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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)")
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