pytorch bert text-classification bn hi hi-en ka-en ma-en mr ta-en ur ur-en en arxiv:2204.12543 transformers license:afl-3.0
---
language: [bn, hi, hi-en, ka-en, ma-en, mr, ta-en, ur, ur-en, en]
license: afl-3.0
---

This model is used detecting **abusive speech** in **Bengali, Devanagari Hindi, Code-mixed Hindi, Code-mixed Kannada, Code-mixed Malayalam, Marathi, Code-mixed Tamil, Urdu, Code-mixed Urdu, and English languages**. The allInOne in the name refers to the Joint training/Cross-lingual training, where the model is trained using all the languages data. It is finetuned on MuRIL model.
The model is trained with learning rates of 2e-5. Training code can be found at this [url](https://github.com/hate-alert/IndicAbusive)

LABEL_0 :-> Normal

LABEL_1 :-> Abusive


### For more details about our paper

Mithun Das, Somnath Banerjee and Animesh Mukherjee. "[Data Bootstrapping Approaches to Improve Low Resource Abusive Language Detection for Indic Languages](https://arxiv.org/abs/2204.12543)". Accepted at ACM HT 2022.

***Please cite our paper in any published work that uses any of these resources.***
~~~
@article{das2022data,
  title={Data Bootstrapping Approaches to Improve Low Resource Abusive Language Detection for Indic Languages},
  author={Das, Mithun and Banerjee, Somnath and Mukherjee, Animesh},
  journal={arXiv preprint arXiv:2204.12543},
  year={2022}
}
~~~