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{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"id":"2Ko1cGE5_8FJ"},"outputs":[],"source":["# !pip uninstall -y transformers\n","# !pip install -U transformers datasets accelerate\n","# !pip show transformers | grep Version"]},{"cell_type":"markdown","source":["#Phân loại"],"metadata":{"id":"_d0n6vLJJmJd"}},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0DEpuPCvp2Ys","executionInfo":{"status":"ok","timestamp":1760813415837,"user_tz":-420,"elapsed":30791,"user":{"displayName":"Lâm Đức Cương","userId":"02362263444637620675"}},"outputId":"05149290-4d1f-42ef-f02d-e7180fca62f1"},"execution_count":1,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","execution_count":2,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":11094,"status":"ok","timestamp":1760813563983,"user":{"displayName":"Lâm Đức Cương","userId":"02362263444637620675"},"user_tz":-420},"id":"j40Z5zXq-8qw","outputId":"964aefc2-9b9f-4c7e-8ead-44880f6c18c2"},"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: transformers in /usr/local/lib/python3.12/dist-packages (4.57.1)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.12/dist-packages (from transformers) (3.20.0)\n","Requirement already satisfied: huggingface-hub<1.0,>=0.34.0 in /usr/local/lib/python3.12/dist-packages (from transformers) (0.35.3)\n","Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.12/dist-packages (from transformers) (2.0.2)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.12/dist-packages (from transformers) (25.0)\n","Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.12/dist-packages (from transformers) (6.0.3)\n","Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.12/dist-packages (from transformers) (2024.11.6)\n","Requirement already satisfied: requests in /usr/local/lib/python3.12/dist-packages (from transformers) (2.32.4)\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) (0.6.2)\n","Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.12/dist-packages (from transformers) (4.67.1)\n","Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<1.0,>=0.34.0->transformers) (2025.3.0)\n","Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<1.0,>=0.34.0->transformers) (4.15.0)\n","Requirement already satisfied: hf-xet<2.0.0,>=1.1.3 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<1.0,>=0.34.0->transformers) (1.1.10)\n","Requirement already satisfied: charset_normalizer<4,>=2 in /usr/local/lib/python3.12/dist-packages (from requests->transformers) (3.4.4)\n","Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.12/dist-packages (from requests->transformers) (3.11)\n","Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.12/dist-packages (from requests->transformers) (2.5.0)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.12/dist-packages (from requests->transformers) (2025.10.5)\n"]}],"source":["!pip install -U transformers"]},{"cell_type":"code","source":["# ===============================================\n","# 1️⃣ CÀI ĐẶT THƯ VIỆN\n","# ===============================================\n","!pip install -q transformers datasets torch scikit-learn\n","\n","# ===============================================\n","# 2️⃣ LOAD DỮ LIỆU THẬT\n","# ===============================================\n","import pandas as pd\n","import os\n","os.environ[\"WANDB_DISABLED\"] = \"true\"\n","\n","file_path = \"/content/drive/MyDrive/Colab Notebooks/train.csv\"\n","df = pd.read_csv(file_path)\n","\n","# Loại bỏ NaN\n","df = df.dropna(subset=['comment', 'label'])\n","\n","# Chuyển nhãn từ 1-5 -> 0-4\n","df['label'] = df['label'].astype(int) - 1\n","\n","print(\"✅ Dữ liệu mẫu:\")\n","print(df.head())\n","print(\"Min label:\", df['label'].min(), \"Max label:\", df['label'].max())\n","\n","# ===============================================\n","# 3️⃣ TẠO DATASET HF\n","# ===============================================\n","from datasets import Dataset, DatasetDict\n","from sklearn.model_selection import train_test_split\n","\n","train_texts, val_texts, train_labels, val_labels = train_test_split(\n"," df['comment'].tolist(),\n"," df['label'].tolist(),\n"," test_size=0.1,\n"," random_state=42,\n"," stratify=df['label']\n",")\n","\n","train_dataset = Dataset.from_dict({\"text\": train_texts, \"label\": train_labels})\n","val_dataset = Dataset.from_dict({\"text\": val_texts, \"label\": val_labels})\n","\n","# ===============================================\n","# 4️⃣ TOKENIZATION (PhoBERT)\n","# ===============================================\n","from transformers import AutoTokenizer\n","\n","model_name = \"vinai/phobert-base\"\n","tokenizer = AutoTokenizer.from_pretrained(model_name)\n","\n","def preprocess_function(examples):\n"," return tokenizer(\n"," examples[\"text\"],\n"," padding=\"max_length\",\n"," truncation=True,\n"," max_length=128\n"," )\n","\n","train_tokenized = train_dataset.map(preprocess_function, batched=True)\n","val_tokenized = val_dataset.map(preprocess_function, batched=True)\n","\n","# ===============================================\n","# 5️⃣ CHUẨN BỊ MÔ HÌNH PhoBERT\n","# ===============================================\n","import torch\n","from transformers import AutoModelForSequenceClassification\n","\n","device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n","model = AutoModelForSequenceClassification.from_pretrained(\n"," model_name,\n"," num_labels=5\n",").to(device)\n","\n","# ===============================================\n","# 6️⃣ HÀM ĐÁNH GIÁ\n","# ===============================================\n","from sklearn.metrics import accuracy_score, f1_score\n","\n","def compute_metrics(eval_pred):\n"," logits, labels = eval_pred\n"," preds = torch.argmax(torch.tensor(logits), dim=1)\n"," acc = accuracy_score(labels, preds)\n"," f1 = f1_score(labels, preds, average=\"weighted\")\n"," return {\"accuracy\": acc, \"f1\": f1}\n","\n","# ===============================================\n","# 7️⃣ CẤU HÌNH HUẤN LUYỆN\n","# ===============================================\n","from transformers import TrainingArguments, Trainer\n","\n","training_args = TrainingArguments(\n"," output_dir=\"./results\",\n"," learning_rate=2e-5,\n"," per_device_train_batch_size=8,\n"," per_device_eval_batch_size=8,\n"," num_train_epochs=3,\n"," weight_decay=0.01,\n"," logging_dir=\"./logs\",\n"," logging_steps=10\n",")\n","\n","\n","trainer = Trainer(\n"," model=model,\n"," args=training_args,\n"," train_dataset=train_tokenized,\n"," eval_dataset=val_tokenized,\n"," tokenizer=tokenizer,\n"," compute_metrics=compute_metrics\n",")\n","\n","# ===============================================\n","# 8️⃣ HUẤN LUYỆN MÔ HÌNH\n","# ===============================================\n","trainer.train()\n","\n","# ===============================================\n","# 9️⃣ ĐÁNH GIÁ\n","# ===============================================\n","eval_results = trainer.evaluate()\n","print(\"\\n📊 Kết quả đánh giá:\", eval_results)\n","\n","# ===============================================\n","# 🔟 THỬ DỰ ĐOÁN\n","# ===============================================\n","text_samples = [\n"," \"Sản phẩm rất tốt, tôi hài lòng\",\n"," \"Chất lượng kém, không đáng tiền\"\n","]\n","\n","inputs = tokenizer(text_samples, padding=True, truncation=True, max_length=128, return_tensors=\"pt\")\n","inputs = {k: v.to(device) for k, v in inputs.items()}\n","outputs = model(**inputs)\n","preds = torch.argmax(outputs.logits, dim=1) + 1 # Chuyển lại 1-5\n","\n","for text, label in zip(text_samples, preds):\n"," print(f\"\\n🗣️ {text}\")\n"," print(\"➡️ Dự đoán:\", label.item())\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["6526189f84ae4cbe8443b30ccd731ec4","be0a8f456b5b49bd8d15ba705453b412","1c3af2f2b5414616a0b0de8e1bc46e30","e88d639d52ce43669639c6916e8e2147","cc1b4cb530c74308aee65030525be8e2","0689c8cd3b484fd38ae3cfd1f8167d79","63c5f894f9574b48abdc693ef4e6f194","1af012edb02b45e6a560e440bbe080e4","03337a6fd63747fda2029fb86ba9740d","68af8db0d0e5408aa61c11a21e83457f","3da29490f3194863922a6c665e4e73db","147e7d4ea26e41f49fca5f1ae13a15a3","b343af6b210248e9b4422fd00a5ade7b","a99dd0f9e7d3443d83903ab31414b602","ab86798be47c409f887f6a6d8f7e8316","4fb688113f8442efaa0b0010e20a8e03","38cc38f786f444949849eafc4fba0f36","8b38052286e54b5591a291650f775d41","080621c51a464588830f130ab43250b3","36c5927df6c64c31ac43786155dbc149","f67615c5d5d543e58a9b768ee59a07fc","cfe9ce405e1546928917705f3cfaf616"]},"id":"saPKyeQ5qpzD","outputId":"94a82328-4535-44d1-ea3e-8e1ac14434b2"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["✅ Dữ liệu mẫu:\n"," comment label\n","0 Mới mua máy này Tại thegioididong thốt nốt cảm... 4\n","1 Pin kém còn lại miễn chê mua 8/3/2019 tình trạ... 4\n","2 Sao lúc gọi điện thoại màn hình bị chấm nhỏ nh... 2\n","3 Mọi người cập nhật phần mềm lại , nó sẽ bớt tố... 2\n","4 Mới mua Sài được 1 tháng thấy pin rất trâu, Sà... 4\n","Min label: 0 Max label: 4\n"]},{"output_type":"display_data","data":{"text/plain":["Map: 0%| | 0/7007 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"6526189f84ae4cbe8443b30ccd731ec4"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Map: 0%| | 0/779 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"147e7d4ea26e41f49fca5f1ae13a15a3"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at vinai/phobert-base 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","Using the `WANDB_DISABLED` environment variable is deprecated and will be removed in v5. Use the --report_to flag to control the integrations used for logging result (for instance --report_to none).\n","/tmp/ipython-input-1488478540.py:103: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n"," trainer = Trainer(\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n"," <div>\n"," \n"," <progress value='1724' max='2628' style='width:300px; height:20px; vertical-align: middle;'></progress>\n"," [1724/2628 06:28 < 03:23, 4.43 it/s, Epoch 1.97/3]\n"," </div>\n"," <table border=\"1\" class=\"dataframe\">\n"," <thead>\n"," <tr style=\"text-align: left;\">\n"," <th>Step</th>\n"," <th>Training Loss</th>\n"," </tr>\n"," </thead>\n"," <tbody>\n"," <tr>\n"," <td>10</td>\n"," <td>1.533300</td>\n"," </tr>\n"," <tr>\n"," <td>20</td>\n"," <td>1.485500</td>\n"," </tr>\n"," <tr>\n"," <td>30</td>\n"," <td>1.407100</td>\n"," </tr>\n"," <tr>\n"," <td>40</td>\n"," <td>1.432600</td>\n"," </tr>\n"," <tr>\n"," <td>50</td>\n"," <td>1.386000</td>\n"," </tr>\n"," <tr>\n"," <td>60</td>\n"," <td>1.298200</td>\n"," </tr>\n"," <tr>\n"," <td>70</td>\n"," <td>1.237300</td>\n"," </tr>\n"," <tr>\n"," <td>80</td>\n"," <td>1.272200</td>\n"," </tr>\n"," <tr>\n"," <td>90</td>\n"," <td>1.340500</td>\n"," </tr>\n"," <tr>\n"," <td>100</td>\n"," <td>1.260100</td>\n"," </tr>\n"," <tr>\n"," <td>110</td>\n"," <td>1.019200</td>\n"," </tr>\n"," <tr>\n"," <td>120</td>\n"," <td>1.249800</td>\n"," </tr>\n"," <tr>\n"," <td>130</td>\n"," <td>1.001600</td>\n"," </tr>\n"," <tr>\n"," <td>140</td>\n"," <td>1.083300</td>\n"," </tr>\n"," <tr>\n"," <td>150</td>\n"," <td>1.097200</td>\n"," </tr>\n"," <tr>\n"," <td>160</td>\n"," <td>1.175400</td>\n"," </tr>\n"," <tr>\n"," <td>170</td>\n"," <td>1.209900</td>\n"," </tr>\n"," <tr>\n"," <td>180</td>\n"," <td>1.214400</td>\n"," </tr>\n"," <tr>\n"," <td>190</td>\n"," <td>1.052900</td>\n"," </tr>\n"," <tr>\n"," <td>200</td>\n"," <td>1.177700</td>\n"," </tr>\n"," <tr>\n"," <td>210</td>\n"," <td>1.010400</td>\n"," </tr>\n"," <tr>\n"," <td>220</td>\n"," <td>1.077200</td>\n"," </tr>\n"," <tr>\n"," <td>230</td>\n"," <td>0.965000</td>\n"," </tr>\n"," <tr>\n"," <td>240</td>\n"," <td>1.199300</td>\n"," </tr>\n"," <tr>\n"," <td>250</td>\n"," <td>1.220400</td>\n"," </tr>\n"," <tr>\n"," <td>260</td>\n"," <td>1.204400</td>\n"," </tr>\n"," <tr>\n"," <td>270</td>\n"," <td>0.985400</td>\n"," </tr>\n"," <tr>\n"," <td>280</td>\n"," <td>0.955100</td>\n"," </tr>\n"," <tr>\n"," <td>290</td>\n"," <td>1.056700</td>\n"," </tr>\n"," <tr>\n"," <td>300</td>\n"," <td>0.931800</td>\n"," </tr>\n"," <tr>\n"," <td>310</td>\n"," <td>1.031100</td>\n"," </tr>\n"," <tr>\n"," <td>320</td>\n"," <td>1.050700</td>\n"," </tr>\n"," <tr>\n"," <td>330</td>\n"," <td>0.975200</td>\n"," </tr>\n"," <tr>\n"," <td>340</td>\n"," <td>0.865500</td>\n"," </tr>\n"," <tr>\n"," <td>350</td>\n"," <td>0.894900</td>\n"," </tr>\n"," <tr>\n"," <td>360</td>\n"," <td>0.914600</td>\n"," </tr>\n"," <tr>\n"," <td>370</td>\n"," <td>0.982700</td>\n"," </tr>\n"," <tr>\n"," <td>380</td>\n"," <td>1.129200</td>\n"," </tr>\n"," <tr>\n"," <td>390</td>\n"," <td>0.931100</td>\n"," </tr>\n"," <tr>\n"," <td>400</td>\n"," <td>0.912500</td>\n"," </tr>\n"," <tr>\n"," <td>410</td>\n"," <td>1.029100</td>\n"," </tr>\n"," <tr>\n"," <td>420</td>\n"," <td>0.980100</td>\n"," </tr>\n"," <tr>\n"," <td>430</td>\n"," <td>0.902200</td>\n"," </tr>\n"," <tr>\n"," <td>440</td>\n"," <td>0.951600</td>\n"," </tr>\n"," <tr>\n"," <td>450</td>\n"," <td>0.919000</td>\n"," </tr>\n"," <tr>\n"," <td>460</td>\n"," <td>1.142400</td>\n"," </tr>\n"," <tr>\n"," <td>470</td>\n"," <td>0.895100</td>\n"," </tr>\n"," <tr>\n"," <td>480</td>\n"," <td>0.987800</td>\n"," </tr>\n"," <tr>\n"," <td>490</td>\n"," <td>1.036800</td>\n"," </tr>\n"," <tr>\n"," <td>500</td>\n"," <td>1.108200</td>\n"," </tr>\n"," <tr>\n"," <td>510</td>\n"," <td>0.746800</td>\n"," </tr>\n"," <tr>\n"," <td>520</td>\n"," <td>1.271200</td>\n"," </tr>\n"," <tr>\n"," <td>530</td>\n"," <td>1.077000</td>\n"," </tr>\n"," <tr>\n"," <td>540</td>\n"," <td>0.980300</td>\n"," </tr>\n"," <tr>\n"," <td>550</td>\n"," <td>1.078500</td>\n"," </tr>\n"," <tr>\n"," <td>560</td>\n"," <td>0.913700</td>\n"," </tr>\n"," <tr>\n"," <td>570</td>\n"," <td>1.097000</td>\n"," </tr>\n"," <tr>\n"," <td>580</td>\n"," <td>0.765600</td>\n"," </tr>\n"," <tr>\n"," <td>590</td>\n"," <td>0.917600</td>\n"," </tr>\n"," <tr>\n"," <td>600</td>\n"," <td>1.219400</td>\n"," </tr>\n"," <tr>\n"," <td>610</td>\n"," <td>1.194400</td>\n"," </tr>\n"," <tr>\n"," <td>620</td>\n"," <td>0.846900</td>\n"," </tr>\n"," <tr>\n"," <td>630</td>\n"," <td>1.109800</td>\n"," </tr>\n"," <tr>\n"," <td>640</td>\n"," <td>0.971700</td>\n"," </tr>\n"," <tr>\n"," <td>650</td>\n"," <td>0.912300</td>\n"," </tr>\n"," <tr>\n"," <td>660</td>\n"," <td>0.909900</td>\n"," </tr>\n"," <tr>\n"," <td>670</td>\n"," <td>0.944300</td>\n"," </tr>\n"," <tr>\n"," <td>680</td>\n"," <td>0.819500</td>\n"," </tr>\n"," <tr>\n"," <td>690</td>\n"," <td>1.213300</td>\n"," </tr>\n"," <tr>\n"," <td>700</td>\n"," <td>1.043900</td>\n"," </tr>\n"," <tr>\n"," <td>710</td>\n"," <td>0.938100</td>\n"," </tr>\n"," <tr>\n"," <td>720</td>\n"," <td>0.969900</td>\n"," </tr>\n"," <tr>\n"," <td>730</td>\n"," <td>0.793900</td>\n"," </tr>\n"," <tr>\n"," <td>740</td>\n"," <td>0.859300</td>\n"," </tr>\n"," <tr>\n"," <td>750</td>\n"," <td>1.056000</td>\n"," </tr>\n"," <tr>\n"," <td>760</td>\n"," <td>0.896100</td>\n"," </tr>\n"," <tr>\n"," <td>770</td>\n"," <td>1.003900</td>\n"," </tr>\n"," <tr>\n"," <td>780</td>\n"," <td>0.961600</td>\n"," </tr>\n"," <tr>\n"," <td>790</td>\n"," <td>0.975200</td>\n"," </tr>\n"," <tr>\n"," <td>800</td>\n"," <td>0.896500</td>\n"," </tr>\n"," <tr>\n"," <td>810</td>\n"," <td>0.798300</td>\n"," </tr>\n"," <tr>\n"," <td>820</td>\n"," <td>0.900500</td>\n"," </tr>\n"," <tr>\n"," <td>830</td>\n"," <td>1.000200</td>\n"," </tr>\n"," <tr>\n"," <td>840</td>\n"," <td>0.927800</td>\n"," </tr>\n"," <tr>\n"," <td>850</td>\n"," <td>1.048800</td>\n"," </tr>\n"," <tr>\n"," <td>860</td>\n"," <td>0.874700</td>\n"," </tr>\n"," <tr>\n"," <td>870</td>\n"," <td>0.990800</td>\n"," </tr>\n"," <tr>\n"," <td>880</td>\n"," <td>0.976900</td>\n"," </tr>\n"," <tr>\n"," <td>890</td>\n"," <td>0.874300</td>\n"," </tr>\n"," <tr>\n"," <td>900</td>\n"," <td>0.852600</td>\n"," </tr>\n"," <tr>\n"," <td>910</td>\n"," <td>0.868300</td>\n"," </tr>\n"," <tr>\n"," <td>920</td>\n"," <td>0.928500</td>\n"," </tr>\n"," <tr>\n"," <td>930</td>\n"," <td>0.830100</td>\n"," </tr>\n"," <tr>\n"," <td>940</td>\n"," <td>1.008700</td>\n"," </tr>\n"," <tr>\n"," <td>950</td>\n"," <td>0.756000</td>\n"," </tr>\n"," <tr>\n"," <td>960</td>\n"," <td>0.894200</td>\n"," </tr>\n"," <tr>\n"," <td>970</td>\n"," <td>1.025800</td>\n"," </tr>\n"," <tr>\n"," <td>980</td>\n"," <td>0.768100</td>\n"," </tr>\n"," <tr>\n"," <td>990</td>\n"," <td>0.951700</td>\n"," </tr>\n"," <tr>\n"," <td>1000</td>\n"," <td>0.946600</td>\n"," </tr>\n"," <tr>\n"," <td>1010</td>\n"," <td>0.903000</td>\n"," </tr>\n"," <tr>\n"," <td>1020</td>\n"," <td>0.758400</td>\n"," </tr>\n"," <tr>\n"," <td>1030</td>\n"," <td>0.808100</td>\n"," </tr>\n"," <tr>\n"," <td>1040</td>\n"," <td>0.859800</td>\n"," </tr>\n"," <tr>\n"," <td>1050</td>\n"," <td>0.804500</td>\n"," </tr>\n"," <tr>\n"," <td>1060</td>\n"," <td>0.699400</td>\n"," </tr>\n"," <tr>\n"," <td>1070</td>\n"," <td>0.851900</td>\n"," </tr>\n"," <tr>\n"," <td>1080</td>\n"," <td>0.933800</td>\n"," </tr>\n"," <tr>\n"," <td>1090</td>\n"," <td>0.814000</td>\n"," </tr>\n"," <tr>\n"," <td>1100</td>\n"," <td>0.878600</td>\n"," </tr>\n"," <tr>\n"," <td>1110</td>\n"," <td>0.648700</td>\n"," </tr>\n"," <tr>\n"," <td>1120</td>\n"," <td>0.798900</td>\n"," </tr>\n"," <tr>\n"," <td>1130</td>\n"," <td>0.900200</td>\n"," </tr>\n"," <tr>\n"," <td>1140</td>\n"," <td>0.885200</td>\n"," </tr>\n"," <tr>\n"," <td>1150</td>\n"," <td>0.919400</td>\n"," </tr>\n"," <tr>\n"," <td>1160</td>\n"," <td>0.850600</td>\n"," </tr>\n"," <tr>\n"," <td>1170</td>\n"," <td>0.859500</td>\n"," </tr>\n"," <tr>\n"," <td>1180</td>\n"," <td>0.750200</td>\n"," </tr>\n"," <tr>\n"," <td>1190</td>\n"," <td>0.813700</td>\n"," </tr>\n"," <tr>\n"," <td>1200</td>\n"," <td>0.863600</td>\n"," </tr>\n"," <tr>\n"," <td>1210</td>\n"," <td>0.883800</td>\n"," </tr>\n"," <tr>\n"," <td>1220</td>\n"," <td>0.675600</td>\n"," </tr>\n"," <tr>\n"," <td>1230</td>\n"," <td>0.685900</td>\n"," </tr>\n"," <tr>\n"," <td>1240</td>\n"," <td>0.718600</td>\n"," </tr>\n"," <tr>\n"," <td>1250</td>\n"," <td>0.658700</td>\n"," </tr>\n"," <tr>\n"," <td>1260</td>\n"," <td>0.965700</td>\n"," </tr>\n"," <tr>\n"," <td>1270</td>\n"," <td>0.847600</td>\n"," </tr>\n"," <tr>\n"," <td>1280</td>\n"," <td>0.915400</td>\n"," </tr>\n"," <tr>\n"," <td>1290</td>\n"," <td>0.843800</td>\n"," </tr>\n"," <tr>\n"," <td>1300</td>\n"," <td>0.892200</td>\n"," </tr>\n"," <tr>\n"," <td>1310</td>\n"," <td>0.766400</td>\n"," </tr>\n"," <tr>\n"," <td>1320</td>\n"," <td>0.748900</td>\n"," </tr>\n"," <tr>\n"," <td>1330</td>\n"," <td>0.748000</td>\n"," </tr>\n"," <tr>\n"," <td>1340</td>\n"," <td>0.660000</td>\n"," </tr>\n"," <tr>\n"," <td>1350</td>\n"," <td>0.901800</td>\n"," </tr>\n"," <tr>\n"," <td>1360</td>\n"," <td>0.772100</td>\n"," </tr>\n"," <tr>\n"," <td>1370</td>\n"," <td>0.841700</td>\n"," </tr>\n"," <tr>\n"," <td>1380</td>\n"," <td>0.804500</td>\n"," </tr>\n"," <tr>\n"," <td>1390</td>\n"," <td>0.812500</td>\n"," </tr>\n"," <tr>\n"," <td>1400</td>\n"," <td>0.739700</td>\n"," </tr>\n"," <tr>\n"," <td>1410</td>\n"," <td>0.926500</td>\n"," </tr>\n"," <tr>\n"," <td>1420</td>\n"," <td>0.806600</td>\n"," </tr>\n"," <tr>\n"," <td>1430</td>\n"," <td>0.867400</td>\n"," </tr>\n"," <tr>\n"," <td>1440</td>\n"," <td>0.810900</td>\n"," </tr>\n"," <tr>\n"," <td>1450</td>\n"," <td>0.914500</td>\n"," </tr>\n"," <tr>\n"," <td>1460</td>\n"," <td>0.922900</td>\n"," </tr>\n"," <tr>\n"," <td>1470</td>\n"," <td>0.699600</td>\n"," </tr>\n"," <tr>\n"," <td>1480</td>\n"," <td>0.740100</td>\n"," </tr>\n"," <tr>\n"," <td>1490</td>\n"," <td>1.015800</td>\n"," </tr>\n"," <tr>\n"," <td>1500</td>\n"," <td>0.789300</td>\n"," </tr>\n"," <tr>\n"," <td>1510</td>\n"," <td>0.877400</td>\n"," </tr>\n"," <tr>\n"," <td>1520</td>\n"," <td>1.086800</td>\n"," </tr>\n"," <tr>\n"," <td>1530</td>\n"," <td>0.899700</td>\n"," </tr>\n"," <tr>\n"," <td>1540</td>\n"," <td>0.813500</td>\n"," </tr>\n"," <tr>\n"," <td>1550</td>\n"," <td>0.798900</td>\n"," </tr>\n"," <tr>\n"," <td>1560</td>\n"," <td>0.921000</td>\n"," </tr>\n"," <tr>\n"," <td>1570</td>\n"," <td>0.583700</td>\n"," </tr>\n"," <tr>\n"," <td>1580</td>\n"," <td>0.841100</td>\n"," </tr>\n"," <tr>\n"," <td>1590</td>\n"," <td>0.992100</td>\n"," </tr>\n"," <tr>\n"," <td>1600</td>\n"," <td>0.660300</td>\n"," </tr>\n"," <tr>\n"," <td>1610</td>\n"," <td>0.882000</td>\n"," </tr>\n"," <tr>\n"," <td>1620</td>\n"," <td>0.757400</td>\n"," </tr>\n"," <tr>\n"," <td>1630</td>\n"," <td>0.865700</td>\n"," </tr>\n"," <tr>\n"," <td>1640</td>\n"," <td>0.710300</td>\n"," </tr>\n"," <tr>\n"," <td>1650</td>\n"," <td>0.841100</td>\n"," </tr>\n"," <tr>\n"," <td>1660</td>\n"," <td>0.942600</td>\n"," </tr>\n"," <tr>\n"," <td>1670</td>\n"," <td>0.810200</td>\n"," </tr>\n"," <tr>\n"," <td>1680</td>\n"," <td>0.851500</td>\n"," </tr>\n"," <tr>\n"," <td>1690</td>\n"," <td>0.851000</td>\n"," </tr>\n"," <tr>\n"," <td>1700</td>\n"," <td>0.703900</td>\n"," </tr>\n"," <tr>\n"," <td>1710</td>\n"," <td>0.893500</td>\n"," </tr>\n"," <tr>\n"," <td>1720</td>\n"," <td>0.820500</td>\n"," </tr>\n"," </tbody>\n","</table><p>"]},"metadata":{}}]},{"cell_type":"code","source":["# training_args = TrainingArguments(\n","# output_dir=\"./results\",\n","# learning_rate=2e-5,\n","# per_device_train_batch_size=8,\n","# per_device_eval_batch_size=8,\n","# num_train_epochs=3,\n","# weight_decay=0.01,\n","# logging_dir=\"./logs\",\n","# logging_steps=10, # thay thế cho evaluation_strategy\n","# )\n"],"metadata":{"id":"exmSvlwFGfnt"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# ===============================================\n","# 1️⃣ CÀI ĐẶT THƯ VIỆN\n","# ===============================================\n","!pip install -q transformers datasets torch\n","\n","from transformers import AutoTokenizer, AutoModelForSequenceClassification, TrainingArguments, Trainer\n","from datasets import load_dataset\n","import numpy as np\n","import torch\n","from sklearn.metrics import accuracy_score, f1_score\n","\n","# ===============================================\n","# 2️⃣ CẤU HÌNH THIẾT BỊ\n","# ===============================================\n","device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n","print(\"🖥️ Device:\", device)\n","\n","# ===============================================\n","# 3️⃣ TẢI DỮ LIỆU VÍ DỤ (IMDB SENTIMENT)\n","# ===============================================\n","dataset = load_dataset(\"imdb\")\n","\n","# Giảm kích thước cho nhanh (chỉ dùng 2000 mẫu train/test)\n","dataset[\"train\"] = dataset[\"train\"].shuffle(seed=42).select(range(2000))\n","dataset[\"test\"] = dataset[\"test\"].shuffle(seed=42).select(range(1000))\n","\n","# ===============================================\n","# 4️⃣ TOKENIZER (BERT BASE UNCASED)\n","# ===============================================\n","model_name = \"bert-base-uncased\"\n","tokenizer = AutoTokenizer.from_pretrained(model_name)\n","\n","def tokenize_function(example):\n"," return tokenizer(\n"," example[\"text\"],\n"," padding=\"max_length\",\n"," truncation=True,\n"," max_length=128\n"," )\n","\n","tokenized_datasets = dataset.map(tokenize_function, batched=True)\n","\n","# Chuyển dữ liệu về torch tensor\n","tokenized_datasets = tokenized_datasets.remove_columns([\"text\"])\n","tokenized_datasets.set_format(\"torch\")\n","\n","train_dataset = tokenized_datasets[\"train\"]\n","test_dataset = tokenized_datasets[\"test\"]\n","\n","# ===============================================\n","# 5️⃣ TẠO MÔ HÌNH BERT\n","# ===============================================\n","model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=2)\n","model.to(device)\n","\n","# ===============================================\n","# 6️⃣ ĐỊNH NGHĨA HÀM ĐÁNH GIÁ\n","# ===============================================\n","def compute_metrics(eval_pred):\n"," logits, labels = eval_pred\n"," preds = np.argmax(logits, axis=-1)\n"," acc = accuracy_score(labels, preds)\n"," f1 = f1_score(labels, preds)\n"," return {\"accuracy\": acc, \"f1\": f1}\n","\n","# ===============================================\n","# 7️⃣ CẤU HÌNH HUẤN LUYỆN\n","# ===============================================\n","training_args = TrainingArguments(\n"," output_dir=\"./results\",\n"," num_train_epochs=2,\n"," per_device_train_batch_size=8,\n"," per_device_eval_batch_size=8,\n"," warmup_steps=50,\n"," weight_decay=0.01,\n"," eval_strategy=\"epoch\", # ⚠️ sửa lại (phiên bản transformers mới dùng \"eval_strategy\")\n"," logging_dir=\"./logs\",\n"," logging_steps=10,\n"," save_strategy=\"no\"\n",")\n","\n","# Tắt WANDB logging\n","import os\n","os.environ[\"WANDB_DISABLED\"] = \"true\"\n","\n","# ===============================================\n","# 8️⃣ HUẤN LUYỆN MÔ HÌNH\n","# ===============================================\n","trainer = Trainer(\n"," model=model,\n"," args=training_args,\n"," train_dataset=train_dataset,\n"," eval_dataset=test_dataset,\n"," compute_metrics=compute_metrics,\n",")\n","\n","trainer.train()\n","\n","# ===============================================\n","# 9️⃣ ĐÁNH GIÁ\n","# ===============================================\n","eval_result = trainer.evaluate()\n","print(\"📊 Evaluation:\", eval_result)\n","\n","# ===============================================\n","# 🔟 DỰ ĐOÁN MẪU MỚI\n","# ===============================================\n","texts = [\n"," \"This movie was absolutely fantastic!\",\n"," \"It was a waste of time and money.\"\n","]\n","\n","inputs = tokenizer(texts, padding=True, truncation=True, max_length=128, return_tensors=\"pt\").to(device)\n","model.eval()\n","\n","with torch.no_grad():\n"," outputs = model(**inputs)\n"," preds = torch.argmax(outputs.logits, dim=1)\n","\n","for text, pred in zip(texts, preds):\n"," label = \"Positive\" if pred.item() == 1 else \"Negative\"\n"," print(f\"📝 {text} → {label}\")\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":619,"referenced_widgets":["849e4a2753174535a07eea1ff9bb3a3a","b6e3aafa4d2b4693802dca526c01e1cd","f0056d535c9744778e0ffdc5825aa15c","73d4141d12054f74be2fa670eb825b4f","df5f0f6b7892473886e4fb8019a04198","307a0b9d234c4a50ad19f362478e8241","b27eab0112db45f6a89c8d945489b7d0","6645894c3588474992a4132450ca828b","9510a8e2b2404c6a81da52c739eba025","054f9714e3094608aac3a792f4edfbd2","a65878f6b7c8497794c054873f66f90d","de2c5f93b2af48afbe95df67e4e9ad61","3c276cd497bd45b493ee77a4b0a72a47","e0844fb9d6a94b1980b7b5381a229fdc","2d3eb9e451c34c16bb46eb0aa47c624c","45957e01b0144223bce2647790fa6416","b3409913826e4b788f920d8c6b17ede1","697109ac40a441cbb80132f3b666f705","d97dd03f5c224a919cda170d17cdcbec","75d54c5da3f143a38ff7a202dc55c4a5","a749333a05c74c7d9f411dfb9e96a19a","c774b9300d2f41549126bd4784d6447a","2efba26208d845ec87c1a11cc6e660a4","1b56457e839c4908b6cfcba148456f45","097eb91a1ecd4b0293dc51bab57bf445","2103051989b94a10b4bb691737579c97","58ce4778e6864a88a6cf7924d24d80a1","25a11c01f4a845c3b7ea321295c1de9f","b6ac45afa8214ab288a7e3b754ce78d5","e5fbc01f5d874ced96a475f9f5d7d620","835908978f024f1e8ecd8a4a8928c5b4","70dba4bc096c4ff5bf00c0704d706248","91899311bfdf481ba084063328ead74f","dca8a0111b42450a86e81e4065a77dba","0857376cd9d9447d8cd6943c26764592","eed6922d5b0a4028b000b03e7aa2440c","87766bce28014377b4573b5b69a5e3bf","1bee68a2aaef4416a9aa788a71453a60","58430178691e4fd98bdc343720848e9c","5536a7517bca489c83a0e8e17cb662d0","61464c762f1e4ee18698f95391ae3acb","87269069b67f4360a4ab63436927e9e3","af8da5e67d1b48dd8639293123f0d3b3","2d2b749467644b659425181504e35c3c","57805d55dfe945429800548a6e068c53","31bb8128b4cd4d4584726c9d5795aaba","c6579b790a77433a8013e61812f1384d","f432dc2184064a62b803de6f5bb840fb","818f5ea300024c02ace55ca2b6e862f7","5ae7897ec16741cc827da74ad7626fc4","003ccf62c6d5446095cfae5502bfdf06","8587eebdb65b4a21822671c8f04d4d23","9888b8003e474883b6e32ee65976113b","7f2e4a4e85af49a389a59d0535f429ce","a322d404fa8d4ec68bb08f6ea253a1f6","a85317594caf465f919645bed803098a","75ac1ce8c8b5433282070e575f0915e5","5d28a284ce0b497db4c3c76c3855423c","81f0cf659ac04d62acbe4eb42249363a","689bb8d8b27c4ccea30316ad5c58819a","b53df09680b54e509d15112f6b3230de","2e821d05370842a9a7d91a815f1d02dc","5375b29fa93647c38969b58c1a661d8a","6787a6b390ac481eb74a238a024e9ae9","8bb3462d837f4ef3b9fea5bb8f2868b1","6137037fd4414b8a8ba739a8597fba91","9eac42aae952457ebcbe9531a9373b61","21caa2011980410b9ff675f44e23ed22","c22258e7e6cf4c44ab639009dacf14e2","3a4dec6a00134760b08c7f594df1b9b0","00fb2ec6306a4d66a5f069363ae1892d","3eebbab9c4824d74a8947cafc7d0de8c","e729ee3f105e4dc883d9f6b67704a52b","d62a3e74cc0545a096d871dd3c31bd07","ec15ae7906cd4fe3b9a7cd49dd64f760","530c8b3095f143fd95fb39fd3a54dd36","d661821391434489937f8c39cef33a00","5fb8c212eaea481a9cee3f707fdef627","d5558a9e6c3e4a34930558676fc9211c","8b7b65fe5dec4948abdab5ee26759b3d","08a6b3ae1c8e48498b5bdba9b280ef67","1dbd067c181a46c0a03d9e81d72175cc","3d6b13bcddbd41ef8652e2da90a9c049","5880ef99861441ce85ed8d5fb44bf2fe","9958dff3e0d84094b99278c7ce33b44d","f033ac7f50564b219e5dda022d0e03ae","6d0fb99add284d058731a4fb8effe0c2","6a726dc9453a428bb3c9d9890e0dc87f","66450de1b34f4d3db937f4f9a70a5720","741d985bab0c4a3aa745f156f3e0b745","d423242cd5144f72aa519e89a686d718","715095e225384e46ae3453431cd73438","50b7aeaf4e164f038a06e6cc8515c47f","1c6d23baac774f50b2ff2ce67149791d","bf8520d6a7a4484f85fb739a13a5cdfa","31357d8963094c0dbf563f64597dba23","4754d880e16e4718a281c7470202a0bf","916f5bef08f641df92afd6a02c7e1083","2c451b6e457c455e9b086b29ce180651","59f50796801a4134b78b5ab414cb39d5","655edc8816354738825db841bb75ae8f","3a921a2caa3a46338259166ef713ed66","cf1198ab5ed646fd908d18fdd01c63d1","726c19f5f4824f2f8c067408a7b702a4","b1f8777eb8b947d1a734fa3c67235ed9","25b48c6362c94298908cb4446a5badb9","7196ada745d7479a93005f695837f383","bd729651fd354f8687996666333f4178","2cb3281e550c403fbf4dac8f38b5edc1","8fb022ffb6694515a8a6ea7475d31086"]},"id":"FA_6kUaSGXtn","executionInfo":{"status":"ok","timestamp":1760804268513,"user_tz":-420,"elapsed":221488,"user":{"displayName":"Lâm Đức Cương","userId":"02362263444637620675"}},"outputId":"1bfd4081-4339-4213-90d6-405a3decf962"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["🖥️ Device: cuda\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":"849e4a2753174535a07eea1ff9bb3a3a"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["plain_text/train-00000-of-00001.parquet: 0%| | 0.00/21.0M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"de2c5f93b2af48afbe95df67e4e9ad61"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["plain_text/test-00000-of-00001.parquet: 0%| | 0.00/20.5M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"2efba26208d845ec87c1a11cc6e660a4"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["plain_text/unsupervised-00000-of-00001.p(…): 0%| | 0.00/42.0M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"dca8a0111b42450a86e81e4065a77dba"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating train split: 0%| | 0/25000 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"57805d55dfe945429800548a6e068c53"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating test split: 0%| | 0/25000 [00:00<?, ? 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