⚠️ DEPRECATED — This model used a non-standard training methodology (Focal Loss + class weighting) and was evaluated on a synthetic test set. For accurate, reproducible results on the official LexGLUE benchmark, use our updated models:

Model μ-F1 m-F1
lexglue-roberta-unfair-tos 96.1 84.4
lexglue-legalbert-unfair-tos 96.0 84.1
lexglue-deberta-unfair-tos 95.6 82.2
lexglue-legalbert-small-unfair-tos 95.0 78.5

legalbert-unfair-tos

Legal-BERT fine-tuned for UNFAIR-ToS classification

Model Description

This model is fine-tuned on the LexGLUE UNFAIR-ToS dataset to detect unfair clauses in Terms of Service documents.

Base Model: nlpaueb/legal-bert-base-uncased

Performance

Metric	Score
Exact Match Accuracy	74.9%
Micro-F1	0.83
Precision	0.98

Risk Categories

The model classifies text into 8 risk categories:

ID	Category
0	Limitation of liability
1	Unilateral termination
2	Unilateral change
3	Content removal
4	Contract by using
5	Choice of law
6	Jurisdiction
7	Arbitration

Usage

python

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "Agreemind/legalbert-unfair-tos"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

text = "We reserve the right to terminate your account at any time."
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)

with torch.no_grad():
    outputs = model(**inputs)
    probs = torch.sigmoid(outputs.logits)

# Get predictions
labels = ["Limitation of liability", "Unilateral termination", "Unilateral change", 
          "Content removal", "Contract by using", "Choice of law", "Jurisdiction", "Arbitration"]
          
for label, prob in zip(labels, probs[0]):
    if prob > 0.5:
        print(f"{label}: {prob:.2%}")

Training

Dataset: LexGLUE UNFAIR-ToS (~5,500 samples)
Loss: Focal Loss with class weighting
Optimizer: AdamW with cosine LR schedule
Epochs: 15 (with early stopping)

Limitations

Arbitration class has lower recall (~38%) due to limited training samples
Optimized for English legal text

Citation

bibtex

@misc{agreemind-unfair-tos,
  author = {Agreemind},
  title = {legalbert-unfair-tos},
  year = {2024},
  publisher = {HuggingFace},
  url = {https://huggingface.co/Agreemind/legalbert-unfair-tos}
}

Model	μ-F1	m-F1
lexglue-roberta-unfair-tos	96.1	84.4
lexglue-legalbert-unfair-tos	96.0	84.1
lexglue-deberta-unfair-tos	95.6	82.2
lexglue-legalbert-small-unfair-tos	95.0	78.5