Automatic Identification of Gender Bias in Hindi,Bengali,Meitei Codemixed Texts
This is a XLM-Align-Base model trained on CoMMA dataset of 12k samples
- This is an extension work from our previous paper: Hypers at ComMA@ICON: Modelling Aggressiveness, Gender Bias and Communal Bias Identification.
Example Usage
import torch
import numpy as np
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
from transformers import set_seed
set_seed(425)
text = "some gender biased text"
pipe = pipeline("text-classification", model="seanbenhur/MuLTiGENBiaS")
def predict_pipe(text):
prediction = pipe(text, return_all_scores=True)[0]
return prediction
if __name__ == "__main__":
target = predict_pipe(text)
print(target)
Some concerns
- Note: The model is trained on relatively lower samples (i.e 12k) but with mix of four languages Hindi, Bengali, Meitei, and English. It contains both native on codemixed scripts, So the model might perform poorly on many text samples and might not generalize well.
Bibtex
@article{Benhur2021HypersAC,
title={Hypers at ComMA@ICON: Modelling Aggressiveness, Gender Bias and Communal Bias Identification},
author={Sean Benhur and Roshan Nayak and Kanchana Sivanraju and Adeep Hande and Subalalitha Chinnaudayar Navaneethakrishnan and Ruba Priyadharshini and Bharathi Raja Chakravarthi6},
journal={ArXiv},
year={2021},
volume={abs/2112.15417}
}
- Downloads last month
- 109
Inference Providers
NEW
This model is not currently available via any of the supported third-party Inference Providers, and
the model is not deployed on the HF Inference API.