HVU_QA

HVU_QA/fine_tune_qg.py

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+import json
+from datasets import Dataset
+from sklearn.model_selection import train_test_split
+from transformers import (
+    T5Tokenizer,
+    T5ForConditionalGeneration,
+    TrainingArguments,
+    Trainer
+)
+
+def load_squad_data(file_path):
+    
+    with open(file_path, "r", encoding="utf-8") as f:
+        squad_data = json.load(f)
+
+    data = []
+    for article in squad_data["data"]:
+        for paragraph in article["paragraphs"]:
+            context = paragraph.get("context", "")
+            for qa in paragraph["qas"]:
+                if not qa.get("is_impossible", False) and qa.get("answers"):
+                    answer = qa["answers"][0]["text"]
+                    question = qa["question"]
+                    input_text = f"answer: {answer} context: {context}"
+                    data.append({"input": input_text, "target": question})
+    return data
+
+def preprocess_function(example, tokenizer, max_input_length=512, max_target_length=64):
+    
+    model_inputs = tokenizer(
+        example["input"],
+        max_length=max_input_length,
+        padding="max_length",
+        truncation=True,
+    )
+    labels = tokenizer(
+        text_target=example["target"],
+        max_length=max_target_length,
+        padding="max_length",
+        truncation=True,
+    )
+    model_inputs["labels"] = labels["input_ids"]
+    return model_inputs
+
+def main():
+    data_path = "30ktrain.json"  
+    output_dir = "t5-viet-qg-finetuned"
+    logs_dir = "logs"
+    model_name = "VietAI/vit5-base"
+
+    print("Tải mô hình và tokenizer...")
+    tokenizer = T5Tokenizer.from_pretrained(model_name)
+    model = T5ForConditionalGeneration.from_pretrained(model_name)
+
+    print("Đọc và chia dữ liệu...")
+    raw_data = load_squad_data(data_path)  
+    train_data, val_data = train_test_split(raw_data, test_size=0.2, random_state=42)
+
+    train_dataset = Dataset.from_list(train_data)
+    val_dataset = Dataset.from_list(val_data)
+
+    tokenized_train = train_dataset.map(
+        lambda x: preprocess_function(x, tokenizer),
+        batched=True,
+        remove_columns=["input", "target"]
+    )
+    tokenized_val = val_dataset.map(
+        lambda x: preprocess_function(x, tokenizer),
+        batched=True,
+        remove_columns=["input", "target"]
+    )
+
+    print("Cấu hình huấn luyện...")
+    training_args = TrainingArguments(
+        output_dir=output_dir,
+        overwrite_output_dir=True,
+        per_device_train_batch_size=1,
+        gradient_accumulation_steps=1,
+        num_train_epochs=3,
+        learning_rate=2e-4,
+        weight_decay=0.01,
+        warmup_steps=0,
+        logging_dir=logs_dir,
+        logging_steps=10,
+        fp16=False
+    )
+
+    print("Huấn luyện mô hình...")
+    trainer = Trainer(
+        model=model,
+        args=training_args,
+        train_dataset=tokenized_train,
+        eval_dataset=tokenized_val,
+        tokenizer=tokenizer,
+    )
+    trainer.train()
+
+    print("Lưu mô hình...")
+    model.save_pretrained(output_dir)
+    tokenizer.save_pretrained(output_dir)
+    print("Huấn luyện hoàn tất!")
+
+if __name__ == "__main__":
+    main()

HVU_QA/generate_question.py

@@ -0,0 +1,139 @@
+import json
+from difflib import SequenceMatcher
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+from transformers.utils import logging as hf_logging
+
+hf_logging.set_verbosity_error()
+
+MODEL_DIR = "t5-viet-qg-finetuned"
+DATA_PATH = "30ktrain.json"
+
+tokenizer = T5Tokenizer.from_pretrained(MODEL_DIR)
+model = T5ForConditionalGeneration.from_pretrained(MODEL_DIR)
+
+def find_best_match_from_context(user_context, squad_data):
+    best_score, best_entry = 0.0, None
+    ui = user_context.lower()
+
+    for article in squad_data.get("data", []):
+        context_title = article.get("title", "")
+        score_title = SequenceMatcher(None, ui, context_title.lower()).ratio()
+
+        for paragraph in article.get("paragraphs", []):
+            context = paragraph.get("context", "")
+            for qa in paragraph.get("qas", []):
+                answers = qa.get("answers", [])
+                if not answers:
+                    continue
+                answer_text = answers[0].get("text", "").strip()
+                question_text = qa.get("question", "").strip()
+
+                score = score_title
+                if score > best_score:
+                    best_score = score
+                    best_entry = (context, answer_text, question_text)
+
+    return best_entry
+
+def _near_duplicate(q, seen, thr=0.90):
+    for s in seen:
+        if SequenceMatcher(None, q, s).ratio() >= thr:
+            return True
+    return False
+
+def generate_questions(user_context,
+                       total_questions=20,
+                       batch_size=10,
+                       top_k=60,
+                       top_p=0.95,
+                       temperature=0.9,  # Tăng temperature để sinh câu hỏi sáng tạo hơn
+                       max_input_len=512,
+                       max_new_tokens=64):
+    with open(DATA_PATH, "r", encoding="utf-8") as f:
+        squad_data = json.load(f)
+
+    best_entry = find_best_match_from_context(user_context, squad_data)
+    if best_entry is None:
+        print("Không tìm thấy dữ liệu phù hợp trong file JSON.")
+        return
+
+    context, answer, _ = best_entry
+
+   
+    input_text = f"answer: {answer} context: {context}"
+    inputs = tokenizer(
+        input_text,
+        return_tensors="pt",
+        truncation=True,
+        max_length=max_input_len
+    )
+
+    unique_questions = []
+    remaining = total_questions
+
+    while remaining > 0:
+        n = min(batch_size, remaining)
+        outputs = model.generate(
+            **inputs,
+            do_sample=True,
+            top_k=top_k,  
+            top_p=top_p,  
+            temperature=temperature,  
+            max_new_tokens=max_new_tokens,
+            num_return_sequences=n,
+            no_repeat_ngram_size=3,
+            repetition_penalty=1.12
+        )
+
+        for out in outputs:
+            q = tokenizer.decode(out, skip_special_tokens=True).strip()
+            if len(q) < 5:
+                continue
+            if not _near_duplicate(q, unique_questions, thr=0.90):
+                unique_questions.append(q)
+
+        remaining = total_questions - len(unique_questions)
+        if remaining <= 0:
+            break
+
+    unique_questions = unique_questions[:total_questions]
+
+    
+    print("Các câu hỏi mới được sinh ra:")
+    for i, q in enumerate(unique_questions, 1):
+        if not q.endswith("?"):
+            q += "?"  
+        print(f"{i}. {q}")
+
+if __name__ == "__main__":
+    user_context = input("\nNhập đoạn văn bản:\n ").strip()
+
+    raw_n = input("\nNhập vào số lượng câu hỏi bạn cần:").strip()
+    if raw_n == "":
+        total_questions = 20
+    else:
+        try:
+            total_questions = int(raw_n)
+        except ValueError:
+            print("Giá trị không hợp lệ. Dùng mặc định 20.")
+            total_questions = 20
+
+    if total_questions < 1:
+        total_questions = 1
+    if total_questions > 200:
+        total_questions = 200
+
+    batch_size = 20 if total_questions >= 30 else min(20, total_questions)
+
+    print("\nĐang phân tích dữ liệu...\n")
+
+    generate_questions(
+        user_context=user_context,
+        total_questions=total_questions,
+        batch_size=batch_size,
+        top_k=60,
+        top_p=0.95,  
+        temperature=0.9,  
+        max_input_len=512,
+        max_new_tokens=64
+    )