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--- |
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license: cc-by-nc-4.0 |
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tags: |
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- bert |
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- text-classification |
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- disability |
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- inclusive-language |
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- academic-writing |
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datasets: |
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- assets |
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library_name: transformers |
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language: |
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- en |
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--- |
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# Identifying Disability-Insensitive Language in Scholarly Works |
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Refer to the code repository and paper here: [GitHub - Insensitive-Lang-Detection](https://github.com/RobyRoshna/Insensitive-Lang-Detection/tree/main) |
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--- |
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## Overview |
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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. |
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The model helps promote more inclusive and respectful communication, aligning with social models of disability and various international guidelines. |
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--- |
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## Intended Use |
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- Academic editors and reviewers who want to check abstracts and papers for disability-insensitive language. |
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- Researchers studying accessibility, inclusive design, or language bias. |
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- Automated writing support tools focused on scholarly communication. |
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--- |
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## Model Details |
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- **Architecture**: BERT-base (uncased) |
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- **Fine-tuned on**: Sentences from ASSETS conference papers (1994–2024) and organizational documents (ADA National Network, UN guidelines). |
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- **Labels**: |
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- `0`: Not insensitive |
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- `1`: Insensitive |
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--- |
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## Training Data |
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- Extracted and manually annotated sentences referencing disability-related terms. |
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- Supported with data augmentation using OpenAI GPT-4o to balance underrepresented phrases. |
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--- |
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## License |
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This model is licensed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license. |
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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. |
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For details, see [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/). |
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--- |
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## How to Use |
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```python |
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from transformers import BertForSequenceClassification, BertTokenizer |
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model = BertForSequenceClassification.from_pretrained("rrroby/insensitive-language-bert") |
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tokenizer = BertTokenizer.from_pretrained("rrroby/insensitive-language-bert") |
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text = "This participant was wheelchair-bound and..." |
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inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512) |
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outputs = model(**inputs) |
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logits = outputs.logits |
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predicted_class = logits.argmax(-1).item() |
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print("Predicted class:", predicted_class) |
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