File size: 5,664 Bytes
254a3c6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 |
# coding=utf-8
# Copyright 2023-present, the HuggingFace Inc. team.
#
# 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.
from typing import TYPE_CHECKING, List, TypedDict
if TYPE_CHECKING:
from PIL import Image
class ClassificationOutput(TypedDict):
"""Dictionary containing the output of a [`~InferenceClient.audio_classification`] and [`~InferenceClient.image_classification`] task.
Args:
label (`str`):
The label predicted by the model.
score (`float`):
The score of the label predicted by the model.
"""
label: str
score: float
class ConversationalOutputConversation(TypedDict):
"""Dictionary containing the "conversation" part of a [`~InferenceClient.conversational`] task.
Args:
generated_responses (`List[str]`):
A list of the responses from the model.
past_user_inputs (`List[str]`):
A list of the inputs from the user. Must be the same length as `generated_responses`.
"""
generated_responses: List[str]
past_user_inputs: List[str]
class ConversationalOutput(TypedDict):
"""Dictionary containing the output of a [`~InferenceClient.conversational`] task.
Args:
generated_text (`str`):
The last response from the model.
conversation (`ConversationalOutputConversation`):
The past conversation.
warnings (`List[str]`):
A list of warnings associated with the process.
"""
conversation: ConversationalOutputConversation
generated_text: str
warnings: List[str]
class FillMaskOutput(TypedDict):
"""Dictionary containing information about a [`~InferenceClient.fill_mask`] task.
Args:
score (`float`):
The probability of the token.
token (`int`):
The id of the token.
token_str (`str`):
The string representation of the token.
sequence (`str`):
The actual sequence of tokens that ran against the model (may contain special tokens).
"""
score: float
token: int
token_str: str
sequence: str
class ImageSegmentationOutput(TypedDict):
"""Dictionary containing information about a [`~InferenceClient.image_segmentation`] task. In practice, image segmentation returns a
list of `ImageSegmentationOutput` with 1 item per mask.
Args:
label (`str`):
The label corresponding to the mask.
mask (`Image`):
An Image object representing the mask predicted by the model.
score (`float`):
The score associated with the label for this mask.
"""
label: str
mask: "Image"
score: float
class ObjectDetectionOutput(TypedDict):
"""Dictionary containing information about a [`~InferenceClient.object_detection`] task.
Args:
label (`str`):
The label corresponding to the detected object.
box (`dict`):
A dict response of bounding box coordinates of
the detected object: xmin, ymin, xmax, ymax
score (`float`):
The score corresponding to the detected object.
"""
label: str
box: dict
score: float
class QuestionAnsweringOutput(TypedDict):
"""Dictionary containing information about a [`~InferenceClient.question_answering`] task.
Args:
score (`float`):
A float that represents how likely that the answer is correct.
start (`int`):
The index (string wise) of the start of the answer within context.
end (`int`):
The index (string wise) of the end of the answer within context.
answer (`str`):
A string that is the answer within the text.
"""
score: float
start: int
end: int
answer: str
class TableQuestionAnsweringOutput(TypedDict):
"""Dictionary containing information about a [`~InferenceClient.table_question_answering`] task.
Args:
answer (`str`):
The plaintext answer.
coordinates (`List[List[int]]`):
A list of coordinates of the cells referenced in the answer.
cells (`List[int]`):
A list of coordinates of the cells contents.
aggregator (`str`):
The aggregator used to get the answer.
"""
answer: str
coordinates: List[List[int]]
cells: List[List[int]]
aggregator: str
class TokenClassificationOutput(TypedDict):
"""Dictionary containing the output of a [`~InferenceClient.token_classification`] task.
Args:
entity_group (`str`):
The type for the entity being recognized (model specific).
score (`float`):
The score of the label predicted by the model.
word (`str`):
The string that was captured.
start (`int`):
The offset stringwise where the answer is located. Useful to disambiguate if word occurs multiple times.
end (`int`):
The offset stringwise where the answer is located. Useful to disambiguate if word occurs multiple times.
"""
entity_group: str
score: float
word: str
start: int
end: int
|