[[["わかりやすい","easyToUnderstand","thumb-up"],["問題の解決に役立った","solvedMyProblem","thumb-up"],["その他","otherUp","thumb-up"]],[["わかりにくい","hardToUnderstand","thumb-down"],["情報またはサンプルコードが不正確","incorrectInformationOrSampleCode","thumb-down"],["必要な情報 / サンプルがない","missingTheInformationSamplesINeed","thumb-down"],["翻訳に関する問題","translationIssue","thumb-down"],["その他","otherDown","thumb-down"]],["最終更新日 2025-09-04 UTC。"],[[["\u003cp\u003eCustom model training and extraction allows building models tailored to specific documents without generative AI, providing complete control over the trained model.\u003c/p\u003e\n"],["\u003cp\u003eA document dataset, consisting of at least three documents, is essential for training, up-training, or evaluating a processor version, as it acts as the source for the model's learning and stability.\u003c/p\u003e\n"],["\u003cp\u003eTraining a model involves using a dataset of documents with ground-truth to improve accuracy, while the test dataset compares the model's predictions against ground truth to measure its accuracy using an F1 score.\u003c/p\u003e\n"],["\u003cp\u003eCreating and evaluating a custom processor involves defining fields, importing documents with auto-labeling, training a new version, and evaluating performance metrics like F1, precision, and recall.\u003c/p\u003e\n"],["\u003cp\u003eAuto-labeling, which can be enhanced with descriptive property information for each entity, uses the foundation model to predict labels and improve extraction accuracy for specific document structures.\u003c/p\u003e\n"]]],[],null,["# Custom-based extraction\n=======================\n\nCustom model training and extraction lets you to build your own model designed specifically for your documents without the use\nof generative AI. It's ideal if you don't want to use generative AI and want to control all aspects of the trained model.\n\n\nDataset configuration\n---------------------\n\nA document dataset is required to train, up-train, or evaluate a processor version. Document AI processors learn from examples, just like humans. Dataset fuels processor stability in terms of performance. \n\n### Train dataset\n\nTo improve the model and its accuracy, train a dataset on your documents. The model is made up of documents with ground-truth. You need a minimum of three documents to train a new model. Ground-truth is the correctly labeled data, as determined by humans.\n\n### Test dataset\n\nThe test dataset is what the model uses to generate an F1 score (accuracy). It is made up of documents with ground-truth. To see how often the model is right, the ground truth is used to compare the model's predictions (extracted fields from the model) with the correct answers. The test dataset should have at least three documents.\n\n\u003cbr /\u003e\n\nBefore getting started\n----------------------\n\nIf not done so already, [enable billing](/document-ai/docs/setup#billing) and the\n[Document AI API](/document-ai/docs/setup).\n\nBuild and evaluate a custom model\n---------------------------------\n\nBegin by building and then evaluating a custom processor.\n\n1. [Create a processor](/document-ai/docs/workbench/build-custom-processor#create_a_processor)\n and [define fields](/document-ai/docs/workbench/build-custom-processor#define_processor_fields)\n you want to extract, which is important because it impacts extraction quality.\n\n | **Note:** The default processor is a foundation model.\n2. Set dataset location: Select the default option folder **Google-managed**.\n This might be done automatically shortly after creating the processor.\n\n3. Navigate to the **Build** tab and select **Import Documents** with auto-labeling\n enabled (see [Auto-labeling with the foundation model](#auto-labeling)). You need a minimum of\n 10 documents in the training set and 10 in the testing set to train a custom model.\n\n4. Train model:\n\n 1. Select **Train new version** and name the processor version.\n 2. Go to **Show advanced options** and select the **Model based** option.\n\n | **Note:** It takes some time for the training to complete.\n5. Evaluation:\n\n - Go to **Evaluate \\& test** , select the version you just trained, then select **View full evaluation**.\n\n - You now see metrics such as [f1, precision, and recall](/document-ai/docs/workbench/evaluate#all-labels) for the entire document and each field.\n - Decide if performance meets your production goals. If it does not, then reevaluate training and testing sets, typically adding documents to the training test set that don't parse well.\n6. Set a new version as the default.\n\n 1. Navigate to **Manage versions**.\n 2. Navigate to the more_vert menu and then select **Set as default**.\n\nYour model is now deployed and documents sent to this processor are now using your\ncustom version. You want to [evaluate the model's performance](/document-ai/docs/workbench/evaluate)\nto check if it requires further training.\n\nEvaluation reference\n--------------------\n\nThe evaluation engine can do both exact match or [fuzzy matching](/document-ai/docs/workbench/evaluate#fuzzy_matching).\nFor an exact match, the extracted value must exactly match the ground truth or is counted as a miss.\n\nFuzzy matching extractions that had slight differences such as capitalization\ndifferences still count as a match. This can be changed at the **Evaluation** screen.\n\nAuto-labeling with the foundation model\n---------------------------------------\n\nThe foundation model can accurately extract fields for a variety of document types,\nbut you can also provide additional training data to improve the accuracy of the\nmodel for specific document structures.\n\nDocument AI uses the label names you define and previous annotations to label\ndocuments at scale with auto-labeling.\n\n1. When you've created a custom processor, go to the **Get Started** tab.\n2. Select **Create new field**.\n3. Provide a descriptive name and fill out the description field. Property description lets you provide additional context, insights, and prior knowledge for each entity to improve extraction accuracy and performance.\n\n| **Note:** Good examples of property descriptions include location information and text patterns of the property values, which help disambiguate potential sources of confusion in the document, guiding the model with rules that ensure more reliable and consistent extractions, regardless of the specific document structure or content variations.\n\n1. Navigate to the **Build** tab, then select **Import documents**.\n\n2. Select the path of the documents and which set the documents should be imported\n into. Check the auto-labeling box, and select the foundation model.\n\n3. In the **Build** tab, select **Manage Dataset**. You should see your imported\n documents. Select one of your documents.\n\nYou now see the predictions from the model highlighted in purple.\n\n1. Review each label predicted by the model and ensure is correct. If there are missing fields, add those as well.\n\n| **Note:** It's important that all fields are as accurate as possible or model performance might affect the results. [More details on labeling](/document-ai/docs/workbench/label-documents).\n\n1. After the document has been reviewed, select **Mark as labeled** . The document is now ready to be used by the model. Make sure the document is in either the **Testing** or **Training** set."]]
