Updated README.md

README.md

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+# 📄 Identifying Disability-Insensitive Language in Scholarly Works
+
+Refer to the code repository here: [GitHub - Insensitive-Lang-Detection](https://github.com/RobyRoshna/Insensitive-Lang-Detection/tree/main)
+
+---
+
+## 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.
+
+---
+
+## 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.
+
+---
+
+## 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
+
+---
+
+## Training Data
+- Extracted and manually annotated sentences referencing disability-related terms.
+- Supported with data augmentation using OpenAI GPT-4o to balance underrepresented phrases.
+
+---
+
+## How to Use
+
+```python
+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)