机器学习提取程序训练器

UiPath.DocumentUnderstanding.ML.Activities.MachineLearningExtractorTrainer

描述

Enables the collection of data that has been processed through Validation Station so that it can be imported into Document Manager. This activity can be used only within the Train Extractors Scope activity.

项目兼容性

Windows - Legacy | Windows

配置

设计器面板

本地存储

• Output Folder - The directory where the collected data is stored. Once the data is stored, it can be imported into machine learning training tools.

选择项目的私有数据集

• Dataset - The dataset where the training data can be uploaded. If the robot is connected to a tenant which has AI Center enabled, you can see all the datasets from AI Center in the dropdown menu and select the folder where to upload the validated documents using the dropdown menu.
• Project - The project where the training data can be uploaded.
  备注：

  Project and dataset selection are enabled only when connected to Orchestrator. Visit Managing datasets for more information about Public/Private Datasets.

提供公共数据集端点

• Dataset ApiKey - The authentication key of the dataset.
• Dataset Endpoint - The endpoint of the dataset where training data can be uploaded. Once a dataset is public, it can be accessed outside UiPath® environment through an endpoint and using API key. Do this if you want to upload datasets to an AI Center instance that you're not connected to (for example in the case of hybrid deployments where the AI Center is on Cloud and the robot is connected to an On premises tenant).

属性面板

常见

• “显示名称”- 活动的显示名称。

本地存储

• Output Folder - The directory where the collected data is stored. Once the data is stored, it can be imported into machine learning training tools.

其他

• “私有”- 选中后将不再以“Verbose”级别记录变量和参数的值。

提供公共数据集端点

• Dataset ApiKey - The authentication key of the dataset.
• Dataset Endpoint - The endpoint of the dataset where training data can be uploaded. Once a dataset is public, it can be accessed outside UiPath® environment through an endpoint and using API key. Do this if you want to upload datasets to an AI Center instance that you're not connected to (for example in the case of hybrid deployments where the AI Center is on Cloud and the robot is connected to an On premises tenant).

选择项目的私有数据集

• Dataset - The dataset where the training data can be uploaded. If the robot is connected to a tenant which has AI Center enabled, you can see all the datasets from AI Center in the dropdown menu and select the folder where to upload the validated documents using the dropdown menu.
• Project - The project where the training data can be uploaded.
  备注：

  Project and dataset selection are enabled only when connected to Orchestrator. Visit Managing datasets for more information about Public/Private Datasets.

服务器

• RetryOnFailure - Retry on transient failure. This field only supports Boolean values (True, False). The default value is True.
• Timeout (milliseconds) - Specifies the amount of time (in milliseconds) to wait for a response from the server before an error is thrown. The default value is 100000 milliseconds (100 seconds).

使用机器学习提取程序训练向导

The Machine Learning Extractor Trainer collects the human feedback for you, in a directory of your choice. Once you collect data and you want to retrain an ML Model, you can just zip the content of the directory and upload it in Document Manager for gathering and filtering data.

如何使用

To use the Machine Learning Extractor Trainer activity, perform the following steps:

1. 使用“分类管理器”向导定义文档类型和字段。

2. Add a Machine Learning Extractor Trainer into a Train Extractors Scope activity.

3. In the Machine Learning Extractor wizard that automatically opens, enter information for the Endpoint field. You can choose one of the public endpoints. Visit Public endpoints for more information about public endpoints.

4. Select the check box for the Update activity arguments if you wish to also use the entered values as input arguments for the activity, more precisely for the Endpoint.

5. Select Get Capabilities. The wizard closes after this operation

6. 输入输出文件夹的值。

7. Select the Configure Extractors option in the Train Extractors Scope. A wizard is displayed.

   Figure 1. The Configure Extractors wizard

   

8. The Machine Learning Extractor Trainer is now ready for configuration. Expand the document type that you want to apply it for, and start selecting the fields you want to train, by selecting the checkboxes next to the appropriate fields.

9. Fill in the text boxes either manually or by selecting, from the available dropdown list, the correct data you wish to map to each field. The dropdown list contains all fields that the Machine Learning Extractor Trainer, using the endpoint entered in the Machine Learning Extractor wizard, declares as extraction capability.

   备注：

   If you select the check box but you leave the text box empty, the latter will be automatically filled in with the Document Type ID from the local taxonomy. The changes apply after saving. Should you want to avoid using a long string for the field ID, we would recommend you to manually enter a value in case you do not have access to the internal taxonomy of the extractor.

10. To check if you are using the latest capabilities of the extractor, you can select the Get or refresh extractor capabilities which opens the Machine Learning Extractor wizard.

11. 从下拉列表中选择一个选项会自动确认该字段。

12. 要根据提取结果训练提取程序，您可以在先前用于提取程序的“框架别名”字段中设置确切的字母数字值。

13. Select Save once all fields are configured properly.

    重要提示：

    您不能为两个不同的字段选择相同的选项。

Document Understanding 集成

The Machine Learning Extractor Trainer activity is part of the Document Understanding solutions. Visit the Document Understanding Guide for more information.