Instructions to use l3cube-pune/marathi-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use l3cube-pune/marathi-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="l3cube-pune/marathi-bert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("l3cube-pune/marathi-bert") model = AutoModelForMaskedLM.from_pretrained("l3cube-pune/marathi-bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from l3cube-pune/marathi-bert: direct link, hf CLI and curl.
- Browser
- Download file 1.05 kB
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https://huggingface.co/l3cube-pune/marathi-bert/resolve/main/README.md
- Command line
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hf download hf://l3cube-pune/marathi-bert/README.md
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curl -L -o README.md https://huggingface.co/l3cube-pune/marathi-bert/resolve/main/README.md
1.05 kB
metadata
license: cc-by-4.0
language: mr
datasets:
- L3Cube-MahaCorpus
MahaBERT
New version of this model is available here: https://huggingface.co/l3cube-pune/marathi-bert-v2
MahaBERT is a Marathi BERT model. It is a multilingual BERT (bert-base-multilingual-cased) model fine-tuned on L3Cube-MahaCorpus and other publicly available Marathi monolingual datasets. [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/2202.01159)
@InProceedings{joshi:2022:WILDRE6,
author = {Joshi, Raviraj},
title = {L3Cube-MahaCorpus and MahaBERT: Marathi Monolingual Corpus, Marathi BERT Language Models, and Resources},
booktitle = {Proceedings of The WILDRE-6 Workshop within the 13th Language Resources and Evaluation Conference},
month = {June},
year = {2022},
address = {Marseille, France},
publisher = {European Language Resources Association},
pages = {97--101}
}