[[["易于理解","easyToUnderstand","thumb-up"],["解决了我的问题","solvedMyProblem","thumb-up"],["其他","otherUp","thumb-up"]],[["很难理解","hardToUnderstand","thumb-down"],["信息或示例代码不正确","incorrectInformationOrSampleCode","thumb-down"],["没有我需要的信息/示例","missingTheInformationSamplesINeed","thumb-down"],["翻译问题","translationIssue","thumb-down"],["其他","otherDown","thumb-down"]],["最后更新时间 (UTC)：2025-03-26。"],[[["Custom extractors are designed to identify and extract specific entities from various document types, including menus and resumes, for which pre-trained processors may not exist."],["The custom extractor employs a combination of layout-aware deep learning models and template-based models to accommodate diverse document structures."],["Three training methods are available for the custom extractor: fine-tuning with foundation models, custom models, and template-based models, each suited for different levels of document layout variability."],["Foundation models are the preferred training option for documents with variable layouts, as they typically require fewer training documents compared to other methods."],["The confidence score, ranging from zero to one, indicates the model's certainty in associating a value with a predicted entity, enabling users to set review thresholds and improve model accuracy."]]],[]]
