Instructions to use mahesh27/vedicberta-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mahesh27/vedicberta-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="mahesh27/vedicberta-base")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("mahesh27/vedicberta-base") model = AutoModelForMaskedLM.from_pretrained("mahesh27/vedicberta-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Pre-trained encoder transformer on GRETIL data (IAST format) on Masked Language Modeling
Citation:
@inproceedings{mahesh-bhattacharya-2023-creation,
title = "Creation of a Digital Rig {V}edic Index (Anukramani) for Computational Linguistic Tasks",
author = "Akavarapu, V.S.D.S.Mahesh and
Bhattacharya, Arnab",
editor = "Kulkarni, Amba and
Hellwig, Oliver",
booktitle = "Proceedings of the Computational {S}anskrit {\&} Digital Humanities: Selected papers presented at the 18th World {S}anskrit Conference",
month = jan,
year = "2023",
address = "Canberra, Australia (Online mode)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.wsc-csdh.6/",
pages = "89--96"
}
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