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metadata 
title: EPCC Contract Intelligence
emoji: 📑
colorFrom: blue
colorTo: indigo
sdk: docker
app_port: 7860
pinned: false
license: mit
short_description: Local clause/risk classifier for EPCC contract triage

EPCC Contract Intelligence — live demo

A leakage-safe, severity-weighted clause/risk classifier for Engineering, Procurement, Construction & Commissioning (EPCC) contract review. Fully local model — no external API calls at inference time.

  • Model: BAAI/bge-small-en-v1.5 sentence embeddings + logistic regression head, trained on the public CUAD dataset (13,155 attorney-labelled clauses across 509 commercial contracts, 41 clause types).
  • Held-out test (102 contracts, ≈2,850 clauses, zero contract overlap with training): macro-F1 0.637, critical-tier false-negative rate 0.075, abstain at p≥0.45 → coverage 0.567 / accuracy 0.903.
  • Source code, executive memo, notebook, and tests: github.com/maralzar/epcc-contract-intelligence

How to use

  1. Classify a clause — paste your own or pick one of the 15 synthetic EPCC excerpts (C1–C15). The slider tunes the abstain threshold; clauses with confidence below it route to human review.
  2. Synthetic EPCC packet — see the full pre-computed risk register for all 15 excerpts.
  3. About / metrics — held-out test metrics, the severity-tier map, and stated limitations.

What you're looking at

The model is a methodology proof: CUAD covers commercial / M&A contracts, not EPCC, so the demo shows transferable behaviour. The classifier predicts confidently on liability-cap / LD / insurance clauses (which match CUAD labels) and correctly abstains on EPCC-native concepts CUAD never saw (force majeure, site conditions, change in law, notice/waiver). The abstain layer is doing real product work — that's the design point.

Limitations

  • Trained on CUAD, not on real EPCC contract data. Production needs SME- labelled EPCC data on top of an extended taxonomy.
  • Single-label per clause; real EPCC review is multi-label.
  • Text-only ingestion. Real bid packages arrive as scanned PDFs; OCR is part of the production architecture, not this demo.
  • The system is assistive, not autonomous. Legal & commercial teams remain final decision-makers.

License: MIT. CUAD is CC-BY-4.0 (Hendrycks et al., NeurIPS 2021).