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55 lines
2.4 KiB
Markdown
55 lines
2.4 KiB
Markdown
<!--Copyright 2020 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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rendered properly in your Markdown viewer.
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-->
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# Trainer
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The [`Trainer`] class provides an API for feature-complete training in PyTorch, and it supports distributed training on multiple GPUs/TPUs, mixed precision for [NVIDIA GPUs](https://nvidia.github.io/apex/), [AMD GPUs](https://rocm.docs.amd.com/en/latest/rocm.html), and [`torch.amp`](https://pytorch.org/docs/stable/amp.html) for PyTorch. [`Trainer`] goes hand-in-hand with the [`TrainingArguments`] class, which offers a wide range of options to customize how a model is trained. Together, these two classes provide a complete training API.
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[`Seq2SeqTrainer`] and [`Seq2SeqTrainingArguments`] inherit from the [`Trainer`] and [`TrainingArguments`] classes and they're adapted for training models for sequence-to-sequence tasks such as summarization or translation.
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<Tip warning={true}>
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The [`Trainer`] class is optimized for 🤗 Transformers models and can have surprising behaviors
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when used with other models. When using it with your own model, make sure:
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- your model always return tuples or subclasses of [`~utils.ModelOutput`]
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- your model can compute the loss if a `labels` argument is provided and that loss is returned as the first
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element of the tuple (if your model returns tuples)
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- your model can accept multiple label arguments (use `label_names` in [`TrainingArguments`] to indicate their name to the [`Trainer`]) but none of them should be named `"label"`
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</Tip>
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## Trainer[[api-reference]]
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[[autodoc]] Trainer
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- all
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## Seq2SeqTrainer
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[[autodoc]] Seq2SeqTrainer
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- evaluate
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- predict
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## TrainingArguments
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[[autodoc]] TrainingArguments
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- all
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## Seq2SeqTrainingArguments
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[[autodoc]] Seq2SeqTrainingArguments
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- all
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