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73 lines
3.3 KiB
ReStructuredText
73 lines
3.3 KiB
ReStructuredText
Trainer
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The :class:`~transformers.Trainer` and :class:`~transformers.TFTrainer` classes provide an API for feature-complete
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training in most standard use cases. It's used in most of the :doc:`example scripts <../examples>`.
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Before instantiating your :class:`~transformers.Trainer`/:class:`~transformers.TFTrainer`, create a
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:class:`~transformers.TrainingArguments`/:class:`~transformers.TFTrainingArguments` to access all the points of
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customization during training.
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The API supports distributed training on multiple GPUs/TPUs, mixed precision through `NVIDIA Apex
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<https://github.com/NVIDIA/apex>`__ for PyTorch and :obj:`tf.keras.mixed_precision` for TensorFlow.
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Both :class:`~transformers.Trainer` and :class:`~transformers.TFTrainer` contain the basic training loop supporting the
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previous features. To inject custom behavior you can subclass them and override the following methods:
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- **get_train_dataloader**/**get_train_tfdataset** -- Creates the training DataLoader (PyTorch) or TF Dataset.
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- **get_eval_dataloader**/**get_eval_tfdataset** -- Creates the evaluation DataLoader (PyTorch) or TF Dataset.
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- **get_test_dataloader**/**get_test_tfdataset** -- Creates the test DataLoader (PyTorch) or TF Dataset.
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- **log** -- Logs information on the various objects watching training.
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- **create_optimizer_and_scheduler** -- Setups the optimizer and learning rate scheduler if they were not passed at
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init.
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- **compute_loss** - Computes the loss on a batch of training inputs.
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- **training_step** -- Performs a training step.
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- **prediction_step** -- Performs an evaluation/test step.
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- **run_model** (TensorFlow only) -- Basic pass through the model.
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- **evaluate** -- Runs an evaluation loop and returns metrics.
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- **predict** -- Returns predictions (with metrics if labels are available) on a test set.
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Here is an example of how to customize :class:`~transformers.Trainer` using a custom loss function:
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.. code-block:: python
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from transformers import Trainer
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class MyTrainer(Trainer):
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def compute_loss(self, model, inputs):
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labels = inputs.pop("labels")
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outputs = models(**inputs)
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logits = outputs[0]
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return my_custom_loss(logits, labels)
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Another way to customize the training loop behavior for the PyTorch :class:`~transformers.Trainer` is to use
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:doc:`callbacks <callback>` that can inspect the training loop state (for progress reporting, logging on TensorBoard or
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other ML platforms...) and take decisions (like early stopping).
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Trainer
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.Trainer
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:members:
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TFTrainer
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.TFTrainer
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:members:
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TrainingArguments
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.TrainingArguments
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:members:
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TFTrainingArguments
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.TFTrainingArguments
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:members:
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