Text Classification
Transformers
PyTorch
Safetensors
Marathi
English
multilingual
bert
codemix
text-embeddings-inference
Instructions to use l3cube-pune/me-hate-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use l3cube-pune/me-hate-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="l3cube-pune/me-hate-bert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("l3cube-pune/me-hate-bert") model = AutoModelForSequenceClassification.from_pretrained("l3cube-pune/me-hate-bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
language:
- mr
- en
- multilingual
license: cc-by-4.0
tags:
- mr
- en
- codemix
datasets:
- L3Cube-MeCorpus
- L3Cube-MeHate
MeHate-BERT
MeHate-BERT is a MeBERT-Mixed-v2 model fine-tuned on L3Cube-MeHate, a codemixed Marathi-English hate speech identification dataset.
[dataset link] (https://github.com/l3cube-pune/MarathiNLP)
LABEL_0 = Non-Hate, LABEL_1 = Hate
More details on the dataset, models, and baseline results can be found in our [paper] (https://arxiv.org/abs/2306.14030)
Other models from the MeBERT family:
MeBERT
MeRoBERTa
MeBERT-Mixed
MeBERT-Mixed-v2
MeRoBERTa-Mixed
MeLID-RoBERTa
MeHate-RoBERTa
MeSent-RoBERTa
MeHate-BERT
MeLID-BERT
Citing:
@article{chavan2023my,
title={My Boli: Code-mixed Marathi-English Corpora, Pretrained Language Models and Evaluation Benchmarks},
author={Chavan, Tanmay and Gokhale, Omkar and Kane, Aditya and Patankar, Shantanu and Joshi, Raviraj},
journal={arXiv preprint arXiv:2306.14030},
year={2023}
}