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
Download pytorch_model.bin from l3cube-pune/marathi-bert: direct link, hf CLI and curl.
- Browser
- Download file 712 MB
-
https://huggingface.co/l3cube-pune/marathi-bert/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://l3cube-pune/marathi-bert/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/l3cube-pune/marathi-bert/resolve/main/pytorch_model.bin
712 MB
- Xet hash:
- fa235e7b212e5e60fb62194a1fed3d7e6ee7b1bef17d23558b47b48bae9723aa
- Size of remote file:
- 712 MB
- SHA256:
- 600d7824bf6c41f7f3955d12ac9cf65880e0ad4e1bd044e7106f3ef59d1284bb
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