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SalmanAbbasi/
lexglue-ledgar-distilbert

Name: lexglue-ledgar-distilbert
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🧠 LexGLUE–LEDGAR DistilBERT Legal Clause Classifier

Author: Salman Abbasi
Affiliation: Beijing Institute of Technology, China
Research Area: NLP × Agentic AI × Prompt Engineering
Date: October 2025

📝 Model Overview

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.

Base Model: distilbert-base-uncased
Dataset: coastalcph/lex_glue (ledgar configuration)
Task Type: Multi-class text classification
Language: English
Domain: Legal / Contractual Texts

This model achieves strong performance despite its small size, making it efficient for deployment in legal NLP systems and agentic AI applications.

🚀 Model Performance

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

📚 Dataset — LexGLUE LEDGAR

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:

json

{
  "text": "This clause sets out the obligations of each party regarding confidentiality.",
  "label": "Confidentiality"
}

SalmanAbbasi/lexglue-ledgar-distilbert

🧠 LexGLUE–LEDGAR DistilBERT Legal Clause Classifier

📝 Model Overview

🚀 Model Performance

📚 Dataset — LexGLUE LEDGAR

SalmanAbbasi/
lexglue-ledgar-distilbert