- 基本情報
- 管理
- ソースとデータセットを管理する
- モデルのトレーニングと保守
- トレーニング
- 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
- Generative extraction
- Using analytics and monitoring
- オートメーションと Communications Mining
- よくある質問など
![](https://docs.uipath.com/_next/static/media/grid.05ebd128.png?w=3840&q=100)
Best practices and considerations
The highlighted general field should cover the entire word (or several) in question, not just part of it. Don't include additional spaces at the end of the field.
Similar to labels, don't partially review your general and extraction fields.
- General fields are reviewed at the paragraph level, not the entire message level. When you review a paragraph for fields, review all the fields in the paragraph.
Not confirming a field in a paragraph where you have labelled other fields, tells the model that you don't consider it a genuine example of the predicted field. This is reflected in the validation scores and the general field performance.
- Extraction fields are reviewed at the message level, not just the paragraph level. When you review an entire message for fields, review all the fields in the message.
Not confirming a field in a message where you have labelled other fields, tells the model that you don’t consider it a genuine example of the predicted field. This is reflected in the validation scores and extraction field performance.
- Global fields cannot overlap with each other, or with another example of itself.
- Global fields and extraction fields can overlap with each other.
- You can use the same span of text as many times as needed by different extraction fields.
- There is currently no general field normalization preview in Communications Mining. Fields that should be normalized will get normalized in the downstream response. Normalization in Communications Mining will be available in the model in the future.
- If a child label has extractions on it, its parent doesn’t inherit the extraction examples automatically. For labels, its parent automatically inherits the extraction examples.
- Providing additional extraction examples does not improve the performance of a label. To improve the performance of a label, focus on label-specific training.
- Improving label performance allows you to increase the likelihood that you capture occurrences where a label (and subsequently its extractions) should have been predicted.
To improve the performance of your extractions, provide validated examples on the extractions itself.