[[["容易理解","easyToUnderstand","thumb-up"],["確實解決了我的問題","solvedMyProblem","thumb-up"],["其他","otherUp","thumb-up"]],[["難以理解","hardToUnderstand","thumb-down"],["資訊或程式碼範例有誤","incorrectInformationOrSampleCode","thumb-down"],["缺少我需要的資訊/範例","missingTheInformationSamplesINeed","thumb-down"],["翻譯問題","translationIssue","thumb-down"],["其他","otherDown","thumb-down"]],["上次更新時間：2025-09-04 (世界標準時間)。"],[[["\u003cp\u003eCustom 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.\u003c/p\u003e\n"],["\u003cp\u003eThe custom extractor employs a combination of layout-aware deep learning models and template-based models to accommodate diverse document structures.\u003c/p\u003e\n"],["\u003cp\u003eThree 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.\u003c/p\u003e\n"],["\u003cp\u003eFoundation models are the preferred training option for documents with variable layouts, as they typically require fewer training documents compared to other methods.\u003c/p\u003e\n"],["\u003cp\u003eThe 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.\u003c/p\u003e\n"]]],[],null,["# Custom extractor overview\n=========================\n\nCustom extractor extracts entities from documents of a particular type. For\nexample, it can extract the items in a menu or the name and contact information\nfrom a resume.\n\nOverview\n--------\n\nThe goal of the custom extractor is to enable Document AI users to build\ncustom entity extraction solutions for new document\ntypes for which no pre-trained processors are available. Custom extractor includes\na combination of layout-aware deep learning models (for generative AI and custom\nmodels) and template-based models.\n\nWhich training method should I use?\n-----------------------------------\n\nCustom extractor supports a wide range of use cases with three different modes.\n\nBecause foundation models typically require fewer training documents, they're\nrecommended as the first option for all variable layouts.\n\nConfidence score\n----------------\n\nThe confidence score communicates how strongly your model associates each entity\nwith the predicted value. The value is between zero and one, the closer it is to\none, the higher the model's confidence that the value corresponds to the entity.\nThis allows users to set triggers for manual review of individual entities when\nthe value is low. For example, determining whether the text in an entity is\n\"Hello, world!\" or \"HeIIo vvorld!\"\n\nThe benefits of this approach allow for spotting individual entities with low\nconfidence, setting thresholds for which predictions are used, selecting the\noptimal [confidence threshold](/document-ai/docs/evaluate#confidence_threshold), and development\nof new strategies for training models with higher accuracy and confidence scores.\n\nFor more information on evaluation concepts and metrics, see [Evaluate\nPerformance](/document-ai/docs/evaluate#all-labels)"]]
