# Model outputs

All models have outputs that are instances of subclasses of [`~utils.ModelOutput`]. Those are

data structures containing all the information returned by the model, but that can also be used as tuples or

dictionaries.

Let's see how this looks in an example:

thon

from transformers import BertTokenizer, BertForSequenceClassification

import torch

tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")

model = BertForSequenceClassification.from_pretrained("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)

The `outputs` object is a [`~modeling_outputs.SequenceClassifierOutput`], as we can see in the

documentation of that class below, it means it has an optional `loss`, a `logits`, an optional `hidden_states` and

an optional `attentions` attribute. Here we have the `loss` since we passed along `labels`, but we don't have

`hidden_states` and `attentions` because we didn't pass `output_hidden_states=True` or

`output_attentions=True`.

When passing `output_hidden_states=True` you may expect the `outputs.hidden_states[-1]` to match `outputs.last_hidden_states` exactly.

However, this is not always the case. Some models apply normalization or subsequent process to the last hidden state when it's returned.

You can access each attribute as you would usually do, and if that attribute has not been returned by the model, you

will get `None`. Here for instance `outputs.loss` is the loss computed by the model, and `outputs.attentions` is

`None`.

When considering our `outputs` object as tuple, it only considers the attributes that don't have `None` values.

Here for instance, it has two elements, `loss` then `logits`, so

thon

outputs[:2]

will return the tuple `(outputs.loss, outputs.logits)` for instance.

When considering our `outputs` object as dictionary, it only considers the attributes that don't have `None`

values. Here for instance, it has two keys that are `loss` and `logits`.

We document here the generic model outputs that are used by more than one model type. Specific output types are

documented on their corresponding model page.

## 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