Instructions to use l3cube-pune/marathi-albert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use l3cube-pune/marathi-albert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="l3cube-pune/marathi-albert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("l3cube-pune/marathi-albert") model = AutoModelForMaskedLM.from_pretrained("l3cube-pune/marathi-albert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from l3cube-pune/marathi-albert: direct link, hf CLI and curl.
- Browser
- Download file 45.9 MB
-
https://huggingface.co/l3cube-pune/marathi-albert/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://l3cube-pune/marathi-albert/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/l3cube-pune/marathi-albert/resolve/main/pytorch_model.bin
45.9 MB
- Xet hash:
- 3b6420fe4c57cd7d814fd8a7f77a69250a08b46b16aa5306c66ab5b115c228fa
- Size of remote file:
- 45.9 MB
- SHA256:
- 4b4a694e962a7b2fd9ae33d659ea421324991d3e47bce639606a6082fa365f6b
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