fine_tuning.py

from transformers import T5Tokenizer, T5ForConditionalGeneration, Trainer, TrainingArguments
from datasets import Dataset
from sklearn.model_selection import train_test_split
import re
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.stem import PorterStemmer

# Stop words ve stemmer
stop_words = set(stopwords.words('english'))
ps = PorterStemmer()

# Metni temizleme fonksiyonu
def clean_text(text):
    text = re.sub(r'[^\w\s]', '', text)
    text = re.sub(r'\d+', '', text)
    text = text.lower()
    text = " ".join([word for word in text.split() if word not in stop_words])
    text = " ".join([ps.stem(word) for word in word_tokenize(text)])
    return text

# Prompts okuma
def read_prompts(file_path):
    input_texts = []
    target_texts = []
    with open(file_path, "r", encoding="utf-8") as file:
        lines = file.readlines()
        for line in lines:
            if line.startswith("input:"):
                input_texts.append(line.replace("input:", "").strip())
            elif line.startswith("target:"):
                target_texts.append(line.replace("target:", "").strip())
    return input_texts, target_texts

# Dataset hazırlama
def prepare_data(input_texts, target_texts):
    inputs = tokenizer(input_texts, max_length=512, truncation=True, padding="max_length")
    targets = tokenizer(target_texts, max_length=512, truncation=True, padding="max_length")
    return {"input_ids": inputs["input_ids"], "attention_mask": inputs["attention_mask"], "labels": targets["input_ids"]}

# Paraphrasing fonksiyonu
def paraphrase_with_model(text, model, tokenizer):
    prompt = "paraphrase: " + text
    inputs = tokenizer(prompt, return_tensors="pt", padding=True, truncation=True, max_length=512)
    output_ids = model.generate(
        inputs["input_ids"],
        do_sample=True,
        top_k=50,
        top_p=0.95,
        temperature=1.0,
        max_length=150,
        no_repeat_ngram_size=2,
        early_stopping=True
    )
    return tokenizer.decode(output_ids[0], skip_special_tokens=True, max_length=150)

# Tokenizer ve model yükleme
model_name = "t5-base"
tokenizer = T5Tokenizer.from_pretrained(model_name)
model = T5ForConditionalGeneration.from_pretrained(model_name)

# Veriyi okuma ve temizleme
input_texts, target_texts = read_prompts("prompts.txt")
input_texts_cleaned = [clean_text(text) for text in input_texts]
target_texts_cleaned = [clean_text(text) for text in target_texts]

# Eğitim ve doğrulama verisini ayırma
train_texts, val_texts, train_labels, val_labels = train_test_split(input_texts_cleaned, target_texts_cleaned, test_size=0.1)

# Augmentasyon ve dataset hazırlama
augmented_input_texts = input_texts_cleaned[:10] + [paraphrase_with_model(text, model, tokenizer) for text in input_texts_cleaned[:10]]
augmented_target_texts = target_texts_cleaned[:10] + [paraphrase_with_model(text, model, tokenizer) for text in target_texts_cleaned[:10]]
train_dataset = Dataset.from_dict(prepare_data(augmented_input_texts, augmented_target_texts))
val_dataset = Dataset.from_dict(prepare_data(val_texts, val_labels))

# Eğitim argümanları
training_args = TrainingArguments(
    output_dir="./results",
    evaluation_strategy="steps",
    learning_rate=5e-5,
    per_device_train_batch_size=4,
    num_train_epochs=3,
    save_steps=500,
    logging_dir="./logs",
    logging_steps=10
)

# Trainer
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    eval_dataset=val_dataset
)

# Eğitim
trainer.train()

# Model kaydetme
model.save_pretrained("./fine_tuned_model")
tokenizer.save_pretrained("./fine_tuned_model")