HVU_QA

HVU_QA/generate_question.py

@@ -1,139 +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
-    )
+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
+    )