Create notebooks/fine_tune.ipynb

notebooks/fine_tune.ipynb

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+# Fine-tuning example notebook using Hugging Face's transformers and datasets libraries.
+import json
+from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments
+from datasets import load_dataset
+
+# Load configuration
+with open('../config/config.json') as f:
+    config = json.load(f)
+
+# Load dataset
+dataset = load_dataset('csv', data_files={'train': '../data/train.csv', 'validation': '../data/valid.csv'})
+
+# Load model and tokenizer
+model = AutoModelForSequenceClassification.from_pretrained(config['model_name'], num_labels=config['num_labels'])
+tokenizer = AutoTokenizer.from_pretrained(config['model_name'])
+
+# Tokenize dataset
+def tokenize_function(examples):
+    return tokenizer(examples['text'], padding="max_length", truncation=True)
+
+tokenized_datasets = dataset.map(tokenize_function, batched=True)
+
+# Training arguments
+training_args = TrainingArguments(
+    output_dir='../results',
+    learning_rate=config['learning_rate'],
+    per_device_train_batch_size=config['batch_size'],
+    num_train_epochs=config['num_epochs'],
+    evaluation_strategy="epoch",
+    save_strategy="epoch",
+    logging_dir='../logs'
+)
+
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=tokenized_datasets['train'],
+    eval_dataset=tokenized_datasets['validation'],
+    tokenizer=tokenizer
+)
+
+trainer.train()
+trainer.save_model('../model')