Instructions to use l3cube-pune/marathi-bert-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use l3cube-pune/marathi-bert-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="l3cube-pune/marathi-bert-small")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("l3cube-pune/marathi-bert-small") model = AutoModelForMaskedLM.from_pretrained("l3cube-pune/marathi-bert-small", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from l3cube-pune/marathi-bert-small: direct link, hf CLI and curl.
- Browser
- Download file 334 MB
-
https://huggingface.co/l3cube-pune/marathi-bert-small/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://l3cube-pune/marathi-bert-small/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/l3cube-pune/marathi-bert-small/resolve/main/pytorch_model.bin
334 MB
- Xet hash:
- d8425c6cd24c3a5d8b1ef96b91782d03a3597e7af549a3fe0d86b7c160734701
- Size of remote file:
- 334 MB
- SHA256:
- 2ec5ae7685f09db65ff88ca50702011bd58f5c3203383e93e8cdc19105292b0d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.