L3Cube-MahaHate: A Tweet-based Marathi Hate Speech Detection Dataset and BERT models
Paper • 2203.13778 • Published
How to use l3cube-pune/mahahate-multi-roberta with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="l3cube-pune/mahahate-multi-roberta") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("l3cube-pune/mahahate-multi-roberta")
model = AutoModelForSequenceClassification.from_pretrained("l3cube-pune/mahahate-multi-roberta")# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("l3cube-pune/mahahate-multi-roberta")
model = AutoModelForSequenceClassification.from_pretrained("l3cube-pune/mahahate-multi-roberta")MahaHate-multi-RoBERTa (Marathi Hate speech identification) is a MahaRoBERTa(l3cube-pune/marathi-roberta) model fine-tuned on L3Cube-MahaHate - a Marathi tweet-based hate speech detection dataset. This is a four-class model with labels as hate, offensive, profane, and not. The 2-class model can be found here
[dataset link] (https://github.com/l3cube-pune/MarathiNLP)
More details on the dataset, models, and baseline results can be found in our [paper] (https://arxiv.org/abs/2203.13778)
@inproceedings{patil2022l3cube,
title={L3Cube-MahaHate: A Tweet-based Marathi Hate Speech Detection Dataset and BERT Models},
author={Patil, Hrushikesh and Velankar, Abhishek and Joshi, Raviraj},
booktitle={Proceedings of the Third Workshop on Threat, Aggression and Cyberbullying (TRAC 2022)},
pages={1--9},
year={2022}
}
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="l3cube-pune/mahahate-multi-roberta")