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kB)\n", + "Requirement already satisfied: numpy>=1.18.0 in /usr/local/lib/python3.12/dist-packages (from Korpora) (2.0.2)\n", + "Requirement already satisfied: tqdm>=4.46.0 in /usr/local/lib/python3.12/dist-packages (from Korpora) (4.67.1)\n", + "Requirement already satisfied: requests>=2.20.0 in /usr/local/lib/python3.12/dist-packages (from Korpora) (2.32.4)\n", + "Requirement already satisfied: xlrd>=1.2.0 in /usr/local/lib/python3.12/dist-packages (from Korpora) (2.0.2)\n", + "Requirement already satisfied: charset_normalizer<4,>=2 in /usr/local/lib/python3.12/dist-packages (from requests>=2.20.0->Korpora) (3.4.4)\n", + "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.12/dist-packages (from requests>=2.20.0->Korpora) (3.11)\n", + "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.12/dist-packages (from requests>=2.20.0->Korpora) (2.5.0)\n", + "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.12/dist-packages (from requests>=2.20.0->Korpora) (2025.11.12)\n", + "Downloading Korpora-0.2.0-py3-none-any.whl (57 kB)\n", + "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/57.8 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m57.8/57.8 kB\u001b[0m \u001b[31m4.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hDownloading dataclasses-0.6-py3-none-any.whl (14 kB)\n", + "Installing collected packages: dataclasses, Korpora\n", + "Successfully installed Korpora-0.2.0 dataclasses-0.6\n" + ] + }, + { + "output_type": "display_data", + "data": { + "application/vnd.colab-display-data+json": { + "pip_warning": { + "packages": [ + "dataclasses" + ] + }, + "id": "113aeab02ef843d7bec37ab405e9441b" + } + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "# 네이버 영화 리뷰 데이터 불러오기\n", + "import numpy as np\n", + "import pandas as pd\n", + "from Korpora import Korpora\n", + "\n", + "corpus = Korpora.load(\"nsmc\")\n", + "df = pd.DataFrame(corpus.test).sample(20000, random_state=42) # 테스트 데이터만 사용\n", + "# 데이터 분리 - 6:2:2\n", + "train, valid, test = np.split(\n", + " df.sample(frac=1, random_state=42), [int(0.6 * len(df)), int(0.8 * len(df))]\n", + ")\n", + "\n", + "print(train.head(5).to_markdown())\n", + "print(f\"Training Data Size : {len(train)}\")\n", + "print(f\"Validation Data Size : {len(valid)}\")\n", + "print(f\"Testing Data Size : {len(test)}\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "gwWBXxhONek3", + "outputId": "dd8f88ea-07d3-48a4-9368-baff11e6a2de" + }, + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + " Korpora 는 다른 분들이 연구 목적으로 공유해주신 말뭉치들을\n", + " 손쉽게 다운로드, 사용할 수 있는 기능만을 제공합니다.\n", + "\n", + " 말뭉치들을 공유해 주신 분들에게 감사드리며, 각 말뭉치 별 설명과 라이센스를 공유 드립니다.\n", + " 해당 말뭉치에 대해 자세히 알고 싶으신 분은 아래의 description 을 참고,\n", + " 해당 말뭉치를 연구/상용의 목적으로 이용하실 때에는 아래의 라이센스를 참고해 주시기 바랍니다.\n", + "\n", + " # Description\n", + " Author : e9t@github\n", + " Repository : https://github.com/e9t/nsmc\n", + " References : www.lucypark.kr/docs/2015-pyconkr/#39\n", + "\n", + " Naver sentiment movie corpus v1.0\n", + " This is a movie review dataset in the Korean language.\n", + " Reviews were scraped from Naver Movies.\n", + "\n", + " The dataset construction is based on the method noted in\n", + " [Large movie review dataset][^1] from Maas et al., 2011.\n", + "\n", + " [^1]: http://ai.stanford.edu/~amaas/data/sentiment/\n", + "\n", + " # License\n", + " CC0 1.0 Universal (CC0 1.0) Public Domain Dedication\n", + " Details in https://creativecommons.org/publicdomain/zero/1.0/\n", + "\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "[nsmc] download ratings_train.txt: 14.6MB [00:00, 110MB/s] \n", + "[nsmc] download ratings_test.txt: 4.90MB [00:00, 50.1MB/s]\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "| | text | label |\n", + "|------:|:---------------------------------------------------------|--------:|\n", + "| 26891 | 역시 코믹액션은 성룡, 홍금보, 원표 삼인방이 최고지!! | 1 |\n", + "| 25024 | 점수 후하게 줘야것네 별 반개~ | 0 |\n", + "| 11666 | 오랜만에 느낄수 있는 [감독] 구타욕구. | 0 |\n", + "| 40303 | 본지는 좀 됬지만 극장서 돈주고 본게 아직까지 아까운 영화 | 0 |\n", + "| 18010 | 징키스칸이란 소재를 가지고 이것밖에 못만드냐 | 0 |\n", + "Training Data Size : 12000\n", + "Validation Data Size : 4000\n", + "Testing Data Size : 4000\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.12/dist-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'DataFrame.swapaxes' is deprecated and will be removed in a future version. Please use 'DataFrame.transpose' instead.\n", + " return bound(*args, **kwds)\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# 데이터 전처리 -> BERT 입력 텐서 생성\n", + "import torch\n", + "from transformers import BertTokenizer # BERT 토크나이저 클래스\n", + "from torch.utils.data import TensorDataset, DataLoader\n", + "from torch.utils.data import RandomSampler, SequentialSampler # 무작위 샘플러, 시퀀셜 샘플러\n", + "\n", + "def make_dataset(data, tokenizer, device):\n", + " tokenized = tokenizer(\n", + " text=data.text.tolist(), # 텍스트 -> 문자열\n", + " padding=\"longest\",\n", + " truncation=True,\n", + " return_tensors=\"pt\" # 텐서로 반환\n", + " )\n", + " input_ids = tokenized[\"input_ids\"].to(device) # 인코딩\n", + " attention_mask = tokenized[\"attention_mask\"].to(device) # 어텐션 마스크\n", + " labels = torch.tensor(data.label.values, dtype=torch.long).to(device) # 레이블\n", + " return TensorDataset(input_ids, attention_mask, labels)\n", + "\n", + "# 샘플러 클래스 -> 데이터를 목적에 따라 샘플링\n", + "## 학습 -> RandomSampler, 검증 및 평가 배치 -> SequentialSampler 사용해서 샘플링\n", + "def get_datalodader(dataset, sampler, batch_size):\n", + " data_sampler = sampler(dataset)\n", + " dataloader = DataLoader(dataset, sampler=data_sampler, batch_size=batch_size)\n", + " return dataloader\n", + "\n", + "epochs = 5\n", + "batch_size = 32\n", + "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", + "# 사전 학습 모델 불러오기\n", + "tokenizer = BertTokenizer.from_pretrained(\n", + " pretrained_model_name_or_path=\"bert-base-multilingual-cased\", # 다중 언어 지원 모델\n", + " do_lower_case=False # 소문자 변환 X\n", + ")\n", + "\n", + "# 텐서 데이터셋 만들고 데이터로더 적용해서 샘플링\n", + "train_dataset = make_dataset(train, tokenizer, device)\n", + "train_dataloader = get_datalodader(train_dataset, RandomSampler, batch_size)\n", + "\n", + "valid_dataset = make_dataset(valid, tokenizer, device)\n", + "valid_dataloader = get_datalodader(valid_dataset, SequentialSampler, batch_size)\n", + "\n", + "test_dataset = make_dataset(test, tokenizer, device)\n", + "test_dataloader = get_datalodader(test_dataset, SequentialSampler, batch_size)\n", + "\n", + "print(train_dataset[0])" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 639, + "referenced_widgets": [ + "7065d9a791ff47288a5bd5f0840e03fd", + "5d185eb3c0ae4430af7c2b1b1a9d9b58", + "57c319c44b134ca9b625d0a816979137", + "1e11c925a2e3478ca183ea873d44f7d9", + "a73401e537a344808317d914318d32f3", + "a4c4d11bbd974f8e8a2c1689863ef970", + "6170f0e7756c42378e76e3e63e6b343e", + "df1e678041dc4c00b7388a85845a29cf", + "97c6f51daa8a4e8c9fd2ca749ec4f842", + "9848b2bd23434822952a003ee5ce325b", + "0891412380344d31972395d34a7c0d00", + "91314cbd671542f9affbc70bd7d7e42a", + "0ec582c56f8d483985d7772aa9b1152e", + "1e55c7bd1c6840e9ba6c882a40d98695", + "ff9d2622bc2743659b1fbb081c9c4b3e", + "346779b77f1f40a99bcb9680a31afabb", + "1ac4365de9f64826a4b5e37a68e49d3d", + "dd2ddd64b1bc474a9ad073e11c89427b", + "b21cb06ee0f045889d33324dbb77167d", + "84a93eba396346ae84c892d485f495a1", + 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(1.22.0)\n", + "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.12/dist-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.17.0)\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# 뉴스 요약 데이터세트 불러오기\n", + "import numpy as np\n", + "from datasets import load_dataset\n", + "\n", + "news = load_dataset(\"argilla/news-summary\", split=\"test\") # 테스트 데이터셋 사용\n", + "df = news.to_pandas().sample(5000, random_state=42)[[\"text\", \"prediction\"]]\n", + "## 문장 요약 작업 - 긴 텍스트를 입력해야 하므로 연산량이 많아져 5000개만 샘플링해 사용\n", + "df[\"prediction\"] = df[\"prediction\"].map(lambda x: x[0][\"text\"])\n", + "train, valid, test = np.split( # 6:2:2\n", + " df.sample(frac=1, random_state=42), [int(0.6 * len(df)), int(0.8 * len(df))]\n", + ")\n", + "\n", + "print(f\"Source News : {train.text.iloc[0][:200]}\")\n", + "print(f\"Summarization : {train.prediction.iloc[0][:50]}\")\n", + "print(f\"Training Data Size : {len(train)}\")\n", + "print(f\"Validation Data Size : {len(valid)}\")\n", + "print(f\"Testing Data Size : {len(test)}\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 380, + "referenced_widgets": [ + "f8e667e3e5704855b894b383f4f64c5c", + "948702048b954b4680dff0956997b741", + "c3ad7f52e6d942ebb565596d66de5c82", + "fcadc0db5f70453eb15e70c09e63160b", + "dc87f04f695c4e39940391f015d50f2f", + "0c4a7df5bb504d03b32a3e5984b95965", + "b778264ad5434910882a65bfbfe40665", + "8d49809a9b524fe8b25bfd08241a2a24", + "91f37d44e88846848f9a4a7d900537d8", + "b2676653416e4c7c92b8ab0758308cce", + "0f9af0e800e348798833b5b1e725cc90", + "0bf5272c3147437d95a4d994630ad185", + "87872e74612f42679b3a16f600e95da8", + "0df4d897dfce452aa0fcf69ceb70c40d", + "212abc98171a4d74b9192ee29150da08", + "284ac890d2ec42a7842fb27335d39633", + "063bd157163a47b496ea0c6e7d9f2f03", + "b16f0b63581d4f08922a8927cb22ee9d", + "ba04490b81e2469d966a49a81d3dd76a", + "1ec619472a00412a8275ee67ee0d6959", + "a6ff538492274deb84c6b6922b232da1", + "370dfa16a6434c6d968d933d13187c6d", + "500eb7b99bab4c43b36ca978e1e1e108", + "e2f6eb8b3b394da49672cc725e95123d", + "97c54bd22bfc4047b72e347f5182dc9d", + "fc38b67bf938457da8a7e3fa555d82d5", + "ee932546147b49c78834a608ebc4471d", + "e1fe12ed16644896906a2093313b1cf3", + "d899b25454e04738aee251aeec024ee3", + "e32ddfda4e014b08a57eda49705a81c4", + "707a1df095614161ab03e68e11b18b6d", + "385d16ff3bd94e7cbd56c08093176d42", + "b5707293b9fb4eb995ff8282fbc21929", + "9f5afc14aac94e3799c92aa95c00e385", + "d948cc371f85489fbc4b3abdb54bb8c6", + "ef0631f551c2409693db6d55072d0f46", + "0835210630fe4bc68310d7c89bc00b86", + "197b3b01d6c5449e9675878b8947fd65", + "9db49afd63104a4f8b8232f2a08f72e9", + "3ab81851462748d3bcfbac7d88612dbf", + "4d6a885787004221af8499c3755b204e", + "8a4a53cb025f480793c3960f6892e8e5", + "f9b161d5e57f4727aee8100d639ec709", + "6397bdb3fcea4174a4fa535676f440c3", + "74f46c3b890149afbb0ed7507a35d401", + "57e2770a385c473d94727b62d040ae17", + "84405d4962c7480ba66aa0276f4cb493", + "3fcb445ba8674b88b39fa4485aaae7fc", + "fa86e36162684b99af5f5effa1a146a0", + "4eb8d7abed7e4357ad40201a25166c09", + "4d843033ffb44506887e6f0c4d7622ae", + "35664c91291c400baa1e83bc666f8ee9", + "0c8acf9e576d4b809bee4500406af039", + "dc4d1fac55e5457c83c6a5d409e4f031", + "ba2d7bde95c74dc19d7072c5e862d454" + ] + }, + "id": "ZFbO9Bt5HcgG", + "outputId": "cf368d39-d92a-49b7-a863-d647ad4f0551" + }, + "execution_count": 8, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "README.md: 0.00B [00:00, ?B/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "f8e667e3e5704855b894b383f4f64c5c" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "data/train-00000-of-00001-ebc48879f34571(…): 0%| | 0.00/1.54M [00:00 인코더 디코더 간 연결 강화\n", + "\n", + "6개 인코더+디코더 계층\n", + "\n", + "마지막 연산에서 layernorm_embedding 계층 통과\n", + "\n", + "- layernorm_embedding: 임베딩 벡터 마지막 차원에 대해 정규화 수행 -> 학습 안정화\n", + "\n", + "인코더 마지막 계층 출력값은 디코더의 모든 계층과 어텐션 연산 수행\n", + "\n", + "-> 마지막 디코더 계층의 출력값은 출력 크기가 단어 사전의 크기인 완전 연결 계층 통과해 언어 모델 형성" + ], + "metadata": { + "id": "cJhCc9ZfK8Ng" + } + }, + { + "cell_type": "code", + "source": [ + "!pip install evaluate rouge_score absl-py" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "I-fVdMkTL63J", + "outputId": "76d350b2-4603-4bb1-b8f9-c5f1993d2d10" + }, + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting evaluate\n", + " Downloading evaluate-0.4.6-py3-none-any.whl.metadata (9.5 kB)\n", + "Collecting rouge_score\n", + " Downloading rouge_score-0.1.2.tar.gz (17 kB)\n", + " Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + "Requirement already satisfied: absl-py in /usr/local/lib/python3.12/dist-packages (1.4.0)\n", + "Requirement already satisfied: datasets>=2.0.0 in /usr/local/lib/python3.12/dist-packages (from 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calc_rouge(preds, labels):\n", + " preds = preds.argmax(axis=-1)\n", + " # preds: 모델이 예측한 요약 토큰 인덱스를 담은 2차원 배열\n", + " ## argmax 함수로 각 토큰에 대해 가장 높은 확률을 가진 인덱스 선택해 1차원 배열로 변경\n", + " labels = np.where(labels != -100, labels, tokenizer.pad_token_id) # 정답 요약문\n", + " ## labels==-100 인 값들 -> 패딩 토큰 인덱스로 변경\n", + "\n", + " # 토큰 인덱스 -> 실제 텍스트 변환\n", + " decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)\n", + " decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n", + "\n", + " # 루지 점수 계산\n", + " rouge2 = rouge_score.compute(\n", + " predictions=decoded_preds,\n", + " references=decoded_labels\n", + " )\n", + " return rouge2[\"rouge2\"]\n", + "\n", + "def train(model, optimizer, dataloader):\n", + " model.train()\n", + " train_loss = 0.0\n", + "\n", + " for input_ids, attention_mask, labels in dataloader:\n", + " outputs = model(\n", + " input_ids=input_ids,\n", + " attention_mask=attention_mask,\n", + " labels=labels\n", + " )\n", + "\n", + " loss = outputs.loss\n", + " train_loss += loss.item()\n", + "\n", + " optimizer.zero_grad()\n", + " loss.backward()\n", + " optimizer.step()\n", + "\n", + " train_loss = train_loss / len(dataloader)\n", + " return train_loss\n", + "\n", + "def evaluation(model, dataloader):\n", + " with torch.no_grad():\n", + " model.eval()\n", + " val_loss, val_rouge = 0.0, 0.0\n", + "\n", + " for input_ids, attention_mask, labels in dataloader:\n", + " outputs = model(\n", + " input_ids=input_ids,\n", + " attention_mask=attention_mask,\n", + " labels=labels\n", + " )\n", + " logits = outputs.logits\n", + " loss = outputs.loss\n", + "\n", + " logits = logits.detach().cpu().numpy()\n", + " label_ids = labels.to(\"cpu\").numpy()\n", + " rouge = calc_rouge(logits, label_ids) # 루지\n", + "\n", + " val_loss += loss.item()\n", + " val_rouge += rouge\n", + "\n", + " val_loss = val_loss / len(dataloader)\n", + " val_rouge = val_rouge / len(dataloader)\n", + " return val_loss, val_rouge\n", + "\n", + "rouge_score = evaluate.load(\"rouge\", tokenizer=tokenizer)\n", + "best_loss = 10000\n", + "for epoch in range(epochs):\n", + " train_loss = train(model, optimizer, train_dataloader)\n", + " val_loss, val_accuracy = evaluation(model, valid_dataloader)\n", + " print(f\"Epoch {epoch + 1}: Train Loss: {train_loss:.4f} Val Loss: {val_loss:.4f} Val Rouge {val_accuracy:.4f}\")\n", + "\n", + " if val_loss < best_loss:\n", + " best_loss = val_loss\n", + " torch.save(model.state_dict(), \"/content/drive/MyDrive/euron/datasets/Week15/models/BartForConditionalGeneration.pt\")\n", + " print(\"Saved the model weights\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 158, + "referenced_widgets": [ + "80840c5bfb7c4c1b8f065d8332988961", + "5ad98e401257491b9326fb05a9cd91c4", + "bd8e3a23268847fa87dfe292dd9e3046", + "51edecb42d9040719b28f2ba502db5bb", + "fb6a5a3b657b4e1a847c1a2efabcd755", + "da9ccf362da1449e840d3650c40aae73", + "38ea5b8dd37b4c72890a8a9426a1d192", + "b877b4881367463b8d6839123cd639dd", + "6ce37380ac66457599f063a6e11aa122", + "7ed74348b3444f0a80b4897a8161c995", + "01a0d746581d43be92753f41628fa4ce" + ] + }, + "id": "ja6zw3bXL3kA", + "outputId": "71ddd608-95bf-4448-9a7a-f3c9a7d81829" + }, + "execution_count": 13, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Downloading builder script: 0.00B [00:00, ?B/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "80840c5bfb7c4c1b8f065d8332988961" + } + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Epoch 1: Train Loss: 2.1514 Val Loss: 1.8342 Val Rouge 0.2643\n", + "Saved the model weights\n", + "Epoch 2: Train Loss: 1.6243 Val Loss: 1.9051 Val Rouge 0.2579\n", + "Epoch 3: Train Loss: 1.2327 Val Loss: 1.9719 Val Rouge 0.2559\n", + "Epoch 4: Train Loss: 0.9527 Val Loss: 2.1186 Val Rouge 0.2470\n", + "Epoch 5: Train Loss: 0.7241 Val Loss: 2.2333 Val Rouge 0.2436\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# BART 모델 평가\n", + "model = BartForConditionalGeneration.from_pretrained(\n", + " pretrained_model_name_or_path=\"facebook/bart-base\"\n", + ").to(device)\n", + "model.load_state_dict(torch.load(\"/content/drive/MyDrive/euron/datasets/Week15/models/BartForConditionalGeneration.pt\"))\n", + "\n", + "test_loss, test_rouge_score = evaluation(model, test_dataloader)\n", + "print(f\"Test Loss : {test_loss:.4f}\")\n", + "print(f\"Test ROUGE-2 Score : {test_rouge_score:.4f}\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "wtTamjMRNmxL", + "outputId": "138a7bb7-a8b0-4c38-9df5-116fc4f7afc2" + }, + "execution_count": 14, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Test Loss : 1.7962\n", + "Test ROUGE-2 Score : 0.2685\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "ROUGE-2\n", + "\n", + "- 0~1 사이의 값\n", + "- 1에 가까울수록 높은 성능" + ], + "metadata": { + "id": "Iw_erY-NPYkE" + } + }, + { + "cell_type": "code", + "source": [ + "# 문장 요약문 직접 비교\n", + "from transformers import pipeline\n", + "\n", + "summarizer = pipeline(\n", + " task=\"summarization\",\n", + " model=model,\n", + " tokenizer=tokenizer,\n", + " max_length=54,\n", + " device=\"cpu\"\n", + ")\n", + "\n", + "for index in range(5):\n", + " news_text = test.text.iloc[index]\n", + " summarization = test.prediction.iloc[index]\n", + " predicted_summarization = summarizer(news_text)[0][\"summary_text\"]\n", + " print(f\"정답 요약문 : {summarization}\")\n", + " print(f\"모델 요약문 : {predicted_summarization}\\n\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "yVbM1cLePp_i", + "outputId": "7c559bfe-33d4-453c-ccb6-f839d8087287" + }, + "execution_count": 15, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Device set to use cpu\n", + "Your max_length is set to 128, but your input_length is only 111. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=55)\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "정답 요약문 : Clinton leads Trump by 4 points in Washington Post: ABC News poll\n", + "모델 요약문 : Clinton leads Trump by 4 points in Washington Post-ABC News poll\n", + "\n", + "정답 요약문 : Democrats question independence of Trump Supreme Court nominee\n", + "모델 요약문 : U.S. senators question whether Supreme Court nominee Gorsuch is independent\n", + "\n", + "정답 요약문 : In push for Yemen aid, U.S. warned Saudis of threats in Congress\n", + "모델 요약문 : U.S. warns Saudi Arabia over humanitarian situation in Yemen\n", + "\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Your max_length is set to 128, but your input_length is only 108. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=54)\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "정답 요약문 : Romanian ruling party leader investigated over 'criminal group'\n", + "모델 요약문 : Romanian anti-graft prosecutors probe leader on suspicion of graft\n", + "\n", + "정답 요약문 : Billionaire environmental activist Tom Steyer endorses Clinton\n", + "모델 요약문 : Environmental activist Steyer backs Clinton for U.S. president\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# ELECTRA" + ], + "metadata": { + "id": "Yc6Yl180Pv9F" + } + }, + { + "cell_type": "markdown", + "source": [ + "허깅 페이스 라이브러리 ELECTRA 모델과 nsmc 데이터셋으로 분류 모델 학습" + ], + "metadata": { + "id": "0thhUSQPdGeI" + } + }, + { + "cell_type": "code", + "source": [ + "# 데이터셋 불러옴\n", + "import numpy as np\n", + "import pandas as pd\n", + "from Korpora import Korpora\n", + "\n", + "corpus = Korpora.load(\"nsmc\")\n", + "df = pd.DataFrame(corpus.test).sample(20000, random_state=42)\n", + "train, valid, test = np.split(\n", + " df.sample(frac=1, random_state=42), [int(0.6 * len(df)), int(0.8 * len(df))]\n", + ")\n", + "\n", + "print(train.head(5).to_markdown())\n", + "print(f\"Training Data Size : {len(train)}\")\n", + "print(f\"Validation Data Size : {len(valid)}\")\n", + "print(f\"Testing Data Size : {len(test)}\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "FUVqHbXWPzyC", + "outputId": "aaedd638-9b2d-4fec-e76f-889e48ddea09" + }, + "execution_count": 16, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + " Korpora 는 다른 분들이 연구 목적으로 공유해주신 말뭉치들을\n", + " 손쉽게 다운로드, 사용할 수 있는 기능만을 제공합니다.\n", + "\n", + " 말뭉치들을 공유해 주신 분들에게 감사드리며, 각 말뭉치 별 설명과 라이센스를 공유 드립니다.\n", + " 해당 말뭉치에 대해 자세히 알고 싶으신 분은 아래의 description 을 참고,\n", + " 해당 말뭉치를 연구/상용의 목적으로 이용하실 때에는 아래의 라이센스를 참고해 주시기 바랍니다.\n", + "\n", + " # Description\n", + " Author : e9t@github\n", + " Repository : https://github.com/e9t/nsmc\n", + " References : www.lucypark.kr/docs/2015-pyconkr/#39\n", + "\n", + " Naver sentiment movie corpus v1.0\n", + " This is a movie review dataset in the Korean language.\n", + " Reviews were scraped from Naver Movies.\n", + "\n", + " The dataset construction is based on the method noted in\n", + " [Large movie review dataset][^1] from Maas et al., 2011.\n", + "\n", + " [^1]: http://ai.stanford.edu/~amaas/data/sentiment/\n", + "\n", + " # License\n", + " CC0 1.0 Universal (CC0 1.0) Public Domain Dedication\n", + " Details in https://creativecommons.org/publicdomain/zero/1.0/\n", + "\n", + "[Korpora] Corpus `nsmc` is already installed at /root/Korpora/nsmc/ratings_train.txt\n", + "[Korpora] Corpus `nsmc` is already installed at /root/Korpora/nsmc/ratings_test.txt\n", + "| | text | label |\n", + "|------:|:---------------------------------------------------------|--------:|\n", + "| 26891 | 역시 코믹액션은 성룡, 홍금보, 원표 삼인방이 최고지!! | 1 |\n", + "| 25024 | 점수 후하게 줘야것네 별 반개~ | 0 |\n", + "| 11666 | 오랜만에 느낄수 있는 [감독] 구타욕구. | 0 |\n", + "| 40303 | 본지는 좀 됬지만 극장서 돈주고 본게 아직까지 아까운 영화 | 0 |\n", + "| 18010 | 징키스칸이란 소재를 가지고 이것밖에 못만드냐 | 0 |\n", + "Training Data Size : 12000\n", + "Validation Data Size : 4000\n", + "Testing Data Size : 4000\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import torch\n", + "from transformers import ElectraTokenizer # ELECTRA 토크나이저 클래스\n", + "from torch.utils.data import TensorDataset, DataLoader\n", + "from torch.utils.data import RandomSampler, SequentialSampler\n", + "\n", + "def make_dataset(data, tokenizer, device):\n", + " tokenized = tokenizer(\n", + " text=data.text.tolist(),\n", + " padding=\"longest\",\n", + " truncation=True,\n", + " return_tensors=\"pt\"\n", + " )\n", + " input_ids = tokenized[\"input_ids\"].to(device)\n", + " attention_mask = tokenized[\"attention_mask\"].to(device)\n", + " labels = torch.tensor(data.label.values, dtype=torch.long).to(device)\n", + " return TensorDataset(input_ids, attention_mask, labels)\n", + "\n", + "def get_datalodader(dataset, sampler, batch_size):\n", + " data_sampler = sampler(dataset)\n", + " dataloader = DataLoader(dataset, sampler=data_sampler, batch_size=batch_size)\n", + " return dataloader\n", + "\n", + "epochs = 5\n", + "batch_size = 32\n", + "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", + "# 한국어 텍스트 분류를 위해 만들어진 KoELECTRA 사용\n", + "# ELECTRA는 판별 모델만을 이용해 다운스트림 작업 수행\n", + "## 생성 모델 - monologg/koelectra-base-generator\n", + "tokenizer = ElectraTokenizer.from_pretrained(\n", + " pretrained_model_name_or_path=\"monologg/koelectra-base-v3-discriminator\",\n", + " do_lower_case=False,\n", + ")\n", + "\n", + "train_dataset = make_dataset(train, tokenizer, device)\n", + "train_dataloader = get_datalodader(train_dataset, RandomSampler, batch_size)\n", + "\n", + "valid_dataset = make_dataset(valid, tokenizer, device)\n", + "valid_dataloader = get_datalodader(valid_dataset, SequentialSampler, batch_size)\n", + "\n", + "test_dataset = make_dataset(test, tokenizer, device)\n", + "test_dataloader = get_datalodader(test_dataset, SequentialSampler, batch_size)\n", + "\n", + "print(train_dataset[0])" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 461, + "referenced_widgets": [ + "11f4575330264aa1bdb036efe52302b7", + "b7dfa5c986f34fc49df2e85fe653251b", + "4f2a09550a7449c297dd996619527b31", + "e42fadca5640440a9341db3e34e30628", + "cd49fa4e568741429402632c54fe897f", + "c815c92f9d3042499785bc76be87c2bf", + "5135e8ddc21d4ec4b9ec0faa7efff4b5", + "c95412f7eb1d492e95c64d88c67b9185", + "242b3e223a484e15a35df6ac655ac015", + "a863ab2db02441b3b9f175c35d4af905", + "c38615c84c1e4fd781fb7d83bb02db2b", + "ecfeddca06224389a708876d444a40fd", + "6932b4b06307466384077fc981c91023", + "e8dbcd4ba80a4a4aa33570d04c2a781a", + "67694b0e694b436bac18678ebca263fd", + "737a0e81d2454d16a5932633b1927cec", + "e65ef1b68409423b867c16a8ddb51e5d", + "7ea91a3b0e85487786350f1d89161ed6", + "d2f569f404e443cd9c540591115265eb", + "2a4fb0d427ee43b3a5e64938e925bd05", + "01e55389332d483e9f49a401ad6c64f3", + "993898e500e5492abaede9c91c50919f", + "db02d36033d644cd969cdfbebb35c079", + "d92a0a695ed847579261c5c79f6c4e8c", + "03d154ff29984329a629bf83ef7310bd", + "ba467c3feb1e49fab53fc0340a014c0d", + "c836d47638a44e3a9e781624aab2e440", + "60be33d2395748548827d65e62965fbf", + "f60cf77b3493404e97b9d8a5649f7736", + "47f7afb39fe145b68cc7e664b2407eb7", + "577fe3571ac34e10ba6190d8f323705f", + "bb5aa2fd059247a0b2f9730ea40a85b9", + "68577c70dff14b49b4a1bba042f98b14" + ] + }, + "id": "UhCuReknaHCg", + "outputId": "5764d2a8-3abc-4b75-ca2c-ed0753aa4213" + }, + "execution_count": 17, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "tokenizer_config.json: 0%| | 0.00/61.0 [00:00 입력 텍스트 상태 값 반환" + ], + "metadata": { + "id": "VIipfnzPdun2" + } + }, + { + "cell_type": "code", + "source": [ + "# 모델 학습\n", + "import numpy as np\n", + "from torch import nn\n", + "\n", + "def calc_accuracy(preds, labels):\n", + " pred_flat = np.argmax(preds, axis=1).flatten()\n", + " labels_flat = labels.flatten()\n", + " return np.sum(pred_flat == labels_flat) / len(labels_flat)\n", + "\n", + "def train(model, optimizer, dataloader):\n", + " model.train()\n", + " train_loss = 0.0\n", + "\n", + " for input_ids, attention_mask, labels in dataloader:\n", + " outputs = model(input_ids=input_ids, attention_mask=attention_mask, labels=labels)\n", + "\n", + " loss = outputs.loss\n", + " train_loss += loss.item()\n", + "\n", + " optimizer.zero_grad()\n", + " loss.backward()\n", + " optimizer.step()\n", + "\n", + " train_loss = train_loss / len(dataloader)\n", + " return train_loss\n", + "\n", + "def evaluation(model, dataloader):\n", + " with torch.no_grad():\n", + " model.eval()\n", + " criterion = nn.CrossEntropyLoss()\n", + " val_loss, val_accuracy = 0.0, 0.0\n", + "\n", + " for input_ids, attention_mask, labels in dataloader:\n", + " outputs = model(input_ids=input_ids, attention_mask=attention_mask, labels=labels)\n", + " logits = outputs.logits\n", + "\n", + " loss = criterion(logits, labels)\n", + " logits = logits.detach().cpu().numpy()\n", + " label_ids = labels.to(\"cpu\").numpy()\n", + " accuracy = calc_accuracy(logits, label_ids)\n", + "\n", + " val_loss += loss.item()\n", + " val_accuracy += accuracy\n", + "\n", + " val_loss = val_loss/len(dataloader)\n", + " val_accuracy = val_accuracy/len(dataloader)\n", + " return val_loss, val_accuracy\n", + "\n", + "best_loss = 10000\n", + "for epoch in range(epochs):\n", + " train_loss = train(model, optimizer, train_dataloader)\n", + " val_loss, val_accuracy = evaluation(model, valid_dataloader)\n", + " print(f\"Epoch {epoch + 1}: Train Loss: {train_loss:.4f} Val Loss: {val_loss:.4f} Val Accuracy {val_accuracy:.4f}\")\n", + "\n", + " if val_loss < best_loss:\n", + " best_loss = val_loss\n", + " torch.save(model.state_dict(), \"/content/drive/MyDrive/euron/datasets/Week15/models/ElectraForSequenceClassification.pt\")\n", + " print(\"Saved the model weights\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "iHuXB5rTaOFM", + "outputId": "f810a489-9df5-41e0-8162-7f156207fe32" + }, + "execution_count": 20, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Epoch 1: Train Loss: 0.4441 Val Loss: 0.3275 Val Accuracy 0.8645\n", + "Saved the model weights\n", + "Epoch 2: Train Loss: 0.2793 Val Loss: 0.3069 Val Accuracy 0.8768\n", + "Saved the model weights\n", + "Epoch 3: Train Loss: 0.2065 Val Loss: 0.3270 Val Accuracy 0.8815\n", + "Epoch 4: Train Loss: 0.1490 Val Loss: 0.3283 Val Accuracy 0.8805\n", + "Epoch 5: Train Loss: 0.1142 Val Loss: 0.4010 Val Accuracy 0.8798\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# 예측 평가\n", + "model = ElectraForSequenceClassification.from_pretrained(\n", + " pretrained_model_name_or_path=\"monologg/koelectra-base-v3-discriminator\",\n", + " num_labels=2\n", + ").to(device)\n", + "model.load_state_dict(torch.load(\"/content/drive/MyDrive/euron/datasets/Week15/models/ElectraForSequenceClassification.pt\"))\n", + "\n", + "test_loss, test_accuracy = evaluation(model, test_dataloader)\n", + "print(f\"Test Loss : {test_loss:.4f}\")\n", + "print(f\"Test Accuracy : {test_accuracy:.4f}\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "dadxVYcZaZdp", + "outputId": "ab5f7ea3-4143-4d46-9b61-3b6c88eb39d8" + }, + "execution_count": 21, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Some weights of ElectraForSequenceClassification were not initialized from the model checkpoint at monologg/koelectra-base-v3-discriminator and are newly initialized: ['classifier.dense.bias', 'classifier.dense.weight', 'classifier.out_proj.bias', 'classifier.out_proj.weight']\n", + "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Test Loss : 0.3191\n", + "Test Accuracy : 0.8745\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# T5" + ], + "metadata": { + "id": "T_ysQcsZadnf" + } + }, + { + "cell_type": "markdown", + "source": [ + "허깅페이스의 T5 인코더-디코더 모델로 문장 요약 작업 수행" + ], + "metadata": { + "id": "PjpwCbDKeanF" + } + }, + { + "cell_type": "code", + "source": [ + "# 뉴스 요약 데이터셋 불러오기\n", + "import numpy as np\n", + "from datasets import load_dataset\n", + "\n", + "news = load_dataset(\"argilla/news-summary\", split=\"test\")\n", + "# text: 뉴스 본문, prediction: 요약된 뉴스\n", + "df = news.to_pandas().sample(5000, random_state=42)[[\"text\", \"prediction\"]]\n", + "# 요약 작업이라는 정보를 모델에 전달\n", + "df[\"text\"] = \"summarize: \" + df[\"text\"]\n", + "df[\"prediction\"] = df[\"prediction\"].map(lambda x: x[0][\"text\"])\n", + "train, valid, test = np.split(\n", + " df.sample(frac=1, random_state=42), [int(0.6 * len(df)), int(0.8 * len(df))]\n", + ")\n", + "\n", + "print(f\"Source News : {train.text.iloc[0][:200]}\")\n", + "print(f\"Summarization : {train.prediction.iloc[0][:50]}\")\n", + "print(f\"Training Data Size : {len(train)}\")\n", + "print(f\"Validation Data Size : {len(valid)}\")\n", + "print(f\"Testing Data Size : {len(test)}\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "fgiRfOA5ehC1", + "outputId": "c49a0d0c-54ac-4822-c0a9-0befad529765" + }, + "execution_count": 27, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Source News : summarize: DANANG, Vietnam (Reuters) - Russian President Vladimir Putin said on Saturday he had a normal dialogue with U.S. leader Donald Trump at a summit in Vietnam, and described Trump as civil, we\n", + "Summarization : Putin says had useful interaction with Trump at Vi\n", + "Training Data Size : 3000\n", + "Validation Data Size : 1000\n", + "Testing Data Size : 1000\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.12/dist-packages/numpy/_core/fromnumeric.py:57: FutureWarning: 'DataFrame.swapaxes' is deprecated and will be removed in a future version. Please use 'DataFrame.transpose' instead.\n", + " return bound(*args, **kwds)\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# 전처리\n", + "import torch\n", + "from transformers import T5Tokenizer # T5 토크나이저 클래스\n", + "from torch.utils.data import TensorDataset, DataLoader\n", + "from torch.utils.data import RandomSampler, SequentialSampler\n", + "from torch.nn.utils.rnn import pad_sequence # 패딩\n", + "\n", + "def make_dataset(data, tokenizer, device):\n", + " source = tokenizer(\n", + " text=data.text.tolist(),\n", + " padding=\"max_length\",\n", + " max_length=128,\n", + " truncation=True,\n", + " return_tensors=\"pt\"\n", + " )\n", + "\n", + " target = tokenizer(\n", + " text=data.prediction.tolist(),\n", + " padding=\"max_length\",\n", + " max_length=128,\n", + " truncation=True,\n", + " return_tensors=\"pt\"\n", + " )\n", + "\n", + " source_ids = source[\"input_ids\"].squeeze().to(device)\n", + " source_mask = source[\"attention_mask\"].squeeze().to(device)\n", + " target_ids = target[\"input_ids\"].squeeze().to(device)\n", + " target_mask = target[\"attention_mask\"].squeeze().to(device)\n", + " return TensorDataset(source_ids, source_mask, target_ids, target_mask)\n", + "\n", + "def get_datalodader(dataset, sampler, batch_size):\n", + " data_sampler = sampler(dataset)\n", + " dataloader = DataLoader(dataset, sampler=data_sampler, batch_size=batch_size)\n", + " return dataloader\n", + "\n", + "epochs = 5\n", + "batch_size = 8\n", + "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", + "tokenizer = T5Tokenizer.from_pretrained(\n", + " pretrained_model_name_or_path=\"t5-small\" # T5 기본 버전\n", + ")\n", + "\n", + "train_dataset = make_dataset(train, tokenizer, device)\n", + "train_dataloader = get_datalodader(train_dataset, RandomSampler, batch_size)\n", + "\n", + "valid_dataset = make_dataset(valid, tokenizer, device)\n", + "valid_dataloader = get_datalodader(valid_dataset, SequentialSampler, batch_size)\n", + "\n", + "test_dataset = make_dataset(test, tokenizer, device)\n", + "test_dataloader = get_datalodader(test_dataset, SequentialSampler, batch_size)\n", + "\n", + "print(next(iter(train_dataloader)))\n", + "print(tokenizer.convert_ids_to_tokens(21603))\n", + "print(tokenizer.convert_ids_to_tokens(10))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "1IpgmVIMenBf", + "outputId": "9b727c4c-8e1f-4872-e8cf-c14cb452791b" + }, + "execution_count": 29, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[tensor([[21603, 10, 272, ..., 1827, 5362, 1],\n", + " [21603, 10, 549, ..., 19021, 227, 1],\n", + " [21603, 10, 446, ..., 11, 2785, 1],\n", + " ...,\n", + " [21603, 10, 549, ..., 1676, 18, 1],\n", + " [21603, 10, 4753, ..., 8417, 6574, 1],\n", + " [21603, 10, 549, ..., 25770, 9220, 1]], device='cuda:0'), tensor([[1, 1, 1, ..., 1, 1, 1],\n", + " [1, 1, 1, ..., 1, 1, 1],\n", + " [1, 1, 1, ..., 1, 1, 1],\n", + " ...,\n", + " [1, 1, 1, ..., 1, 1, 1],\n", + " [1, 1, 1, ..., 1, 1, 1],\n", + " [1, 1, 1, ..., 1, 1, 1]], device='cuda:0'), tensor([[22198, 343, 7, ..., 0, 0, 0],\n", + " [ 412, 5, 134, ..., 0, 0, 0],\n", + " [30499, 6, 2523, ..., 0, 0, 0],\n", + " ...,\n", + " [ 412, 5, 134, ..., 0, 0, 0],\n", + " [25699, 45, 1117, ..., 0, 0, 0],\n", + " [ 3, 20331, 1489, ..., 0, 0, 0]], device='cuda:0'), tensor([[1, 1, 1, ..., 0, 0, 0],\n", + " [1, 1, 1, ..., 0, 0, 0],\n", + " [1, 1, 1, ..., 0, 0, 0],\n", + " ...,\n", + " [1, 1, 1, ..., 0, 0, 0],\n", + " [1, 1, 1, ..., 0, 0, 0],\n", + " [1, 1, 1, ..., 0, 0, 0]], device='cuda:0')]\n", + "▁summarize\n", + ":\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# T5 모델 선언\n", + "from torch import optim\n", + "from transformers import T5ForConditionalGeneration\n", + "\n", + "model = T5ForConditionalGeneration.from_pretrained(\n", + " pretrained_model_name_or_path=\"t5-small\",\n", + ").to(device)\n", + "optimizer = optim.AdamW(model.parameters(), lr=1e-5, eps=1e-8)" + ], + "metadata": { + "id": "M7knWq89e1oB" + }, + "execution_count": 32, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# 모델 학습 및 평가\n", + "from torch import optim\n", + "from transformers import T5ForConditionalGeneration\n", + "\n", + "model = T5ForConditionalGeneration.from_pretrained(\n", + " pretrained_model_name_or_path=\"t5-small\",\n", + ").to(device)\n", + "optimizer = optim.AdamW(model.parameters(), lr=1e-5, eps=1e-8)\n", + "import numpy as np\n", + "from torch import nn\n", + "\n", + "def calc_accuracy(preds, labels):\n", + " pred_flat = np.argmax(preds, axis=1).flatten()\n", + " labels_flat = labels.flatten()\n", + " return np.sum(pred_flat == labels_flat) / len(labels_flat)\n", + "\n", + "def train(model, optimizer, dataloader):\n", + " model.train()\n", + " train_loss = 0.0\n", + "\n", + " for source_ids, source_mask, target_ids, target_mask in dataloader:\n", + " # 디코더에 입력될 시퀀스를 인코딩한 텐서\n", + " decoder_input_ids = target_ids[:, :-1].contiguous() # 마지막 제외 나머지 토큰 사용\n", + " ## contiguous 메소드: 텐서를 메모리상에 연속된 블록으로 저장하는 역할\n", + " labels = target_ids[:, 1:].clone().detach()\n", + " labels[target_ids[:, 1:] == tokenizer.pad_token_id] = -100 # 패딩 토큰 무시\n", + "\n", + " outputs = model(\n", + " input_ids=source_ids,\n", + " attention_mask=source_mask,\n", + " decoder_input_ids=decoder_input_ids,\n", + " labels=labels,\n", + " )\n", + "\n", + " loss = outputs.loss\n", + " train_loss += loss.item()\n", + "\n", + " optimizer.zero_grad()\n", + " loss.backward()\n", + " optimizer.step()\n", + "\n", + " train_loss = train_loss / len(dataloader)\n", + " return train_loss\n", + "\n", + "def evaluation(model, dataloader):\n", + " with torch.no_grad():\n", + " model.eval()\n", + " val_loss = 0.0\n", + "\n", + " for source_ids, source_mask, target_ids, target_mask in dataloader:\n", + " decoder_input_ids = target_ids[:, :-1].contiguous()\n", + " labels = target_ids[:, 1:].clone().detach()\n", + " labels[target_ids[:, 1:] == tokenizer.pad_token_id] = -100\n", + "\n", + " outputs = model(\n", + " input_ids=source_ids,\n", + " attention_mask=source_mask,\n", + " decoder_input_ids=decoder_input_ids,\n", + " labels=labels,\n", + " )\n", + "\n", + " loss = outputs.loss\n", + " val_loss += loss.item()\n", + "\n", + " val_loss = val_loss / len(dataloader)\n", + " return val_loss\n", + "\n", + "\n", + "best_loss = 10000\n", + "for epoch in range(epochs):\n", + " train_loss = train(model, optimizer, train_dataloader)\n", + " val_loss = evaluation(model, valid_dataloader)\n", + " print(f\"Epoch {epoch + 1}: Train Loss: {train_loss:.4f} Val Loss: {val_loss:.4f}\")\n", + "\n", + " if val_loss < best_loss:\n", + " best_loss = val_loss\n", + " torch.save(model.state_dict(), \"/content/drive/MyDrive/euron/datasets/Week15/models/T5ForConditionalGeneration.pt\")\n", + " print(\"Saved the model weights\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "bn2H0zBVe8ML", + "outputId": "eeaacbed-3c6b-4f6b-8939-19e83ea998ad" + }, + "execution_count": 33, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Epoch 1: Train Loss: 4.3426 Val Loss: 3.3304\n", + "Saved the model weights\n", + "Epoch 2: Train Loss: 3.4250 Val Loss: 2.9178\n", + "Saved the model weights\n", + "Epoch 3: Train Loss: 3.1341 Val Loss: 2.7643\n", + "Saved the model weights\n", + "Epoch 4: Train Loss: 2.9846 Val Loss: 2.6755\n", + "Saved the model weights\n", + "Epoch 5: Train Loss: 2.8995 Val Loss: 2.6094\n", + "Saved the model weights\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# T5 생성 모델 테스트\n", + "model.eval()\n", + "with torch.no_grad():\n", + " for source_ids, source_mask, target_ids, target_mask in test_dataloader:\n", + " # 생성 메소드: 입력 문장(입력 시퀀스) -> 요약문(출력 시퀀스) 생성\n", + " generated_ids = model.generate(\n", + " input_ids=source_ids, # 토큰 인덱스\n", + " attention_mask=source_mask, # 어텐션 마스크 (마스킹 정보)\n", + " max_length=128, # 생성될 요약문 최대 길이\n", + " num_beams=3, # 빔 개수: beam search 알고리즘 빔 크기\n", + " repetition_penalty=2.5, # 반복 패널티: 중복 토큰 제어 값\n", + " length_penalty=1.0, # 길이 패널티: 생성된 시퀀스 길이에 대한 보상 제어\n", + " early_stopping=True, # 조기 중단: EOS 토큰 생성되면 중단\n", + " )\n", + "\n", + " for generated, target in zip(generated_ids, target_ids):\n", + " # 디코더 모델: 다수 후보 단어 생성\n", + " ## 빔서치 알고리즘이 미리 지정한 빔 크기만큼 후보 단어 시퀀스 유지, 나머지 삭제\n", + " ## 가장 높은 확률을 가진 시퀀스 선택\n", + " pred = tokenizer.decode(\n", + " generated, skip_special_tokens=True, clean_up_tokenization_spaces=True\n", + " ) # skip_special_tokens: 특수 토큰 제거, clean_up_tokenization_spaces: 불필요 공백 제거\n", + " actual = tokenizer.decode(\n", + " target, skip_special_tokens=True, clean_up_tokenization_spaces=True\n", + " )\n", + " print(\"Generated Headline Text:\", pred)\n", + " print(\"Actual Headline Text :\", actual)\n", + " break" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "pqAms92yfGmT", + "outputId": "9af7b023-829a-478d-cac8-0ba4718e7925" + }, + "execution_count": 34, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Generated Headline Text: Clinton leads Trump by 4 percentage points in four-war race for Nov. 8 election\n", + "Actual Headline Text : Clinton leads Trump by 4 points in Washington Post: ABC News poll\n", + "Generated Headline Text: U.S. senators sharpen potential line of attack against Gorsuch's nomination to Supreme Court\n", + "Actual Headline Text : Democrats question independence of Trump Supreme Court nominee\n", + "Generated Headline Text: U.S. warns Saudi Arabia over Yemen's humanitarian situation could constrain U.S. aid, official says.\n", + "Actual Headline Text : In push for Yemen aid, U.S. warned Saudis of threats in Congress\n", + "Generated Headline Text: Romanian anti-corruption prosecutors open investigation into Liviu Dragnea on suspicion of forming criminal group to siphon off cash from state projects\n", + "Actual Headline Text : Romanian ruling party leader investigated over 'criminal group'\n", + "Generated Headline Text: environmental activist endorsed Hillary Clinton for U.S. president\n", + "Actual Headline Text : Billionaire environmental activist Tom Steyer endorses Clinton\n", + "Generated Headline Text: tv presenter delivers news of Pyongyang nuclear test with her usual gusto.\n", + "Actual Headline Text : Voice of triumph or doom: North Korean presenter back in limelight for nuclear test\n", + "Generated Headline Text: Delson Guarate and Yon Goicoechea among nearly 400 jailed anti-Maduro activists.\n", + "Actual Headline Text : Venezuela frees two anti-Maduro activists; scores still jailed\n", + "Generated Headline Text: House Majority Leader says he still troubled by Clinton email server\n", + "Actual Headline Text : House No. 2 Republican says still questions Clinton's judgment in email matter\n" + ] + } + ] + } + ] +} \ No newline at end of file diff --git "a/Week15_\354\230\210\354\212\265\352\263\274\354\240\234_\352\271\200\355\203\234\354\230\201.pdf" 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From 80023171641b75e169f3377aa1098d0a783df672 Mon Sep 17 00:00:00 2001
From: tykim0O0 
Date: Mon, 22 Dec 2025 20:17:31 +0900
Subject: [PATCH 2/2] =?UTF-8?q?15=EC=A3=BC=EC=B0=A8=20=EB=B3=B5=EC=8A=B5?=
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---
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diff --git "a/Week15_\353\263\265\354\212\265\352\263\274\354\240\234_\352\271\200\355\203\234\354\230\201.ipynb" "b/Week15_\353\263\265\354\212\265\352\263\274\354\240\234_\352\271\200\355\203\234\354\230\201.ipynb"
new file mode 100644
index 0000000..105d8a0
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+      }
+    }
+  },
+  "cells": [
+    {
+      "id": "4644be45",
+      "cell_type": "markdown",
+      "source": [
+        "# BERT와 ELECTRA 모델 비교 실습\n",
+        "\n",
+        "- 이번 복습과제에서는 SST-2 데이터셋을 기반으로 BERT와 ELECTRA 모델을 학습시켜보고 성능과 구조의 차이를 알아보겠습니다.\n",
+        "- 코드 실행시간이 매우 길 수 있습니다.\n",
+        "  - 최대한 끝까지 실행해보시되, 시간 부족으로 인해 중간에 중지하신 실행 결과를 제출하셔도 괜찮습니다.\n",
+        "  - 제출 이후에는 꼭 끝까지 실행시켜 비교해보시기 바랍니다!"
+      ],
+      "metadata": {
+        "id": "4644be45"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from google.colab import drive\n",
+        "drive.mount('/content/drive')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "9L3k-Cw1GU0O",
+        "outputId": "c47561f0-43d3-4e5c-8024-090a0049ebf7"
+      },
+      "id": "9L3k-Cw1GU0O",
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mounted at /content/drive\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip uninstall huggingface_hub fsspec -y"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "oC2lHBZlG5V-",
+        "outputId": "21358102-5659-4a33-c406-14cad55b2268"
+      },
+      "id": "oC2lHBZlG5V-",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Found existing installation: huggingface_hub 1.2.3\n",
+            "Uninstalling huggingface_hub-1.2.3:\n",
+            "  Successfully uninstalled huggingface_hub-1.2.3\n",
+            "Found existing installation: fsspec 2025.10.0\n",
+            "Uninstalling fsspec-2025.10.0:\n",
+            "  Successfully uninstalled fsspec-2025.10.0\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install --upgrade --quiet datasets fsspec==2025.3.0 \"huggingface_hub>=0.34.0,<1.0\""
+      ],
+      "metadata": {
+        "id": "2eSXxqg4a9SF",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "e77dc5c3-e0c4-4c04-91cf-f63e54e9b776"
+      },
+      "id": "2eSXxqg4a9SF",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/193.6 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m193.6/193.6 kB\u001b[0m \u001b[31m11.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/566.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m566.1/566.1 kB\u001b[0m \u001b[31m24.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "---------------\n",
+        "여기까지만 실행\n",
+        "---------------\n",
+        "그 다음,  런타임 > 세션 다시 시작 > 아래 셀부터 실행"
+      ],
+      "metadata": {
+        "id": "4rq77NfBbByn"
+      },
+      "id": "4rq77NfBbByn"
+    },
+    {
+      "id": "ac52f320",
+      "cell_type": "code",
+      "metadata": {
+        "id": "ac52f320"
+      },
+      "execution_count": 2,
+      "source": [
+        "import torch\n",
+        "from torch.utils.data import DataLoader\n",
+        "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
+        "from datasets import load_dataset\n",
+        "from torch.optim import AdamW\n",
+        "from tqdm import tqdm"
+      ],
+      "outputs": []
+    },
+    {
+      "id": "393f4136",
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "393f4136",
+        "outputId": "e1ace32b-efcd-4fc5-df9f-d2585f04972a"
+      },
+      "execution_count": 3,
+      "source": [
+        "# batch_size와 epochs를 조정해보세요!\n",
+        "batch_size = 16\n",
+        "epochs = 2\n",
+        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+        "print(f\"Using device: {device}\")"
+      ],
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Using device: cuda\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 데이터셋 로드\n",
+        "raw_datasets = load_dataset(\"sst2\")\n",
+        "raw_datasets"
+      ],
+      "metadata": {
+        "colab": {
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+        "id": "QTVKkGiIflzk",
+        "outputId": "76febad4-7268-4e59-8261-386eb7a37397"
+      },
+      "id": "QTVKkGiIflzk",
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
+            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
+            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
+            "You will be able to reuse this secret in all of your notebooks.\n",
+            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "README.md: 0.00B [00:00, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "423863b2cb0947499b62690215490266"
+            }
+          },
+          "metadata": {}
+        },
+        {
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+          "data": {
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