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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:

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.

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

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