Communications Mining
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- 入门指南
- 管理
- 管理来源和数据集
- 模型训练和维护
- 训练
- Defining and setting up your general fields
- Understanding general fields
- Which pre-trained general fields are available?
- Enabling, disabling, updating and creating general fields
- General field filtering
- Reviewing and applying general fields
- Validation for general fields
- Improving general field performance
- Building custom regex general fields
- 生成式提取
- 使用分析和监控
- 自动化和 Communications Mining
- 常见问题及解答
![](https://docs.uipath.com/_next/static/media/grid.05ebd128.png?w=3840&q=100)
Communications Mining 用户指南
Last updated 2024年7月2日
正在生成提取内容
备注: Pre-requisites. Pick a label that has no performance indicators/warnings and is at a precision/recall level that’s appropriate for your use case.
- The Extraction validation process is required to understand the performance of these extractions via Validation.
Decide on the extraction that you want to train out. We use Report > Statement of Accounts as an example of a schema we want to train out.
To automate this process, extract the following data points to input into a downstream system:
Note: This is only applicable if you are training in Explore. In Train, clicking into an extraction training batch pre-loads the extractions.Use this training mode as required, to boost the number of training examples for each extraction (i.e., a set of fields assigned to a label) to at least 25, allowing the model to accurately estimate the performance of the extraction.
- Go to Explore then Label, and select the label you want to generate extractions on.
- Select Predict extractions. Predict extractions generates extractions on a per page basis in Explore (i.e.- this applies predictions on all the comments on a given page).
Note: Each time you go to the next page, you need to select Predict extractions again.
You can also generate extractions on an individual comment level. Select Annotate Fields, then Predict extractions icon.
- The model uses generative models and maps each of the data points that you previously defined (in our extraction schema), to relate to them to an intent (label).
- It extracts and returns them in a structured schema, for an SME to go through and confirm.
- The structured schema is intended to enable more complex automations, and is structured in JSON format in the API for consumption by any downstream automations.
- After making the extraction predictions, if the model picked up field extractions on the comment, it highlights the relevant span in the text (if applicable). The model displays the extracted value on the right-hand side. Check the Validating and annotating extractions page to learn how to validate the predicted values.