| --- |
| 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`](https://huggingface.co/datasets/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). |
|
|
