#
추론 타입

이 페이지에는 Hugging Face Hub에서 지원하는 타입(예: 데이터 클래스)이 나열되어 있습니다. 각 작업은 JSON 스키마를 사용하여 지정되며, 이러한 스키마에 의해서 타입이 생성됩니다. 이때 Python 요구 사항으로 인해 일부 사용자 정의가 있을 수 있습니다.

각 작업의 JSON 스키마를 확인하려면 @huggingface.js/tasks를 확인하세요.

라이브러리에서 이 부분은 아직 개발 중이며, 향후 릴리즈에서 개선될 예정입니다.

##
audio_classification

###
class huggingface_hub.AudioClassificationInput

< source >

( inputs: str parameters: typing.Optional[huggingface_hub.inference._generated.types.audio_classification.AudioClassificationParameters] = None )

Inputs for Audio Classification inference

###
class huggingface_hub.AudioClassificationOutputElement

< source >

( label: str score: float )

Outputs for Audio Classification inference

###
class huggingface_hub.AudioClassificationParameters

< source >

( function_to_apply: typing.Optional[ForwardRef('AudioClassificationOutputTransform')] = None top_k: typing.Optional[int] = None )

Additional inference parameters for Audio Classification

##
audio_to_audio

###
class huggingface_hub.AudioToAudioInput

< source >

( inputs: typing.Any )

Inputs for Audio to Audio inference

###
class huggingface_hub.AudioToAudioOutputElement

< source >

( blob: typing.Any content_type: str label: str )

Outputs of inference for the Audio To Audio task A generated audio file with its label.

##
automatic_speech_recognition

###
class huggingface_hub.AutomaticSpeechRecognitionGenerationParameters

< source >

( do_sample: typing.Optional[bool] = None early_stopping: typing.Union[bool, ForwardRef('AutomaticSpeechRecognitionEarlyStoppingEnum'), NoneType] = None epsilon_cutoff: typing.Optional[float] = None eta_cutoff: typing.Optional[float] = None max_length: typing.Optional[int] = None max_new_tokens: typing.Optional[int] = None min_length: typing.Optional[int] = None min_new_tokens: typing.Optional[int] = None num_beam_groups: typing.Optional[int] = None num_beams: typing.Optional[int] = None penalty_alpha: typing.Optional[float] = None temperature: typing.Optional[float] = None top_k: typing.Optional[int] = None top_p: typing.Optional[float] = None typical_p: typing.Optional[float] = None use_cache: typing.Optional[bool] = None )

Parametrization of the text generation process

###
class huggingface_hub.AutomaticSpeechRecognitionInput

< source >

( inputs: str parameters: typing.Optional[huggingface_hub.inference._generated.types.automatic_speech_recognition.AutomaticSpeechRecognitionParameters] = None )

Inputs for Automatic Speech Recognition inference

###
class huggingface_hub.AutomaticSpeechRecognitionOutput

< source >

( text: str chunks: typing.Optional[list[huggingface_hub.inference._generated.types.automatic_speech_recognition.AutomaticSpeechRecognitionOutputChunk]] = None )

Outputs of inference for the Automatic Speech Recognition task

###
class huggingface_hub.AutomaticSpeechRecognitionOutputChunk

< source >

( text: str timestamp: list )

###
class huggingface_hub.AutomaticSpeechRecognitionParameters

< source >

( generation_parameters: typing.Optional[huggingface_hub.inference._generated.types.automatic_speech_recognition.AutomaticSpeechRecognitionGenerationParameters] = None return_timestamps: typing.Optional[bool] = None )

Additional inference parameters for Automatic Speech Recognition

##
chat_completion

###
class huggingface_hub.ChatCompletionInput

< source >

( messages: list frequency_penalty: typing.Optional[float] = None logit_bias: typing.Optional[list[float]] = None logprobs: typing.Optional[bool] = None max_tokens: typing.Optional[int] = None model: typing.Optional[str] = None n: typing.Optional[int] = None presence_penalty: typing.Optional[float] = None response_format: typing.Union[huggingface_hub.inference._generated.types.chat_completion.ChatCompletionInputResponseFormatText, huggingface_hub.inference._generated.types.chat_completion.ChatCompletionInputResponseFormatJSONSchema, huggingface_hub.inference._generated.types.chat_completion.ChatCompletionInputResponseFormatJSONObject, NoneType] = None seed: typing.Optional[int] = None stop: typing.Optional[list[str]] = None stream: typing.Optional[bool] = None stream_options: typing.Optional[huggingface_hub.inference._generated.types.chat_completion.ChatCompletionInputStreamOptions] = None temperature: typing.Optional[float] = None tool_choice: typing.Union[huggingface_hub.inference._generated.types.chat_completion.ChatCompletionInputToolChoiceClass, ForwardRef('ChatCompletionInputToolChoiceEnum'), NoneType] = None tool_prompt: typing.Optional[str] = None tools: typing.Optional[list[huggingface_hub.inference._generated.types.chat_completion.ChatCompletionInputTool]] = None top_logprobs: typing.Optional[int] = None top_p: typing.Optional[float] = None )

Chat Completion Input. Auto-generated from TGI specs. For more details, check out https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-tgi-import.ts.

###
class huggingface_hub.ChatCompletionInputFunctionDefinition

< source >

( name: str parameters: typing.Any description: typing.Optional[str] = None )

###
class huggingface_hub.ChatCompletionInputFunctionName

< source >

( name: str )

###
class huggingface_hub.ChatCompletionInputJSONSchema

< source >

( name: str description: typing.Optional[str] = None schema: typing.Optional[dict[str, object]] = None strict: typing.Optional[bool] = None )

###
class huggingface_hub.ChatCompletionInputMessage

< source >

( role: str content: typing.Union[list[huggingface_hub.inference._generated.types.chat_completion.ChatCompletionInputMessageChunk], str, NoneType] = None name: typing.Optional[str] = None tool_calls: typing.Optional[list[huggingface_hub.inference._generated.types.chat_completion.ChatCompletionInputToolCall]] = None )

###
class huggingface_hub.ChatCompletionInputMessageChunk

< source >

( type: ChatCompletionInputMessageChunkType image_url: typing.Optional[huggingface_hub.inference._generated.types.chat_completion.ChatCompletionInputURL] = None text: typing.Optional[str] = None )

###
class huggingface_hub.ChatCompletionInputResponseFormatJSONObject

< source >

( type: typing.Literal['json_object'] )

###
class huggingface_hub.ChatCompletionInputResponseFormatJSONSchema

< source >

( type: typing.Literal['json_schema'] json_schema: ChatCompletionInputJSONSchema )

###
class huggingface_hub.ChatCompletionInputResponseFormatText

< source >

( type: typing.Literal['text'] )

###
class huggingface_hub.ChatCompletionInputStreamOptions

< source >

( include_usage: typing.Optional[bool] = None )

###
class huggingface_hub.ChatCompletionInputTool

< source >

( function: ChatCompletionInputFunctionDefinition type: str )

###
class huggingface_hub.ChatCompletionInputToolCall

< source >

( function: ChatCompletionInputFunctionDefinition id: str type: str )

###
class huggingface_hub.ChatCompletionInputToolChoiceClass

< source >

( function: ChatCompletionInputFunctionName )

###
class huggingface_hub.ChatCompletionInputURL

< source >

( url: str )

###
class huggingface_hub.ChatCompletionOutput

< source >

( choices: list created: int id: str model: str system_fingerprint: str usage: ChatCompletionOutputUsage )

Chat Completion Output. Auto-generated from TGI specs. For more details, check out https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-tgi-import.ts.

###
class huggingface_hub.ChatCompletionOutputComplete

< source >

( finish_reason: str index: int message: ChatCompletionOutputMessage logprobs: typing.Optional[huggingface_hub.inference._generated.types.chat_completion.ChatCompletionOutputLogprobs] = None )

###
class huggingface_hub.ChatCompletionOutputFunctionDefinition

< source >

( arguments: str name: str description: typing.Optional[str] = None )

###
class huggingface_hub.ChatCompletionOutputLogprob

< source >

( logprob: float token: str top_logprobs: list )

###
class huggingface_hub.ChatCompletionOutputLogprobs

< source >

( content: list )

###
class huggingface_hub.ChatCompletionOutputMessage

< source >

( role: str content: typing.Optional[str] = None reasoning: typing.Optional[str] = None tool_call_id: typing.Optional[str] = None tool_calls: typing.Optional[list[huggingface_hub.inference._generated.types.chat_completion.ChatCompletionOutputToolCall]] = None )

###
class huggingface_hub.ChatCompletionOutputToolCall

< source >

( function: ChatCompletionOutputFunctionDefinition id: str type: str )

###
class huggingface_hub.ChatCompletionOutputTopLogprob

< source >

( logprob: float token: str )

###
class huggingface_hub.ChatCompletionOutputUsage

< source >

( completion_tokens: int prompt_tokens: int total_tokens: int )

###
class huggingface_hub.ChatCompletionStreamOutput

< source >

( choices: list created: int id: str model: str system_fingerprint: str usage: typing.Optional[huggingface_hub.inference._generated.types.chat_completion.ChatCompletionStreamOutputUsage] = None )

Chat Completion Stream Output. Auto-generated from TGI specs. For more details, check out https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-tgi-import.ts.

###
class huggingface_hub.ChatCompletionStreamOutputChoice

< source >

( delta: ChatCompletionStreamOutputDelta index: int finish_reason: typing.Optional[str] = None logprobs: typing.Optional[huggingface_hub.inference._generated.types.chat_completion.ChatCompletionStreamOutputLogprobs] = None )

###
class huggingface_hub.ChatCompletionStreamOutputDelta

< source >

( role: str content: typing.Optional[str] = None reasoning: typing.Optional[str] = None tool_call_id: typing.Optional[str] = None tool_calls: typing.Optional[list[huggingface_hub.inference._generated.types.chat_completion.ChatCompletionStreamOutputDeltaToolCall]] = None )

###
class huggingface_hub.ChatCompletionStreamOutputDeltaToolCall

< source >

( function: ChatCompletionStreamOutputFunction id: str index: int type: str )

###
class huggingface_hub.ChatCompletionStreamOutputFunction

< source >

( arguments: str name: typing.Optional[str] = None )

###
class huggingface_hub.ChatCompletionStreamOutputLogprob

< source >

( logprob: float token: str top_logprobs: list )

###
class huggingface_hub.ChatCompletionStreamOutputLogprobs

< source >

( content: list )

###
class huggingface_hub.ChatCompletionStreamOutputTopLogprob

< source >

( logprob: float token: str )

###
class huggingface_hub.ChatCompletionStreamOutputUsage

< source >

( completion_tokens: int prompt_tokens: int total_tokens: int )

##
depth_estimation

###
class huggingface_hub.DepthEstimationInput

< source >

( inputs: typing.Any parameters: typing.Optional[dict[str, typing.Any]] = None )

Inputs for Depth Estimation inference

###
class huggingface_hub.DepthEstimationOutput

< source >

( depth: typing.Any predicted_depth: typing.Any )

Outputs of inference for the Depth Estimation task

##
document_question_answering

###
class huggingface_hub.DocumentQuestionAnsweringInput

< source >

( inputs: DocumentQuestionAnsweringInputData parameters: typing.Optional[huggingface_hub.inference._generated.types.document_question_answering.DocumentQuestionAnsweringParameters] = None )

Inputs for Document Question Answering inference

###
class huggingface_hub.DocumentQuestionAnsweringInputData

< source >

( image: typing.Any question: str )

One (document, question) pair to answer

###
class huggingface_hub.DocumentQuestionAnsweringOutputElement

< source >

( answer: str end: int score: float start: int )

Outputs of inference for the Document Question Answering task

###
class huggingface_hub.DocumentQuestionAnsweringParameters

< source >

( doc_stride: typing.Optional[int] = None handle_impossible_answer: typing.Optional[bool] = None lang: typing.Optional[str] = None max_answer_len: typing.Optional[int] = None max_question_len: typing.Optional[int] = None max_seq_len: typing.Optional[int] = None top_k: typing.Optional[int] = None word_boxes: typing.Optional[list[typing.Union[list[float], str]]] = None )

Additional inference parameters for Document Question Answering

##
feature_extraction

###
class huggingface_hub.FeatureExtractionInput

< source >

( inputs: typing.Union[list[str], str] normalize: typing.Optional[bool] = None prompt_name: typing.Optional[str] = None truncate: typing.Optional[bool] = None truncation_direction: typing.Optional[ForwardRef('FeatureExtractionInputTruncationDirection')] = None )

Feature Extraction Input. Auto-generated from TEI specs. For more details, check out https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-tei-import.ts.

##
fill_mask

###
class huggingface_hub.FillMaskInput

< source >

( inputs: str parameters: typing.Optional[huggingface_hub.inference._generated.types.fill_mask.FillMaskParameters] = None )

Inputs for Fill Mask inference

###
class huggingface_hub.FillMaskOutputElement

< source >

( score: float sequence: str token: int token_str: typing.Any fill_mask_output_token_str: typing.Optional[str] = None )

Outputs of inference for the Fill Mask task

###
class huggingface_hub.FillMaskParameters

< source >

( targets: typing.Optional[list[str]] = None top_k: typing.Optional[int] = None )

Additional inference parameters for Fill Mask

##
image_classification

###
class huggingface_hub.ImageClassificationInput

< source >

( inputs: str parameters: typing.Optional[huggingface_hub.inference._generated.types.image_classification.ImageClassificationParameters] = None )

Inputs for Image Classification inference

###
class huggingface_hub.ImageClassificationOutputElement

< source >

( label: str score: float )

Outputs of inference for the Image Classification task

###
class huggingface_hub.ImageClassificationParameters

< source >

( function_to_apply: typing.Optional[ForwardRef('ImageClassificationOutputTransform')] = None top_k: typing.Optional[int] = None )

Additional inference parameters for Image Classification

##
image_segmentation

###
class huggingface_hub.ImageSegmentationInput

< source >

( inputs: str parameters: typing.Optional[huggingface_hub.inference._generated.types.image_segmentation.ImageSegmentationParameters] = None )

Inputs for Image Segmentation inference

###
class huggingface_hub.ImageSegmentationOutputElement

< source >

( label: str mask: str score: typing.Optional[float] = None )

Outputs of inference for the Image Segmentation task A predicted mask / segment

###
class huggingface_hub.ImageSegmentationParameters

< source >

( mask_threshold: typing.Optional[float] = None overlap_mask_area_threshold: typing.Optional[float] = None subtask: typing.Optional[ForwardRef('ImageSegmentationSubtask')] = None threshold: typing.Optional[float] = None )

Additional inference parameters for Image Segmentation

##
image_to_image

###
class huggingface_hub.ImageToImageInput

< source >

( inputs: str parameters: typing.Optional[huggingface_hub.inference._generated.types.image_to_image.ImageToImageParameters] = None )

Inputs for Image To Image inference

###
class huggingface_hub.ImageToImageOutput

< source >

( image: typing.Any )

Outputs of inference for the Image To Image task

###
class huggingface_hub.ImageToImageParameters

< source >

( guidance_scale: typing.Optional[float] = None negative_prompt: typing.Optional[str] = None num_inference_steps: typing.Optional[int] = None prompt: typing.Optional[str] = None target_size: typing.Optional[huggingface_hub.inference._generated.types.image_to_image.ImageToImageTargetSize] = None )

Additional inference parameters for Image To Image

###
class huggingface_hub.ImageToImageTargetSize

< source >

( height: int width: int )

The size in pixels of the output image. This parameter is only supported by some providers and for specific models. It will be ignored when unsupported.

##
image_to_text

###
class huggingface_hub.ImageToTextGenerationParameters

< source >

( do_sample: typing.Optional[bool] = None early_stopping: typing.Union[bool, ForwardRef('ImageToTextEarlyStoppingEnum'), NoneType] = None epsilon_cutoff: typing.Optional[float] = None eta_cutoff: typing.Optional[float] = None max_length: typing.Optional[int] = None max_new_tokens: typing.Optional[int] = None min_length: typing.Optional[int] = None min_new_tokens: typing.Optional[int] = None num_beam_groups: typing.Optional[int] = None num_beams: typing.Optional[int] = None penalty_alpha: typing.Optional[float] = None temperature: typing.Optional[float] = None top_k: typing.Optional[int] = None top_p: typing.Optional[float] = None typical_p: typing.Optional[float] = None use_cache: typing.Optional[bool] = None )

Parametrization of the text generation process

###
class huggingface_hub.ImageToTextInput

< source >

( inputs: typing.Any parameters: typing.Optional[huggingface_hub.inference._generated.types.image_to_text.ImageToTextParameters] = None )

Inputs for Image To Text inference

###
class huggingface_hub.ImageToTextOutput

< source >

( generated_text: typing.Any image_to_text_output_generated_text: typing.Optional[str] = None )

Outputs of inference for the Image To Text task

###
class huggingface_hub.ImageToTextParameters

< source >

( generation_parameters: typing.Optional[huggingface_hub.inference._generated.types.image_to_text.ImageToTextGenerationParameters] = None max_new_tokens: typing.Optional[int] = None )

Additional inference parameters for Image To Text

##
image_to_video

###
class huggingface_hub.ImageToVideoInput

< source >

( inputs: str parameters: typing.Optional[huggingface_hub.inference._generated.types.image_to_video.ImageToVideoParameters] = None )

Inputs for Image To Video inference

###
class huggingface_hub.ImageToVideoOutput

< source >

( video: typing.Any )

Outputs of inference for the Image To Video task

###
class huggingface_hub.ImageToVideoParameters

< source >

( guidance_scale: typing.Optional[float] = None negative_prompt: typing.Optional[str] = None num_frames: typing.Optional[float] = None num_inference_steps: typing.Optional[int] = None prompt: typing.Optional[str] = None seed: typing.Optional[int] = None target_size: typing.Optional[huggingface_hub.inference._generated.types.image_to_video.ImageToVideoTargetSize] = None )

Additional inference parameters for Image To Video

###
class huggingface_hub.ImageToVideoTargetSize

< source >

( height: int width: int )

The size in pixel of the output video frames.

##
object_detection

###
class huggingface_hub.ObjectDetectionBoundingBox

< source >

( xmax: int xmin: int ymax: int ymin: int )

The predicted bounding box. Coordinates are relative to the top left corner of the input image.

###
class huggingface_hub.ObjectDetectionInput

< source >

( inputs: str parameters: typing.Optional[huggingface_hub.inference._generated.types.object_detection.ObjectDetectionParameters] = None )

Inputs for Object Detection inference

###
class huggingface_hub.ObjectDetectionOutputElement

< source >

( box: ObjectDetectionBoundingBox label: str score: float )

Outputs of inference for the Object Detection task

###
class huggingface_hub.ObjectDetectionParameters

< source >

( threshold: typing.Optional[float] = None )

Additional inference parameters for Object Detection

##
question_answering

###
class huggingface_hub.QuestionAnsweringInput

< source >

( inputs: QuestionAnsweringInputData parameters: typing.Optional[huggingface_hub.inference._generated.types.question_answering.QuestionAnsweringParameters] = None )

Inputs for Question Answering inference

###
class huggingface_hub.QuestionAnsweringInputData

< source >

( context: str question: str )

One (context, question) pair to answer

###
class huggingface_hub.QuestionAnsweringOutputElement

< source >

( answer: str end: int score: float start: int )

Outputs of inference for the Question Answering task

###
class huggingface_hub.QuestionAnsweringParameters

< source >

( align_to_words: typing.Optional[bool] = None doc_stride: typing.Optional[int] = None handle_impossible_answer: typing.Optional[bool] = None max_answer_len: typing.Optional[int] = None max_question_len: typing.Optional[int] = None max_seq_len: typing.Optional[int] = None top_k: typing.Optional[int] = None )

Additional inference parameters for Question Answering

##
sentence_similarity

###
class huggingface_hub.SentenceSimilarityInput

< source >

( inputs: SentenceSimilarityInputData parameters: typing.Optional[dict[str, typing.Any]] = None )

Inputs for Sentence similarity inference

###
class huggingface_hub.SentenceSimilarityInputData

< source >

( sentences: list source_sentence: str )

##
summarization

###
class huggingface_hub.SummarizationInput

< source >

( inputs: str parameters: typing.Optional[huggingface_hub.inference._generated.types.summarization.SummarizationParameters] = None )

Inputs for Summarization inference

###
class huggingface_hub.SummarizationOutput

< source >

( summary_text: str )

Outputs of inference for the Summarization task

###
class huggingface_hub.SummarizationParameters

< source >

( clean_up_tokenization_spaces: typing.Optional[bool] = None generate_parameters: typing.Optional[dict[str, typing.Any]] = None truncation: typing.Optional[ForwardRef('SummarizationTruncationStrategy')] = None )

Additional inference parameters for summarization.

##
table_question_answering

###
class huggingface_hub.TableQuestionAnsweringInput

< source >

( inputs: TableQuestionAnsweringInputData parameters: typing.Optional[huggingface_hub.inference._generated.types.table_question_answering.TableQuestionAnsweringParameters] = None )

Inputs for Table Question Answering inference

###
class huggingface_hub.TableQuestionAnsweringInputData

< source >

( question: str table: dict )

One (table, question) pair to answer

###
class huggingface_hub.TableQuestionAnsweringOutputElement

< source >

( answer: str cells: list coordinates: list aggregator: typing.Optional[str] = None )

Outputs of inference for the Table Question Answering task

###
class huggingface_hub.TableQuestionAnsweringParameters

< source >

( padding: typing.Optional[ForwardRef('Padding')] = None sequential: typing.Optional[bool] = None truncation: typing.Optional[bool] = None )

Additional inference parameters for Table Question Answering

##
text2text_generation

###
class huggingface_hub.Text2TextGenerationInput

< source >

( inputs: str parameters: typing.Optional[huggingface_hub.inference._generated.types.text2text_generation.Text2TextGenerationParameters] = None )

Inputs for Text2text Generation inference

###
class huggingface_hub.Text2TextGenerationOutput

< source >

( generated_text: typing.Any text2_text_generation_output_generated_text: typing.Optional[str] = None )

Outputs of inference for the Text2text Generation task

###
class huggingface_hub.Text2TextGenerationParameters

< source >

( clean_up_tokenization_spaces: typing.Optional[bool] = None generate_parameters: typing.Optional[dict[str, typing.Any]] = None truncation: typing.Optional[ForwardRef('Text2TextGenerationTruncationStrategy')] = None )

Additional inference parameters for Text2text Generation

##
text_classification

###
class huggingface_hub.TextClassificationInput

< source >

( inputs: str parameters: typing.Optional[huggingface_hub.inference._generated.types.text_classification.TextClassificationParameters] = None )

Inputs for Text Classification inference

###
class huggingface_hub.TextClassificationOutputElement

< source >

( label: str score: float )

Outputs of inference for the Text Classification task

###
class huggingface_hub.TextClassificationParameters

< source >

( function_to_apply: typing.Optional[ForwardRef('TextClassificationOutputTransform')] = None top_k: typing.Optional[int] = None )

Additional inference parameters for Text Classification

##
text_generation

###
class huggingface_hub.TextGenerationInput

< source >

( inputs: str parameters: typing.Optional[huggingface_hub.inference._generated.types.text_generation.TextGenerationInputGenerateParameters] = None stream: typing.Optional[bool] = None )

Text Generation Input. Auto-generated from TGI specs. For more details, check out https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-tgi-import.ts.

###
class huggingface_hub.TextGenerationInputGenerateParameters

< source >

( adapter_id: typing.Optional[str] = None best_of: typing.Optional[int] = None decoder_input_details: typing.Optional[bool] = None details: typing.Optional[bool] = None do_sample: typing.Optional[bool] = None frequency_penalty: typing.Optional[float] = None grammar: typing.Optional[huggingface_hub.inference._generated.types.text_generation.TextGenerationInputGrammarType] = None max_new_tokens: typing.Optional[int] = None repetition_penalty: typing.Optional[float] = None return_full_text: typing.Optional[bool] = None seed: typing.Optional[int] = None stop: typing.Optional[list[str]] = None temperature: typing.Optional[float] = None top_k: typing.Optional[int] = None top_n_tokens: typing.Optional[int] = None top_p: typing.Optional[float] = None truncate: typing.Optional[int] = None typical_p: typing.Optional[float] = None watermark: typing.Optional[bool] = None )

###
class huggingface_hub.TextGenerationInputGrammarType

< source >

( type: TypeEnum value: typing.Any )

###
class huggingface_hub.TextGenerationOutput

< source >

( generated_text: str details: typing.Optional[huggingface_hub.inference._generated.types.text_generation.TextGenerationOutputDetails] = None )

Text Generation Output. Auto-generated from TGI specs. For more details, check out https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-tgi-import.ts.

###
class huggingface_hub.TextGenerationOutputBestOfSequence

< source >

( finish_reason: TextGenerationOutputFinishReason generated_text: str generated_tokens: int prefill: list tokens: list seed: typing.Optional[int] = None top_tokens: typing.Optional[list[list[huggingface_hub.inference._generated.types.text_generation.TextGenerationOutputToken]]] = None )

###
class huggingface_hub.TextGenerationOutputDetails

< source >

( finish_reason: TextGenerationOutputFinishReason generated_tokens: int prefill: list tokens: list best_of_sequences: typing.Optional[list[huggingface_hub.inference._generated.types.text_generation.TextGenerationOutputBestOfSequence]] = None seed: typing.Optional[int] = None top_tokens: typing.Optional[list[list[huggingface_hub.inference._generated.types.text_generation.TextGenerationOutputToken]]] = None )

###
class huggingface_hub.TextGenerationOutputPrefillToken

< source >

( id: int logprob: float text: str )

###
class huggingface_hub.TextGenerationOutputToken

< source >

( id: int logprob: float special: bool text: str )

###
class huggingface_hub.TextGenerationStreamOutput

< source >

( index: int token: TextGenerationStreamOutputToken details: typing.Optional[huggingface_hub.inference._generated.types.text_generation.TextGenerationStreamOutputStreamDetails] = None generated_text: typing.Optional[str] = None top_tokens: typing.Optional[list[huggingface_hub.inference._generated.types.text_generation.TextGenerationStreamOutputToken]] = None )

Text Generation Stream Output. Auto-generated from TGI specs. For more details, check out https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-tgi-import.ts.

###
class huggingface_hub.TextGenerationStreamOutputStreamDetails

< source >

( finish_reason: TextGenerationOutputFinishReason generated_tokens: int input_length: int seed: typing.Optional[int] = None )

###
class huggingface_hub.TextGenerationStreamOutputToken

< source >

( id: int logprob: float special: bool text: str )

##
text_to_audio

###
class huggingface_hub.TextToAudioGenerationParameters

< source >

( do_sample: typing.Optional[bool] = None early_stopping: typing.Union[bool, ForwardRef('TextToAudioEarlyStoppingEnum'), NoneType] = None epsilon_cutoff: typing.Optional[float] = None eta_cutoff: typing.Optional[float] = None max_length: typing.Optional[int] = None max_new_tokens: typing.Optional[int] = None min_length: typing.Optional[int] = None min_new_tokens: typing.Optional[int] = None num_beam_groups: typing.Optional[int] = None num_beams: typing.Optional[int] = None penalty_alpha: typing.Optional[float] = None temperature: typing.Optional[float] = None top_k: typing.Optional[int] = None top_p: typing.Optional[float] = None typical_p: typing.Optional[float] = None use_cache: typing.Optional[bool] = None )

Parametrization of the text generation process

###
class huggingface_hub.TextToAudioInput

< source >

( inputs: str parameters: typing.Optional[huggingface_hub.inference._generated.types.text_to_audio.TextToAudioParameters] = None )

Inputs for Text To Audio inference

###
class huggingface_hub.TextToAudioOutput

< source >

( audio: typing.Any sampling_rate: float )

Outputs of inference for the Text To Audio task

###
class huggingface_hub.TextToAudioParameters

< source >

( generation_parameters: typing.Optional[huggingface_hub.inference._generated.types.text_to_audio.TextToAudioGenerationParameters] = None )

Additional inference parameters for Text To Audio

##
text_to_image

###
class huggingface_hub.TextToImageInput

< source >

( inputs: str parameters: typing.Optional[huggingface_hub.inference._generated.types.text_to_image.TextToImageParameters] = None )

Inputs for Text To Image inference

###
class huggingface_hub.TextToImageOutput

< source >

( image: typing.Any )

Outputs of inference for the Text To Image task

###
class huggingface_hub.TextToImageParameters

< source >

( guidance_scale: typing.Optional[float] = None height: typing.Optional[int] = None negative_prompt: typing.Optional[str] = None num_inference_steps: typing.Optional[int] = None scheduler: typing.Optional[str] = None seed: typing.Optional[int] = None width: typing.Optional[int] = None )

Additional inference parameters for Text To Image

##
text_to_speech

###
class huggingface_hub.TextToSpeechGenerationParameters

< source >

( do_sample: typing.Optional[bool] = None early_stopping: typing.Union[bool, ForwardRef('TextToSpeechEarlyStoppingEnum'), NoneType] = None epsilon_cutoff: typing.Optional[float] = None eta_cutoff: typing.Optional[float] = None max_length: typing.Optional[int] = None max_new_tokens: typing.Optional[int] = None min_length: typing.Optional[int] = None min_new_tokens: typing.Optional[int] = None num_beam_groups: typing.Optional[int] = None num_beams: typing.Optional[int] = None penalty_alpha: typing.Optional[float] = None temperature: typing.Optional[float] = None top_k: typing.Optional[int] = None top_p: typing.Optional[float] = None typical_p: typing.Optional[float] = None use_cache: typing.Optional[bool] = None )

Parametrization of the text generation process

###
class huggingface_hub.TextToSpeechInput

< source >

( inputs: str parameters: typing.Optional[huggingface_hub.inference._generated.types.text_to_speech.TextToSpeechParameters] = None )

Inputs for Text To Speech inference

###
class huggingface_hub.TextToSpeechOutput

< source >

( audio: typing.Any sampling_rate: typing.Optional[float] = None )

Outputs of inference for the Text To Speech task

###
class huggingface_hub.TextToSpeechParameters

< source >

( generation_parameters: typing.Optional[huggingface_hub.inference._generated.types.text_to_speech.TextToSpeechGenerationParameters] = None )

Additional inference parameters for Text To Speech

##
text_to_video

###
class huggingface_hub.TextToVideoInput

< source >

( inputs: str parameters: typing.Optional[huggingface_hub.inference._generated.types.text_to_video.TextToVideoParameters] = None )

Inputs for Text To Video inference

###
class huggingface_hub.TextToVideoOutput

< source >

( video: typing.Any )

Outputs of inference for the Text To Video task

###
class huggingface_hub.TextToVideoParameters

< source >

( guidance_scale: typing.Optional[float] = None negative_prompt: typing.Optional[list[str]] = None num_frames: typing.Optional[float] = None num_inference_steps: typing.Optional[int] = None seed: typing.Optional[int] = None )

Additional inference parameters for Text To Video

##
token_classification

###
class huggingface_hub.TokenClassificationInput

< source >

( inputs: str parameters: typing.Optional[huggingface_hub.inference._generated.types.token_classification.TokenClassificationParameters] = None )

Inputs for Token Classification inference

###
class huggingface_hub.TokenClassificationOutputElement

< source >

( end: int score: float start: int word: str entity: typing.Optional[str] = None entity_group: typing.Optional[str] = None )

Outputs of inference for the Token Classification task

###
class huggingface_hub.TokenClassificationParameters

< source >

( aggregation_strategy: typing.Optional[ForwardRef('TokenClassificationAggregationStrategy')] = None ignore_labels: typing.Optional[list[str]] = None stride: typing.Optional[int] = None )

Additional inference parameters for Token Classification

##
translation

###
class huggingface_hub.TranslationInput

< source >

( inputs: str parameters: typing.Optional[huggingface_hub.inference._generated.types.translation.TranslationParameters] = None )

Inputs for Translation inference

###
class huggingface_hub.TranslationOutput

< source >

( translation_text: str )

Outputs of inference for the Translation task

###
class huggingface_hub.TranslationParameters

< source >

( clean_up_tokenization_spaces: typing.Optional[bool] = None generate_parameters: typing.Optional[dict[str, typing.Any]] = None src_lang: typing.Optional[str] = None tgt_lang: typing.Optional[str] = None truncation: typing.Optional[ForwardRef('TranslationTruncationStrategy')] = None )

Additional inference parameters for Translation

##
video_classification

###
class huggingface_hub.VideoClassificationInput

< source >

( inputs: typing.Any parameters: typing.Optional[huggingface_hub.inference._generated.types.video_classification.VideoClassificationParameters] = None )

Inputs for Video Classification inference

###
class huggingface_hub.VideoClassificationOutputElement

< source >

( label: str score: float )

Outputs of inference for the Video Classification task

###
class huggingface_hub.VideoClassificationParameters

< source >

( frame_sampling_rate: typing.Optional[int] = None function_to_apply: typing.Optional[ForwardRef('VideoClassificationOutputTransform')] = None num_frames: typing.Optional[int] = None top_k: typing.Optional[int] = None )

Additional inference parameters for Video Classification

##
visual_question_answering

###
class huggingface_hub.VisualQuestionAnsweringInput

< source >

( inputs: VisualQuestionAnsweringInputData parameters: typing.Optional[huggingface_hub.inference._generated.types.visual_question_answering.VisualQuestionAnsweringParameters] = None )

Inputs for Visual Question Answering inference

###
class huggingface_hub.VisualQuestionAnsweringInputData

< source >

( image: typing.Any question: str )

One (image, question) pair to answer

###
class huggingface_hub.VisualQuestionAnsweringOutputElement

< source >

( score: float answer: typing.Optional[str] = None )

Outputs of inference for the Visual Question Answering task

###
class huggingface_hub.VisualQuestionAnsweringParameters

< source >

( top_k: typing.Optional[int] = None )

Additional inference parameters for Visual Question Answering

##
zero_shot_classification

###
class huggingface_hub.ZeroShotClassificationInput

< source >

( inputs: str parameters: ZeroShotClassificationParameters )

Inputs for Zero Shot Classification inference

###
class huggingface_hub.ZeroShotClassificationOutputElement

< source >

( label: str score: float )

Outputs of inference for the Zero Shot Classification task

###
class huggingface_hub.ZeroShotClassificationParameters

< source >

( candidate_labels: list hypothesis_template: typing.Optional[str] = None multi_label: typing.Optional[bool] = None )

Additional inference parameters for Zero Shot Classification

##
zero_shot_image_classification

###
class huggingface_hub.ZeroShotImageClassificationInput

< source >

( inputs: str parameters: ZeroShotImageClassificationParameters )

Inputs for Zero Shot Image Classification inference

###
class huggingface_hub.ZeroShotImageClassificationOutputElement

< source >

( label: str score: float )

Outputs of inference for the Zero Shot Image Classification task

###
class huggingface_hub.ZeroShotImageClassificationParameters

< source >

( candidate_labels: list hypothesis_template: typing.Optional[str] = None )

Additional inference parameters for Zero Shot Image Classification

##
zero_shot_object_detection

###
class huggingface_hub.ZeroShotObjectDetectionBoundingBox

< source >

( xmax: int xmin: int ymax: int ymin: int )

The predicted bounding box. Coordinates are relative to the top left corner of the input image.

###
class huggingface_hub.ZeroShotObjectDetectionInput

< source >

( inputs: str parameters: ZeroShotObjectDetectionParameters )

Inputs for Zero Shot Object Detection inference

###
class huggingface_hub.ZeroShotObjectDetectionOutputElement

< source >

( box: ZeroShotObjectDetectionBoundingBox label: str score: float )

Outputs of inference for the Zero Shot Object Detection task

###
class huggingface_hub.ZeroShotObjectDetectionParameters

< source >

( candidate_labels: list )

Additional inference parameters for Zero Shot Object Detection

Update on GitHub