| Before passing your predictions to compute, you need to convert the logits to predictions (remember all 🤗 Transformers models return logits): | |
| def compute_metrics(eval_pred): | |
| logits, labels = eval_pred | |
| predictions = np.argmax(logits, axis=-1) | |
| return metric.compute(predictions=predictions, references=labels) | |
| If you'd like to monitor your evaluation metrics during fine-tuning, specify the evaluation_strategy parameter in your training arguments to report the evaluation metric at the end of each epoch: | |
| from transformers import TrainingArguments, Trainer | |
| training_args = TrainingArguments(output_dir="test_trainer", evaluation_strategy="epoch") | |
| Trainer | |
| Create a [Trainer] object with your model, training arguments, training and test datasets, and evaluation function: | |
| trainer = Trainer( | |
| model=model, | |
| args=training_args, | |
| train_dataset=small_train_dataset, | |
| eval_dataset=small_eval_dataset, | |
| compute_metrics=compute_metrics, | |
| ) | |
| Then fine-tune your model by calling [~transformers.Trainer.train]: | |
| trainer.train() | |
| Train a TensorFlow model with Keras | |
| You can also train 🤗 Transformers models in TensorFlow with the Keras API! |