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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 | |
| ## TFBaseModelOutput | |
| [[autodoc]] modeling_tf_outputs.TFBaseModelOutput | |
| ## TFBaseModelOutputWithPooling | |
| [[autodoc]] modeling_tf_outputs.TFBaseModelOutputWithPooling | |
| ## TFBaseModelOutputWithPoolingAndCrossAttentions | |
| [[autodoc]] modeling_tf_outputs.TFBaseModelOutputWithPoolingAndCrossAttentions | |
| ## TFBaseModelOutputWithPast | |
| [[autodoc]] modeling_tf_outputs.TFBaseModelOutputWithPast | |
| ## TFBaseModelOutputWithPastAndCrossAttentions | |
| [[autodoc]] modeling_tf_outputs.TFBaseModelOutputWithPastAndCrossAttentions | |
| ## TFSeq2SeqModelOutput | |
| [[autodoc]] modeling_tf_outputs.TFSeq2SeqModelOutput | |
| ## TFCausalLMOutput | |
| [[autodoc]] modeling_tf_outputs.TFCausalLMOutput | |
| ## TFCausalLMOutputWithCrossAttentions | |
| [[autodoc]] modeling_tf_outputs.TFCausalLMOutputWithCrossAttentions | |
| ## TFCausalLMOutputWithPast | |
| [[autodoc]] modeling_tf_outputs.TFCausalLMOutputWithPast | |
| ## TFMaskedLMOutput | |
| [[autodoc]] modeling_tf_outputs.TFMaskedLMOutput | |
| ## TFSeq2SeqLMOutput | |
| [[autodoc]] modeling_tf_outputs.TFSeq2SeqLMOutput | |
| ## TFNextSentencePredictorOutput | |
| [[autodoc]] modeling_tf_outputs.TFNextSentencePredictorOutput | |
| ## TFSequenceClassifierOutput | |
| [[autodoc]] modeling_tf_outputs.TFSequenceClassifierOutput | |
| ## TFSeq2SeqSequenceClassifierOutput | |
| [[autodoc]] modeling_tf_outputs.TFSeq2SeqSequenceClassifierOutput | |
| ## TFMultipleChoiceModelOutput | |
| [[autodoc]] modeling_tf_outputs.TFMultipleChoiceModelOutput | |
| ## TFTokenClassifierOutput | |
| [[autodoc]] modeling_tf_outputs.TFTokenClassifierOutput | |
| ## TFQuestionAnsweringModelOutput | |
| [[autodoc]] modeling_tf_outputs.TFQuestionAnsweringModelOutput | |
| ## TFSeq2SeqQuestionAnsweringModelOutput | |
| [[autodoc]] modeling_tf_outputs.TFSeq2SeqQuestionAnsweringModelOutput | |
| ## FlaxBaseModelOutput | |
| [[autodoc]] modeling_flax_outputs.FlaxBaseModelOutput | |
| ## FlaxBaseModelOutputWithPast | |
| [[autodoc]] modeling_flax_outputs.FlaxBaseModelOutputWithPast | |
| ## FlaxBaseModelOutputWithPooling | |
| [[autodoc]] modeling_flax_outputs.FlaxBaseModelOutputWithPooling | |
| ## FlaxBaseModelOutputWithPastAndCrossAttentions | |
| [[autodoc]] modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions | |
| ## FlaxSeq2SeqModelOutput | |
| [[autodoc]] modeling_flax_outputs.FlaxSeq2SeqModelOutput | |
| ## FlaxCausalLMOutputWithCrossAttentions | |
| [[autodoc]] modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions | |
| ## FlaxMaskedLMOutput | |
| [[autodoc]] modeling_flax_outputs.FlaxMaskedLMOutput | |
| ## FlaxSeq2SeqLMOutput | |
| [[autodoc]] modeling_flax_outputs.FlaxSeq2SeqLMOutput | |
| ## FlaxNextSentencePredictorOutput | |
| [[autodoc]] modeling_flax_outputs.FlaxNextSentencePredictorOutput | |
| ## FlaxSequenceClassifierOutput | |
| [[autodoc]] modeling_flax_outputs.FlaxSequenceClassifierOutput | |
| ## FlaxSeq2SeqSequenceClassifierOutput | |
| [[autodoc]] modeling_flax_outputs.FlaxSeq2SeqSequenceClassifierOutput | |
| ## FlaxMultipleChoiceModelOutput | |
| [[autodoc]] modeling_flax_outputs.FlaxMultipleChoiceModelOutput | |
| ## FlaxTokenClassifierOutput | |
| [[autodoc]] modeling_flax_outputs.FlaxTokenClassifierOutput | |
| ## FlaxQuestionAnsweringModelOutput | |
| [[autodoc]] modeling_flax_outputs.FlaxQuestionAnsweringModelOutput | |
| ## FlaxSeq2SeqQuestionAnsweringModelOutput | |
| [[autodoc]] modeling_flax_outputs.FlaxSeq2SeqQuestionAnsweringModelOutput | |