What is Trainer in transformers?

This recipe explains what is Trainer in transformers.

Recipe Objective - What is Trainer in transformers?

The Trainer and TFTrainer classes provide APIs for functionally complete training in most standard use cases.
Both Trainer and TFTrainer contain basic training loops that support the above functions. To inject custom behaviors, you can subclass them and override the following methods:
1. get_train_dataloader/get_train_tfdataset – Creates the training DataLoader (PyTorch) or TF Dataset.
2. get_eval_dataloader/get_eval_tfdataset – Creates the evaluation DataLoader (PyTorch) or TF Dataset.
3. get_test_dataloader/get_test_tfdataset – Creates the test DataLoader (PyTorch) or TF Dataset.
4. log – Logs information on the various objects watching training.
5. create_optimizer_and_scheduler – Sets up the optimizer and learning rate scheduler if they were not passed at init. Note, that you simply also can subclass or override the create_optimizer and create_scheduler methods separately.
6. create_optimizer – Sets up the optimizer if it wasn’t passed at init.
7. create_scheduler – Sets up the learning rate scheduler if it wasn’t passed at init.
8. compute_loss - Computes the loss on a batch of training inputs.
9. training_step – Performs a training step.
10. prediction_step – Performs an evaluation/test step.
11. run_model (TensorFlow only) – Basic pass through the model.
12. evaluate – Runs an evaluation loop and returns metrics.
13. predict – Returns predictions (with metrics if labels are available) on a test set.

For more related projects -

/projects/data-science-projects/deep-learning-projects
/projects/data-science-projects/tensorflow-projects

Example -

Let's see how to customize Trainer using a custom loss function for multi-label classification:

# Importing libraries
from torch import nn
from transformers import Trainer

# Customize trainer using custom loss function
class MultilabelTrainer(Trainer):
 def compute_loss(self, model, inputs, return_outputs=False):
  labels = inputs.pop("labels")
  outputs = model(**inputs)
  logits = outputs.logits
  loss_fct = nn.BCEWithLogitsLoss()
  loss = loss_fct(logits.view(-1, self.model.config.num_labels), labels.float().view(-1, self.model.config.num_labels))
  return (loss, outputs) if return_outputs else loss

In this way, we can customize trainer using some functions in transformers.

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