README.md

metadata

license: cc-by-4.0
task_categories:
  - text-classification
language:
  - en
size_categories:
  - 10K<n<100K
tags:
  - legal
  - contracts
  - clause-classification
  - CUAD

CUAD Clause Classification — with negative ("None Of The Above") samples

This dataset is dvgodoy/CUAD_v1_Contract_Understanding_clause_classification (13,155 attorney-labeled clauses over 41 clause types from 509 commercial contracts) with a 42nd reject class added: class_id = 41, label = "None Of The Above".

The base dataset contains only positively-tagged clauses, so a tagger trained on it is forced to assign one of the 41 labels to any input. The added negatives let the model learn to say "this is none of the 41 clause types."

The format is identical to the source: 7 columns (file_name, clause, pages, class_id, label, start_at, end_at), single train split.

Rows

	count
Positive clauses (class 0–40)	13,155
Negatives (class 41)	2,600
Total	15,755

How the negatives were built

Two complementary sources, both mapped to class_id 41:

1. In-domain (~82%) — real, unannotated spans mined from the same 509 source contracts (only ~19% of each contract is annotated; the rest is genuine contract prose — definitions, confidentiality, indemnification, notices, etc. — that is not one of the 41 tracked types). Text is verbatim from the contracts. For these rows file_name is the source contract.
2. Out-of-domain (~18%) — synthetic non-contract sentences (e.g. everyday facts, reviews, messages) so the model also rejects clearly non-contract input. For these rows file_name is empty.

Negative rows use start_at = end_at = -1 (they are not a single contiguous annotated span). A light signature filter drops gap chunks that strongly match one of the 41 clause types, to avoid mislabeling missed annotations as negatives.

Recommended use

• Train with per-class F1 / macro-F1, not raw accuracy (the data is heavily imbalanced, ~98:1, and class 41 is now one of the largest classes).
• Optionally add a serving-time confidence threshold (reject when the top softmax probability is below τ) as an extra layer over the learned reject class.

License & attribution

cc-by-4.0, inheriting from CUAD v1 (The Atticus Project) and the source dataset. Please cite CUAD: Hendrycks et al., CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review, NeurIPS 2021 (arXiv:2103.06268).