Instructions to use l3cube-pune/marathi-bert-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use l3cube-pune/marathi-bert-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="l3cube-pune/marathi-bert-v2")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("l3cube-pune/marathi-bert-v2") model = AutoModelForMaskedLM.from_pretrained("l3cube-pune/marathi-bert-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 78075f210f709bcf48d319cd9c90fee5686df6a31cd292f9c8a9b1fbfbc089fb
- Size of remote file:
- 951 MB
- SHA256:
- f971c6d3c93afa406a5c81f0a3e677812bdc784a959d093ae14b54862346d826
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