As of January 1, 2020 this library no longer supports Python 2 on the latest released version. Library versions released prior to that date will continue to be available. For more information please visit Python 2 support on Google Cloud.

Source code for google.cloud.automl_v1beta1.types.text

# -*- 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

from google.cloud.automl_v1beta1.types import classification


__protobuf__ = proto.module(
    package="google.cloud.automl.v1beta1",
    manifest={
        "TextClassificationDatasetMetadata",
        "TextClassificationModelMetadata",
        "TextExtractionDatasetMetadata",
        "TextExtractionModelMetadata",
        "TextSentimentDatasetMetadata",
        "TextSentimentModelMetadata",
    },
)


[docs]class TextClassificationDatasetMetadata(proto.Message): r"""Dataset metadata for classification. Attributes: classification_type (google.cloud.automl_v1beta1.types.ClassificationType): Required. Type of the classification problem. """ classification_type = proto.Field( proto.ENUM, number=1, enum=classification.ClassificationType, )
[docs]class TextClassificationModelMetadata(proto.Message): r"""Model metadata that is specific to text classification. Attributes: classification_type (google.cloud.automl_v1beta1.types.ClassificationType): Output only. Classification type of the dataset used to train this model. """ classification_type = proto.Field( proto.ENUM, number=3, enum=classification.ClassificationType, )
[docs]class TextExtractionDatasetMetadata(proto.Message): r"""Dataset metadata that is specific to text extraction """
[docs]class TextExtractionModelMetadata(proto.Message): r"""Model metadata that is specific to text extraction. Attributes: model_hint (str): Indicates the scope of model use case. - ``default``: Use to train a general text extraction model. Default value. - ``health_care``: Use to train a text extraction model that is tuned for healthcare applications. """ model_hint = proto.Field(proto.STRING, number=3,)
[docs]class TextSentimentDatasetMetadata(proto.Message): r"""Dataset metadata for text sentiment. Attributes: sentiment_max (int): Required. A sentiment is expressed as an integer ordinal, where higher value means a more positive sentiment. The range of sentiments that will be used is between 0 and sentiment_max (inclusive on both ends), and all the values in the range must be represented in the dataset before a model can be created. sentiment_max value must be between 1 and 10 (inclusive). """ sentiment_max = proto.Field(proto.INT32, number=1,)
[docs]class TextSentimentModelMetadata(proto.Message): r"""Model metadata that is specific to text sentiment. """
__all__ = tuple(sorted(__protobuf__.manifest))