310 lines
8.4 KiB
Markdown
310 lines
8.4 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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# 模型输出
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所有模型的输出都是 [`~utils.ModelOutput`] 的子类的实例。这些是包含模型返回的所有信息的数据结构，但也可以用作元组或字典。
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让我们看一个例子：
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```python
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from transformers import BertTokenizer, BertForSequenceClassification
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import torch
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tokenizer = BertTokenizer.from_pretrained("google-bert/bert-base-uncased")
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model = BertForSequenceClassification.from_pretrained("google-bert/bert-base-uncased")
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inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
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labels = torch.tensor([1]).unsqueeze(0) # Batch size 1
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outputs = model(**inputs, labels=labels)
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```
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`outputs` 对象是 [`~modeling_outputs.SequenceClassifierOutput`]，如下面该类的文档中所示，它表示它有一个可选的 `loss`，一个 `logits`，一个可选的 `hidden_states` 和一个可选的 `attentions` 属性。在这里，我们有 `loss`，因为我们传递了 `labels`，但我们没有 `hidden_states` 和 `attentions`，因为我们没有传递 `output_hidden_states=True` 或 `output_attentions=True`。
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<Tip>
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当传递 `output_hidden_states=True` 时，您可能希望 `outputs.hidden_states[-1]` 与 `outputs.last_hidden_states` 完全匹配。然而，这并不总是成立。一些模型在返回最后的 hidden state时对其应用归一化或其他后续处理。
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</Tip>
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您可以像往常一样访问每个属性，如果模型未返回该属性，您将得到 `None`。在这里，例如，`outputs.loss` 是模型计算的损失，而 `outputs.attentions` 是 `None`。
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当将我们的 `outputs` 对象视为元组时，它仅考虑那些没有 `None` 值的属性。例如这里它有两个元素，`loss` 和 `logits`，所以
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```python
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outputs[:2]
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```
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将返回元组 `(outputs.loss, outputs.logits)`。
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将我们的 `outputs` 对象视为字典时，它仅考虑那些没有 `None` 值的属性。例如在这里它有两个键，分别是 `loss` 和 `logits`。
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我们在这里记录了被多个类型模型使用的通用模型输出。特定输出类型在其相应的模型页面上有文档。
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## ModelOutput
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[[autodoc]] utils.ModelOutput
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- to_tuple
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## BaseModelOutput
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[[autodoc]] modeling_outputs.BaseModelOutput
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## BaseModelOutputWithPooling
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[[autodoc]] modeling_outputs.BaseModelOutputWithPooling
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## BaseModelOutputWithCrossAttentions
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[[autodoc]] modeling_outputs.BaseModelOutputWithCrossAttentions
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## BaseModelOutputWithPoolingAndCrossAttentions
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[[autodoc]] modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions
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## BaseModelOutputWithPast
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[[autodoc]] modeling_outputs.BaseModelOutputWithPast
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## BaseModelOutputWithPastAndCrossAttentions
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[[autodoc]] modeling_outputs.BaseModelOutputWithPastAndCrossAttentions
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## Seq2SeqModelOutput
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[[autodoc]] modeling_outputs.Seq2SeqModelOutput
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## CausalLMOutput
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[[autodoc]] modeling_outputs.CausalLMOutput
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## CausalLMOutputWithCrossAttentions
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[[autodoc]] modeling_outputs.CausalLMOutputWithCrossAttentions
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## CausalLMOutputWithPast
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[[autodoc]] modeling_outputs.CausalLMOutputWithPast
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## MaskedLMOutput
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[[autodoc]] modeling_outputs.MaskedLMOutput
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## Seq2SeqLMOutput
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[[autodoc]] modeling_outputs.Seq2SeqLMOutput
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## NextSentencePredictorOutput
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[[autodoc]] modeling_outputs.NextSentencePredictorOutput
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## SequenceClassifierOutput
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[[autodoc]] modeling_outputs.SequenceClassifierOutput
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## Seq2SeqSequenceClassifierOutput
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[[autodoc]] modeling_outputs.Seq2SeqSequenceClassifierOutput
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## MultipleChoiceModelOutput
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[[autodoc]] modeling_outputs.MultipleChoiceModelOutput
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## TokenClassifierOutput
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[[autodoc]] modeling_outputs.TokenClassifierOutput
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## QuestionAnsweringModelOutput
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[[autodoc]] modeling_outputs.QuestionAnsweringModelOutput
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## Seq2SeqQuestionAnsweringModelOutput
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[[autodoc]] modeling_outputs.Seq2SeqQuestionAnsweringModelOutput
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## Seq2SeqSpectrogramOutput
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[[autodoc]] modeling_outputs.Seq2SeqSpectrogramOutput
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## SemanticSegmenterOutput
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[[autodoc]] modeling_outputs.SemanticSegmenterOutput
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## ImageClassifierOutput
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[[autodoc]] modeling_outputs.ImageClassifierOutput
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## ImageClassifierOutputWithNoAttention
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[[autodoc]] modeling_outputs.ImageClassifierOutputWithNoAttention
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## DepthEstimatorOutput
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[[autodoc]] modeling_outputs.DepthEstimatorOutput
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## Wav2Vec2BaseModelOutput
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[[autodoc]] modeling_outputs.Wav2Vec2BaseModelOutput
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## XVectorOutput
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[[autodoc]] modeling_outputs.XVectorOutput
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## Seq2SeqTSModelOutput
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[[autodoc]] modeling_outputs.Seq2SeqTSModelOutput
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## Seq2SeqTSPredictionOutput
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[[autodoc]] modeling_outputs.Seq2SeqTSPredictionOutput
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## SampleTSPredictionOutput
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[[autodoc]] modeling_outputs.SampleTSPredictionOutput
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## TFBaseModelOutput
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[[autodoc]] modeling_tf_outputs.TFBaseModelOutput
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## TFBaseModelOutputWithPooling
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[[autodoc]] modeling_tf_outputs.TFBaseModelOutputWithPooling
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## TFBaseModelOutputWithPoolingAndCrossAttentions
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[[autodoc]] modeling_tf_outputs.TFBaseModelOutputWithPoolingAndCrossAttentions
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## TFBaseModelOutputWithPast
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[[autodoc]] modeling_tf_outputs.TFBaseModelOutputWithPast
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## TFBaseModelOutputWithPastAndCrossAttentions
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[[autodoc]] modeling_tf_outputs.TFBaseModelOutputWithPastAndCrossAttentions
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## TFSeq2SeqModelOutput
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[[autodoc]] modeling_tf_outputs.TFSeq2SeqModelOutput
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## TFCausalLMOutput
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[[autodoc]] modeling_tf_outputs.TFCausalLMOutput
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## TFCausalLMOutputWithCrossAttentions
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[[autodoc]] modeling_tf_outputs.TFCausalLMOutputWithCrossAttentions
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## TFCausalLMOutputWithPast
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[[autodoc]] modeling_tf_outputs.TFCausalLMOutputWithPast
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## TFMaskedLMOutput
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[[autodoc]] modeling_tf_outputs.TFMaskedLMOutput
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## TFSeq2SeqLMOutput
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[[autodoc]] modeling_tf_outputs.TFSeq2SeqLMOutput
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## TFNextSentencePredictorOutput
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[[autodoc]] modeling_tf_outputs.TFNextSentencePredictorOutput
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## TFSequenceClassifierOutput
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[[autodoc]] modeling_tf_outputs.TFSequenceClassifierOutput
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## TFSeq2SeqSequenceClassifierOutput
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[[autodoc]] modeling_tf_outputs.TFSeq2SeqSequenceClassifierOutput
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## TFMultipleChoiceModelOutput
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[[autodoc]] modeling_tf_outputs.TFMultipleChoiceModelOutput
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## TFTokenClassifierOutput
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[[autodoc]] modeling_tf_outputs.TFTokenClassifierOutput
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## TFQuestionAnsweringModelOutput
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[[autodoc]] modeling_tf_outputs.TFQuestionAnsweringModelOutput
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## TFSeq2SeqQuestionAnsweringModelOutput
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[[autodoc]] modeling_tf_outputs.TFSeq2SeqQuestionAnsweringModelOutput
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## FlaxBaseModelOutput
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[[autodoc]] modeling_flax_outputs.FlaxBaseModelOutput
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## FlaxBaseModelOutputWithPast
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[[autodoc]] modeling_flax_outputs.FlaxBaseModelOutputWithPast
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## FlaxBaseModelOutputWithPooling
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[[autodoc]] modeling_flax_outputs.FlaxBaseModelOutputWithPooling
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## FlaxBaseModelOutputWithPastAndCrossAttentions
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[[autodoc]] modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions
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## FlaxSeq2SeqModelOutput
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[[autodoc]] modeling_flax_outputs.FlaxSeq2SeqModelOutput
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## FlaxCausalLMOutputWithCrossAttentions
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[[autodoc]] modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions
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## FlaxMaskedLMOutput
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[[autodoc]] modeling_flax_outputs.FlaxMaskedLMOutput
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## FlaxSeq2SeqLMOutput
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[[autodoc]] modeling_flax_outputs.FlaxSeq2SeqLMOutput
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## FlaxNextSentencePredictorOutput
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[[autodoc]] modeling_flax_outputs.FlaxNextSentencePredictorOutput
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## FlaxSequenceClassifierOutput
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[[autodoc]] modeling_flax_outputs.FlaxSequenceClassifierOutput
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## FlaxSeq2SeqSequenceClassifierOutput
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[[autodoc]] modeling_flax_outputs.FlaxSeq2SeqSequenceClassifierOutput
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## FlaxMultipleChoiceModelOutput
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[[autodoc]] modeling_flax_outputs.FlaxMultipleChoiceModelOutput
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## FlaxTokenClassifierOutput
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[[autodoc]] modeling_flax_outputs.FlaxTokenClassifierOutput
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## FlaxQuestionAnsweringModelOutput
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[[autodoc]] modeling_flax_outputs.FlaxQuestionAnsweringModelOutput
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## FlaxSeq2SeqQuestionAnsweringModelOutput
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[[autodoc]] modeling_flax_outputs.FlaxSeq2SeqQuestionAnsweringModelOutput
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