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SalmanAbbasi/lexglue-ledgar-distilbert
lexglue-ledgar-distilbert is a text classification model from SalmanAbbasi. Use it when you need a label for a piece of text. The card lists the license as mit.
Author: Salman Abbasi Affiliation: Beijing Institute of Technology, China Research Area: NLP × Agentic AI × Prompt Engineering Date: October 2025
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From the Hugging Face model README
Author: Salman Abbasi
Affiliation: Beijing Institute of Technology, China
Research Area: NLP × Agentic AI × Prompt Engineering
Date: October 2025
This model is a DistilBERT-based legal clause classifier fine-tuned on the LexGLUE–LEDGAR dataset.
It predicts the type of contract clause (e.g., Confidentiality, Termination, Liability, Payment, etc.) given a legal paragraph or clause.
distilbert-base-uncasedcoastalcph/lex_glue (ledgar configuration)This model achieves strong performance despite its small size, making it efficient for deployment in legal NLP systems and agentic AI applications.
| Metric | Score |
|---|---|
| Accuracy | 0.8618 |
| Macro F1 | 0.7842 |
| Validation Loss | 0.5756 |
| Epochs | 3 |
| Batch Size | 8 |
| Learning Rate | 2e-5 |
| Max Sequence Length | 512 |
The LEDGAR dataset contains contract clauses annotated with one of 100+ provision types.
It is part of the LexGLUE benchmark, which evaluates legal text understanding models.
Each sample includes:
{
"text": "This clause sets out the obligations of each party regarding confidentiality.",
"label": "Confidentiality"
}