Social Bias Ner

🚀 Social Bias NER

This NER model, fine - tuned from BERT, is designed for multi - label token classification of generalizations, unfairness, and stereotypes, offering a solution for detecting social bias in text.

🚀 Quick Start

Transformers pipeline doesn't have a class for multi - label token classification, but you can use this code to load the model, run it, and format the output.

Basic Usage

import json
import torch
from transformers import BertTokenizerFast, BertForTokenClassification
import gradio as gr

# init important things
tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased')
model = BertForTokenClassification.from_pretrained('ethical-spectacle/social-bias-ner')
model.eval()
model.to('cuda' if torch.cuda.is_available() else 'cpu')

# ids to labels we want to display
id2label = {
    0: 'O',
    1: 'B-STEREO',
    2: 'I-STEREO',
    3: 'B-GEN',
    4: 'I-GEN',
    5: 'B-UNFAIR',
    6: 'I-UNFAIR'
}

# predict function you'll want to use if using in your own code
def predict_ner_tags(sentence):
    inputs = tokenizer(sentence, return_tensors="pt", padding=True, truncation=True, max_length=128)
    input_ids = inputs['input_ids'].to(model.device)
    attention_mask = inputs['attention_mask'].to(model.device)

    with torch.no_grad():
        outputs = model(input_ids=input_ids, attention_mask=attention_mask)
        logits = outputs.logits
        probabilities = torch.sigmoid(logits)
        predicted_labels = (probabilities > 0.5).int() # remember to try your own threshold

    result = []
    tokens = tokenizer.convert_ids_to_tokens(input_ids[0])
    for i, token in enumerate(tokens):
        if token not in tokenizer.all_special_tokens:
            label_indices = (predicted_labels[0][i] == 1).nonzero(as_tuple=False).squeeze(-1)
            labels = [id2label[idx.item()] for idx in label_indices] if label_indices.numel() > 0 else ['O']
            result.append({"token": token, "labels": labels})

    return json.dumps(result, indent=4)

✨ Features

This NER model is fine - tuned from BERT, for multi - label token classification of:

• (GEN)eralizations
• (UNFAIR)ness
• (STEREO)types

You can try it out in spaces :).

📚 Documentation

GUS - Net Project Details:

Resources:

• Please visit this collection for the datasets and model presented in the GUS - Net paper.
• GUS - Net was implemented as part of The Fair - ly Project, in a Chrome Extension, and PyPI package.

Please cite:

@article{powers2024gusnet,
  title={{GUS-Net: Social Bias Classification in Text with Generalizations, Unfairness, and Stereotypes}},
  author={Maximus Powers and Umang Mavani and Harshitha Reddy Jonala and Ansh Tiwari and Hua Wei},
  journal={arXiv preprint arXiv:2410.08388},
  year={2024},
  url={https://arxiv.org/abs/2410.08388}
}

Give our research group, Ethical Spectacle, a follow ;).

📄 License

This project is licensed under the MIT license.