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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,
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}\ncontext: {context}\nquestion:"
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
)
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