Preparing training data

• Updated on Jul 22, 2026
• Published on Jun 8, 2026
• 8 minute(s) read

The quality of your Training Data directly affects how well your Identification model performs. Before training a model, prepare a Dataset that accurately represents the documents you expect to process in production.

While you'll first create a Semi-structured Layout to upload documents in Training Data Management (TDM), the quality, diversity, and consistency of your training data have the greatest impact on model performance.

In this step, you'll prepare a representative dataset that reflects the document patterns, formats, and edge cases your model is expected to process.

Choose representative documents

Selecting the right documents is the most important part of preparing your training data. A representative dataset allows the model to learn patterns that reflect the documents it will process in production.

Identify your document types

Start by identifying the document types (e.g., invoices, paystubs, claim forms):

• Determine the main document patterns (e.g., structures, fields, tables)
• Ensure that the fields or tables you need to extract are present in these documents.

Document distribution

Your training data should reflect the real distribution of documents you expect to process.

Include examples from the document types you expect to process with this model

• For Field Identification models: include at least 15 documents per type.
• For Table Identification models: include at least 20 documents per type.

Ensure documents contain the required data

Your documents must support the data you want to extract.

• Ensure that every field you want to extract appears in your dataset.
• Include enough examples of each field across documents.
• If documents with missing information are expected in production, include representative examples in your training dataset.

Ensure dataset diversity

Your training dataset should reflect the range of document patterns your model will encounter in production. Including diverse examples helps the model generalize and perform consistently across different document formats.

Include diverse document patterns

Documents of the same type may differ in how they present information. That’s why it is important to include documents where:

• fields appear in different locations
• sections are arranged differently
• tables vary in size.

Handle edge cases

Not all documents should be treated equally. Some documents differ significantly from the main dataset and can negatively impact model performance if included without consideration.

Clean your dataset

Before using your documents for training, remove any data that could negatively impact model performance. Low-quality or irrelevant documents can introduce noise and reduce the model’s ability to learn consistent patterns.

Prepare a testing set

Before training your model, set aside a portion of your documents for evaluation. A testing set allows you to measure model performance accurately after training.

Common issues

Using too few documents

Training with a small dataset limits the model’s ability to learn patterns and generalize to new documents. Make sure you meet the minimum requirement (100 documents) and aim for a larger, well-distributed dataset for better performance.