JohnLicode/ethics-review-deberta

Ethics Review DeBERTa Model

A fine-tuned DeBERTa-v3-base model for classifying research ethics guideline compliance.

Model Description

This model classifies text segments from research proposals to determine if they adequately address specific ethics review guidelines.

Labels

• LABEL_0 / ADDRESSED: The text adequately addresses the ethics guideline
• LABEL_1 / NEEDS_REVISION: The text needs revision or doesn't address the guideline

Usage

from transformers import pipeline

classifier = pipeline("text-classification", model="JohnLicode/ethics-review-deberta")

# Example
text = "Guideline 1.1 Objectives: The general objective is to develop an AI ethics review system."
result = classifier(text)
print(result)
# [{'label': 'LABEL_0', 'score': 0.95}]  # ADDRESSED

Training

• Base model: microsoft/deberta-v3-base
• Task: Binary text classification
• Training data: Custom ethics review dataset with 30 guideline categories

Intended Use

This model is designed to assist ethics review committees by providing preliminary assessments of research proposals against institutional guidelines.