cuda()\n",
        "    gen_tokens = cls_model.generate(input_ids=token_ids, attention_mask=attn_mask,\n",
        "                                    max_new_tokens=1, pad_token_id=0)\n",
        "    pred = tokenizer.batch_decode(gen_tokens[:, -1])[0].strip()\n",
        "\n",
        "    pred_tokens.append(pred)\n",
        "    real_labels.append('긍정' if test_label == 1 else '부정')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8ufjRihAzNBK",
        "outputId": "7cfd6e38-72c3-465d-993f-1a10ad353f21"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.81\n"
          ]
        }
      ],
      "source": [
        "accuracy_match = [p == t for p, t in zip(pred_tokens, real_labels)]\n",
        "accuracy = len([m for m in accuracy_match if m]) / len(real_labels)\n",
        "\n",
        "print(accuracy)"
      ]
    }
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