README.md

metadata

license: cc-by-nc-4.0
tags:
  - bert
  - text-classification
  - disability
  - inclusive-language
  - academic-writing
datasets:
  - assets
library_name: transformers
language:
  - en

Identifying Disability-Insensitive Language in Scholarly Works

Refer to the code repository and paper here: GitHub - Insensitive-Lang-Detection

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Overview

This is a fine-tuned BERT model designed to detect potentially insensitive or non-inclusive language relating to disability, specifically in academic and scholarly writing.

The model helps promote more inclusive and respectful communication, aligning with social models of disability and various international guidelines.

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Intended Use

• Academic editors and reviewers who want to check abstracts and papers for disability-insensitive language.
• Researchers studying accessibility, inclusive design, or language bias.
• Automated writing support tools focused on scholarly communication.

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Model Details

• Architecture: BERT-base (uncased)
• Fine-tuned on: Sentences from ASSETS conference papers (1994–2024) and organizational documents (ADA National Network, UN guidelines).
• Labels:
  • 0: Not insensitive
  • 1: Insensitive

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Training Data

• Extracted and manually annotated sentences referencing disability-related terms.
• Supported with data augmentation using OpenAI GPT-4o to balance underrepresented phrases.

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License

This model is licensed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.

This means you are free to share and adapt the model for non-commercial purposes, as long as appropriate credit is given. Commercial use is not permitted without explicit permission.

For details, see CC BY-NC 4.0.

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How to Use

from transformers import BertForSequenceClassification, BertTokenizer

model = BertForSequenceClassification.from_pretrained("rrroby/insensitive-language-bert")
tokenizer = BertTokenizer.from_pretrained("rrroby/insensitive-language-bert")

text = "This participant was wheelchair-bound and..."
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
outputs = model(**inputs)
logits = outputs.logits
predicted_class = logits.argmax(-1).item()

print("Predicted class:", predicted_class)