From bc09b3ac4752b34c576e999aa3241562cf6b9280 Mon Sep 17 00:00:00 2001 From: boyunJang Date: Tue, 10 Sep 2024 22:35:12 +0900 Subject: [PATCH 1/3] docs: ko: model_doc/auto.md --- docs/source/ko/_toctree.yml | 4 +- docs/source/ko/model_doc/auto.md | 383 +++++++++++++++++++++++++++++++ 2 files changed, 385 insertions(+), 2 deletions(-) create mode 100644 docs/source/ko/model_doc/auto.md diff --git a/docs/source/ko/_toctree.yml b/docs/source/ko/_toctree.yml index eafd389994ad52..081b4000fd0015 100644 --- a/docs/source/ko/_toctree.yml +++ b/docs/source/ko/_toctree.yml @@ -270,8 +270,8 @@ - sections: - local: main_classes/agent title: 에이전트와 도구 - - local: in_translation - title: (번역중) Auto Classes + - local: model_doc/auto + title: 자동 클래스 - local: in_translation title: (번역중) Backbones - local: in_translation diff --git a/docs/source/ko/model_doc/auto.md b/docs/source/ko/model_doc/auto.md new file mode 100644 index 00000000000000..ab42c24d83e82d --- /dev/null +++ b/docs/source/ko/model_doc/auto.md @@ -0,0 +1,383 @@ + + +# Auto Classes + +In many cases, the architecture you want to use can be guessed from the name or the path of the pretrained model you +are supplying to the `from_pretrained()` method. AutoClasses are here to do this job for you so that you +automatically retrieve the relevant model given the name/path to the pretrained weights/config/vocabulary. + +Instantiating one of [`AutoConfig`], [`AutoModel`], and +[`AutoTokenizer`] will directly create a class of the relevant architecture. For instance + + +```python +model = AutoModel.from_pretrained("google-bert/bert-base-cased") +``` + +will create a model that is an instance of [`BertModel`]. + +There is one class of `AutoModel` for each task, and for each backend (PyTorch, TensorFlow, or Flax). + +## Extending the Auto Classes + +Each of the auto classes has a method to be extended with your custom classes. For instance, if you have defined a +custom class of model `NewModel`, make sure you have a `NewModelConfig` then you can add those to the auto +classes like this: + +```python +from transformers import AutoConfig, AutoModel + +AutoConfig.register("new-model", NewModelConfig) +AutoModel.register(NewModelConfig, NewModel) +``` + +You will then be able to use the auto classes like you would usually do! + + + +If your `NewModelConfig` is a subclass of [`~transformers.PretrainedConfig`], make sure its +`model_type` attribute is set to the same key you use when registering the config (here `"new-model"`). + +Likewise, if your `NewModel` is a subclass of [`PreTrainedModel`], make sure its +`config_class` attribute is set to the same class you use when registering the model (here +`NewModelConfig`). + + + +## AutoConfig + +[[autodoc]] AutoConfig + +## AutoTokenizer + +[[autodoc]] AutoTokenizer + +## AutoFeatureExtractor + +[[autodoc]] AutoFeatureExtractor + +## AutoImageProcessor + +[[autodoc]] AutoImageProcessor + +## AutoProcessor + +[[autodoc]] AutoProcessor + +## Generic model classes + +The following auto classes are available for instantiating a base model class without a specific head. + +### AutoModel + +[[autodoc]] AutoModel + +### TFAutoModel + +[[autodoc]] TFAutoModel + +### FlaxAutoModel + +[[autodoc]] FlaxAutoModel + +## Generic pretraining classes + +The following auto classes are available for instantiating a model with a pretraining head. + +### AutoModelForPreTraining + +[[autodoc]] AutoModelForPreTraining + +### TFAutoModelForPreTraining + +[[autodoc]] TFAutoModelForPreTraining + +### FlaxAutoModelForPreTraining + +[[autodoc]] FlaxAutoModelForPreTraining + +## Natural Language Processing + +The following auto classes are available for the following natural language processing tasks. + +### AutoModelForCausalLM + +[[autodoc]] AutoModelForCausalLM + +### TFAutoModelForCausalLM + +[[autodoc]] TFAutoModelForCausalLM + +### FlaxAutoModelForCausalLM + +[[autodoc]] FlaxAutoModelForCausalLM + +### AutoModelForMaskedLM + +[[autodoc]] AutoModelForMaskedLM + +### TFAutoModelForMaskedLM + +[[autodoc]] TFAutoModelForMaskedLM + +### FlaxAutoModelForMaskedLM + +[[autodoc]] FlaxAutoModelForMaskedLM + +### AutoModelForMaskGeneration + +[[autodoc]] AutoModelForMaskGeneration + +### TFAutoModelForMaskGeneration + +[[autodoc]] TFAutoModelForMaskGeneration + +### AutoModelForSeq2SeqLM + +[[autodoc]] AutoModelForSeq2SeqLM + +### TFAutoModelForSeq2SeqLM + +[[autodoc]] TFAutoModelForSeq2SeqLM + +### FlaxAutoModelForSeq2SeqLM + +[[autodoc]] FlaxAutoModelForSeq2SeqLM + +### AutoModelForSequenceClassification + +[[autodoc]] AutoModelForSequenceClassification + +### TFAutoModelForSequenceClassification + +[[autodoc]] TFAutoModelForSequenceClassification + +### FlaxAutoModelForSequenceClassification + +[[autodoc]] FlaxAutoModelForSequenceClassification + +### AutoModelForMultipleChoice + +[[autodoc]] AutoModelForMultipleChoice + +### TFAutoModelForMultipleChoice + +[[autodoc]] TFAutoModelForMultipleChoice + +### FlaxAutoModelForMultipleChoice + +[[autodoc]] FlaxAutoModelForMultipleChoice + +### AutoModelForNextSentencePrediction + +[[autodoc]] AutoModelForNextSentencePrediction + +### TFAutoModelForNextSentencePrediction + +[[autodoc]] TFAutoModelForNextSentencePrediction + +### FlaxAutoModelForNextSentencePrediction + +[[autodoc]] FlaxAutoModelForNextSentencePrediction + +### AutoModelForTokenClassification + +[[autodoc]] AutoModelForTokenClassification + +### TFAutoModelForTokenClassification + +[[autodoc]] TFAutoModelForTokenClassification + +### FlaxAutoModelForTokenClassification + +[[autodoc]] FlaxAutoModelForTokenClassification + +### AutoModelForQuestionAnswering + +[[autodoc]] AutoModelForQuestionAnswering + +### TFAutoModelForQuestionAnswering + +[[autodoc]] TFAutoModelForQuestionAnswering + +### FlaxAutoModelForQuestionAnswering + +[[autodoc]] FlaxAutoModelForQuestionAnswering + +### AutoModelForTextEncoding + +[[autodoc]] AutoModelForTextEncoding + +### TFAutoModelForTextEncoding + +[[autodoc]] TFAutoModelForTextEncoding + +## Computer vision + +The following auto classes are available for the following computer vision tasks. + +### AutoModelForDepthEstimation + +[[autodoc]] AutoModelForDepthEstimation + +### AutoModelForImageClassification + +[[autodoc]] AutoModelForImageClassification + +### TFAutoModelForImageClassification + +[[autodoc]] TFAutoModelForImageClassification + +### FlaxAutoModelForImageClassification + +[[autodoc]] FlaxAutoModelForImageClassification + +### AutoModelForVideoClassification + +[[autodoc]] AutoModelForVideoClassification + +### AutoModelForKeypointDetection + +[[autodoc]] AutoModelForKeypointDetection + +### AutoModelForMaskedImageModeling + +[[autodoc]] AutoModelForMaskedImageModeling + +### TFAutoModelForMaskedImageModeling + +[[autodoc]] TFAutoModelForMaskedImageModeling + +### AutoModelForObjectDetection + +[[autodoc]] AutoModelForObjectDetection + +### AutoModelForImageSegmentation + +[[autodoc]] AutoModelForImageSegmentation + +### AutoModelForImageToImage + +[[autodoc]] AutoModelForImageToImage + +### AutoModelForSemanticSegmentation + +[[autodoc]] AutoModelForSemanticSegmentation + +### TFAutoModelForSemanticSegmentation + +[[autodoc]] TFAutoModelForSemanticSegmentation + +### AutoModelForInstanceSegmentation + +[[autodoc]] AutoModelForInstanceSegmentation + +### AutoModelForUniversalSegmentation + +[[autodoc]] AutoModelForUniversalSegmentation + +### AutoModelForZeroShotImageClassification + +[[autodoc]] AutoModelForZeroShotImageClassification + +### TFAutoModelForZeroShotImageClassification + +[[autodoc]] TFAutoModelForZeroShotImageClassification + +### AutoModelForZeroShotObjectDetection + +[[autodoc]] AutoModelForZeroShotObjectDetection + +## Audio + +The following auto classes are available for the following audio tasks. + +### AutoModelForAudioClassification + +[[autodoc]] AutoModelForAudioClassification + +### AutoModelForAudioFrameClassification + +[[autodoc]] TFAutoModelForAudioClassification + +### TFAutoModelForAudioFrameClassification + +[[autodoc]] AutoModelForAudioFrameClassification + +### AutoModelForCTC + +[[autodoc]] AutoModelForCTC + +### AutoModelForSpeechSeq2Seq + +[[autodoc]] AutoModelForSpeechSeq2Seq + +### TFAutoModelForSpeechSeq2Seq + +[[autodoc]] TFAutoModelForSpeechSeq2Seq + +### FlaxAutoModelForSpeechSeq2Seq + +[[autodoc]] FlaxAutoModelForSpeechSeq2Seq + +### AutoModelForAudioXVector + +[[autodoc]] AutoModelForAudioXVector + +### AutoModelForTextToSpectrogram + +[[autodoc]] AutoModelForTextToSpectrogram + +### AutoModelForTextToWaveform + +[[autodoc]] AutoModelForTextToWaveform + +## Multimodal + +The following auto classes are available for the following multimodal tasks. + +### AutoModelForTableQuestionAnswering + +[[autodoc]] AutoModelForTableQuestionAnswering + +### TFAutoModelForTableQuestionAnswering + +[[autodoc]] TFAutoModelForTableQuestionAnswering + +### AutoModelForDocumentQuestionAnswering + +[[autodoc]] AutoModelForDocumentQuestionAnswering + +### TFAutoModelForDocumentQuestionAnswering + +[[autodoc]] TFAutoModelForDocumentQuestionAnswering + +### AutoModelForVisualQuestionAnswering + +[[autodoc]] AutoModelForVisualQuestionAnswering + +### AutoModelForVision2Seq + +[[autodoc]] AutoModelForVision2Seq + +### TFAutoModelForVision2Seq + +[[autodoc]] TFAutoModelForVision2Seq + +### FlaxAutoModelForVision2Seq + +[[autodoc]] FlaxAutoModelForVision2Seq From 476b5a6b0fcf66ce5073a320d5ff6952342133e8 Mon Sep 17 00:00:00 2001 From: boyunJang Date: Tue, 10 Sep 2024 23:10:19 +0900 Subject: [PATCH 2/3] feat: nmt draft --- docs/source/ko/model_doc/auto.md | 202 +++++++++++++++---------------- 1 file changed, 97 insertions(+), 105 deletions(-) diff --git a/docs/source/ko/model_doc/auto.md b/docs/source/ko/model_doc/auto.md index ab42c24d83e82d..9746c02f4218ea 100644 --- a/docs/source/ko/model_doc/auto.md +++ b/docs/source/ko/model_doc/auto.md @@ -14,29 +14,24 @@ rendered properly in your Markdown viewer. --> -# Auto Classes +# 자동 클래스[[auto-classes]] -In many cases, the architecture you want to use can be guessed from the name or the path of the pretrained model you -are supplying to the `from_pretrained()` method. AutoClasses are here to do this job for you so that you -automatically retrieve the relevant model given the name/path to the pretrained weights/config/vocabulary. +많은 경우, 사용하려는 아키텍처는 `from_pretrained()` 메서드에 제공하는 사전 훈련된 모델의 이름이나 경로로부터 유추할 수 있습니다. AutoClasses는 이러한 작업을 대신하여, 사전 훈련된 가중치/구성/어휘에 대한 이름/경로를 제공하면 자동으로 관련 모델을 가져오도록 도와줍니다. -Instantiating one of [`AutoConfig`], [`AutoModel`], and -[`AutoTokenizer`] will directly create a class of the relevant architecture. For instance +[`AutoConfig`], [`AutoModel`], [`AutoTokenizer`] 중 하나를 인스턴스화하면 해당 아키텍처의 클래스를 직접 생성합니다. 예를 들어, ```python model = AutoModel.from_pretrained("google-bert/bert-base-cased") ``` -will create a model that is an instance of [`BertModel`]. +[`BertModel`]의 인스턴스인 모델을 생성합니다. -There is one class of `AutoModel` for each task, and for each backend (PyTorch, TensorFlow, or Flax). +각 작업에 대해 하나의 `AutoModel` 클래스가 있으며, 각각의 백엔드(PyTorch, TensorFlow 또는 Flax)에 해당하는 클래스가 존재합니다. -## Extending the Auto Classes +## Auto 클래스 확장[[extending-the-auto-classes]] -Each of the auto classes has a method to be extended with your custom classes. For instance, if you have defined a -custom class of model `NewModel`, make sure you have a `NewModelConfig` then you can add those to the auto -classes like this: +각 Auto 클래스는 사용자의 커스텀 클래스로 확장될 수 있는 메서드를 가지고 있습니다. 예를 들어, `NewModel`이라는 커스텀 모델 클래스를 정의했다면, `NewModelConfig`를 준비한 후 다음과 같이 Auto 클래스에 추가할 수 있습니다: ```python from transformers import AutoConfig, AutoModel @@ -45,339 +40,336 @@ AutoConfig.register("new-model", NewModelConfig) AutoModel.register(NewModelConfig, NewModel) ``` -You will then be able to use the auto classes like you would usually do! +그 후에는 평소처럼 Auto 클래스를 사용할 수 있게 됩니다! -If your `NewModelConfig` is a subclass of [`~transformers.PretrainedConfig`], make sure its -`model_type` attribute is set to the same key you use when registering the config (here `"new-model"`). +만약 `NewModelConfig`가 [`~transformers.PretrainedConfig`]의 서브클래스라면, 해당 `model_type` 속성이 등록할 때 사용하는 키(여기서는 `"new-model"`)와 동일하게 설정되어 있는지 확인하세요. -Likewise, if your `NewModel` is a subclass of [`PreTrainedModel`], make sure its -`config_class` attribute is set to the same class you use when registering the model (here -`NewModelConfig`). +마찬가지로, `NewModel`이 [`PreTrainedModel`]의 서브클래스라면, 해당 `config_class` 속성이 등록할 때 사용하는 클래스(여기서는 `NewModelConfig`)와 동일하게 설정되어 있는지 확인하세요. -## AutoConfig +## AutoConfig[[transformers.AutoConfig]] [[autodoc]] AutoConfig -## AutoTokenizer +## AutoTokenizer[[transformers.AutoTokenizer]] [[autodoc]] AutoTokenizer -## AutoFeatureExtractor +## AutoFeatureExtractor[[transformers.AutoFeatureExtractor]] [[autodoc]] AutoFeatureExtractor -## AutoImageProcessor +## AutoImageProcessor[[transformers.AutoImageProcessor]] [[autodoc]] AutoImageProcessor -## AutoProcessor +## AutoProcessor[[transformers.AutoProcessor]] [[autodoc]] AutoProcessor -## Generic model classes +## 일반적인 모델 클래스[[generic-model-classes]] -The following auto classes are available for instantiating a base model class without a specific head. +다음 Auto 클래스들은 특정 헤드 없이 기본 모델 클래스를 인스턴스화하는 데 사용할 수 있습니다. -### AutoModel +### AutoModel[[transformers.AutoModel]] [[autodoc]] AutoModel -### TFAutoModel +### TFAutoModel[[transformers.TFAutoModel]] [[autodoc]] TFAutoModel -### FlaxAutoModel +### FlaxAutoModel[[transformers.FlaxAutoModel]] [[autodoc]] FlaxAutoModel -## Generic pretraining classes +## 일반적인 사전 학습 클래스[[generic-pretraining-classes]] -The following auto classes are available for instantiating a model with a pretraining head. +다음 Auto 클래스들은 사전 훈련 헤드가 포함된 모델을 인스턴스화하는 데 사용할 수 있습니다. -### AutoModelForPreTraining +### AutoModelForPreTraining[[transformers.AutoModelForPreTraining]] [[autodoc]] AutoModelForPreTraining -### TFAutoModelForPreTraining +### TFAutoModelForPreTraining[[transformers.TFAutoModelForPreTraining]] [[autodoc]] TFAutoModelForPreTraining -### FlaxAutoModelForPreTraining +### FlaxAutoModelForPreTraining[[transformers.FlaxAutoModelForPreTraining]] [[autodoc]] FlaxAutoModelForPreTraining -## Natural Language Processing +## 자연어 처리[[natural-language-processing]] -The following auto classes are available for the following natural language processing tasks. +다음 Auto 클래스들은 아래의 자연어 처리 작업에 사용할 수 있습니다. -### AutoModelForCausalLM +### AutoModelForCausalLM[[transformers.AutoModelForCausalLM]] [[autodoc]] AutoModelForCausalLM -### TFAutoModelForCausalLM +### TFAutoModelForCausalLM[[transformers.TFAutoModelForCausalLM]] [[autodoc]] TFAutoModelForCausalLM -### FlaxAutoModelForCausalLM +### FlaxAutoModelForCausalLM[[transformers.FlaxAutoModelForCausalLM]] [[autodoc]] FlaxAutoModelForCausalLM -### AutoModelForMaskedLM +### AutoModelForMaskedLM[[transformers.AutoModelForMaskedLM]] [[autodoc]] AutoModelForMaskedLM -### TFAutoModelForMaskedLM +### TFAutoModelForMaskedLM[[transformers.TFAutoModelForMaskedLM]] [[autodoc]] TFAutoModelForMaskedLM -### FlaxAutoModelForMaskedLM +### FlaxAutoModelForMaskedLM[[transformers.FlaxAutoModelForMaskedLM]] [[autodoc]] FlaxAutoModelForMaskedLM -### AutoModelForMaskGeneration +### AutoModelForMaskGeneration[[transformers.AutoModelForMaskGeneration]] [[autodoc]] AutoModelForMaskGeneration -### TFAutoModelForMaskGeneration +### TFAutoModelForMaskGeneration[[transformers.TFAutoModelForMaskGeneration]] [[autodoc]] TFAutoModelForMaskGeneration -### AutoModelForSeq2SeqLM +### AutoModelForSeq2SeqLM[[transformers.AutoModelForSeq2SeqLM]] [[autodoc]] AutoModelForSeq2SeqLM -### TFAutoModelForSeq2SeqLM +### TFAutoModelForSeq2SeqLM[[transformers.TFAutoModelForSeq2SeqLM]] [[autodoc]] TFAutoModelForSeq2SeqLM -### FlaxAutoModelForSeq2SeqLM +### FlaxAutoModelForSeq2SeqLM[[transformers.FlaxAutoModelForSeq2SeqLM]] [[autodoc]] FlaxAutoModelForSeq2SeqLM -### AutoModelForSequenceClassification +### AutoModelForSequenceClassification[[transformers.AutoModelForSequenceClassification]] [[autodoc]] AutoModelForSequenceClassification -### TFAutoModelForSequenceClassification +### TFAutoModelForSequenceClassification[[transformers.TFAutoModelForSequenceClassification]] [[autodoc]] TFAutoModelForSequenceClassification -### FlaxAutoModelForSequenceClassification +### FlaxAutoModelForSequenceClassification[[transformers.FlaxAutoModelForSequenceClassification]] [[autodoc]] FlaxAutoModelForSequenceClassification -### AutoModelForMultipleChoice +### AutoModelForMultipleChoice[[transformers.AutoModelForMultipleChoice]] [[autodoc]] AutoModelForMultipleChoice -### TFAutoModelForMultipleChoice +### TFAutoModelForMultipleChoice[[transformers.TFAutoModelForMultipleChoice]] [[autodoc]] TFAutoModelForMultipleChoice -### FlaxAutoModelForMultipleChoice +### FlaxAutoModelForMultipleChoice[[transformers.FlaxAutoModelForMultipleChoice]] [[autodoc]] FlaxAutoModelForMultipleChoice -### AutoModelForNextSentencePrediction +### AutoModelForNextSentencePrediction[[transformers.AutoModelForNextSentencePrediction]] [[autodoc]] AutoModelForNextSentencePrediction -### TFAutoModelForNextSentencePrediction +### TFAutoModelForNextSentencePrediction[[transformers.TFAutoModelForNextSentencePrediction]] [[autodoc]] TFAutoModelForNextSentencePrediction -### FlaxAutoModelForNextSentencePrediction +### FlaxAutoModelForNextSentencePrediction[[transformers.FlaxAutoModelForNextSentencePrediction]] [[autodoc]] FlaxAutoModelForNextSentencePrediction -### AutoModelForTokenClassification +### AutoModelForTokenClassification[[transformers.AutoModelForTokenClassification]] [[autodoc]] AutoModelForTokenClassification -### TFAutoModelForTokenClassification +### TFAutoModelForTokenClassification[[transformers.TFAutoModelForTokenClassification]] [[autodoc]] TFAutoModelForTokenClassification -### FlaxAutoModelForTokenClassification +### FlaxAutoModelForTokenClassification[[transformers.FlaxAutoModelForTokenClassification]] [[autodoc]] FlaxAutoModelForTokenClassification -### AutoModelForQuestionAnswering +### AutoModelForQuestionAnswering[[transformers.AutoModelForQuestionAnswering]] [[autodoc]] AutoModelForQuestionAnswering -### TFAutoModelForQuestionAnswering +### TFAutoModelForQuestionAnswering[[transformers.TFAutoModelForQuestionAnswering]] [[autodoc]] TFAutoModelForQuestionAnswering -### FlaxAutoModelForQuestionAnswering +### FlaxAutoModelForQuestionAnswering[[transformers.FlaxAutoModelForQuestionAnswering]] [[autodoc]] FlaxAutoModelForQuestionAnswering -### AutoModelForTextEncoding +### AutoModelForTextEncoding[[transformers.AutoModelForTextEncoding]] [[autodoc]] AutoModelForTextEncoding -### TFAutoModelForTextEncoding +### TFAutoModelForTextEncoding[[transformers.TFAutoModelForTextEncoding]] [[autodoc]] TFAutoModelForTextEncoding -## Computer vision +## 컴퓨터 비전[[computer-vision]] -The following auto classes are available for the following computer vision tasks. +다음 Auto 클래스들은 아래의 컴퓨터 비전 작업에 사용할 수 있습니다. -### AutoModelForDepthEstimation +### AutoModelForDepthEstimation[[transformers.AutoModelForDepthEstimation]] [[autodoc]] AutoModelForDepthEstimation -### AutoModelForImageClassification +### AutoModelForImageClassification[[transformers.AutoModelForImageClassification]] [[autodoc]] AutoModelForImageClassification -### TFAutoModelForImageClassification +### TFAutoModelForImageClassification[[transformers.TFAutoModelForImageClassification]] [[autodoc]] TFAutoModelForImageClassification -### FlaxAutoModelForImageClassification +### FlaxAutoModelForImageClassification[[transformers.FlaxAutoModelForImageClassification]] [[autodoc]] FlaxAutoModelForImageClassification -### AutoModelForVideoClassification +### AutoModelForVideoClassification[[transformers.AutoModelForVideoClassification]] [[autodoc]] AutoModelForVideoClassification -### AutoModelForKeypointDetection +### AutoModelForKeypointDetection[[transformers.AutoModelForKeypointDetection]] [[autodoc]] AutoModelForKeypointDetection -### AutoModelForMaskedImageModeling +### AutoModelForMaskedImageModeling[[transformers.AutoModelForMaskedImageModeling]] [[autodoc]] AutoModelForMaskedImageModeling -### TFAutoModelForMaskedImageModeling +### TFAutoModelForMaskedImageModeling[[transformers.TFAutoModelForMaskedImageModeling]] [[autodoc]] TFAutoModelForMaskedImageModeling -### AutoModelForObjectDetection +### AutoModelForObjectDetection[[transformers.AutoModelForObjectDetection]] [[autodoc]] AutoModelForObjectDetection -### AutoModelForImageSegmentation +### AutoModelForImageSegmentation[[transformers.AutoModelForImageSegmentation]] [[autodoc]] AutoModelForImageSegmentation -### AutoModelForImageToImage +### AutoModelForImageToImage[[transformers.AutoModelForImageToImage]] [[autodoc]] AutoModelForImageToImage -### AutoModelForSemanticSegmentation +### AutoModelForSemanticSegmentation[[transformers.AutoModelForSemanticSegmentation]] [[autodoc]] AutoModelForSemanticSegmentation -### TFAutoModelForSemanticSegmentation +### TFAutoModelForSemanticSegmentation[[transformers.TFAutoModelForSemanticSegmentation]] [[autodoc]] TFAutoModelForSemanticSegmentation -### AutoModelForInstanceSegmentation +### AutoModelForInstanceSegmentation[[transformers.AutoModelForInstanceSegmentation]] [[autodoc]] AutoModelForInstanceSegmentation -### AutoModelForUniversalSegmentation +### AutoModelForUniversalSegmentation[[transformers.AutoModelForUniversalSegmentation]] [[autodoc]] AutoModelForUniversalSegmentation -### AutoModelForZeroShotImageClassification +### AutoModelForZeroShotImageClassification[[transformers.AutoModelForZeroShotImageClassification]] [[autodoc]] AutoModelForZeroShotImageClassification -### TFAutoModelForZeroShotImageClassification +### TFAutoModelForZeroShotImageClassification[[transformers.TFAutoModelForZeroShotImageClassification]] [[autodoc]] TFAutoModelForZeroShotImageClassification -### AutoModelForZeroShotObjectDetection +### AutoModelForZeroShotObjectDetection[[transformers.AutoModelForZeroShotObjectDetection]] [[autodoc]] AutoModelForZeroShotObjectDetection -## Audio +## 오디오[[audio]] -The following auto classes are available for the following audio tasks. +다음 Auto 클래스들은 아래의 오디오 작업에 사용할 수 있습니다. -### AutoModelForAudioClassification +### AutoModelForAudioClassification[[transformers.AutoModelForAudioClassification]] [[autodoc]] AutoModelForAudioClassification -### AutoModelForAudioFrameClassification +### TFAutoModelForAudioClassification[[transformers.TFAutoModelForAudioClassification]] [[autodoc]] TFAutoModelForAudioClassification -### TFAutoModelForAudioFrameClassification +### AutoModelForAudioFrameClassification[[transformers.AutoModelForAudioFrameClassification]] [[autodoc]] AutoModelForAudioFrameClassification -### AutoModelForCTC +### AutoModelForCTC[[transformers.AutoModelForCTC]] [[autodoc]] AutoModelForCTC -### AutoModelForSpeechSeq2Seq +### AutoModelForSpeechSeq2Seq[[transformers.AutoModelForSpeechSeq2Seq]] [[autodoc]] AutoModelForSpeechSeq2Seq -### TFAutoModelForSpeechSeq2Seq +### TFAutoModelForSpeechSeq2Seq[[transformers.TFAutoModelForSpeechSeq2Seq]] [[autodoc]] TFAutoModelForSpeechSeq2Seq -### FlaxAutoModelForSpeechSeq2Seq +### FlaxAutoModelForSpeechSeq2Seq[[transformers.FlaxAutoModelForSpeechSeq2Seq]] [[autodoc]] FlaxAutoModelForSpeechSeq2Seq -### AutoModelForAudioXVector +### AutoModelForAudioXVector[[transformers.AutoModelForAudioXVector]] [[autodoc]] AutoModelForAudioXVector -### AutoModelForTextToSpectrogram +### AutoModelForTextToSpectrogram[[transformers.AutoModelForTextToSpectrogram]] [[autodoc]] AutoModelForTextToSpectrogram -### AutoModelForTextToWaveform +### AutoModelForTextToWaveform[[transformers.AutoModelForTextToWaveform]] [[autodoc]] AutoModelForTextToWaveform -## Multimodal +## 멀티모달[[multimodal]] -The following auto classes are available for the following multimodal tasks. +다음 Auto 클래스들은 아래의 멀티모달 작업에 사용할 수 있습니다. -### AutoModelForTableQuestionAnswering +### AutoModelForTableQuestionAnswering[[transformers.AutoModelForTableQuestionAnswering]] [[autodoc]] AutoModelForTableQuestionAnswering -### TFAutoModelForTableQuestionAnswering +### TFAutoModelForTableQuestionAnswering[[transformers.TFAutoModelForTableQuestionAnswering]] [[autodoc]] TFAutoModelForTableQuestionAnswering -### AutoModelForDocumentQuestionAnswering +### AutoModelForDocumentQuestionAnswering[[transformers.AutoModelForDocumentQuestionAnswering]] [[autodoc]] AutoModelForDocumentQuestionAnswering -### TFAutoModelForDocumentQuestionAnswering +### TFAutoModelForDocumentQuestionAnswering[[transformers.TFAutoModelForDocumentQuestionAnswering]] [[autodoc]] TFAutoModelForDocumentQuestionAnswering -### AutoModelForVisualQuestionAnswering +### AutoModelForVisualQuestionAnswering[[transformers.AutoModelForVisualQuestionAnswering]] [[autodoc]] AutoModelForVisualQuestionAnswering -### AutoModelForVision2Seq +### AutoModelForVision2Seq[[transformers.AutoModelForVision2Seq]] [[autodoc]] AutoModelForVision2Seq -### TFAutoModelForVision2Seq +### TFAutoModelForVision2Seq[[transformers.TFAutoModelForVision2Seq]] [[autodoc]] TFAutoModelForVision2Seq -### FlaxAutoModelForVision2Seq +### FlaxAutoModelForVision2Seq[[transformers.FlaxAutoModelForVision2Seq]] [[autodoc]] FlaxAutoModelForVision2Seq From 81478c2b118c2694203a0d497d90744cdb583171 Mon Sep 17 00:00:00 2001 From: boyunJang Date: Tue, 10 Sep 2024 23:18:25 +0900 Subject: [PATCH 3/3] fix: manual edits --- docs/source/ko/model_doc/auto.md | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/docs/source/ko/model_doc/auto.md b/docs/source/ko/model_doc/auto.md index 9746c02f4218ea..3d07b62748d307 100644 --- a/docs/source/ko/model_doc/auto.md +++ b/docs/source/ko/model_doc/auto.md @@ -16,7 +16,7 @@ rendered properly in your Markdown viewer. # 자동 클래스[[auto-classes]] -많은 경우, 사용하려는 아키텍처는 `from_pretrained()` 메서드에 제공하는 사전 훈련된 모델의 이름이나 경로로부터 유추할 수 있습니다. AutoClasses는 이러한 작업을 대신하여, 사전 훈련된 가중치/구성/어휘에 대한 이름/경로를 제공하면 자동으로 관련 모델을 가져오도록 도와줍니다. +많은 경우, 사용하려는 아키텍처는 `from_pretrained()` 메소드에서 제공하는 사전 학습된 모델의 이름이나 경로로부터 유추할 수 있습니다. AutoClasses는 이 작업을 위해 존재하며, 사전 학습된 weight/config/vocabulary에 대한 이름/경로를 제공하면 자동으로 관련 모델을 가져오도록 도와줍니다. [`AutoConfig`], [`AutoModel`], [`AutoTokenizer`] 중 하나를 인스턴스화하면 해당 아키텍처의 클래스를 직접 생성합니다. 예를 들어, @@ -25,13 +25,13 @@ rendered properly in your Markdown viewer. model = AutoModel.from_pretrained("google-bert/bert-base-cased") ``` -[`BertModel`]의 인스턴스인 모델을 생성합니다. +위 코드는 [`BertModel`]의 인스턴스인 모델을 생성합니다. 각 작업에 대해 하나의 `AutoModel` 클래스가 있으며, 각각의 백엔드(PyTorch, TensorFlow 또는 Flax)에 해당하는 클래스가 존재합니다. -## Auto 클래스 확장[[extending-the-auto-classes]] +## 자동 클래스 확장[[extending-the-auto-classes]] -각 Auto 클래스는 사용자의 커스텀 클래스로 확장될 수 있는 메서드를 가지고 있습니다. 예를 들어, `NewModel`이라는 커스텀 모델 클래스를 정의했다면, `NewModelConfig`를 준비한 후 다음과 같이 Auto 클래스에 추가할 수 있습니다: +각 자동 클래스는 사용자의 커스텀 클래스로 확장될 수 있는 메소드를 가지고 있습니다. 예를 들어, `NewModel`이라는 커스텀 모델 클래스를 정의했다면, `NewModelConfig`를 준비한 후 다음과 같이 자동 클래스에 추가할 수 있습니다: ```python from transformers import AutoConfig, AutoModel @@ -40,7 +40,7 @@ AutoConfig.register("new-model", NewModelConfig) AutoModel.register(NewModelConfig, NewModel) ``` -그 후에는 평소처럼 Auto 클래스를 사용할 수 있게 됩니다! +그 후에는 평소처럼 자동 클래스를 사용할 수 있게 됩니다! @@ -72,7 +72,7 @@ AutoModel.register(NewModelConfig, NewModel) ## 일반적인 모델 클래스[[generic-model-classes]] -다음 Auto 클래스들은 특정 헤드 없이 기본 모델 클래스를 인스턴스화하는 데 사용할 수 있습니다. +다음 자동 클래스들은 특정 헤드 없이 기본 모델 클래스를 인스턴스화하는 데 사용할 수 있습니다. ### AutoModel[[transformers.AutoModel]] @@ -88,7 +88,7 @@ AutoModel.register(NewModelConfig, NewModel) ## 일반적인 사전 학습 클래스[[generic-pretraining-classes]] -다음 Auto 클래스들은 사전 훈련 헤드가 포함된 모델을 인스턴스화하는 데 사용할 수 있습니다. +다음 자동 클래스들은 사전 학습 헤드가 포함된 모델을 인스턴스화하는 데 사용할 수 있습니다. ### AutoModelForPreTraining[[transformers.AutoModelForPreTraining]] @@ -104,7 +104,7 @@ AutoModel.register(NewModelConfig, NewModel) ## 자연어 처리[[natural-language-processing]] -다음 Auto 클래스들은 아래의 자연어 처리 작업에 사용할 수 있습니다. +다음 자동 클래스들은 아래의 자연어 처리 작업에 사용할 수 있습니다. ### AutoModelForCausalLM[[transformers.AutoModelForCausalLM]] @@ -220,7 +220,7 @@ AutoModel.register(NewModelConfig, NewModel) ## 컴퓨터 비전[[computer-vision]] -다음 Auto 클래스들은 아래의 컴퓨터 비전 작업에 사용할 수 있습니다. +다음 자동 클래스들은 아래의 컴퓨터 비전 작업에 사용할 수 있습니다. ### AutoModelForDepthEstimation[[transformers.AutoModelForDepthEstimation]] @@ -296,7 +296,7 @@ AutoModel.register(NewModelConfig, NewModel) ## 오디오[[audio]] -다음 Auto 클래스들은 아래의 오디오 작업에 사용할 수 있습니다. +다음 자동 클래스들은 아래의 오디오 작업에 사용할 수 있습니다. ### AutoModelForAudioClassification[[transformers.AutoModelForAudioClassification]] @@ -340,7 +340,7 @@ AutoModel.register(NewModelConfig, NewModel) ## 멀티모달[[multimodal]] -다음 Auto 클래스들은 아래의 멀티모달 작업에 사용할 수 있습니다. +다음 자동 클래스들은 아래의 멀티모달 작업에 사용할 수 있습니다. ### AutoModelForTableQuestionAnswering[[transformers.AutoModelForTableQuestionAnswering]]