Delete HVU_QA/generate_question.py

HVU_QA/generate_question.py

@@ -1,153 +0,0 @@
-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}")