Update README.md

README.md

@@ -1,3 +1,54 @@
----
-license: agpl-3.0
----
+---
+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()