enginex-mlu370-any2any/transformers/docs/source/en/main_classes/output.md

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# 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:

```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)
```

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`.

<Tip>

When passing `output_hidden_states=True` you may expect the `outputs.hidden_states[-1]` to match `outputs.last_hidden_state` exactly.
However, this is not always the case. Some models apply normalization or subsequent process to the last hidden state when it's returned.

</Tip>

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

```python
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