Agreemind/lexglue-legalbert-unfair-tos
017
lexglue-legalbert-unfair-tos
Legal-BERT fine-tuned on LexGLUE UNFAIR-ToS — matches published benchmark
Model Description
This model is fine-tuned on the LexGLUE UNFAIR-ToS benchmark dataset to detect unfair clauses in Terms of Service documents.
Base Model: nlpaueb/legal-bert-base-uncased Training: Standard BCEWithLogitsLoss, lr=3e-5, batch_size=8, linear scheduler, up to 20 epochs with early stopping (matching Chalkidis et al., 2022)
Performance
Evaluated on the official LexGLUE test set (1,607 samples) using the paper's evaluation methodology (includes implicit "fair" class in micro-F1 computation).
Comparison with LexGLUE Leaderboard
Risk Categories
The model classifies text into 8 types of potentially unfair clauses:
Usage
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model_id = "Agreemind/lexglue-legalbert-unfair-tos"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id)
labels = [
"Limitation of liability", "Unilateral termination",
"Unilateral change", "Content removal",
"Contract by using", "Choice of law",
"Jurisdiction", "Arbitration",
]
text = "We may terminate your account at any time without notice."
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=128)
with torch.no_grad():
probs = torch.sigmoid(model(**inputs).logits).squeeze()
for label, prob in sorted(zip(labels, probs), key=lambda x: x[1], reverse=True):
if prob > 0.5:
print(f" {label}: {prob:.3f}")Citation
If you use this model, please cite:
@article{chalkidis2022lexglue,
title={LexGLUE: A Benchmark Dataset for Legal Language Understanding in English},
author={Chalkidis, Ilias and Jana, Abhik and Hartung, Dirk and Bommarito, Michael and Androutsopoulos, Ion and Katz, Daniel Martin and Aletras, Nikolaos},
journal={arXiv preprint arXiv:2110.00976},
year={2022}
}License
MIT