mirror of https://github.com/huggingface/transformers.git synced 2025-07-03 12:50:06 +06:00

Nilay Bhatnagar eedd21b9e7

Fixed Majority of the Typos in transformers[en] Documentation (#33350 )

* Fixed typo: insted to instead

* Fixed typo: relase to release

* Fixed typo: nighlty to nightly

* Fixed typos: versatible, benchamarks, becnhmark to versatile, benchmark, benchmarks

* Fixed typo in comment: quantizd to quantized

* Fixed typo: architecutre to architecture

* Fixed typo: contibution to contribution

* Fixed typo: Presequities to Prerequisites

* Fixed typo: faste to faster

* Fixed typo: extendeding to extending

* Fixed typo: segmetantion_maps to segmentation_maps

* Fixed typo: Alternativelly to Alternatively

* Fixed incorrectly defined variable: output to output_disabled

* Fixed typo in library name: tranformers.onnx to transformers.onnx

* Fixed missing import: import tensorflow as tf

* Fixed incorrectly defined variable: token_tensor to tokens_tensor

* Fixed missing import: import torch

* Fixed incorrectly defined variable and typo: uromaize to uromanize

* Fixed incorrectly defined variable and typo: uromaize to uromanize

* Fixed typo in function args: numpy.ndarry to numpy.ndarray

* Fixed Inconsistent Library Name: Torchscript to TorchScript

* Fixed Inconsistent Class Name: OneformerProcessor to OneFormerProcessor

* Fixed Inconsistent Class Named Typo: TFLNetForMultipleChoice to TFXLNetForMultipleChoice

* Fixed Inconsistent Library Name Typo: Pytorch to PyTorch

* Fixed Inconsistent Function Name Typo: captureWarning to captureWarnings

* Fixed Inconsistent Library Name Typo: Pytorch to PyTorch

* Fixed Inconsistent Class Name Typo: TrainingArgument to TrainingArguments

* Fixed Inconsistent Model Name Typo: Swin2R to Swin2SR

* Fixed Inconsistent Model Name Typo: EART to BERT

* Fixed Inconsistent Library Name Typo: TensorFLow to TensorFlow

* Fixed Broken Link for Speech Emotion Classification with Wav2Vec2

* Fixed minor missing word Typo

* Fixed minor missing word Typo

* Fixed minor missing word Typo

* Fixed minor missing word Typo

* Fixed minor missing word Typo

* Fixed minor missing word Typo

* Fixed minor missing word Typo

* Fixed minor missing word Typo

* Fixed Punctuation: Two commas

* Fixed Punctuation: No Space between XLM-R and is

* Fixed Punctuation: No Space between [~accelerate.Accelerator.backward] and method

* Added backticks to display model.fit() in codeblock

* Added backticks to display openai-community/gpt2 in codeblock

* Fixed Minor Typo: will to with

* Fixed Minor Typo: is to are

* Fixed Minor Typo: in to on

* Fixed Minor Typo: inhibits to exhibits

* Fixed Minor Typo: they need to it needs

* Fixed Minor Typo: cast the load the checkpoints To load the checkpoints

* Fixed Inconsistent Class Name Typo: TFCamembertForCasualLM to TFCamembertForCausalLM

* Fixed typo in attribute name: outputs.last_hidden_states to outputs.last_hidden_state

* Added missing verbosity level: fatal

* Fixed Minor Typo: take To takes

* Fixed Minor Typo: heuristic To heuristics

* Fixed Minor Typo: setting To settings

* Fixed Minor Typo: Content To Contents

* Fixed Minor Typo: millions To million

* Fixed Minor Typo: difference To differences

* Fixed Minor Typo: while extract To which extracts

* Fixed Minor Typo: Hereby To Here

* Fixed Minor Typo: addition To additional

* Fixed Minor Typo: supports To supported

* Fixed Minor Typo: so that benchmark results TO as a consequence, benchmark

* Fixed Minor Typo: a To an

* Fixed Minor Typo: a To an

* Fixed Minor Typo: Chain-of-though To Chain-of-thought

2024-09-09 10:47:24 +02:00

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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_state 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

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