> For the complete documentation index, see [llms.txt](https://docs.docbits.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.docbits.com/chinese/guan-li-yu-she-zhi/settings/global-settings/document-types/model-training/manage-training-data.md).

# 管理训练数据

## 管理现有训练数据，包括添加、编辑或移除记录。

**有效管理训练数据，可以采取以下步骤：**

**添加新记录：**

* 收集新文档，作为模型的训练数据。
* 确保这些文档能够代表模型所要处理的不同类型的数据。
* 将新记录上传到训练数据存储库。

**编辑现有记录**：

* 定期检查现有训练数据，并根据需要进行更新。这可能包括编辑文档元数据、添加更多标签，或移除有错误或缺乏代表性的记录。

**移除记录：**

* 找出过时、不准确或不再相关的记录，并将其从训练数据集中移除。
* 确保有明确的记录移除决策流程，并记录该流程。

**训练数据版本管理：**

* 为训练数据实施版本控制系统，以跟踪更改并清晰记录数据集的变更历史。这样，您可以在需要时恢复训练数据的旧版本，并跟踪更改。

**训练数据安全：**

* 确保训练数据得到妥善保护，尤其是在其中包含敏感或机密信息时。实施访问控制，确保只有获授权的用户才能访问训练数据，并在传输和存储过程中对数据进行加密。

**文档记录与跟踪：**

* 记录训练数据的所有更改，包括添加、编辑和移除数据集。这样，您可以跟踪训练数据的历史，并确保用于训练模型的数据始终是最新且相关的。

定期管理和更新训练数据，可以确保模型使用最新且具有代表性的数据进行训练，并获得最佳性能。


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