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# 模型输出

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。

让我们看一个例子：

```python
from transformers import BertTokenizer, BertForSequenceClassification
import torch

tokenizer = BertTokenizer.from_pretrained("google-bert/bert-base-uncased")
model = BertForSequenceClassification.from_pretrained("google-bert/bert-base-uncased")

inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
labels = torch.tensor([1]).unsqueeze(0)  # Batch size 1
outputs = model(**inputs, labels=labels)
```

`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`。

<Tip>

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。

</Tip>


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`。

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

```python
outputs[:2]
```

将返回元组 `(outputs.loss, outputs.logits)`。

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`。

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。

## ModelOutput

[[autodoc]] utils.ModelOutput
    - to_tuple

## BaseModelOutput

[[autodoc]] modeling_outputs.BaseModelOutput

## BaseModelOutputWithPooling

[[autodoc]] modeling_outputs.BaseModelOutputWithPooling

## BaseModelOutputWithCrossAttentions

[[autodoc]] modeling_outputs.BaseModelOutputWithCrossAttentions

## BaseModelOutputWithPoolingAndCrossAttentions

[[autodoc]] modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions

## BaseModelOutputWithPast

[[autodoc]] modeling_outputs.BaseModelOutputWithPast

## BaseModelOutputWithPastAndCrossAttentions

[[autodoc]] modeling_outputs.BaseModelOutputWithPastAndCrossAttentions

## Seq2SeqModelOutput

[[autodoc]] modeling_outputs.Seq2SeqModelOutput

## CausalLMOutput

[[autodoc]] modeling_outputs.CausalLMOutput

## CausalLMOutputWithCrossAttentions

[[autodoc]] modeling_outputs.CausalLMOutputWithCrossAttentions

## CausalLMOutputWithPast

[[autodoc]] modeling_outputs.CausalLMOutputWithPast

## MaskedLMOutput

[[autodoc]] modeling_outputs.MaskedLMOutput

## Seq2SeqLMOutput

[[autodoc]] modeling_outputs.Seq2SeqLMOutput

## NextSentencePredictorOutput

[[autodoc]] modeling_outputs.NextSentencePredictorOutput

## SequenceClassifierOutput

[[autodoc]] modeling_outputs.SequenceClassifierOutput

## Seq2SeqSequenceClassifierOutput

[[autodoc]] modeling_outputs.Seq2SeqSequenceClassifierOutput

## MultipleChoiceModelOutput

[[autodoc]] modeling_outputs.MultipleChoiceModelOutput

## TokenClassifierOutput

[[autodoc]] modeling_outputs.TokenClassifierOutput

## QuestionAnsweringModelOutput

[[autodoc]] modeling_outputs.QuestionAnsweringModelOutput

## Seq2SeqQuestionAnsweringModelOutput

[[autodoc]] modeling_outputs.Seq2SeqQuestionAnsweringModelOutput

## Seq2SeqSpectrogramOutput

[[autodoc]] modeling_outputs.Seq2SeqSpectrogramOutput

## SemanticSegmenterOutput

[[autodoc]] modeling_outputs.SemanticSegmenterOutput

## ImageClassifierOutput

[[autodoc]] modeling_outputs.ImageClassifierOutput

## ImageClassifierOutputWithNoAttention

[[autodoc]] modeling_outputs.ImageClassifierOutputWithNoAttention

## DepthEstimatorOutput

[[autodoc]] modeling_outputs.DepthEstimatorOutput

## Wav2Vec2BaseModelOutput

[[autodoc]] modeling_outputs.Wav2Vec2BaseModelOutput

## XVectorOutput

[[autodoc]] modeling_outputs.XVectorOutput

## Seq2SeqTSModelOutput

[[autodoc]] modeling_outputs.Seq2SeqTSModelOutput

## Seq2SeqTSPredictionOutput

[[autodoc]] modeling_outputs.Seq2SeqTSPredictionOutput

## SampleTSPredictionOutput

[[autodoc]] modeling_outputs.SampleTSPredictionOutput