L3Cube-HindBERT and DevBERT: Pre-Trained BERT Transformer models for Devanagari based Hindi and Marathi Languages
Paper • 2211.11418 • Published
How to use l3cube-pune/hindi-bert-v1 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("fill-mask", model="l3cube-pune/hindi-bert-v1") # Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("l3cube-pune/hindi-bert-v1")
model = AutoModelForMaskedLM.from_pretrained("l3cube-pune/hindi-bert-v1")HindBERT is a Hindi BERT model. It is a multilingual BERT (bert-base-multilingual-cased) model fine-tuned on publicly available Hindi monolingual datasets. [project link] (https://github.com/l3cube-pune/MarathiNLP)
More details on the dataset, models, and baseline results can be found in our [ paper ]
A new version of model is shared here
Citing:
@article{joshi2022l3cubehind,
title={L3Cube-HindBERT and DevBERT: Pre-Trained BERT Transformer models for Devanagari based Hindi and Marathi Languages},
author={Joshi, Raviraj},
journal={arXiv preprint arXiv:2211.11418},
year={2022}
}