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