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"2eSXxqg4a9SF" + }, + "id": "2eSXxqg4a9SF", + "execution_count": 2, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "!pip install -U transformers huggingface_hub==0.34.4" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "4oZoUkkFSWFY", + "outputId": "0e9722d5-0507-4567-b083-6293252a6a1b" + }, + "id": "4oZoUkkFSWFY", + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Requirement already satisfied: transformers in /usr/local/lib/python3.12/dist-packages (4.57.3)\n", + "Collecting huggingface_hub==0.34.4\n", + " Downloading huggingface_hub-0.34.4-py3-none-any.whl.metadata (14 kB)\n", + "Requirement already satisfied: filelock in /usr/local/lib/python3.12/dist-packages (from huggingface_hub==0.34.4) (3.20.0)\n", + "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.12/dist-packages (from huggingface_hub==0.34.4) (2025.10.0)\n", + "Requirement already satisfied: 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/usr/local/lib/python3.12/dist-packages (from transformers) (2025.11.3)\n", + "Requirement already satisfied: tokenizers<=0.23.0,>=0.22.0 in /usr/local/lib/python3.12/dist-packages (from transformers) (0.22.1)\n", + "Requirement already satisfied: safetensors>=0.4.3 in /usr/local/lib/python3.12/dist-packages (from transformers) (0.7.0)\n", + "Requirement already satisfied: charset_normalizer<4,>=2 in /usr/local/lib/python3.12/dist-packages (from requests->huggingface_hub==0.34.4) (3.4.4)\n", + "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.12/dist-packages (from requests->huggingface_hub==0.34.4) (3.11)\n", + "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.12/dist-packages (from requests->huggingface_hub==0.34.4) (2.5.0)\n", + "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.12/dist-packages (from requests->huggingface_hub==0.34.4) (2025.11.12)\n", + "Downloading huggingface_hub-0.34.4-py3-none-any.whl (561 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m561.5/561.5 kB\u001b[0m \u001b[31m14.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hInstalling collected packages: huggingface_hub\n", + " Attempting uninstall: huggingface_hub\n", + " Found existing installation: huggingface_hub 1.2.3\n", + " Uninstalling huggingface_hub-1.2.3:\n", + " Successfully uninstalled huggingface_hub-1.2.3\n", + "Successfully installed huggingface_hub-0.34.4\n" + ] + } + ] + }, + { + "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": 1, + "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": "415d09f8-0ad3-43da-a306-1a22bdaad992" + }, + "execution_count": 5, + "source": [ + "# batch_size와 epochs를 조정해보세요!\n", + "batch_size = 16\n", + "epochs = 1\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": { + "base_uri": "https://localhost:8080/" + }, + "id": "QTVKkGiIflzk", + "outputId": "ed926525-7036-411e-9d68-cfa469cb7227" + }, + "id": "QTVKkGiIflzk", + "execution_count": 6, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "DatasetDict({\n", + " train: Dataset({\n", + " features: ['idx', 'sentence', 'label'],\n", + " num_rows: 67349\n", + " })\n", + " validation: Dataset({\n", + " features: ['idx', 'sentence', 'label'],\n", + " num_rows: 872\n", + " })\n", + " test: Dataset({\n", + " features: ['idx', 'sentence', 'label'],\n", + " num_rows: 1821\n", + " })\n", + "})" + ] + }, + "metadata": {}, + "execution_count": 6 + } + ] + }, + { + "cell_type": "code", + "source": [ + "# 전처리\n", + "def tokenize_function(examples, tokenizer):\n", + " return tokenizer(examples[\"sentence\"], padding=\"max_length\", truncation=True, max_length=128)" + ], + "metadata": { + "id": "Y_zJtt34ftoZ" + }, + "id": "Y_zJtt34ftoZ", + "execution_count": 4, + "outputs": [] + }, + { + "id": "4e3b88e4", + "cell_type": "markdown", + "source": [ + "## 🔹 BERT와 ELECTRA 실험" + ], + "metadata": { + "id": "4e3b88e4" + } + }, + { + "cell_type": "code", + "source": [ + "# 학습 함수 정의\n", + "def train_and_evaluate(model_name):\n", + " print(f\"\\n======== Now Training: {model_name} ========\")\n", + "\n", + " tokenizer = AutoTokenizer.from_pretrained(model_name)\n", + " tokenized_datasets = raw_datasets.map(lambda x: tokenize_function(x, tokenizer), batched=True)\n", + "\n", + " tokenized_datasets = tokenized_datasets.rename_column(\"label\", \"labels\")\n", + " tokenized_datasets.set_format(\"torch\", columns=[\"input_ids\", \"attention_mask\", \"labels\"])\n", + " train_dataset = tokenized_datasets[\"train\"]\n", + " valid_dataset = tokenized_datasets[\"validation\"]\n", + " train_dataset = train_dataset.select(range(10000))\n", + "\n", + " train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n", + " valid_loader = DataLoader(valid_dataset, batch_size=batch_size)\n", + "\n", + " model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=2).to(device)\n", + " optimizer = AdamW(model.parameters(), lr=2e-5)\n", + "\n", + " model.train()\n", + " for epoch in range(epochs):\n", + " total_loss = 0\n", + " for batch in tqdm(train_loader, desc=f\"Epoch {epoch+1}\"):\n", + " batch = {k: v.to(device) for k, v in batch.items()}\n", + " outputs = model(\n", + " input_ids=batch[\"input_ids\"],\n", + " attention_mask=batch[\"attention_mask\"],\n", + " labels=batch[\"labels\"],\n", + " )\n", + " loss = outputs.loss\n", + " total_loss += loss.item()\n", + "\n", + " loss.backward()\n", + " optimizer.step()\n", + " optimizer.zero_grad()\n", + "\n", + " avg_loss = total_loss / len(train_loader)\n", + " print(f\"Epoch {epoch+1} - Avg Train Loss: {avg_loss:.4f}\")\n", + "\n", + " model.eval()\n", + " correct = 0\n", + " total = 0\n", + " with torch.no_grad():\n", + " for batch in valid_loader:\n", + " batch = {k: v.to(device) for k, v in batch.items()}\n", + " outputs = model(**batch)\n", + " predictions = torch.argmax(outputs.logits, dim=-1)\n", + " correct += (predictions == batch[\"labels\"]).sum().item()\n", + " total += batch[\"labels\"].size(0)\n", + "\n", + " acc = correct / total\n", + " print(f\"Validation Accuracy ({model_name}): {acc:.4f}\")\n", + " return acc\n", + "\n", + "# 실행 및 평가\n", + "bert_acc = train_and_evaluate(\"bert-base-uncased\")\n", + "electra_acc = train_and_evaluate(\"google/electra-base-discriminator\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 855, + "referenced_widgets": [ + "8a35ff1731c34b228904eb416e4549e0", + "db782b5fa0ca4376aaa0acd68c09edcc", + "3f4c1f67ec51468082d310c03c477ac5", + "72d7f5850a874dc2845ec00f914ce6d4", + "1838651aa89a474d9da6958de12854e8", + "24930ad877a440f98f698762980accee", + "874048759c2640b49221189f27329c6e", + "3c7c7bb7c1f64b2c8e9e58220ef56c4d", + "aedf2d972cae45aa811591acb04e583e", + "81f27d58b86a45f7b32a20c9e21eb659", + 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+ "text": [ + "\n", + "======== Now Training: bert-base-uncased ========\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "tokenizer_config.json: 0%| | 0.00/48.0 [00:00