---
license: apache-2.0
language:
- en
library_name: pythae
tags:
- music
---
---
license: agpl-3.0
---from transformers import GPT2Tokenizer, GPT2LMHeadModel, Trainer, TrainingArguments
from datasets import load_dataset
import numpy as np

# Carica il modello e il tokenizer
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = GPT2LMHeadModel.from_pretrained('gpt2')

# Carica un dataset personalizzato (esempio con CSV)
dataset = load_dataset('csv', data_files={'train': 'path/to/train.csv', 'test': 'path/to/test.csv'})

# Tokenizzazione del dataset
def tokenize_function(examples):
    return tokenizer(examples['text'], padding='max_length', truncation=True, max_length=128)

tokenized_datasets = dataset.map(tokenize_function, batched=True)

# Configura i parametri di addestramento
training_args = TrainingArguments(
    output_dir='./results',
    num_train_epochs=3,
    per_device_train_batch_size=4,
    save_steps=10_000,
    save_total_limit=2,
    evaluation_strategy="epoch"
)

# Funzione per calcolare le metriche
def compute_metrics(eval_pred):
    logits, labels = eval_pred
    predictions = np.argmax(logits, axis=-1)
    return metric.compute(predictions=predictions, references=labels)

# Crea il trainer
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=tokenized_datasets['train'],
    eval_dataset=tokenized_datasets['test'],
    compute_metrics=compute_metrics
)

# Esegui l'addestramento
trainer.train()