# Hugging Face with Bias Data in CoNLL Format

## Introduction

This README provides guidance on how to use the Hugging Face platform with bias-tagged datasets in the CoNLL format.
Such datasets are essential for studying and mitigating bias in AI models.
This dataset is curated by **Shaina Raza**. 
The methods and formatting discussed here are based on the seminal work "Nbias: A natural language processing framework for BIAS identification in text" by Raza et al. (2024) (see citation below).

## Prerequisites
- Install the Hugging Face `transformers` and `datasets` libraries:
  ```bash
  pip install transformers datasets
  ```

## Data Format

Bias data in CoNLL format can be structured similarly to standard CoNLL, but with labels indicating bias instead of named entities:
```
The     O
book    O
written B-BIAS
by      I-BIAS
egoist  I-BIAS
women   I-BIAS
is      O
good    O
.       O

```
Here, `B-` prefixes indicate the beginning of a biased term,`I-` indicates inside biased terms, and `O` stands for outside any biased entity.

## Steps to Use with Hugging Face

1. **Loading Bias-tagged CoNLL Data with Hugging Face**
   - If your bias-tagged dataset in CoNLL format is publicly available on the Hugging Face `datasets` hub, use:
     ```python
     from datasets import load_dataset
     dataset = load_dataset("newsmediabias/BIAS-CONLL")
     ```
   - For custom datasets, ensure they are formatted correctly and use a local path to load them.
     If the dataset is gated/private, make sure you have run `huggingface-cli login`


2. **Preprocessing the Data**
   - Tokenization:
     ```python
     from transformers import AutoTokenizer
     tokenizer = AutoTokenizer.from_pretrained("YOUR_PREFERRED_MODEL_CHECKPOINT")
     tokenized_input = tokenizer(dataset['train']['tokens'])
     ```

3. **Training a Model on Bias-tagged CoNLL Data**
   - Depending on your task, you may fine-tune a model on the bias data using Hugging Face's `Trainer` class or native PyTorch/TensorFlow code.

4. **Evaluation**
   - After training, evaluate the model's ability to recognize and possibly mitigate bias.
   - This might involve measuring the model's precision, recall, and F1 score on recognizing bias in text.

5. **Deployment**
   - Once satisfied with the model's performance, deploy it for real-world applications, always being mindful of its limitations and potential implications.



Please cite us if you use it.

**Reference to cite us**
```
@article{raza2024nbias,
  title={Nbias: A natural language processing framework for BIAS identification in text},
  author={Raza, Shaina and Garg, Muskan and Reji, Deepak John and Bashir, Syed Raza and Ding, Chen},
  journal={Expert Systems with Applications},
  volume={237},
  pages={121542},
  year={2024},
  publisher={Elsevier}
}
```