Scaling document classification to 100k+ labels

TL;DR

Existing methods for classifying documents into large taxonomies of over 100,000 labels often fail to meet accuracy and cost requirements. Databricks developed a solution that combines AI Classify with vector search, significantly improving performance.

✦ Why It Matters

Engineers can adopt Databricks' AI Classify and vector search to enhance their document classification systems today.

Key Takeaways

Use vector search to shortlist labels before classification.
Leverage the AI Classify function for improved accuracy.
Update taxonomies without retraining models.
Benchmark different methods to find the best cost-quality balance.

How It Works

The workflow first embeds labels and documents using the Qwen3-Embedding-8B model, scoring labels based on semantic and lexical similarity. It then merges results from vector search and AI Classify to predict the best label, optimizing for accuracy and cost.

Scaling document classification to 100k+ labels

tailwindlabs / tailwindcss

denoland / deno

angular / angular

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tailwindlabs / tailwindcss

denoland / deno

angular / angular

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