| 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("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 | |
| 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 | |
| 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 |