Загрузить файлы в «BART_eng»
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135
BART_eng/BART_CLEAN_15.py
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135
BART_eng/BART_CLEAN_15.py
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import os
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import torch
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from transformers import (
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BartTokenizer,
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BartForConditionalGeneration,
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Trainer,
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TrainingArguments,
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)
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# ----------------------------------------------------------
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# 1. Zistenie aktuálneho adresára skriptu
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# ----------------------------------------------------------
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# Umožňuje uložiť výsledný model do rovnakého priečinka ako tento skript
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script_dir = os.path.dirname(os.path.abspath(__file__))
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# ----------------------------------------------------------
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# 2. Pomocná funkcia na načítanie všetkých .txt súborov z priečinka
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# ----------------------------------------------------------
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def load_texts_from_folder(folder_path):
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texts = []
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for filename in os.listdir(folder_path):
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if filename.endswith(".txt"):
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with open(
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os.path.join(folder_path, filename), "r", encoding="utf-8"
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) as file:
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texts.append(file.read().strip())
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return texts
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# ----------------------------------------------------------
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# 3. Cesty k dátam – zdravé a choré texty
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# ----------------------------------------------------------
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healthy_folder = r"project\text\zdr_eng_cleaned"
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dementia_folder = r"project\text\chory_eng_cleaned"
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# Načítanie textov z oboch priečinkov
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healthy_texts = load_texts_from_folder(healthy_folder)
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dementia_texts = load_texts_from_folder(dementia_folder)
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# Kontrola: počet textov musí byť rovnaký
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assert len(healthy_texts) == len(
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dementia_texts
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), "Počet textov v oboch priečinkoch sa musí zhodovať!"
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# Vytvorenie trénovacích dvojíc (vstup: zdravý text, výstup: text s príznakmi demencie)
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train_data = [{"input": h, "output": d} for h, d in zip(healthy_texts, dementia_texts)]
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# ----------------------------------------------------------
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# 4. Načítanie predtrénovaného BART modelu a tokenizátora
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# ----------------------------------------------------------
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local_model_path = r"project\BART_eng\facebook_bart_large"
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tokenizer = BartTokenizer.from_pretrained(local_model_path)
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model = BartForConditionalGeneration.from_pretrained(local_model_path)
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# ----------------------------------------------------------
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# 5. Tokenizácia dát pre vstup do modelu
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# ----------------------------------------------------------
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# Tokenizácia so skrátením na max. 256 tokenov
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def tokenize_function(examples):
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inputs = tokenizer(
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examples["input"], padding="max_length", truncation=True, max_length=256
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)
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outputs = tokenizer(
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examples["output"], padding="max_length", truncation=True, max_length=256
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)
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inputs["labels"] = outputs["input_ids"]
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return inputs
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# Aplikácia tokenizácie na všetky vstupy
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tokenized_data = list(map(tokenize_function, train_data))
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# ----------------------------------------------------------
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# 6. Vytvorenie vlastného datasetu kompatibilného s PyTorch
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# ----------------------------------------------------------
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class CustomDataset(torch.utils.data.Dataset):
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def __init__(self, encodings):
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self.encodings = encodings
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def __getitem__(self, idx):
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return {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
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def __len__(self):
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return len(self.encodings["input_ids"])
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# Dataset na trénovanie
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train_dataset = CustomDataset(
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{
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"input_ids": [item["input_ids"] for item in tokenized_data],
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"labels": [item["labels"] for item in tokenized_data],
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"attention_mask": [item["attention_mask"] for item in tokenized_data],
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}
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)
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# ----------------------------------------------------------
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# 7. Konfigurácia trénovania modelu
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# ----------------------------------------------------------
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# Žiadne checkpointy, logy, ani validácia – len čisté učenie
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training_args = TrainingArguments(
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output_dir=".", # Povinné, ale nepoužíva sa
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evaluation_strategy="no", # Bez evaluačnej množiny
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save_strategy="no", # Bez ukladania medzivýsledkov (checkpointov)
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per_device_train_batch_size=4, # Veľkosť dávky na zariadenie (CPU/GPU)
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num_train_epochs=15, # Počet epôch
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learning_rate=3e-5, # Rýchlosť učenia
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weight_decay=0.01, # Penalizácia veľkých váh (regularizácia)
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)
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# ----------------------------------------------------------
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# 8. Inicializácia trénovania pomocou Trainer API
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# ----------------------------------------------------------
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trainer = Trainer(
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model=model,
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args=training_args,
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train_dataset=train_dataset,
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)
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# ----------------------------------------------------------
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# 9. Spustenie trénovania modelu
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# ----------------------------------------------------------
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print("Trénovanie modelu prebieha...")
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trainer.train()
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print("Trénovanie dokončené.")
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# ----------------------------------------------------------
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# 10. Uloženie finálneho modelu a tokenizátora na disk
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# ----------------------------------------------------------
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model_save_path = os.path.join(script_dir, "trained_bart_model_CLEAN_15")
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model.save_pretrained(model_save_path)
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tokenizer.save_pretrained(model_save_path)
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print(f"Model a tokenizátor boli uložené do: {model_save_path}")
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112
BART_eng/CLEAN_15.py
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BART_eng/CLEAN_15.py
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import os
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import torch
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from transformers import BartTokenizer, BartForConditionalGeneration
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# ----------------------------------------------------------
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# Načítanie trénovaného BART modelu a tokenizátora
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# ----------------------------------------------------------
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# Cesta k priečinku s trénovaným modelom
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script_dir = os.path.dirname(os.path.abspath(__file__))
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model_path = os.path.abspath(
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os.path.join(script_dir, "..", "BART_eng", "trained_bart_model_CLEAN_15")
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)
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# Načítanie modelu a tokenizátora z disku
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tokenizer = BartTokenizer.from_pretrained(model_path)
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model = BartForConditionalGeneration.from_pretrained(model_path)
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# Výber zariadenia (GPU ak je dostupné, inak CPU)
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device = "cuda" if torch.cuda.is_available() else "cpu"
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model.to(device)
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print("Model bol úspešne načítaný a je pripravený na použitie.")
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# ----------------------------------------------------------
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# Vstupný text zdravého človeka na transformáciu
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# ----------------------------------------------------------
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healthy_text = """there's a boy reaching into the cookie jar while standing on a stool
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and he already took one cookie and is handing it to the girl next to him
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and girl seems to be telling him to be quiet so their mom doesn't notice
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and the sink is overflowing with water and nobody is paying attention
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meanwhile, their mother is turned away, busy with something else
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"""
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# Tokenizácia vstupného textu
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inputs = tokenizer(healthy_text, return_tensors="pt").to(device)
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input_token_length = len(tokenizer.encode(healthy_text, add_special_tokens=False))
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# Výpočet dynamických limitov
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min_length_ratio = 0.6
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max_length_ratio = 1.1
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min_length = int(input_token_length * min_length_ratio)
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max_length = int(input_token_length * max_length_ratio)
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print(f"Počet tokenov vo vstupe: {input_token_length}")
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print(f"Minimálna dĺžka výstupu: {min_length}, maximálna: {max_length}")
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# ----------------------------------------------------------
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# Konfigurácie generovania textu
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# ----------------------------------------------------------
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generation_configs = [
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{
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"max_length": max_length,
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"min_length": min_length,
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"num_beams": 5,
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"no_repeat_ngram_size": 4,
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"repetition_penalty": 2.2,
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"length_penalty": 0.6,
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"early_stopping": True,
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},
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{
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"max_length": max_length + 20,
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"min_length": min_length + 10,
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"num_beams": 7,
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"no_repeat_ngram_size": 5,
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"repetition_penalty": 2.2,
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"length_penalty": 0.8,
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"early_stopping": True,
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},
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{
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"max_length": max_length + 40,
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"min_length": min_length + 20,
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"num_beams": 9,
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"no_repeat_ngram_size": 6,
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"repetition_penalty": 2.2,
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"length_penalty": 1.0,
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"early_stopping": True,
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},
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]
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# ----------------------------------------------------------
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# Generovanie výstupov na základe rôznych konfigurácií
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# ----------------------------------------------------------
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generated_texts = []
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for i, config in enumerate(generation_configs):
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print(f"\nGenerácia textu č. {i+1}:")
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# Generovanie bez výpočtu gradientov
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with torch.no_grad():
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outputs = model.generate(
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inputs["input_ids"],
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max_length=config["max_length"],
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min_length=config["min_length"],
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num_beams=config["num_beams"],
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no_repeat_ngram_size=config["no_repeat_ngram_size"],
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repetition_penalty=config["repetition_penalty"],
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length_penalty=config["length_penalty"],
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early_stopping=config["early_stopping"],
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)
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# Dekódovanie výstupu na text
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generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
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generated_texts.append(generated_text)
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print("Vygenerovaný text:")
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print(generated_text)
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print("-" * 80)
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154
BART_eng/METRIKY.py
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154
BART_eng/METRIKY.py
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import os
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import nltk
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from rouge_score import rouge_scorer
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from bert_score import score
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import warnings
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import logging
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from transformers.utils import logging as hf_logging
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# ----------------------------------------------------------
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# 1. Nastavenie prostredia – potlačenie varovaní a logov
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# ----------------------------------------------------------
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# Automatické stiahnutie 'punkt' ak ešte nie je k dispozícii
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try:
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nltk.data.find("tokenizers/punkt")
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except LookupError:
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nltk.download("punkt")
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# Skrytie varovaní BERTScore pri načítavaní modelov
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warnings.filterwarnings(
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"ignore", message="Some weights of RobertaModel were not initialized"
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)
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# Potlačenie logov z knižnice transformers
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hf_logging.set_verbosity_error()
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logging.getLogger("transformers.modeling_utils").setLevel(logging.ERROR)
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logging.getLogger("transformers.configuration_utils").setLevel(logging.ERROR)
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logging.getLogger("transformers.tokenization_utils_base").setLevel(logging.ERROR)
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# ----------------------------------------------------------
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# 2. Načítanie referenčných textov pacientov
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# ----------------------------------------------------------
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# Pomocná funkcia na načítanie obsahu všetkých .txt súborov v priečinku
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def load_texts_from_folder(folder_path):
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texts = []
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for filename in os.listdir(folder_path):
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if filename.endswith(".txt"):
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with open(
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os.path.join(folder_path, filename), "r", encoding="utf-8"
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) as file:
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texts.append(file.read().strip())
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return texts
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# Cesta k textom pacientov s Alzheimerovou chorobou
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script_dir = os.path.dirname(os.path.abspath(__file__))
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project_root = os.path.abspath(os.path.join(script_dir, ".."))
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dementia_folder = os.path.join(project_root, "text", "chory_eng_cleaned")
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reference_texts_dementia = load_texts_from_folder(dementia_folder)
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# Overenie, že priečinok obsahuje súbory
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if not reference_texts_dementia:
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raise ValueError("V zložke nie sú žiadne .txt súbory! Skontroluj cestu.")
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print(f"Načítaných {len(reference_texts_dementia)} chorých textov na porovnanie.")
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# ----------------------------------------------------------
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# 3. Vzorky na porovnanie (originálny zdravý a vygenerovaný text)
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# ----------------------------------------------------------
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original_text_healthy = """there's a boy reaching into the cookie jar while standing on a stool
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and he already took one cookie and is handing it to the girl next to him
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and girl seems to be telling him to be quiet so their mom doesn't notice
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and the sink is overflowing with water and nobody is paying attention
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meanwhile, their mother is turned away, busy with something else"""
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generated_text = """okay exc there's a boy taking cookies out of the cookie jar
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and he's standing on a stool
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and he's handing one to his sister
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and she's laughing at him
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and I don't know what else you want exc
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I don't know exc
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what else do you want exc
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uh... that's all I see exc"""
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# ----------------------------------------------------------
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# 4. Výpočet ROUGE metriky (porovnávanie na úrovni tokenov/fráz)
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# ----------------------------------------------------------
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def evaluate_text(reference_texts, generated_text):
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scorer = rouge_scorer.RougeScorer(["rouge1", "rougeL"], use_stemmer=True)
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rouge1_scores = []
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rougeL_scores = []
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for ref_text in reference_texts:
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scores = scorer.score(ref_text, generated_text)
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rouge1_scores.append(scores["rouge1"].fmeasure)
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rougeL_scores.append(scores["rougeL"].fmeasure)
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return {
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"ROUGE-1-best": max(rouge1_scores),
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"ROUGE-1-mean": sum(rouge1_scores) / len(rouge1_scores),
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"ROUGE-L-best": max(rougeL_scores),
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"ROUGE-L-mean": sum(rougeL_scores) / len(rougeL_scores),
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}
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# ----------------------------------------------------------
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# 5. Výpočet BERTScore metriky (porovnávanie na úrovni významu)
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# ----------------------------------------------------------
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def compute_bert_score(reference_texts, generated_text):
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try:
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P, R, F1 = score(
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cands=[generated_text]
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* len(
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reference_texts
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), # rovnaký kandidát sa porovnáva so všetkými referenciami
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refs=[
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[ref] for ref in reference_texts
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], # každá referencia musí byť v samostatnom zozname
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lang="en",
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rescale_with_baseline=False,
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)
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return {"BERTScore-F1": F1.mean().item()}
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except Exception as e:
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print(f"Chyba pri výpočte BERTScore: {e}")
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return {"BERTScore-F1": 0}
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# ----------------------------------------------------------
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# 6. Vyhodnotenie kvality vygenerovaného textu
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# ----------------------------------------------------------
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results_to_dementia = evaluate_text(reference_texts_dementia, generated_text)
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bert_results_dementia = compute_bert_score(reference_texts_dementia, generated_text)
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results_to_original = evaluate_text([original_text_healthy], generated_text)
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bert_results_original = compute_bert_score([original_text_healthy], generated_text)
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# ----------------------------------------------------------
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# 7. Výpis výsledkov pre hodnotenie v bakalárskej práci
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# ----------------------------------------------------------
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print(
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"\nPorovnanie s textami pacientov s Alzheimerovou chorobou (ČÍM VYŠŠIE, TÝM LEPŠIE):"
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)
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print(
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f"ROUGE-1 (najvyššie): {results_to_dementia['ROUGE-1-best']:.4f} | (priemer): {results_to_dementia['ROUGE-1-mean']:.4f}"
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)
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print(
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f"ROUGE-L (najvyššie): {results_to_dementia['ROUGE-L-best']:.4f} | (priemer): {results_to_dementia['ROUGE-L-mean']:.4f}"
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)
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print(f"BERTScore-F1 (priemer): {bert_results_dementia['BERTScore-F1']:.4f}\n")
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print("Porovnanie s pôvodným zdravým textom (ČÍM NIŽŠIE, TÝM LEPŠIE):")
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print("(Porovnanie s jedným textom – hodnoty 'naj' a 'priemer' sú rovnaké)")
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print(f"ROUGE-1: {results_to_original['ROUGE-1-best']:.4f}")
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print(f"ROUGE-L: {results_to_original['ROUGE-L-best']:.4f}")
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print(f"BERTScore-F1: {bert_results_original['BERTScore-F1']:.4f}")
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