| | import re |
| | import torch |
| | 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" |
| |
|
| | PATTERN = re.compile( |
| | r""" |
| | “([^”]{3,120})” |
| | | "([^"]{3,120})" |
| | | \b(?:là|gồm|do|theo)\s+([^,.;:\n]{3,120}) |
| | | \b\d{4}\b |
| | | \b(?:Điều|Khoản)\s+\d+\b |
| | """, |
| | re.VERBOSE | re.IGNORECASE, |
| | ) |
| |
|
| |
|
| | def norm(s: str) -> str: |
| | return re.sub(r"\s+", " ", s).strip() |
| |
|
| |
|
| | def is_dup(q: str, qs: list[str], thr: float = 0.85) -> bool: |
| | ql = q.lower() |
| | for x in qs: |
| | if SequenceMatcher(None, ql, x.lower()).ratio() >= thr: |
| | return True |
| | return False |
| |
|
| |
|
| | def extract_answers(ctx: str, max_n: int = 60) -> list[str]: |
| | ctx = norm(ctx) |
| | answers, seen = [], set() |
| |
|
| | for m in PATTERN.finditer(ctx): |
| | for g in m.groups(): |
| | if not g: |
| | continue |
| | g = norm(g) |
| | k = g.lower() |
| | if 3 <= len(g) <= 120 and k not in seen: |
| | seen.add(k) |
| | answers.append(g) |
| | if len(answers) >= max_n: |
| | return answers |
| |
|
| | if len(answers) < 8: |
| | for i in range(0, min(len(ctx), 500), 60): |
| | ch = norm(ctx[i : i + 60]) |
| | k = ch.lower() |
| | if len(ch) >= 15 and k not in seen: |
| | seen.add(k) |
| | answers.append(ch) |
| | if len(answers) >= max_n: |
| | break |
| |
|
| | if not answers and ctx: |
| | answers = [ctx[:120]] |
| |
|
| | return answers |
| |
|
| |
|
| | def load_model(): |
| | tok = T5Tokenizer.from_pretrained(MODEL_DIR) |
| | mdl = T5ForConditionalGeneration.from_pretrained(MODEL_DIR) |
| |
|
| | dev = "cuda" if torch.cuda.is_available() else "cpu" |
| | try: |
| | mdl = mdl.to(dev) |
| | except RuntimeError: |
| | dev = "cpu" |
| | try: |
| | torch.cuda.empty_cache() |
| | except Exception: |
| | pass |
| | mdl = mdl.to(dev) |
| |
|
| | mdl.eval() |
| | return tok, mdl, dev |
| |
|
| |
|
| | tokenizer, model, device = load_model() |
| |
|
| |
|
| | def generate_questions(context: str, n: int = 20) -> list[str]: |
| | ctx = norm(context) |
| | answers = extract_answers(ctx, max_n=80) |
| | questions = [] |
| |
|
| | gen_cfg = dict( |
| | do_sample=True, |
| | top_k=80, |
| | top_p=0.98, |
| | temperature=1.05, |
| | max_new_tokens=72, |
| | no_repeat_ngram_size=3, |
| | repetition_penalty=1.08, |
| | ) |
| |
|
| | num_ret = 8 if n <= 20 else 10 |
| |
|
| | def run_prompt(ans: str, rounds: int): |
| | nonlocal gen_cfg |
| | prompt = f"answer: {ans}\ncontext: {ctx}\nquestion:" |
| | inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=512).to(device) |
| |
|
| | for _ in range(rounds): |
| | outs = model.generate(**inputs, num_return_sequences=num_ret, **gen_cfg) |
| | added = 0 |
| | for o in outs: |
| | q = norm(tokenizer.decode(o, skip_special_tokens=True)) |
| | if not q: |
| | continue |
| | if not q.endswith("?"): |
| | q += "?" |
| | if len(q) >= 6 and not is_dup(q, questions, thr=0.85): |
| | questions.append(q) |
| | added += 1 |
| | if len(questions) >= n: |
| | return |
| | if added == 0: |
| | gen_cfg["temperature"] = min(1.25, gen_cfg["temperature"] + 0.05) |
| | gen_cfg["top_p"] = min(0.995, gen_cfg["top_p"] + 0.005) |
| |
|
| | with torch.inference_mode(): |
| | for ans in answers: |
| | if len(questions) >= n: |
| | break |
| | run_prompt(ans, rounds=6) |
| |
|
| | if len(questions) < n: |
| | run_prompt(ctx[:120], rounds=12) |
| |
|
| | return questions[:n] |
| |
|
| |
|
| | if __name__ == "__main__": |
| | ctx = input("\nNhập đoạn văn bản:\n").strip() |
| | try: |
| | n = int((input("\nNhập số lượng câu hỏi cần sinh: ").strip() or "")) |
| | except ValueError: |
| | n = 20 |
| |
|
| | n = max(1, min(n, 200)) |
| | qs = generate_questions(ctx, n) |
| |
|
| | print("\nCác câu hỏi sinh ra:") |
| | for i, q in enumerate(qs, 1): |
| | print(f"{i}. {q}") |
| |
|
