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"2eSXxqg4a9SF", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "7cfb9763-6124-4403-8a5c-df64e4091de6" + }, + "id": "2eSXxqg4a9SF", + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/512.3 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m512.3/512.3 kB\u001b[0m \u001b[31m17.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/201.0 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m201.0/201.0 kB\u001b[0m \u001b[31m10.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m521.0/521.0 kB\u001b[0m \u001b[31m21.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m47.7/47.7 MB\u001b[0m \u001b[31m15.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", + "transformers 4.57.3 requires huggingface-hub<1.0,>=0.34.0, but you have huggingface-hub 1.2.3 which is incompatible.\n", + "gcsfs 2025.3.0 requires fsspec==2025.3.0, but you have fsspec 2025.10.0 which is incompatible.\u001b[0m\u001b[31m\n", + "\u001b[0m" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "!pip uninstall -y huggingface-hub transformers datasets\n", + "!pip install huggingface-hub==0.35.1 transformers==4.45.2 datasets==2.21.0\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "JgRVJtJzEBat", + "outputId": "73e8785b-f96a-467f-b5ec-102df627b4da" + }, + "id": "JgRVJtJzEBat", + "execution_count": 2, + "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: transformers 4.57.3\n", + "Uninstalling transformers-4.57.3:\n", + " Successfully uninstalled transformers-4.57.3\n", + "Found existing installation: datasets 4.4.2\n", + "Uninstalling datasets-4.4.2:\n", + " Successfully uninstalled datasets-4.4.2\n", + "Collecting huggingface-hub==0.35.1\n", + " Downloading huggingface_hub-0.35.1-py3-none-any.whl.metadata (14 kB)\n", + "Collecting transformers==4.45.2\n", + " Downloading transformers-4.45.2-py3-none-any.whl.metadata (44 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.4/44.4 kB\u001b[0m \u001b[31m2.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hCollecting datasets==2.21.0\n", + " Downloading datasets-2.21.0-py3-none-any.whl.metadata (21 kB)\n", + "Requirement already satisfied: filelock in /usr/local/lib/python3.12/dist-packages (from huggingface-hub==0.35.1) (3.20.0)\n", + "Requirement already satisfied: fsspec>=2023.5.0 in 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(from transformers==4.45.2) (2.0.2)\n", + "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.12/dist-packages (from transformers==4.45.2) (2025.11.3)\n", + "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.12/dist-packages (from transformers==4.45.2) (0.7.0)\n", + "Collecting tokenizers<0.21,>=0.20 (from transformers==4.45.2)\n", + " Downloading tokenizers-0.20.3-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (6.7 kB)\n", + "Requirement already satisfied: pyarrow>=15.0.0 in /usr/local/lib/python3.12/dist-packages (from datasets==2.21.0) (22.0.0)\n", + "Requirement already satisfied: dill<0.3.9,>=0.3.0 in /usr/local/lib/python3.12/dist-packages (from datasets==2.21.0) (0.3.8)\n", + "Requirement already satisfied: pandas in /usr/local/lib/python3.12/dist-packages (from datasets==2.21.0) (2.2.2)\n", + "Requirement already satisfied: xxhash in /usr/local/lib/python3.12/dist-packages (from datasets==2.21.0) (3.6.0)\n", + "Requirement already satisfied: multiprocess in /usr/local/lib/python3.12/dist-packages (from datasets==2.21.0) (0.70.16)\n", + "Collecting fsspec>=2023.5.0 (from huggingface-hub==0.35.1)\n", + " Downloading fsspec-2024.6.1-py3-none-any.whl.metadata (11 kB)\n", + "Requirement already satisfied: aiohttp in /usr/local/lib/python3.12/dist-packages (from datasets==2.21.0) (3.13.2)\n", + "Requirement already satisfied: aiohappyeyeballs>=2.5.0 in /usr/local/lib/python3.12/dist-packages (from aiohttp->datasets==2.21.0) (2.6.1)\n", + "Requirement already satisfied: aiosignal>=1.4.0 in /usr/local/lib/python3.12/dist-packages (from aiohttp->datasets==2.21.0) (1.4.0)\n", + "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.12/dist-packages (from aiohttp->datasets==2.21.0) (25.4.0)\n", + "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.12/dist-packages (from aiohttp->datasets==2.21.0) (1.8.0)\n", + "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.12/dist-packages (from aiohttp->datasets==2.21.0) (6.7.0)\n", + "Requirement already satisfied: propcache>=0.2.0 in /usr/local/lib/python3.12/dist-packages (from aiohttp->datasets==2.21.0) (0.4.1)\n", + "Requirement already satisfied: yarl<2.0,>=1.17.0 in /usr/local/lib/python3.12/dist-packages (from aiohttp->datasets==2.21.0) (1.22.0)\n", + "Requirement already satisfied: charset_normalizer<4,>=2 in /usr/local/lib/python3.12/dist-packages (from requests->huggingface-hub==0.35.1) (3.4.4)\n", + "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.12/dist-packages (from requests->huggingface-hub==0.35.1) (3.11)\n", + "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.12/dist-packages (from requests->huggingface-hub==0.35.1) (2.5.0)\n", + "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.12/dist-packages (from requests->huggingface-hub==0.35.1) (2025.11.12)\n", + "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.12/dist-packages (from pandas->datasets==2.21.0) (2.9.0.post0)\n", + "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.12/dist-packages (from pandas->datasets==2.21.0) (2025.2)\n", + "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.12/dist-packages (from pandas->datasets==2.21.0) (2025.3)\n", + "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.12/dist-packages (from python-dateutil>=2.8.2->pandas->datasets==2.21.0) (1.17.0)\n", + "Downloading huggingface_hub-0.35.1-py3-none-any.whl (563 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m563.3/563.3 kB\u001b[0m \u001b[31m21.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hDownloading transformers-4.45.2-py3-none-any.whl (9.9 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m9.9/9.9 MB\u001b[0m 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installation: fsspec 2025.10.0\n", + " Uninstalling fsspec-2025.10.0:\n", + " Successfully uninstalled fsspec-2025.10.0\n", + " Attempting uninstall: tokenizers\n", + " Found existing installation: tokenizers 0.22.1\n", + " Uninstalling tokenizers-0.22.1:\n", + " Successfully uninstalled tokenizers-0.22.1\n", + "\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", + "gcsfs 2025.3.0 requires fsspec==2025.3.0, but you have fsspec 2024.6.1 which is incompatible.\u001b[0m\u001b[31m\n", + "\u001b[0mSuccessfully installed datasets-2.21.0 fsspec-2024.6.1 huggingface-hub-0.35.1 tokenizers-0.20.3 transformers-4.45.2\n" + ] + }, + { + "output_type": "display_data", + "data": { + "application/vnd.colab-display-data+json": { + "pip_warning": { + "packages": [ + "huggingface_hub" + ] + }, + "id": "689e13137d3c462ab83eca3a6ea1fd78" + } + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "import os\n", + "os.kill(os.getpid(), 9)\n" + ], + "metadata": { + "id": "Uz8bgKR0EIgC" + }, + "id": "Uz8bgKR0EIgC", + "execution_count": null, + "outputs": [] + }, + { + "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", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "7d111eea-e726-43f8-ded6-7a54265ddc32" + }, + "execution_count": 7, + "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\n", + "\n", + "\n", + "print(\"✅ Import 성공\")\n" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "✅ Import 성공\n" + ] + } + ] + }, + { + "id": "393f4136", + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "393f4136", + "outputId": "fa94cb5d-c424-4720-ff07-00d517098c5e" + }, + "execution_count": 2, + "source": [ + "# batch_size와 epochs를 조정해보세요!\n", + "batch_size = 16\n", + "epochs = 2\n", + "\n", + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "print(f\"Using device: {device}\")\n" + ], + "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/", + "height": 624, + "referenced_widgets": [ + "a76700c0fc0842bda2b761e068b0aa6a", + "70b8b9408eaf43d4b22913e843608e85", + "058ce626dbcc4fad8f475e31028827cb", + "7db398ea0085448692ff5507d3e29c33", + "0922c35722a145b699e2cd1d56f9f33c", + "31afb9e05bca4470b23ea7a5cc1c3739", + "5b2be27b480a4351aa525f444c2493f2", + "9ab8c8757e354340925678ba403f84fe", + "1cc195488fd94811b9c7dfb63c4ed322", + "ce577bdd5df540f0afea63086def9ef8", + "622503308cbc4a96b47d11d9e7f9f88a", + "71a8015b2b674538a88b794525672eab", + "c8d0efa676314c9fbe5e3639509479fa", + "92f592702ff942a2981b410554723e4c", + "e06727bcebce42d6a3c4678f1fac766f", + "b81d3d332f104eed8b88d0378f9c1f29", + "0d0f37e429b94994af4ea49e7c577e51", + "0143e3d2778e4539a3d5aa19b7285b3c", + "491e6b60a21041f3b49f501c288c2633", + "8cd4043a9cc647b9a1d9a086e773dd15", + "2f44c06078b34fafb7fd900e65f03dab", + "934844c673aa4d9e926393e009c4f4da", + "147d6017337345949eacbbe7209c3f81", + "98a0ca3b9a1d4a079987516ccef0b842", + "949a82824a19407ca4d082237db3f0c9", + "667abff3a57943609ebd5a2a35121307", + "1f7f9eb7480f4ff2a75bd3bcf69f2ec4", + "b5b4fb2b82544e318d4fd30812c5e73d", + "74251d04e66b4f0086c0316c7a6bcfaa", + "517d333b6a4b4ee3bbc14f429d68c38c", + "968545ab1edf4397b38cdd4d686d5a17", + "74e19c377fc2480099f972833edfe8bd", + "e25493090f484f0fbdb7dca505c46982", + "e11f7ad575e84e30825771ec161c985f", + "b9570b2e6b8047e98f9800ba45f59c82", + "107eeb8f71bc47488eb99e3dbe7a2c96", + "d2ed95f73fe64ff1b4b4822245ae6230", + "48f4dd957d314b0e825beb2be62eb212", + "7ef6f9b98721414dbb20bd23604b23d5", + "7fff7e5dbd234408817f5240bec4a21f", + "8ce0721d11b14866a69f2a9aed2091d7", + "124f4bb5a4b54938adc28748f66ccece", + "b6372ac2c4b644f9b1115cc191c29ff3", + "512ba97fdf524f119d0f7a8099a38ddf", + "bbca25bb210646c48ca35a358591ec49", + "4daf997d1ccc46ef8611e811d5cae717", + "8ceb71b03088481db11734f6d861eaf1", + "16efbbfa780f4850abac85cc0c6cc494", + "aa52eeb8b58a4381aa48fd60373e478a", + "0f50e2c6ff4f4dac922476d5e145273d", + "449df1eaadb54701963055d7b002d9c8", + "54d0ec7671bf49b48b087aba44417c1a", + "444c4ae5738d4cf4ab043e32e6cee7b2", + "88a9326fa2af4ec1914c135a9c0f7783", + "fa2291804b354205942fdd19f45a1b12", + "eddd099b37db4b258382a15e6833bd75", + "ea6c03257f3a4f82b0ab67a7a1f15004", + "973f15bbe19245b8bba6f4599556c886", + "f94027315e0447d4b6c137ebb825f152", + "c46be4604d26480aae82e51c958a6bc1", + "9cee26568a3f4e89bfe56044ed8ee1f5", + "1a737a7f8a3849f5bc5af3e94d95c276", + "4b8e54c7ca0647fc8fd5f56ac05b1e64", + "4f71f02ef7d54780be22331955515ed9", + "cb2cadec1a5b4903a703efd0768cde1c", + "9fea3cdbcfc140f59b3f9e88490ccdaa", + "1f545db8f7e04d90924fd4a38ed99969", + "a851554edfa149058628dcd9619395a0", + "f003a94c940e4a3986dd44be0e06b341", + "ed58280ed9c8460081619b4175c59941", + "3f5b19b7851e4ac2adb49cfa0d1368a0", + "10d88a1db7334a35b1c551ecc53be5ff", + "7d8acecf9edb49afbadf5e8a1ea4f1f3", + "a16f9304ac7c443289e92d0b969819c1", + "774e614dce594e30890ebdc7fba549cb", + "6010263f543e422fbc0877bb427c3a47", + "55b033687c6c4cf4b3992c6d2b666d2d" + ] + }, + "id": "QTVKkGiIflzk", + "outputId": "54061119-e5fd-47b4-da03-289392816378" + }, + "id": "QTVKkGiIflzk", + "execution_count": 3, + "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": [ + "Downloading readme: 0%| | 0.00/5.27k [00:00= max_steps:\n", + " break\n", + "\n", + " input_ids = input_ids.to(device, non_blocking=True)\n", + " attention_mask = attention_mask.to(device, non_blocking=True)\n", + " labels = labels.to(device, non_blocking=True)\n", + "\n", + " optimizer.zero_grad(set_to_none=True)\n", + "\n", + " with autocast(enabled=(device.type == \"cuda\")):\n", + " out = model(input_ids=input_ids, attention_mask=attention_mask, labels=labels)\n", + " loss = out.loss\n", + "\n", + " scaler.scale(loss).backward()\n", + " scaler.step(optimizer)\n", + " scaler.update()\n", + "\n", + " total_loss += loss.item()\n", + " steps += 1\n", + "\n", + " return total_loss / max(steps, 1)\n", + "\n", + "@torch.no_grad()\n", + "def eval_cls(model, loader, max_steps=None):\n", + " model.eval()\n", + " total_loss = 0.0\n", + " correct = 0\n", + " total = 0\n", + " steps = 0\n", + "\n", + " for step, (input_ids, attention_mask, labels) in enumerate(loader):\n", + " if max_steps is not None and step >= max_steps:\n", + " break\n", + "\n", + " input_ids = input_ids.to(device, non_blocking=True)\n", + " attention_mask = attention_mask.to(device, non_blocking=True)\n", + " labels = labels.to(device, non_blocking=True)\n", + "\n", + " out = model(input_ids=input_ids, attention_mask=attention_mask, labels=labels)\n", + " loss = out.loss\n", + " preds = out.logits.argmax(dim=-1)\n", + "\n", + " total_loss += loss.item()\n", + " correct += (preds == labels).sum().item()\n", + " total += labels.size(0)\n", + " steps += 1\n", + "\n", + " return total_loss / max(steps, 1), correct / max(total, 1)\n", + "\n", + "best_loss = float(\"inf\")\n", + "ckpt_path = os.path.join(SAVE_DIR, \"BertForSequenceClassification.pt\")\n", + "\n", + "for epoch in range(CLS_EPOCHS):\n", + " tr_loss = train_cls_one_epoch(model, train_loader, max_steps=CLS_MAX_STEPS)\n", + " va_loss, va_acc = eval_cls(model, valid_loader)\n", + " print(f\"Epoch {epoch+1}: train {tr_loss:.4f} | val {va_loss:.4f} | acc {va_acc:.4f}\")\n", + "\n", + " if va_loss < best_loss:\n", + " best_loss = va_loss\n", + " torch.save(model.state_dict(), ckpt_path)\n", + " print(\"Saved ->\", ckpt_path)\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 253, + "referenced_widgets": [ + "a9117de43ab040d281df25dacc9e475b", + "6419a8a0a6134b2f86a2c0ab5df5d9ad", + "a90ea8655315407c9529657330503c61", + "eac2d6eaf830433cae4897fc6ee994cc", + "180d9ba404074926819b3bf9372dcec4", + "17c1c9f32a0646bfbea9a71767da71a3", + "5b0b8df8eb9449f0926aa6733dc87fdd", + "43491f39e4634498a95b6c8cf4eb5cc2", + "0584156f72094cc094d33a0c7803a471", + "6e033971d377403289ad9a0883589985", + "f2b1945e2a004acb9c9623b528007046" + ] + }, + "id": "1EoIaxkasLbX", + "outputId": "476fb12c-3480-42e7-b436-a012522faf2d" + }, + "execution_count": 5, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "model.safetensors: 0%| | 0.00/714M [00:00 /content/models/BertForSequenceClassification.pt\n", + "Epoch 2: train 0.4531 | val 0.4530 | acc 0.7883\n", + "Saved -> /content/models/BertForSequenceClassification.pt\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# [셀 5] (예제 7.17) 저장 모델 로드 후 Test 평가\n", + "model2 = BertForSequenceClassification.from_pretrained(\n", + " \"bert-base-multilingual-cased\",\n", + " num_labels=2\n", + ").to(device)\n", + "\n", + "state = torch.load(os.path.join(SAVE_DIR, \"BertForSequenceClassification.pt\"), map_location=device)\n", + "model2.load_state_dict(state)\n", + "\n", + "te_loss, te_acc = eval_cls(model2, test_loader)\n", + "print(f\"Test Loss: {te_loss:.4f}\")\n", + "print(f\"Test Acc : {te_acc:.4f}\")\n", + "\n", + "del model2\n", + "torch.cuda.empty_cache()\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "S1ndJy05sNQ-", + "outputId": "9cc91947-50ff-4881-d7de-ac020a30107a" + }, + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-multilingual-cased and are newly initialized: ['classifier.bias', 'classifier.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: 0.4592\n", + "Test Acc : 0.7817\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# [셀 6] (예제 7.18) 데이터 로드/샘플링/분할\n", + "from datasets import load_dataset\n", + "\n", + "news = load_dataset(\"argilla/news-summary\", split=\"test\")\n", + "df = news.to_pandas().sample(SUM_N_SAMPLES, random_state=42)[[\"text\", \"prediction\"]].reset_index(drop=True)\n", + "df[\"prediction\"] = df[\"prediction\"].map(lambda x: x[0][\"text\"])\n", + "\n", + "n = len(df)\n", + "train_df = df.iloc[: int(0.6*n)].reset_index(drop=True)\n", + "valid_df = df.iloc[int(0.6*n): int(0.8*n)].reset_index(drop=True)\n", + "test_df = df.iloc[int(0.8*n):].reset_index(drop=True)\n", + "\n", + "print(\"news:\", train_df[\"text\"].iloc[0][:200])\n", + "print(\"sum :\", train_df[\"prediction\"].iloc[0][:80])\n", + "print(\"sizes:\", len(train_df), len(valid_df), len(test_df))\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 308, + "referenced_widgets": [ + "5493f3c75651453e9e41bccd65ffa8fa", + "d2fe9318796a4ca18a0b9e1f00e8700b", + "cb66cd86bf9847b28f893cf04c2f2624", + "0714377ce25244bcb57b3d49a6fda58f", + "43c0880ae67b4e99ab075faf5532b7e4", + "a8717eaab155427da59d6cca66686e41", + "630e19237bfa459b941312d523af09bf", + "26e0c0d93bd94c098876ef75da051f9a", + "aef0dba225e544cbbeaf4a66c251e1e9", + "93b95facb6b44b348b6756e3f71f7095", + "6ca373cadc214b9bbf19c4faf1818bff", + "4a700cafdab14083ad4419099e58275a", + "a76ae6e125434eb68465a26c82768c3a", + "946fb5cf5a5146ddb9fa3b6fc454f8a6", + "ffe28571a4e446ffa01527d38ba46663", + "728c124db21149f5b37cd4ef9888d223", + "40b860300bdf4a0bbbff63650ec222b1", + "af8ff1b773604bda81fd454f4a91978e", + "309d5480dd6b4c84a6d3a501bb375a0c", + "8b7cbeab8eab46808cebe337f9e52f0a", + "efc8b9bb3ec24022a7eaa5334b090113", + "6675f0c9a95044159815a5dfd2d5bb37", + "b96c810d99484195b579e928af762c1f", + "f13106f571da4a3389a7840ad95725c1", + "3f1684239dbc49dab865c784007921cb", + "e599ab57fe784a26a394028623dc7d19", + "921c9aa493c74a8697be6f35e0f08aaa", + "cda0a98c9e5046218637c70044fd7dd6", + "704c90eb0d514f84adfbaae9faa3a527", + "307660a0144d47cca83a0aa52a5a607d", + "d884afd3068047bba4abaa64c7c8b5af", + "e4495820a3334a98bfc4c1fb86985f65", + "f0265d6a2b204b169d9dfb929cd244fe", + "43a0ed0237974265b83fd018707b76ce", + "18a7f32050b744cda565656dea66c5fc", + "38934f3fdfea43edab229e61da8f42ed", + "9934274db17f4293b6687d11537802bb", + "9ed9dd2108974b0e8ba14c9e291959cf", + "0a7b519b28b548988216b0e111f8ff41", + "bfbfe3d5da374a68b83f3b2d8ed09c26", + "47b7f2fa02e341c19d1106d91aee7f9d", + "a0955ab28ece4ffd8380acb47159b1d7", + "34f3e993dc854a808349d9b363b39c28", + "821205edc58e45ffa1995222d63d7e4f", + "5ea6400ac6a0468385c16bd63dc041c8", + "838046590904421ea3df8b4292771fe7", + "ae0aea01c1ab486285e0ecc492ff86ca", + "56575136b2d143fca4e1d32290c08b86", + "cef9fa748529427e8ec4d5011a90aef6", + "ca32edabe16f4be6be1e9cf34595eae7", + "f24a227418bc4136a2f57de381a8b91f", + "a095b04b26464cb3ba8f2842338fc4c7", + "e87d1fa1133542eca2ddb4925cbd6c9c", + "fcc5c48740b54517b37f032e9682f88d", + "c5377a543b7a415ab64e979f8038b724" + ] + }, + "id": "N6IXS1nZsPSd", + "outputId": "12152244-c707-4265-bfce-f536d1fe1276" + }, + "execution_count": 7, + "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": "5493f3c75651453e9e41bccd65ffa8fa" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "data/train-00000-of-00001-ebc48879f34571(…): 0%| | 0.00/1.54M [00:00= max_steps:\n", + " break\n", + "\n", + " input_ids = input_ids.to(device, non_blocking=True)\n", + " attention_mask = attention_mask.to(device, non_blocking=True)\n", + " labels = labels.to(device, non_blocking=True)\n", + "\n", + " optimizer.zero_grad(set_to_none=True)\n", + "\n", + " with autocast(enabled=(device.type == \"cuda\")):\n", + " out = model(input_ids=input_ids, attention_mask=attention_mask, labels=labels)\n", + " loss = out.loss\n", + "\n", + " bart_scaler.scale(loss).backward()\n", + " bart_scaler.step(optimizer)\n", + " bart_scaler.update()\n", + "\n", + " total += loss.item()\n", + " steps += 1\n", + "\n", + " return total / max(steps, 1)\n", + "\n", + "@torch.no_grad()\n", + "def eval_sum_loss(model, loader, max_batches=10):\n", + " model.eval()\n", + " total = 0.0\n", + " steps = 0\n", + " for step, (input_ids, attention_mask, labels) in enumerate(loader):\n", + " if step >= max_batches:\n", + " break\n", + " input_ids = input_ids.to(device, non_blocking=True)\n", + " attention_mask = attention_mask.to(device, non_blocking=True)\n", + " labels = labels.to(device, non_blocking=True)\n", + " out = model(input_ids=input_ids, attention_mask=attention_mask, labels=labels)\n", + " total += out.loss.item()\n", + " steps += 1\n", + " return total / max(steps, 1)\n", + "\n", + "bart_best = float(\"inf\")\n", + "bart_ckpt = os.path.join(SAVE_DIR, \"BartForConditionalGeneration.pt\")\n", + "\n", + "for epoch in range(SUM_EPOCHS):\n", + " tr = train_sum_one_epoch(bart, bart_train_loader, bart_opt, max_steps=SUM_MAX_STEPS)\n", + " va = eval_sum_loss(bart, bart_valid_loader, max_batches=10)\n", + " print(f\"Epoch {epoch+1}: train {tr:.4f} | val {va:.4f}\")\n", + "\n", + " if va < bart_best:\n", + " bart_best = va\n", + " torch.save(bart.state_dict(), bart_ckpt)\n", + " print(\"Saved ->\", bart_ckpt)\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 179, + "referenced_widgets": [ + "37bdb28b9ce34755bfc8ac346133f660", + "8020d02c46c449a08349e891f3061639", + "7e73a0a31d904f01845e35d31329c855", + "a04222f45a8348a38d6764639e0ddd89", + "01ad820c172847a9b5fd70b3f80c6ca5", + "f1b553189bbd4081b4e5fe18252b66ca", + "c574614a3e5246f28cc2ce29ddd7caa1", + "8cc04b74436f4f5486646343525fbd6d", + "3062cab472f447a48579633b373166cd", + "c9868f3dd7a24089bf93f572ba767d3b", + "c407a351d7124848876ac49cc0d978d9" + ] + }, + "id": "DvdFB11JsSuE", + "outputId": "f1e0b0f5-ddf9-4dd9-f34f-25070a9ef06a" + }, + "execution_count": 9, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "model.safetensors: 0%| | 0.00/558M [00:00 /content/models/BartForConditionalGeneration.pt\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# [셀 9] (예제 7.23) ROUGE-2(빠른 샘플 평가) + 몇 개 생성 비교\n", + "import evaluate\n", + "\n", + "rouge = evaluate.load(\"rouge\")\n", + "\n", + "@torch.no_grad()\n", + "def quick_rouge2(model, loader, max_batches=5):\n", + " model.eval()\n", + " preds, refs = [], []\n", + "\n", + " for b, (input_ids, attention_mask, labels) in enumerate(loader):\n", + " if b >= max_batches:\n", + " break\n", + "\n", + " input_ids = input_ids.to(device, non_blocking=True)\n", + " attention_mask = attention_mask.to(device, non_blocking=True)\n", + " labels = labels.clone()\n", + " labels[labels == -100] = bart_tok.pad_token_id\n", + "\n", + " gen = model.generate(\n", + " input_ids=input_ids,\n", + " attention_mask=attention_mask,\n", + " num_beams=2,\n", + " max_new_tokens=SUM_TGT_LEN,\n", + " )\n", + "\n", + " preds += bart_tok.batch_decode(gen, skip_special_tokens=True)\n", + " refs += bart_tok.batch_decode(labels, skip_special_tokens=True)\n", + "\n", + " scores = rouge.compute(predictions=preds, references=refs)\n", + " return scores[\"rouge2\"], preds[:3], refs[:3]\n", + "\n", + "# 저장본 로드 후 평가\n", + "bart2 = BartForConditionalGeneration.from_pretrained(\"facebook/bart-base\").to(device)\n", + "bart2.load_state_dict(torch.load(bart_ckpt, map_location=device))\n", + "\n", + "r2, p3, g3 = quick_rouge2(bart2, bart_test_loader, max_batches=5)\n", + "print(\"Quick ROUGE-2:\", r2)\n", + "for i in range(3):\n", + " print(\"\\n[정답]\\n\", g3[i])\n", + " print(\"[모델]\\n\", p3[i])\n", + "\n", + "del bart2\n", + "torch.cuda.empty_cache()\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 343, + "referenced_widgets": [ + "84308d9f73d74f7e977d565695268a4e", + "49657d9a96b644b4a2047c5485470330", + "e28e604722e1467eac99311c3225b1cb", + "67dc2a99cea141768f02b5093f707dae", + "8bbcf9ac09404df3bf488d57b7fcacbc", + "c4026dee9f7e47f893cd2eb04debe02f", + "fd4ef198da30486299696e0df9012a3c", + "40c41ecb5ce54506812a8d7ffa275729", + "8d382dfbf87e49288ac39cbc78737abb", + "0460fcef56444422bcce7ab470a9b899", + "b173bebd50cf437e98a4cc258c6fc14d" + ] + }, + "id": "joMlbwHFsUw4", + "outputId": "57c79d04-7c54-4177-e8f5-f92fcbd824c8" + }, + "execution_count": 10, + "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": "84308d9f73d74f7e977d565695268a4e" + } + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Quick ROUGE-2: 0.1931295433153018\n", + "\n", + "[정답]\n", + " No word Tuesday on Supreme Court nomination: White House\n", + "[모델]\n", + " White House does not plan announcement on Supreme Court nominee\n", + "\n", + "[정답]\n", + " Former Trump campaign staffer files discrimination complaint: NYT\n", + "[모델]\n", + " Ex-Trump campaign staffer says campaign discrimination\n", + "\n", + "[정답]\n", + " China calls for restraint when asked about North Korea hydrogen bomb threat\n", + "[모델]\n", + " China calls on restraint after North Korea's foreign minister quoted as saying he wants hydrogen bomb test\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# [셀 10] (옵션) pipeline로 요약 보기 (GPU면 device=0)\n", + "from transformers import pipeline\n", + "\n", + "pipe_device = 0 if device.type == \"cuda\" else -1\n", + "\n", + "bart_pipe_model = BartForConditionalGeneration.from_pretrained(\"facebook/bart-base\").to(device)\n", + "bart_pipe_model.load_state_dict(torch.load(bart_ckpt, map_location=device))\n", + "\n", + "summarizer = pipeline(\n", + " \"summarization\",\n", + " model=bart_pipe_model,\n", + " tokenizer=bart_tok,\n", + " device=pipe_device\n", + ")\n", + "\n", + "for i in range(3):\n", + " txt = test_df[\"text\"].iloc[i]\n", + " gold = test_df[\"prediction\"].iloc[i]\n", + " pred = summarizer(txt, max_new_tokens=SUM_TGT_LEN, num_beams=2)[0][\"summary_text\"]\n", + " print(\"\\n정답:\", gold)\n", + " print(\"모델:\", pred)\n", + "\n", + "del bart_pipe_model\n", + "torch.cuda.empty_cache()\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "D-dc0rw7sXOS", + "outputId": "3246dd29-cc7a-4e66-b0c0-eb90ce344b85" + }, + "execution_count": 11, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Device set to use cuda:0\n", + "Your max_length is set to 128, but your input_length is only 105. 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=52)\n", + "Your max_length is set to 128, but your input_length is only 100. 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=50)\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "정답: No word Tuesday on Supreme Court nomination: White House\n", + "모델: White House does not plan announcement on Supreme Court nominee\n", + "\n", + "정답: Former Trump campaign staffer files discrimination complaint: NYT\n", + "모델: Ex-Trump campaign staffer says campaign discriminated against her: New York Times\n", + "\n", + "정답: China calls for restraint when asked about North Korea hydrogen bomb threat\n", + "모델: China calls on restraint after North Korea's foreign minister quoted as saying he wants hydrogen bomb test\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# [셀 11] (예제 7.24) 데이터 로드/샘플링/분할\n", + "import pandas as pd\n", + "from Korpora import Korpora\n", + "\n", + "corpus = Korpora.load(\"nsmc\")\n", + "df = pd.DataFrame(corpus.test).sample(CLS_N_SAMPLES, random_state=42).reset_index(drop=True)\n", + "\n", + "n = len(df)\n", + "train_df = df.iloc[: int(0.6*n)].reset_index(drop=True)\n", + "valid_df = df.iloc[int(0.6*n): int(0.8*n)].reset_index(drop=True)\n", + "test_df = df.iloc[int(0.8*n):].reset_index(drop=True)\n", + "\n", + "print(\"sizes:\", len(train_df), len(valid_df), len(test_df))\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "f6gqE2eksa1a", + "outputId": "4c277bd8-a508-4a84-8a20-9ad00376d52a" + }, + "execution_count": 12, + "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", + "sizes: 7200 2400 2400\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# [셀 12] (예제 7.24) 토크나이즈 + DataLoader\n", + "from transformers import ElectraTokenizerFast\n", + "from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler\n", + "\n", + "elec_tok = ElectraTokenizerFast.from_pretrained(\"monologg/koelectra-base-v3-discriminator\", do_lower_case=False)\n", + "\n", + "def make_elec_dataset(df_):\n", + " enc = elec_tok(\n", + " df_[\"text\"].tolist(),\n", + " padding=\"max_length\",\n", + " truncation=True,\n", + " max_length=CLS_MAX_LEN,\n", + " return_tensors=\"pt\",\n", + " )\n", + " labels = torch.tensor(df_[\"label\"].values, dtype=torch.long)\n", + " return TensorDataset(enc[\"input_ids\"], enc[\"attention_mask\"], labels)\n", + "\n", + "def make_elec_loader(dataset, train=True):\n", + " sampler = RandomSampler(dataset) if train else SequentialSampler(dataset)\n", + " return DataLoader(dataset, sampler=sampler, batch_size=CLS_BATCH,\n", + " pin_memory=(device.type==\"cuda\"), num_workers=2)\n", + "\n", + "elec_train_loader = make_elec_loader(make_elec_dataset(train_df), train=True)\n", + "elec_valid_loader = make_elec_loader(make_elec_dataset(valid_df), train=False)\n", + "elec_test_loader = make_elec_loader(make_elec_dataset(test_df), train=False)\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 113, + "referenced_widgets": [ + "82b2e9079d174144b3bd15333495ed27", + "5fcd83472c124102b8960f4d5afb1c6d", + "79eb7e4e503a458aa53552c83144871a", + "ffeda635d86d4ebf91a24b9120192252", + "82575782b9dd48f288c1293d3a58c7cd", + "0db9f66edd8a4748ac416277bb90d661", + "5d705364722844deb27ebb91fc9a3045", + "e15c9ba23614482f9a5255b0443fe95a", + "b5f057b387bd43a597f3682e15eae6fd", + "47b0b69a737e41bebb098fa060c129ca", + "c166055cbe7b497bbb4274038719a9ed", + "ba69c33d3bab47d3865557d51d716c51", + "ba0cda8f5e9541d8a68f9dcd28be2934", + "8a9330bb35994c5e94a4aaa7c5a975d2", + "cfba402521a94e2f990f64c0fe4e5b71", + "6cb9ab9c38e342c98b70c896e23be2ed", + "8347491fafa54f07b696414aa907dfd1", + "05bd799936ed45728b9c996521aa6611", + "e8f5f2a77f7e48d08c6dc54a1f56b88c", + "a3d770c6f32448198d16292472f4d3cb", + "2b04e1c760a5410385e51aa65cf16ba6", + "d9459b40123347879a4a6842c0972b29", + "d5c018ffff9141c1aaf8a91c1ad3f900", + "103afe7c383b40e6a71502e10e6f90f9", + "b693f97d166b43ce8b5931d292419e37", + "eeed370486a7413894e808bb593a32cd", + "0d65814e884a4a678827d9687cd03344", + "0f6c1b982b45417d9b11c3037caf0593", + "e709bf26d1094aab9d6800a87bc5ffc2", + "990438818275486fb641df5cdb1f9dc0", + "6a7b0a2d15db436fb309e9450dc1e010", + "1ca4d82bdd6846c1af749c2e1a1a937b", + "a2354fc8a22c427fb458a5ac8bc05f18" + ] + }, + "id": "q0QqF97is-JL", + "outputId": "0e1e108d-d17e-4b1a-d423-c11d073104d4" + }, + "execution_count": 13, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "tokenizer_config.json: 0%| | 0.00/61.0 [00:00= max_steps:\n", + " break\n", + " input_ids = input_ids.to(device, non_blocking=True)\n", + " attention_mask = attention_mask.to(device, non_blocking=True)\n", + " labels = labels.to(device, non_blocking=True)\n", + "\n", + " optimizer.zero_grad(set_to_none=True)\n", + " with autocast(enabled=(device.type==\"cuda\")):\n", + " out = model(input_ids=input_ids, attention_mask=attention_mask, labels=labels)\n", + " loss = out.loss\n", + "\n", + " scaler.scale(loss).backward()\n", + " scaler.step(optimizer)\n", + " scaler.update()\n", + "\n", + " total += loss.item()\n", + " steps += 1\n", + " return total / max(steps, 1)\n", + "\n", + "@torch.no_grad()\n", + "def eval_cls_simple(model, loader):\n", + " model.eval()\n", + " total_loss = 0.0\n", + " correct = 0\n", + " total = 0\n", + " steps = 0\n", + " for input_ids, attention_mask, labels in loader:\n", + " input_ids = input_ids.to(device, non_blocking=True)\n", + " attention_mask = attention_mask.to(device, non_blocking=True)\n", + " labels = labels.to(device, non_blocking=True)\n", + " out = model(input_ids=input_ids, attention_mask=attention_mask, labels=labels)\n", + " total_loss += out.loss.item()\n", + " correct += (out.logits.argmax(-1) == labels).sum().item()\n", + " total += labels.size(0)\n", + " steps += 1\n", + " return total_loss / max(steps, 1), correct / max(total, 1)\n", + "\n", + "elec_best = float(\"inf\")\n", + "elec_ckpt = os.path.join(SAVE_DIR, \"ElectraForSequenceClassification.pt\")\n", + "\n", + "for epoch in range(CLS_EPOCHS):\n", + " tr = train_cls_one_epoch_amp(elec, elec_train_loader, elec_opt, elec_scaler, max_steps=CLS_MAX_STEPS)\n", + " va_loss, va_acc = eval_cls_simple(elec, elec_valid_loader)\n", + " print(f\"Epoch {epoch+1}: train {tr:.4f} | val {va_loss:.4f} | acc {va_acc:.4f}\")\n", + " if va_loss < elec_best:\n", + " elec_best = va_loss\n", + " torch.save(elec.state_dict(), elec_ckpt)\n", + " print(\"Saved ->\", elec_ckpt)\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 285, + "referenced_widgets": [ + "94c7098a5d504413b18521d7845143b3", + "f9cc2a5264d748bea195ecf0c8e618f1", + "dced0bf91cf345168f0afd7dd60b5be3", + "549f2288e2334cb9838a2d9571f42945", + "12f576f695ac42daa9f7b235647a1842", + "0461d3e7fc2c4f17851ecf2287c7de9b", + "29a59b39b0b74eb688644a480fe7622f", + "ff07b8da8d214999bc23b023df2424ab", + "53980c02f65b41e5a6b8e5e31b08fdf1", + "ae9ba74d3dd34cd4a58f0cd231a82131", + "da4693714266414f95ae9c77a4c2a9de", + "198ce7faae8f46698e53c39bba389d5d", + "a3506607a4664816a12545141d84836c", + "6cd487c8a01a4ae790a5c145f4c366bd", + "bcdca404bbf34f34b7d959ade2d87f14", + "5859f54991724d76954bd78a6fffd7f4", + "35a13669403441d9b45ec234cc4db5df", + "d511d30928d44925aa7d31828d61f052", + "6a05d5752e5c49aeac72712d1f4ac7e7", + "20580e9b66b04198be9722bc6cd86ac6", + "5cff6ef3c770459ab858a5ba49af71e4", + "c737c03114e54756ab5dd5fc7e5f5fa7" + ] + }, + "id": "pfUQnRDNtALO", + "outputId": "3948985c-b356-4937-94f0-075429b14c79" + }, + "execution_count": 14, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "pytorch_model.bin: 0%| | 0.00/452M [00:00 /content/models/ElectraForSequenceClassification.pt\n", + "Epoch 2: train 0.2930 | val 0.3155 | acc 0.8679\n", + "Saved -> /content/models/ElectraForSequenceClassification.pt\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# [셀 14] Test 평가\n", + "elec2 = ElectraForSequenceClassification.from_pretrained(\n", + " \"monologg/koelectra-base-v3-discriminator\",\n", + " num_labels=2\n", + ").to(device)\n", + "elec2.load_state_dict(torch.load(elec_ckpt, map_location=device))\n", + "\n", + "te_loss, te_acc = eval_cls_simple(elec2, elec_test_loader)\n", + "print(f\"Test Loss: {te_loss:.4f}\")\n", + "print(f\"Test Acc : {te_acc:.4f}\")\n", + "\n", + "del elec2\n", + "torch.cuda.empty_cache()\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "RbHa9u9otBkQ", + "outputId": "4d40f0ff-b096-4651-d9af-7fa485b542f8" + }, + "execution_count": 15, + "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: 0.3687\n", + "Test Acc : 0.8512\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# [셀 15] (예제 7.26) 데이터 로드/샘플링/분할 + prefix\n", + "from datasets import load_dataset\n", + "\n", + "news = load_dataset(\"argilla/news-summary\", split=\"test\")\n", + "df = news.to_pandas().sample(SUM_N_SAMPLES, random_state=42)[[\"text\", \"prediction\"]].reset_index(drop=True)\n", + "df[\"text\"] = \"summarize: \" + df[\"text\"]\n", + "df[\"prediction\"] = df[\"prediction\"].map(lambda x: x[0][\"text\"])\n", + "\n", + "n = len(df)\n", + "train_df = df.iloc[: int(0.6*n)].reset_index(drop=True)\n", + "valid_df = df.iloc[int(0.6*n): int(0.8*n)].reset_index(drop=True)\n", + "test_df = df.iloc[int(0.8*n):].reset_index(drop=True)\n", + "\n", + "print(\"src:\", train_df[\"text\"].iloc[0][:200])\n", + "print(\"tgt:\", train_df[\"prediction\"].iloc[0][:80])\n", + "print(\"sizes:\", len(train_df), len(valid_df), len(test_df))\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "7v5gT3E5tFW3", + "outputId": "8f074c6a-5309-427e-cb38-d0e93074cd87" + }, + "execution_count": 16, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "src: summarize: WASHINGTON (Reuters) - President Barack Obama did not specify a candidate preference in the race for the Democratic presidential nomination at a Democratic National Committee fundraising ev\n", + "tgt: Obama did not indicate preference for Democratic candidate: White House\n", + "sizes: 720 240 240\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# [셀 16] (예제 7.27) 토크나이즈 + DataLoader (CPU → 배치만 GPU)\n", + "from transformers import T5TokenizerFast\n", + "from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler\n", + "\n", + "t5_tok = T5TokenizerFast.from_pretrained(\"t5-small\")\n", + "\n", + "def make_t5_dataset(df_):\n", + " src = t5_tok(\n", + " df_[\"text\"].tolist(),\n", + " padding=\"max_length\",\n", + " truncation=True,\n", + " max_length=SUM_SRC_LEN,\n", + " return_tensors=\"pt\",\n", + " )\n", + " tgt = t5_tok(\n", + " df_[\"prediction\"].tolist(),\n", + " padding=\"max_length\",\n", + " truncation=True,\n", + " max_length=SUM_TGT_LEN,\n", + " return_tensors=\"pt\",\n", + " )\n", + " labels = tgt[\"input_ids\"]\n", + " labels[labels == t5_tok.pad_token_id] = -100\n", + " return TensorDataset(src[\"input_ids\"], src[\"attention_mask\"], labels)\n", + "\n", + "def make_t5_loader(dataset, train=True, batch_size=8):\n", + " sampler = RandomSampler(dataset) if train else SequentialSampler(dataset)\n", + " return DataLoader(dataset, sampler=sampler, batch_size=batch_size,\n", + " pin_memory=(device.type==\"cuda\"), num_workers=2)\n", + "\n", + "t5_train_loader = make_t5_loader(make_t5_dataset(train_df), train=True, batch_size=T5_BATCH)\n", + "t5_valid_loader = make_t5_loader(make_t5_dataset(valid_df), train=False, batch_size=T5_BATCH)\n", + "t5_test_loader = make_t5_loader(make_t5_dataset(test_df), train=False, batch_size=T5_BATCH)\n", + "\n", + "print([x.shape for x in next(iter(t5_train_loader))])\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 131, + "referenced_widgets": [ + "a7f8916d41984a7faadcce9bf9fe3a00", + "9e4b9faa113e4349b2396a7fe61c44af", + "e3dfc29204724d06af75f43f7bc655a4", + "3cc240f4b6d34884b95e8bf0735ed499", + "6abdef7b6fda47a8b422dd72dfca3898", + "1d92bd951f264e6ab31f55e7fcb25666", + "2b0a6d8958844ca0be7da56600d04755", + "6c98017c64174b2e87e9fe4c1bb32a8d", + "8299ca6136fd4c9aaaff12cbf29689f5", + "e644e3976e084a6ca58a31f93deb05db", + "087c1fa8e8fd45cba8e3894d93902bac", + "783ec1ad91a143aba38218aa5820f239", + "c0b7b13a10fb47baa598e2e2b543a861", + "663c6a3dd10d4215b8350575c96a66d7", + "f110a9619cb54a2d8ec3f567a0ce92f2", + "ab8ecee6c8f840beb4db9a5d230eaf53", + "3951c69822634b62bed442dae7072d5b", + "b44cd724b04d4237b0e435c548cc0243", + "390046a7990748aa902aafa32612202a", + "f6749580281346438e87e7e22301afce", + "0411e814d18d468187ef0c96fcead162", + "9cdbd630e15c463f981ce0fafaa99c2c", + "124fc0f205464d8fa3f9683cd970ee62", + "24ba237715a445afa0ccdaa11cfbeefa", + "bb5cbe6503c147228f582f83eaac9885", + "01208a0117514ac4a8859e347f7311e6", + "35b9ee9c29d24922b73bac52c6574330", + "ff02374c9d174f2dab41705c9ab80290", + "a1ad34d8efee41f2902c631ccc94a9ad", + "191f2c4435f64e499169db46ffed77f9", + "4223383192cf4e32a0522870eb6b889a", + "0d6225805cb54a928f73735d045ee023", + "dded6412425f4db7bc7258cbc90a875b" + ] + }, + "id": "_LKFQMQrtG31", + "outputId": "540a4f5f-a172-4bb1-9d7d-ac506cb9b42a" + }, + "execution_count": 17, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "tokenizer_config.json: 0%| | 0.00/2.32k [00:00= max_steps:\n", + " break\n", + "\n", + " input_ids = input_ids.to(device, non_blocking=True)\n", + " attention_mask = attention_mask.to(device, non_blocking=True)\n", + " labels = labels.to(device, non_blocking=True)\n", + "\n", + " optimizer.zero_grad(set_to_none=True)\n", + " with autocast(enabled=(device.type==\"cuda\")):\n", + " out = model(input_ids=input_ids, attention_mask=attention_mask, labels=labels)\n", + " loss = out.loss\n", + "\n", + " scaler.scale(loss).backward()\n", + " scaler.step(optimizer)\n", + " scaler.update()\n", + "\n", + " total += loss.item()\n", + " steps += 1\n", + " return total / max(steps, 1)\n", + "\n", + "@torch.no_grad()\n", + "def eval_t5_loss(model, loader, max_batches=10):\n", + " model.eval()\n", + " total = 0.0\n", + " steps = 0\n", + " for b, (input_ids, attention_mask, labels) in enumerate(loader):\n", + " if b >= max_batches:\n", + " break\n", + " input_ids = input_ids.to(device, non_blocking=True)\n", + " attention_mask = attention_mask.to(device, non_blocking=True)\n", + " labels = labels.to(device, non_blocking=True)\n", + " out = model(input_ids=input_ids, attention_mask=attention_mask, labels=labels)\n", + " total += out.loss.item()\n", + " steps += 1\n", + " return total / max(steps, 1)\n", + "\n", + "t5_best = float(\"inf\")\n", + "t5_ckpt = os.path.join(SAVE_DIR, \"T5ForConditionalGeneration.pt\")\n", + "\n", + "for epoch in range(SUM_EPOCHS):\n", + " tr = train_t5_one_epoch(t5, t5_train_loader, t5_opt, t5_scaler, max_steps=SUM_MAX_STEPS)\n", + " va = eval_t5_loss(t5, t5_valid_loader, max_batches=10)\n", + " print(f\"Epoch {epoch+1}: train {tr:.4f} | val {va:.4f}\")\n", + " if va < t5_best:\n", + " t5_best = va\n", + " torch.save(t5.state_dict(), t5_ckpt)\n", + " print(\"Saved ->\", t5_ckpt)\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 243, + "referenced_widgets": [ + "9129f6b39a7743cfa573e562b8132fbe", + "acf1a57c234a43c98fa79e572480ed40", + "ff5e0041b13348ce839f5d428cdbbe2a", + "752f7d3114a246d48c9510dd8d7e95eb", + "d95d38a2513441979e22dd3345c60436", + "1bf86134dec84567ab982bec6c4b6961", + "e4020e8be18047cc886c25a151d3d29a", + "b7a5f2ce124b4808a192126f547cf5f7", + "842733cd8b6b4a42ac292ffd83bc9af2", + "7ee87444edfb443da3987ee005dab342", + "c2b8b7d955084310ad1361f8207e3520", + "e4d176398d544790932078ec691dbf63", + "b9ee75bed43244fa8f4de79dbc2084f8", + "90203590cbe94fa7867c858b07e83f21", + "d9285d1ed8d9446489af465680995822", + "735c09cb1e164df4892634fb3c72fb54", + "ac6e76523df24d6495d2c0f4ccc632b9", + "9cbe502c73d14bcd8dea757662b42eed", + "0b92c62dc85e48ea8566c9282e411c00", + "17db699b664a4a57b0500a8541102be5", + "c539af080a294299a7130b595a17d581", + "75570475f7cd41fabfeb945fb5052d13", + "2444532639ba4fef86c45778f1cc6248", + "0d4a2f29afde431c81f1c1419acb9554", + "7b9707185a624d2b841b83dbe2cd9dae", + "130354dd46bb4a1bb6e1f71ec01f678a", + "bb70643916c84e158d7de9862d647357", + "c23858500e29444f83c9c3af27e0faeb", + "55e57e1171dc4c8aa2ffa65b766a8a84", + "5314c95f2522458abe7cfe7f52b4db5b", + "f6687148e09a47699c808bd8d2afe3a7", + "7ba0b85f78cc44049546f928fbcecf89", + "f6c4e273c6de4e719d09163326544af6" + ] + }, + "id": "zf7y3IkItIdC", + "outputId": "5fe91ae5-58c7-4d57-ba71-25d208c630d6" + }, + "execution_count": 18, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "config.json: 0%| | 0.00/1.21k [00:00 /content/models/T5ForConditionalGeneration.pt\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# [셀 18] (예제 7.30) 생성 결과 확인 (몇 개만)\n", + "t5_gen = T5ForConditionalGeneration.from_pretrained(\"t5-small\").to(device)\n", + "t5_gen.load_state_dict(torch.load(t5_ckpt, map_location=device))\n", + "t5_gen.eval()\n", + "\n", + "with torch.no_grad():\n", + " input_ids, attention_mask, labels = next(iter(t5_test_loader))\n", + " input_ids = input_ids.to(device)\n", + " attention_mask = attention_mask.to(device)\n", + "\n", + " gen = t5_gen.generate(\n", + " input_ids=input_ids,\n", + " attention_mask=attention_mask,\n", + " num_beams=2,\n", + " max_new_tokens=SUM_TGT_LEN,\n", + " repetition_penalty=2.0,\n", + " )\n", + "\n", + " # labels 디코딩용 복원\n", + " labels = labels.clone()\n", + " labels[labels == -100] = t5_tok.pad_token_id\n", + "\n", + " preds = t5_tok.batch_decode(gen, skip_special_tokens=True)\n", + " refs = t5_tok.batch_decode(labels, skip_special_tokens=True)\n", + "\n", + "for i in range(3):\n", + " print(\"\\nGenerated:\", preds[i])\n", + " print(\"Actual :\", refs[i])\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "8ac0rBrUtKOh", + "outputId": "981c11ee-fa86-4b9b-e51c-f0ec90618126" + }, + "execution_count": 19, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Generated: the white house does not plan any announcement on a nominee for the Supreme Court. president Barack Obama has said he plans to soon present a nominee to fill the vacancy left by the Feb. 13 death of conservative Justice Antonin Scalia.\n", + "Actual : No word Tuesday on Supreme Court nomination: White House\n", + "\n", + "Generated: Elizabeth Mae Davidson filed complaint with the Iowa Civil Rights Commission. she claimed female staffers were paid less than male staffers, report says. Davidson also claimed that Trump addressed her and a young female volunteer with a remark.\n", + "Actual : Former Trump campaign staffer files discrimination complaint: NYT\n", + "\n", + "Generated: China calls on all parties to exercise restraint after north Korea's foreign minister quoted as saying he believes the North could consider conducting a hydrogen bomb test in the Pacific Ocean. his comments came after U.S. president Donald Trump ordered new sanctions against North Korea on Thursday over its nuclear and missile programs\n", + "Actual : China calls for restraint when asked about North Korea hydrogen bomb threat\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "CQgU5-cMtMI8" + }, + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file diff --git "a/Week15_\354\230\210\354\212\265\352\263\274\354\240\234_\354\225\210\354\230\210\354\235\200.pdf" "b/Week15_\354\230\210\354\212\265\352\263\274\354\240\234_\354\225\210\354\230\210\354\235\200.pdf" new file mode 100644 index 0000000..32a170b Binary files /dev/null and "b/Week15_\354\230\210\354\212\265\352\263\274\354\240\234_\354\225\210\354\230\210\354\235\200.pdf" differ