322 lines
8.5 KiB
Markdown
322 lines
8.5 KiB
Markdown
<!--Copyright 2020 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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rendered properly in your Markdown viewer.
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-->
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# Model outputs
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All models have outputs that are instances of subclasses of [`~utils.ModelOutput`]. Those are
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data structures containing all the information returned by the model, but that can also be used as tuples or
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dictionaries.
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Let's see how this looks in an example:
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```python
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from transformers import BertTokenizer, BertForSequenceClassification
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import torch
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tokenizer = BertTokenizer.from_pretrained("google-bert/bert-base-uncased")
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model = BertForSequenceClassification.from_pretrained("google-bert/bert-base-uncased")
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inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
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labels = torch.tensor([1]).unsqueeze(0) # Batch size 1
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outputs = model(**inputs, labels=labels)
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```
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The `outputs` object is a [`~modeling_outputs.SequenceClassifierOutput`], as we can see in the
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documentation of that class below, it means it has an optional `loss`, a `logits`, an optional `hidden_states` and
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an optional `attentions` attribute. Here we have the `loss` since we passed along `labels`, but we don't have
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`hidden_states` and `attentions` because we didn't pass `output_hidden_states=True` or
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`output_attentions=True`.
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<Tip>
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When passing `output_hidden_states=True` you may expect the `outputs.hidden_states[-1]` to match `outputs.last_hidden_states` exactly.
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However, this is not always the case. Some models apply normalization or subsequent process to the last hidden state when it's returned.
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</Tip>
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You can access each attribute as you would usually do, and if that attribute has not been returned by the model, you
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will get `None`. Here for instance `outputs.loss` is the loss computed by the model, and `outputs.attentions` is
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`None`.
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When considering our `outputs` object as tuple, it only considers the attributes that don't have `None` values.
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Here for instance, it has two elements, `loss` then `logits`, so
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```python
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outputs[:2]
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```
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will return the tuple `(outputs.loss, outputs.logits)` for instance.
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When considering our `outputs` object as dictionary, it only considers the attributes that don't have `None`
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values. Here for instance, it has two keys that are `loss` and `logits`.
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We document here the generic model outputs that are used by more than one model type. Specific output types are
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documented on their corresponding model page.
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## ModelOutput
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[[autodoc]] utils.ModelOutput
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- to_tuple
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## BaseModelOutput
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[[autodoc]] modeling_outputs.BaseModelOutput
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## BaseModelOutputWithPooling
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[[autodoc]] modeling_outputs.BaseModelOutputWithPooling
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## BaseModelOutputWithCrossAttentions
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[[autodoc]] modeling_outputs.BaseModelOutputWithCrossAttentions
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## BaseModelOutputWithPoolingAndCrossAttentions
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[[autodoc]] modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions
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## BaseModelOutputWithPast
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[[autodoc]] modeling_outputs.BaseModelOutputWithPast
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## BaseModelOutputWithPastAndCrossAttentions
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[[autodoc]] modeling_outputs.BaseModelOutputWithPastAndCrossAttentions
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## Seq2SeqModelOutput
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[[autodoc]] modeling_outputs.Seq2SeqModelOutput
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## CausalLMOutput
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[[autodoc]] modeling_outputs.CausalLMOutput
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## CausalLMOutputWithCrossAttentions
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[[autodoc]] modeling_outputs.CausalLMOutputWithCrossAttentions
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## CausalLMOutputWithPast
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[[autodoc]] modeling_outputs.CausalLMOutputWithPast
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## MaskedLMOutput
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[[autodoc]] modeling_outputs.MaskedLMOutput
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## Seq2SeqLMOutput
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[[autodoc]] modeling_outputs.Seq2SeqLMOutput
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## NextSentencePredictorOutput
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[[autodoc]] modeling_outputs.NextSentencePredictorOutput
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## SequenceClassifierOutput
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[[autodoc]] modeling_outputs.SequenceClassifierOutput
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## Seq2SeqSequenceClassifierOutput
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[[autodoc]] modeling_outputs.Seq2SeqSequenceClassifierOutput
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## MultipleChoiceModelOutput
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[[autodoc]] modeling_outputs.MultipleChoiceModelOutput
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## TokenClassifierOutput
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[[autodoc]] modeling_outputs.TokenClassifierOutput
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## QuestionAnsweringModelOutput
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[[autodoc]] modeling_outputs.QuestionAnsweringModelOutput
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## Seq2SeqQuestionAnsweringModelOutput
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[[autodoc]] modeling_outputs.Seq2SeqQuestionAnsweringModelOutput
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## Seq2SeqSpectrogramOutput
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[[autodoc]] modeling_outputs.Seq2SeqSpectrogramOutput
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## SemanticSegmenterOutput
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[[autodoc]] modeling_outputs.SemanticSegmenterOutput
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## ImageClassifierOutput
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[[autodoc]] modeling_outputs.ImageClassifierOutput
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## ImageClassifierOutputWithNoAttention
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[[autodoc]] modeling_outputs.ImageClassifierOutputWithNoAttention
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## DepthEstimatorOutput
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[[autodoc]] modeling_outputs.DepthEstimatorOutput
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## Wav2Vec2BaseModelOutput
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[[autodoc]] modeling_outputs.Wav2Vec2BaseModelOutput
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## XVectorOutput
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[[autodoc]] modeling_outputs.XVectorOutput
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## Seq2SeqTSModelOutput
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[[autodoc]] modeling_outputs.Seq2SeqTSModelOutput
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## Seq2SeqTSPredictionOutput
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[[autodoc]] modeling_outputs.Seq2SeqTSPredictionOutput
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## SampleTSPredictionOutput
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[[autodoc]] modeling_outputs.SampleTSPredictionOutput
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## TFBaseModelOutput
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[[autodoc]] modeling_tf_outputs.TFBaseModelOutput
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## TFBaseModelOutputWithPooling
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[[autodoc]] modeling_tf_outputs.TFBaseModelOutputWithPooling
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## TFBaseModelOutputWithPoolingAndCrossAttentions
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[[autodoc]] modeling_tf_outputs.TFBaseModelOutputWithPoolingAndCrossAttentions
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## TFBaseModelOutputWithPast
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[[autodoc]] modeling_tf_outputs.TFBaseModelOutputWithPast
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## TFBaseModelOutputWithPastAndCrossAttentions
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[[autodoc]] modeling_tf_outputs.TFBaseModelOutputWithPastAndCrossAttentions
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## TFSeq2SeqModelOutput
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[[autodoc]] modeling_tf_outputs.TFSeq2SeqModelOutput
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## TFCausalLMOutput
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[[autodoc]] modeling_tf_outputs.TFCausalLMOutput
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## TFCausalLMOutputWithCrossAttentions
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[[autodoc]] modeling_tf_outputs.TFCausalLMOutputWithCrossAttentions
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## TFCausalLMOutputWithPast
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[[autodoc]] modeling_tf_outputs.TFCausalLMOutputWithPast
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## TFMaskedLMOutput
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[[autodoc]] modeling_tf_outputs.TFMaskedLMOutput
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## TFSeq2SeqLMOutput
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[[autodoc]] modeling_tf_outputs.TFSeq2SeqLMOutput
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## TFNextSentencePredictorOutput
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[[autodoc]] modeling_tf_outputs.TFNextSentencePredictorOutput
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## TFSequenceClassifierOutput
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[[autodoc]] modeling_tf_outputs.TFSequenceClassifierOutput
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## TFSeq2SeqSequenceClassifierOutput
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[[autodoc]] modeling_tf_outputs.TFSeq2SeqSequenceClassifierOutput
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## TFMultipleChoiceModelOutput
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[[autodoc]] modeling_tf_outputs.TFMultipleChoiceModelOutput
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## TFTokenClassifierOutput
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[[autodoc]] modeling_tf_outputs.TFTokenClassifierOutput
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## TFQuestionAnsweringModelOutput
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[[autodoc]] modeling_tf_outputs.TFQuestionAnsweringModelOutput
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## TFSeq2SeqQuestionAnsweringModelOutput
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[[autodoc]] modeling_tf_outputs.TFSeq2SeqQuestionAnsweringModelOutput
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## FlaxBaseModelOutput
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[[autodoc]] modeling_flax_outputs.FlaxBaseModelOutput
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## FlaxBaseModelOutputWithPast
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[[autodoc]] modeling_flax_outputs.FlaxBaseModelOutputWithPast
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## FlaxBaseModelOutputWithPooling
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[[autodoc]] modeling_flax_outputs.FlaxBaseModelOutputWithPooling
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## FlaxBaseModelOutputWithPastAndCrossAttentions
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[[autodoc]] modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions
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## FlaxSeq2SeqModelOutput
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[[autodoc]] modeling_flax_outputs.FlaxSeq2SeqModelOutput
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## FlaxCausalLMOutputWithCrossAttentions
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[[autodoc]] modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions
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## FlaxMaskedLMOutput
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[[autodoc]] modeling_flax_outputs.FlaxMaskedLMOutput
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## FlaxSeq2SeqLMOutput
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[[autodoc]] modeling_flax_outputs.FlaxSeq2SeqLMOutput
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## FlaxNextSentencePredictorOutput
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[[autodoc]] modeling_flax_outputs.FlaxNextSentencePredictorOutput
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## FlaxSequenceClassifierOutput
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[[autodoc]] modeling_flax_outputs.FlaxSequenceClassifierOutput
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## FlaxSeq2SeqSequenceClassifierOutput
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[[autodoc]] modeling_flax_outputs.FlaxSeq2SeqSequenceClassifierOutput
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## FlaxMultipleChoiceModelOutput
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[[autodoc]] modeling_flax_outputs.FlaxMultipleChoiceModelOutput
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## FlaxTokenClassifierOutput
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[[autodoc]] modeling_flax_outputs.FlaxTokenClassifierOutput
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## FlaxQuestionAnsweringModelOutput
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[[autodoc]] modeling_flax_outputs.FlaxQuestionAnsweringModelOutput
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## FlaxSeq2SeqQuestionAnsweringModelOutput
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[[autodoc]] modeling_flax_outputs.FlaxSeq2SeqQuestionAnsweringModelOutput
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