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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("bert-base-uncased") |
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model = BertForSequenceClassification.from_pretrained("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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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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