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🌐 [i18n-KO] Translated output.md to Korean (#33607)
* nmt draft * fix toctree * minor fix * Apply suggestions from code review * Apply suggestions from code review * Apply suggestions from code review Co-authored-by: boyunJang <[email protected]> Co-authored-by: wony617 <[email protected]> * Apply suggestions from code review * Apply suggestions from code review * Update docs/source/ko/main_classes/output.md * Update docs/source/ko/_toctree.yml Co-authored-by: Steven Liu <[email protected]> --------- Co-authored-by: boyunJang <[email protected]> Co-authored-by: wony617 <[email protected]> Co-authored-by: Steven Liu <[email protected]>
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| <!--Copyright 2020 The HuggingFace Team. All rights reserved. | ||
| Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with | ||
| the License. You may obtain a copy of the License at | ||
| http://www.apache.org/licenses/LICENSE-2.0 | ||
| Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on | ||
| an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the | ||
| specific language governing permissions and limitations under the License. | ||
| ⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be | ||
| rendered properly in your Markdown viewer. | ||
| --> | ||
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| # 모델 출력[[model-outputs]] | ||
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| 모든 모델에는 [`~utils.ModelOutput`]의 서브클래스의 인스턴스인 모델 출력이 있습니다. 이들은 | ||
| 모델에서 반환되는 모든 정보를 포함하는 데이터 구조이지만 튜플이나 딕셔너리로도 사용할 수 있습니다. | ||
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| 예제를 통해 살펴보겠습니다: | ||
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| ```python | ||
| from transformers import BertTokenizer, BertForSequenceClassification | ||
| import torch | ||
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| tokenizer = BertTokenizer.from_pretrained("google-bert/bert-base-uncased") | ||
| model = BertForSequenceClassification.from_pretrained("google-bert/bert-base-uncased") | ||
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| inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") | ||
| labels = torch.tensor([1]).unsqueeze(0) # 배치 크기 1 | ||
| outputs = model(**inputs, labels=labels) | ||
| ``` | ||
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| `outputs` 객체는 [`~modeling_outputs.SequenceClassifierOutput`]입니다. | ||
| 아래 해당 클래스의 문서에서 볼 수 있듯이, `loss`(선택적), `logits`, `hidden_states`(선택적) 및 `attentions`(선택적) 항목이 있습니다. 여기에서는 `labels`를 전달했기 때문에 `loss`가 있지만 `hidden_states`와 `attentions`가 없는데, 이는 `output_hidden_states=True` 또는 `output_attentions=True`를 전달하지 않았기 때문입니다. | ||
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| <Tip> | ||
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| `output_hidden_states=True`를 전달할 때 `outputs.hidden_states[-1]`가 `outputs.last_hidden_state`와 정확히 일치할 것으로 예상할 수 있습니다. | ||
| 하지만 항상 그런 것은 아닙니다. 일부 모델은 마지막 은닉 상태가 반환될 때 정규화를 적용하거나 다른 후속 프로세스를 적용합니다. | ||
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| </Tip> | ||
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| 일반적으로 사용할 때와 동일하게 각 속성들에 접근할 수 있으며, 모델이 해당 속성을 반환하지 않은 경우 `None`이 반환됩니다. 예시에서는 `outputs.loss`는 모델에서 계산한 손실이고 `outputs.attentions`는 `None`입니다. | ||
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| `outputs` 객체를 튜플로 간주할 때는 `None` 값이 없는 속성만 고려합니다. | ||
| 예시에서는 `loss`와 `logits`라는 두 개의 요소가 있습니다. 그러므로, | ||
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| ```python | ||
| outputs[:2] | ||
| ``` | ||
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| 는 `(outputs.loss, outputs.logits)` 튜플을 반환합니다. | ||
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| `outputs` 객체를 딕셔너리로 간주할 때는 `None` 값이 없는 속성만 고려합니다. | ||
| 예시에는 `loss`와 `logits`라는 두 개의 키가 있습니다. | ||
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| 여기서부터는 두 가지 이상의 모델 유형에서 사용되는 일반 모델 출력을 다룹니다. 구체적인 출력 유형은 해당 모델 페이지에 문서화되어 있습니다. | ||
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| ## ModelOutput[[transformers.utils.ModelOutput]] | ||
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| [[autodoc]] utils.ModelOutput | ||
| - to_tuple | ||
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| ## BaseModelOutput[[transformers.BaseModelOutput]] | ||
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| [[autodoc]] modeling_outputs.BaseModelOutput | ||
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| ## BaseModelOutputWithPooling[[transformers.modeling_outputs.BaseModelOutputWithPooling]] | ||
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| [[autodoc]] modeling_outputs.BaseModelOutputWithPooling | ||
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| ## BaseModelOutputWithCrossAttentions[[transformers.modeling_outputs.BaseModelOutputWithCrossAttentions]] | ||
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| [[autodoc]] modeling_outputs.BaseModelOutputWithCrossAttentions | ||
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| ## BaseModelOutputWithPoolingAndCrossAttentions[[transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions]] | ||
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| [[autodoc]] modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions | ||
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| ## BaseModelOutputWithPast[[transformers.modeling_outputs.BaseModelOutputWithPast]] | ||
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| [[autodoc]] modeling_outputs.BaseModelOutputWithPast | ||
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| ## BaseModelOutputWithPastAndCrossAttentions[[transformers.modeling_outputs.BaseModelOutputWithPastAndCrossAttentions]] | ||
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| [[autodoc]] modeling_outputs.BaseModelOutputWithPastAndCrossAttentions | ||
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| ## Seq2SeqModelOutput[[transformers.modeling_outputs.Seq2SeqModelOutput]] | ||
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| [[autodoc]] modeling_outputs.Seq2SeqModelOutput | ||
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| ## CausalLMOutput[[transformers.modeling_outputs.CausalLMOutput]] | ||
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| [[autodoc]] modeling_outputs.CausalLMOutput | ||
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| ## CausalLMOutputWithCrossAttentions[[transformers.modeling_outputs.CausalLMOutputWithCrossAttentions]] | ||
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| [[autodoc]] modeling_outputs.CausalLMOutputWithCrossAttentions | ||
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| ## CausalLMOutputWithPast[[transformers.modeling_outputs.CausalLMOutputWithPast]] | ||
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| [[autodoc]] modeling_outputs.CausalLMOutputWithPast | ||
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| ## MaskedLMOutput[[transformers.modeling_outputs.MaskedLMOutput]] | ||
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| [[autodoc]] modeling_outputs.MaskedLMOutput | ||
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| ## Seq2SeqLMOutput[[transformers.modeling_outputs.Seq2SeqLMOutput]] | ||
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| [[autodoc]] modeling_outputs.Seq2SeqLMOutput | ||
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| ## NextSentencePredictorOutput[[transformers.modeling_outputs.NextSentencePredictorOutput]] | ||
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| [[autodoc]] modeling_outputs.NextSentencePredictorOutput | ||
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| ## SequenceClassifierOutput[[transformers.modeling_outputs.SequenceClassifierOutput]] | ||
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| [[autodoc]] modeling_outputs.SequenceClassifierOutput | ||
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| ## Seq2SeqSequenceClassifierOutput[[transformers.modeling_outputs.Seq2SeqSequenceClassifierOutput]] | ||
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| [[autodoc]] modeling_outputs.Seq2SeqSequenceClassifierOutput | ||
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| ## MultipleChoiceModelOutput[[transformers.modeling_outputs.MultipleChoiceModelOutput]] | ||
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| [[autodoc]] modeling_outputs.MultipleChoiceModelOutput | ||
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| ## TokenClassifierOutput[[transformers.modeling_outputs.TokenClassifierOutput]] | ||
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| [[autodoc]] modeling_outputs.TokenClassifierOutput | ||
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| ## QuestionAnsweringModelOutput[[transformers.modeling_outputs.QuestionAnsweringModelOutput]] | ||
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| [[autodoc]] modeling_outputs.QuestionAnsweringModelOutput | ||
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| ## Seq2SeqQuestionAnsweringModelOutput[[transformers.modeling_outputs.Seq2SeqQuestionAnsweringModelOutput]] | ||
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| [[autodoc]] modeling_outputs.Seq2SeqQuestionAnsweringModelOutput | ||
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| ## Seq2SeqSpectrogramOutput[[transformers.modeling_outputs.Seq2SeqSpectrogramOutput]] | ||
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| [[autodoc]] modeling_outputs.Seq2SeqSpectrogramOutput | ||
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| ## SemanticSegmenterOutput[[transformers.modeling_outputs.SemanticSegmenterOutput]] | ||
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| [[autodoc]] modeling_outputs.SemanticSegmenterOutput | ||
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| ## ImageClassifierOutput[[transformers.modeling_outputs.ImageClassifierOutput]] | ||
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| [[autodoc]] modeling_outputs.ImageClassifierOutput | ||
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| ## ImageClassifierOutputWithNoAttention[[transformers.modeling_outputs.ImageClassifierOutputWithNoAttention]] | ||
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| [[autodoc]] modeling_outputs.ImageClassifierOutputWithNoAttention | ||
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| ## DepthEstimatorOutput[[transformers.modeling_outputs.DepthEstimatorOutput]] | ||
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| [[autodoc]] modeling_outputs.DepthEstimatorOutput | ||
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| ## Wav2Vec2BaseModelOutput[[transformers.modeling_outputs.Wav2Vec2BaseModelOutput]] | ||
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| [[autodoc]] modeling_outputs.Wav2Vec2BaseModelOutput | ||
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| ## XVectorOutput[[transformers.modeling_outputs.XVectorOutput]] | ||
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| [[autodoc]] modeling_outputs.XVectorOutput | ||
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| ## Seq2SeqTSModelOutput[[transformers.modeling_outputs.Seq2SeqTSModelOutput]] | ||
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| [[autodoc]] modeling_outputs.Seq2SeqTSModelOutput | ||
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| ## Seq2SeqTSPredictionOutput[[transformers.modeling_outputs.Seq2SeqTSPredictionOutput]] | ||
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| [[autodoc]] modeling_outputs.Seq2SeqTSPredictionOutput | ||
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| ## SampleTSPredictionOutput[[transformers.modeling_outputs.SampleTSPredictionOutput]] | ||
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| [[autodoc]] modeling_outputs.SampleTSPredictionOutput | ||
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| ## TFBaseModelOutput[[transformers.modeling_outputs.TFBaseModelOutput]] | ||
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| [[autodoc]] modeling_tf_outputs.TFBaseModelOutput | ||
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| ## TFBaseModelOutputWithPooling[[transformers.modeling_tf_outputs.TFBaseModelOutputWithPooling]] | ||
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| [[autodoc]] modeling_tf_outputs.TFBaseModelOutputWithPooling | ||
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| ## TFBaseModelOutputWithPoolingAndCrossAttentions[[transformers.modeling_tf_outputs.TFBaseModelOutputWithPoolingAndCrossAttentions]] | ||
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| [[autodoc]] modeling_tf_outputs.TFBaseModelOutputWithPoolingAndCrossAttentions | ||
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| ## TFBaseModelOutputWithPast[[transformers.modeling_tf_outputs.TFBaseModelOutputWithPast]] | ||
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| [[autodoc]] modeling_tf_outputs.TFBaseModelOutputWithPast | ||
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| ## TFBaseModelOutputWithPastAndCrossAttentions[[transformers.modeling_tf_outputs.TFBaseModelOutputWithPastAndCrossAttentions]] | ||
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| [[autodoc]] modeling_tf_outputs.TFBaseModelOutputWithPastAndCrossAttentions | ||
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| ## TFSeq2SeqModelOutput[[transformers.modeling_tf_outputs.TFSeq2SeqModelOutput]] | ||
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| [[autodoc]] modeling_tf_outputs.TFSeq2SeqModelOutput | ||
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| ## TFCausalLMOutput[[transformers.modeling_tf_outputs.TFCausalLMOutput]] | ||
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| [[autodoc]] modeling_tf_outputs.TFCausalLMOutput | ||
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| ## TFCausalLMOutputWithCrossAttentions[[transformers.modeling_tf_outputs.TFCausalLMOutputWithCrossAttentions]] | ||
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| [[autodoc]] modeling_tf_outputs.TFCausalLMOutputWithCrossAttentions | ||
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| ## TFCausalLMOutputWithPast[[transformers.modeling_tf_outputs.TFCausalLMOutputWithPast]] | ||
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| [[autodoc]] modeling_tf_outputs.TFCausalLMOutputWithPast | ||
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| ## TFMaskedLMOutput[[transformers.modeling_tf_outputs.TFMaskedLMOutput]] | ||
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| [[autodoc]] modeling_tf_outputs.TFMaskedLMOutput | ||
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| ## TFSeq2SeqLMOutput[[transformers.modeling_tf_outputs.TFSeq2SeqLMOutput]] | ||
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| [[autodoc]] modeling_tf_outputs.TFSeq2SeqLMOutput | ||
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| ## TFNextSentencePredictorOutput[[transformers.modeling_tf_outputs.TFNextSentencePredictorOutput]] | ||
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| [[autodoc]] modeling_tf_outputs.TFNextSentencePredictorOutput | ||
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| ## TFSequenceClassifierOutput[[transformers.modeling_tf_outputs.TFSequenceClassifierOutput]] | ||
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| [[autodoc]] modeling_tf_outputs.TFSequenceClassifierOutput | ||
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| ## TFSeq2SeqSequenceClassifierOutput[[transformers.modeling_tf_outputs.TFSeq2SeqSequenceClassifierOutput]] | ||
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| [[autodoc]] modeling_tf_outputs.TFSeq2SeqSequenceClassifierOutput | ||
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| ## TFMultipleChoiceModelOutput[[transformers.modeling_tf_outputs.TFMultipleChoiceModelOutput]] | ||
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| [[autodoc]] modeling_tf_outputs.TFMultipleChoiceModelOutput | ||
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| ## TFTokenClassifierOutput[[transformers.modeling_tf_outputs.TFTokenClassifierOutput]] | ||
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| [[autodoc]] modeling_tf_outputs.TFTokenClassifierOutput | ||
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| ## TFQuestionAnsweringModelOutput[[transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput]] | ||
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| [[autodoc]] modeling_tf_outputs.TFQuestionAnsweringModelOutput | ||
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| ## TFSeq2SeqQuestionAnsweringModelOutput[[transformers.modeling_tf_outputs.TFSeq2SeqQuestionAnsweringModelOutput]] | ||
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| [[autodoc]] modeling_tf_outputs.TFSeq2SeqQuestionAnsweringModelOutput | ||
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| ## FlaxBaseModelOutput[[transformers.modeling_flax_outputs.FlaxBaseModelOutput]] | ||
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| [[autodoc]] modeling_flax_outputs.FlaxBaseModelOutput | ||
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| ## FlaxBaseModelOutputWithPast[[transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPast]] | ||
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| [[autodoc]] modeling_flax_outputs.FlaxBaseModelOutputWithPast | ||
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| ## FlaxBaseModelOutputWithPooling[[transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling]] | ||
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| [[autodoc]] modeling_flax_outputs.FlaxBaseModelOutputWithPooling | ||
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| ## FlaxBaseModelOutputWithPastAndCrossAttentions[[transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions]] | ||
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| [[autodoc]] modeling_flax_outputs.FlaxBaseModelOutputWithPastAndCrossAttentions | ||
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| ## FlaxSeq2SeqModelOutput[[transformers.modeling_flax_outputs.FlaxSeq2SeqModelOutput]] | ||
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| [[autodoc]] modeling_flax_outputs.FlaxSeq2SeqModelOutput | ||
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| ## FlaxCausalLMOutputWithCrossAttentions[[transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions]] | ||
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| [[autodoc]] modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions | ||
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| ## FlaxMaskedLMOutput[[transformers.modeling_flax_outputs.FlaxMaskedLMOutput]] | ||
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| [[autodoc]] modeling_flax_outputs.FlaxMaskedLMOutput | ||
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| ## FlaxSeq2SeqLMOutput[[transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput]] | ||
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| [[autodoc]] modeling_flax_outputs.FlaxSeq2SeqLMOutput | ||
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| ## FlaxNextSentencePredictorOutput[[transformers.modeling_flax_outputs.FlaxNextSentencePredictorOutput]] | ||
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| [[autodoc]] modeling_flax_outputs.FlaxNextSentencePredictorOutput | ||
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| ## FlaxSequenceClassifierOutput[[transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput]] | ||
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| [[autodoc]] modeling_flax_outputs.FlaxSequenceClassifierOutput | ||
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| ## FlaxSeq2SeqSequenceClassifierOutput[[transformers.modeling_flax_outputs.FlaxSeq2SeqSequenceClassifierOutput]] | ||
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| [[autodoc]] modeling_flax_outputs.FlaxSeq2SeqSequenceClassifierOutput | ||
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| ## FlaxMultipleChoiceModelOutput[[transformers.modeling_flax_outputs.FlaxMultipleChoiceModelOutput]] | ||
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| [[autodoc]] modeling_flax_outputs.FlaxMultipleChoiceModelOutput | ||
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| ## FlaxTokenClassifierOutput[[transformers.modeling_flax_outputs.FlaxTokenClassifierOutput]] | ||
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| [[autodoc]] modeling_flax_outputs.FlaxTokenClassifierOutput | ||
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| ## FlaxQuestionAnsweringModelOutput[[transformers.modeling_flax_outputs.FlaxQuestionAnsweringModelOutput]] | ||
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| [[autodoc]] modeling_flax_outputs.FlaxQuestionAnsweringModelOutput | ||
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| ## FlaxSeq2SeqQuestionAnsweringModelOutput[[transformers.modeling_flax_outputs.FlaxSeq2SeqQuestionAnsweringModelOutput]] | ||
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| [[autodoc]] modeling_flax_outputs.FlaxSeq2SeqQuestionAnsweringModelOutput |