/
text_extraction.py
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/
text_extraction.py
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# -*- coding: utf-8 -*-
# Copyright 2020 Google LLC
#
# 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.
#
import proto # type: ignore
__protobuf__ = proto.module(
package="google.cloud.aiplatform.v1.schema.predict.prediction",
manifest={"TextExtractionPredictionResult",},
)
class TextExtractionPredictionResult(proto.Message):
r"""Prediction output format for Text Extraction.
Attributes:
ids (Sequence[int]):
The resource IDs of the AnnotationSpecs that
had been identified, ordered by the confidence
score descendingly.
display_names (Sequence[str]):
The display names of the AnnotationSpecs that
had been identified, order matches the IDs.
text_segment_start_offsets (Sequence[int]):
The start offsets, inclusive, of the text
segment in which the AnnotationSpec has been
identified. Expressed as a zero-based number of
characters as measured from the start of the
text snippet.
text_segment_end_offsets (Sequence[int]):
The end offsets, inclusive, of the text
segment in which the AnnotationSpec has been
identified. Expressed as a zero-based number of
characters as measured from the start of the
text snippet.
confidences (Sequence[float]):
The Model's confidences in correctness of the
predicted IDs, higher value means higher
confidence. Order matches the Ids.
"""
ids = proto.RepeatedField(proto.INT64, number=1,)
display_names = proto.RepeatedField(proto.STRING, number=2,)
text_segment_start_offsets = proto.RepeatedField(proto.INT64, number=3,)
text_segment_end_offsets = proto.RepeatedField(proto.INT64, number=4,)
confidences = proto.RepeatedField(proto.FLOAT, number=5,)
__all__ = tuple(sorted(__protobuf__.manifest))