The Problem
The useful facts in a contract — who the parties are, the agreement date, and the document type — are buried in dense, highly variable legal prose.
At scale, in deal rooms, due diligence, and document registries, extracting those facts by hand is a slow, costly bottleneck that keeps a stack of PDFs from becoming searchable, structured metadata.
Our Solution
We built and benchmarked a legal-document named-entity-recognition system that automatically tags key entities in commercial contracts.
The approach
- We converted a corpus of 500+ expert-annotated real commercial contracts into a token-classification dataset covering parties, agreement dates, and document names.
- A single reproducible training harness makes the model backbone a one-line swap, so we fine-tuned and compared a ladder of transformer models — from lightweight DistilBERT to large DeBERTa-v2 — under identical conditions.
- Every experiment was tracked for reproducibility, and models were evaluated with entity-level metrics rather than misleading token-level accuracy.
Why it's rigorous
Training on genuine contracts (not templated examples) means the models learn real legal phrasing, and controlled benchmarking identifies the best accuracy-versus-cost trade-off for production.
Outcome
Contracts become structured, searchable data automatically — parties, dates, and document types extracted at scale, converting manual contract review into an indexed, queryable dataset with the best model chosen through rigorous, reproducible benchmarking.
Tools & Technologies
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