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Agreemind/contractnli-legalbert-nda-weighted

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contractnli-legalbert-nda-weighted

Legal-BERT fine-tuned on ContractNLI with weighted CE — highest accuracy

Task

Document-level NLI for Non-Disclosure Agreements (NDAs)

Given an NDA contract and a hypothesis about a standard provision, classify as:

—Entailment: The provision is present in the contract
—Contradiction: The provision is explicitly excluded
—NotMentioned: The contract does not address this provision

Performance

Metric	Score
Micro-F1	87.3%
Macro-F1	79.3%
Parameters	110M
Loss	Weighted Cross-Entropy

Comparison

Model	Micro-F1	Macro-F1	Params	Notes
Rule-based baseline	20.9%	16.0%	—	Keyword matching
BERT-base (paper)	~83%	—	110M	ContractNLI paper reference
contractnli-legalbert-nda-weighted	87.3%	79.3%	110M	highest accuracy ← this model
contractnli-legalbert-nda-standard	86.7%	77.0%	110M
contractnli-bert-nda-standard	86.9%	76.7%	110M	paper reproduction
contractnli-bert-nda-weighted	86.3%	77.9%	110M
contractnli-distilbert-nda	86.0%	76.2%	66M	fastest inference, recommended for production

17 NDA Provisions Checked

ID	Provision
nda-1	Explicit identification
nda-2	Non-inclusion of non-technical information
nda-3	Inclusion of verbally conveyed information
nda-4	Limited use
nda-5	Sharing with employees
nda-7	Sharing with third-parties
nda-8	Notice on compelled disclosure
nda-10	Confidentiality of Agreement
nda-11	No reverse engineering
nda-12	Permissible development of similar information
nda-13	Permissible acquirement of similar information
nda-15	No licensing
nda-16	Return of confidential information
nda-17	Permissible copy
nda-18	No solicitation
nda-19	Survival of obligations
nda-20	Permissible post-agreement possession

Usage

python

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "Agreemind/contractnli-legalbert-nda-weighted"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

hypothesis = "All Confidential Information shall be expressly identified by the Disclosing Party."
premise = "Section 2.1: Any information disclosed must be marked as Confidential..."

inputs = tokenizer(hypothesis, premise, return_tensors="pt", truncation=True, max_length=512)
with torch.no_grad():
    logits = model(**inputs).logits
    probs = torch.softmax(logits, dim=-1)
    pred = ["Entailment", "Contradiction", "NotMentioned"][probs.argmax()]
    print(f"Prediction: {pred} (confidence: {probs.max():.3f})")

Training Details

—Dataset: ContractNLI (607 NDAs, 17 hypotheses)
—Train/Dev/Test: 423/61/123 documents → 33,974/5,131/9,373 span-level examples
—Base model: nlpaueb/legal-bert-base-uncased
—Loss: Weighted Cross-Entropy
—Learning rate: 3e-5 (aligned with ContractNLI paper)
—Epochs: 5 with early stopping (patience=3)
—Batch size: 8
—Max sequence length: 512

Citation

bibtex

@inproceedings{koreeda-manning-2021-contractnli,
    title = "ContractNLI: A Dataset for Document-level Natural Language Inference for Contracts",
    author = "Koreeda, Yuta and Manning, Christopher",
    booktitle = "Findings of EMNLP 2021",
    year = "2021",
}

License

MIT