| Specify the number of labels | |
| along with the label mappings: | |
| from transformers import ViltForQuestionAnswering | |
| model = ViltForQuestionAnswering.from_pretrained(model_checkpoint, num_labels=len(id2label), id2label=id2label, label2id=label2id) | |
| At this point, only three steps remain: | |
| Define your training hyperparameters in [TrainingArguments]: | |
| from transformers import TrainingArguments | |
| repo_id = "MariaK/vilt_finetuned_200" | |
| training_args = TrainingArguments( | |
| output_dir=repo_id, | |
| per_device_train_batch_size=4, | |
| num_train_epochs=20, | |
| save_steps=200, | |
| logging_steps=50, | |
| learning_rate=5e-5, | |
| save_total_limit=2, | |
| remove_unused_columns=False, | |
| push_to_hub=True, | |
| ) | |
| Pass the training arguments to [Trainer] along with the model, dataset, processor, and data collator. |