Update fine_tuning.py

fine_tuning.py

@@ -1,3 +1,4 @@
+import logging
 from transformers import T5Tokenizer, T5ForConditionalGeneration, Trainer, TrainingArguments
 from datasets import Dataset
 from sklearn.model_selection import train_test_split
@@ -6,6 +7,10 @@ from nltk.corpus import stopwords
 from nltk.tokenize import word_tokenize
 from nltk.stem import PorterStemmer
 
+# Logging Ayarları
+logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
+logger = logging.getLogger(__name__)
+
 
 stop_words = set(stopwords.words('english'))
 ps = PorterStemmer()
@@ -86,4 +91,38 @@ trainer = Trainer(
 trainer.train()
 
 model.save_pretrained("./fine_tuned_model")
-tokenizer.save_pretrained("./fine_tuned_model")
+tokenizer.save_pretrained("./fine_tuned_model")
+
+try:
+    logger.info("Loading tokenizer and model.")
+    model_name = "t5-base"
+    tokenizer = T5Tokenizer.from_pretrained(model_name)
+    model = T5ForConditionalGeneration.from_pretrained(model_name)
+
+    logger.info("Reading and cleaning prompts.")
+    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]
+
+    logger.info("Splitting dataset into training and validation sets.")
+    train_texts, val_texts, train_labels, val_labels = train_test_split(input_texts_cleaned, target_texts_cleaned, test_size=0.1)
+
+    logger.info("Preparing datasets for training.")
+    train_dataset = Dataset.from_dict(prepare_data(train_texts, train_labels))
+    val_dataset = Dataset.from_dict(prepare_data(val_texts, val_labels))
+
+    logger.info("Starting model training.")
+    trainer = Trainer(
+        model=model,
+        args=training_args,
+        train_dataset=train_dataset,
+        eval_dataset=val_dataset
+    )
+    trainer.train()
+
+    logger.info("Saving fine-tuned model.")
+    model.save_pretrained("./fine_tuned_model")
+    tokenizer.save_pretrained("./fine_tuned_model")
+
+except Exception as e:
+    logger.error(f"An error occurred during fine-tuning: {str(e)}")