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from typing import TYPE_CHECKING, List, TypedDict |
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if TYPE_CHECKING: |
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from PIL import Image |
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class ClassificationOutput(TypedDict): |
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"""Dictionary containing the output of a [`~InferenceClient.audio_classification`] and [`~InferenceClient.image_classification`] task. |
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Args: |
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label (`str`): |
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The label predicted by the model. |
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score (`float`): |
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The score of the label predicted by the model. |
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""" |
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label: str |
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score: float |
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class ConversationalOutputConversation(TypedDict): |
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"""Dictionary containing the "conversation" part of a [`~InferenceClient.conversational`] task. |
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Args: |
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generated_responses (`List[str]`): |
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A list of the responses from the model. |
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past_user_inputs (`List[str]`): |
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A list of the inputs from the user. Must be the same length as `generated_responses`. |
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""" |
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generated_responses: List[str] |
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past_user_inputs: List[str] |
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class ConversationalOutput(TypedDict): |
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"""Dictionary containing the output of a [`~InferenceClient.conversational`] task. |
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Args: |
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generated_text (`str`): |
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The last response from the model. |
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conversation (`ConversationalOutputConversation`): |
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The past conversation. |
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warnings (`List[str]`): |
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A list of warnings associated with the process. |
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""" |
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conversation: ConversationalOutputConversation |
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generated_text: str |
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warnings: List[str] |
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class FillMaskOutput(TypedDict): |
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"""Dictionary containing information about a [`~InferenceClient.fill_mask`] task. |
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Args: |
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score (`float`): |
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The probability of the token. |
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token (`int`): |
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The id of the token. |
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token_str (`str`): |
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The string representation of the token. |
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sequence (`str`): |
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The actual sequence of tokens that ran against the model (may contain special tokens). |
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""" |
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score: float |
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token: int |
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token_str: str |
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sequence: str |
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class ImageSegmentationOutput(TypedDict): |
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"""Dictionary containing information about a [`~InferenceClient.image_segmentation`] task. In practice, image segmentation returns a |
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list of `ImageSegmentationOutput` with 1 item per mask. |
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Args: |
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label (`str`): |
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The label corresponding to the mask. |
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mask (`Image`): |
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An Image object representing the mask predicted by the model. |
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score (`float`): |
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The score associated with the label for this mask. |
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""" |
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label: str |
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mask: "Image" |
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score: float |
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class ObjectDetectionOutput(TypedDict): |
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"""Dictionary containing information about a [`~InferenceClient.object_detection`] task. |
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Args: |
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label (`str`): |
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The label corresponding to the detected object. |
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box (`dict`): |
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A dict response of bounding box coordinates of |
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the detected object: xmin, ymin, xmax, ymax |
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score (`float`): |
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The score corresponding to the detected object. |
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""" |
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label: str |
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box: dict |
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score: float |
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class QuestionAnsweringOutput(TypedDict): |
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"""Dictionary containing information about a [`~InferenceClient.question_answering`] task. |
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Args: |
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score (`float`): |
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A float that represents how likely that the answer is correct. |
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start (`int`): |
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The index (string wise) of the start of the answer within context. |
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end (`int`): |
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The index (string wise) of the end of the answer within context. |
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answer (`str`): |
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A string that is the answer within the text. |
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""" |
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score: float |
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start: int |
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end: int |
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answer: str |
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class TableQuestionAnsweringOutput(TypedDict): |
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"""Dictionary containing information about a [`~InferenceClient.table_question_answering`] task. |
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Args: |
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answer (`str`): |
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The plaintext answer. |
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coordinates (`List[List[int]]`): |
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A list of coordinates of the cells referenced in the answer. |
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cells (`List[int]`): |
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A list of coordinates of the cells contents. |
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aggregator (`str`): |
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The aggregator used to get the answer. |
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""" |
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answer: str |
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coordinates: List[List[int]] |
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cells: List[List[int]] |
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aggregator: str |
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class TokenClassificationOutput(TypedDict): |
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"""Dictionary containing the output of a [`~InferenceClient.token_classification`] task. |
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Args: |
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entity_group (`str`): |
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The type for the entity being recognized (model specific). |
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score (`float`): |
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The score of the label predicted by the model. |
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word (`str`): |
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The string that was captured. |
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start (`int`): |
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The offset stringwise where the answer is located. Useful to disambiguate if word occurs multiple times. |
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end (`int`): |
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The offset stringwise where the answer is located. Useful to disambiguate if word occurs multiple times. |
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""" |
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entity_group: str |
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score: float |
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word: str |
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start: int |
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end: int |
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