Create BERT.ibpynb

BERT.ibpynb

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+#!pip install -U transformers
+from transformers import TrainingArguments
+
+training_args = TrainingArguments(
+    output_dir="./results",
+    learning_rate=2e-5,
+    per_device_train_batch_size=4,
+    per_device_eval_batch_size=4,
+    num_train_epochs=3,
+    weight_decay=0.01,
+    logging_dir="./logs",
+    logging_steps=10
+)
+
+!pip uninstall -y transformers
+!pip install -U transformers datasets accelerate
+!pip show transformers | grep Version
+
+import os
+os.environ["WANDB_DISABLED"] = "true"
+
+
+# ===============================================
+# 1️⃣ CÀI ĐẶT THƯ VIỆN
+# ===============================================
+!pip install -q transformers datasets torch
+
+# ===============================================
+# 2️⃣ TẠO DỮ LIỆU GIẢ LẬP (CSV)
+# ===============================================
+import pandas as pd
+
+data = {
+    "text": [
+        "I love this movie, it was fantastic!",
+        "This product is terrible and useless.",
+        "What a great experience, I will come again!",
+        "I hate this item, waste of money.",
+        "Absolutely amazing service and food.",
+        "Worst app I have ever used.",
+        "The phone works perfectly and fast.",
+        "It broke after two days, horrible!",
+        "Very happy with my purchase.",
+        "Not worth the price at all."
+    ],
+    "label": [1,0,1,0,1,0,1,0,1,0]
+}
+
+df = pd.DataFrame(data)
+df.to_csv("sentiment_data.csv", index=False)
+print("✅ Dữ liệu mẫu đã được tạo:\n")
+print(df.head())
+
+# ===============================================
+# 3️⃣ TẢI DỮ LIỆU & CHUẨN HÓA
+# ===============================================
+from datasets import load_dataset
+
+dataset = load_dataset("csv", data_files="sentiment_data.csv")
+dataset = dataset["train"].train_test_split(test_size=0.3, seed=42)
+
+train_dataset = dataset["train"]
+test_dataset = dataset["test"]
+
+print("\n🔹 Số mẫu train:", len(train_dataset))
+print("🔹 Số mẫu test:", len(test_dataset))
+
+# ===============================================
+# 4️⃣ TOKENIZATION (CHUYỂN TEXT THÀNH INPUT CHO BERT)
+# ===============================================
+from transformers import AutoTokenizer
+
+model_name = "bert-base-uncased"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+def preprocess_function(examples):
+    return tokenizer(
+        examples["text"],
+        padding="max_length",
+        truncation=True,
+        max_length=64,
+    )
+
+train_tokenized = train_dataset.map(preprocess_function, batched=True)
+test_tokenized = test_dataset.map(preprocess_function, batched=True)
+
+# ===============================================
+# 5️⃣ CHUẨN BỊ MÔ HÌNH BERT CHO PHÂN LOẠI
+# ===============================================
+import torch
+from transformers import AutoModelForSequenceClassification, TrainingArguments, Trainer
+
+model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=2)
+
+# ===============================================
+# 6️⃣ ĐỊNH NGHĨA HÀM ĐÁNH GIÁ
+# ===============================================
+from sklearn.metrics import accuracy_score, f1_score
+
+def compute_metrics(eval_pred):
+    logits, labels = eval_pred
+    preds = torch.argmax(torch.tensor(logits), dim=1)
+    acc = accuracy_score(labels, preds)
+    f1 = f1_score(labels, preds)
+    return {"accuracy": acc, "f1": f1}
+
+# ===============================================
+# 7️⃣ CẤU HÌNH HUẤN LUYỆN
+# ===============================================
+training_args = TrainingArguments(
+    output_dir="./results",
+    learning_rate=2e-5,
+    per_device_train_batch_size=4,
+    per_device_eval_batch_size=4,
+    num_train_epochs=3,
+    weight_decay=0.01,
+    evaluation_strategy="epoch",
+    save_strategy="epoch",
+    logging_dir="./logs",
+    logging_steps=10
+)
+
+# ===============================================
+# 8️⃣ HUẤN LUYỆN MÔ HÌNH
+# ===============================================
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=train_tokenized,
+    eval_dataset=test_tokenized,
+    tokenizer=tokenizer,
+    compute_metrics=compute_metrics
+)
+
+trainer.train()
+
+# ===============================================
+# 9️⃣ ĐÁNH GIÁ
+# ===============================================
+eval_results = trainer.evaluate()
+print("\n📊 Kết quả đánh giá:", eval_results)
+
+# ===============================================
+# 🔟 THỬ DỰ ĐOÁN
+# ===============================================
+text_samples = [
+    "I really love this product!",
+    "This is the worst movie ever."
+]
+
+inputs = tokenizer(text_samples, padding=True, truncation=True, max_length=64, return_tensors="pt")
+outputs = model(**inputs)
+preds = torch.argmax(outputs.logits, dim=1)
+
+for text, label in zip(text_samples, preds):
+    print(f"\n🗣️ {text}")
+    print("➡️ Dự đoán:", "Tích cực (1)" if label == 1 else "Tiêu cực (0)")