Upload talgat.py

talgat.py

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+# -*- coding: utf-8 -*-
+import re, json, sys, subprocess
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from datasets import load_dataset
+from tqdm import tqdm
+
+# ===== Параметры =====
+BASE_MODEL   = "google/gemma-3-4b-it"
+MODEL_PATH = "talgatzh/gemma-finetuned-model2"
+OUTPUT_FILE  = "gemma_inference_results_from_multidomain_fixedzxcs555.jsonl"
+MAX_NEW_TOKENS = 60
+MAX_TEXTS    = 20  # увеличь для более стабильной метрики (>=200)
+
+# ===== ROUGE (установим при необходимости) =====
+try:
+    import evaluate
+except ImportError:
+    subprocess.check_call([sys.executable, "-m", "pip", "install", "-q", "evaluate"])
+    import evaluate
+
+# ===== Модель и токенизатор =====
+tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_PATH,
+    device_map="auto",
+    torch_dtype=torch.float16,
+    trust_remote_code=True
+).eval()
+
+# pad_token для стабильности (у Gemma pad = eos)
+if tokenizer.pad_token_id is None:
+    tokenizer.pad_token = tokenizer.eos_token
+
+# ===== Утилиты =====
+def is_kazakh(text: str) -> bool:
+    return any(c in text.lower() for c in "қәөүңғұһі")
+
+_SENT_SPLIT = re.compile(r'(?<=[\.\!\?…])\s+|\n+')
+def lead_n(text: str, n=3) -> str:
+    sents = [s.strip() for s in _SENT_SPLIT.split(text.strip()) if s.strip()]
+    return " ".join(sents[:n])
+
+def build_chat_prompt(text: str) -> str:
+    instr = (
+        "Мақсат: Экстрактивті қысқаша мазмұн.\n"
+        "Ереже: Тек бастапқы мәтіндегі сөйлемдерді көшір. Өз сөзіңмен жазба. Синоним қолданба.\n"
+        "Мәтіннен тек 2–3 ең маңызды сөйлемді таңда да, сол күйінде жаз.\n"
+        "Формат: тек сөйлемдер, жаңа сөздер қоспа.\n\n"
+        "Мәтін:\n"
+        f"{text.strip()}\n\n"
+        "Қысқаша мазмұн:"
+    )
+    messages = [{"role": "user", "content": instr}]
+    # Правильный chat-template для Gemma-IT
+    return tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+
+# ===== Данные =====
+dataset = load_dataset("kz-transformers/multidomain-kazakh-dataset",
+                       split="train", streaming=True)
+
+INPUT_TEXTS = []
+for ex in dataset:
+    txt = (ex.get("text") or "").strip()
+    if is_kazakh(txt) and len(txt.split()) > 20:
+        INPUT_TEXTS.append(txt)
+    if len(INPUT_TEXTS) >= MAX_TEXTS:
+        break
+
+print(f"✔ Отобрано {len(INPUT_TEXTS)} казахских текстов из multidomain")
+
+# ===== Генерация =====
+results, preds, refs = [], [], []
+
+for text in tqdm(INPUT_TEXTS, desc="Generating summaries"):
+    prompt_text = build_chat_prompt(text)
+
+    toks = tokenizer(prompt_text, return_tensors="pt", truncation=True, max_length=2048)
+    toks = {k: v.to(model.device) for k, v in toks.items()}
+
+    with torch.no_grad():
+        out = model.generate(
+            **toks,
+            max_new_tokens=MAX_NEW_TOKENS,
+            do_sample=False,
+            temperature=0.0,
+            repetition_penalty=1.05,
+            no_repeat_ngram_size=6,
+            eos_token_id=tokenizer.eos_token_id,
+            pad_token_id=tokenizer.pad_token_id,
+            use_cache=True,
+        )
+
+    # === Берём ТОЛЬКО новые токены после входа ===
+    input_len = toks["input_ids"].shape[1]
+    gen_ids = out[0, input_len:]
+    generated = tokenizer.decode(gen_ids, skip_special_tokens=True).strip()
+
+    # Чистим возможные «утечки» ролей/маркеров
+    for bad in ("model", "<start_of_turn>", "<end_of_turn>"):
+        if generated.lower().startswith(bad):
+            generated = generated[len(bad):].lstrip(": ").strip()
+        generated = generated.replace(bad, "").strip()
+
+    # Fallback: если пусто — берём первые 2–3 предложения исходника
+    if not generated:
+        generated = lead_n(text, n=3)
+
+    reference = lead_n(text, n=3)
+
+    results.append({"text": text, "summary": generated, "reference": reference})
+    preds.append(generated)
+    refs.append(reference)
+
+# ===== Сохранение =====
+with open(OUTPUT_FILE, "w", encoding="utf-8") as f:
+    for r in results:
+        f.write(json.dumps(r, ensure_ascii=False) + "\n")
+
+print(f"✅ Сохранено {len(results)} суммаризаций → {OUTPUT_FILE}")
+
+# ===== ROUGE к Lead-3 (прокси для быстрой диагностики) =====
+rouge = evaluate.load("rouge")
+scores = rouge.compute(predictions=preds, references=refs, use_stemmer=True)
+scores_pct = {k: round(v * 100, 2) for k, v in scores.items()}
+print("🔎 ROUGE vs Lead-3:")
+for k in ("rouge1", "rouge2", "rougeL", "rougeLsum"):
+    print(f"{k.upper()}: {scores_pct.get(k, 0)}%")
+
+# ===== Быстрый дебаг первых 3 пар =====
+for i in range(min(3, len(results))):
+    print("\n--- SAMPLE", i+1, "---")
+    print("PRED:", results[i]["summary"])
+    print("REF :", results[i]["reference"])