Instructions to use l3cube-pune/marathi-bert-scratch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use l3cube-pune/marathi-bert-scratch with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="l3cube-pune/marathi-bert-scratch")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("l3cube-pune/marathi-bert-scratch") model = AutoModelForMaskedLM.from_pretrained("l3cube-pune/marathi-bert-scratch", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from l3cube-pune/marathi-bert-scratch: direct link, hf CLI and curl.
- Browser
- Download file 1.78 MB
-
https://huggingface.co/l3cube-pune/marathi-bert-scratch/resolve/main/tokenizer.json
- Command line
-
hf download hf://l3cube-pune/marathi-bert-scratch/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/l3cube-pune/marathi-bert-scratch/resolve/main/tokenizer.json
1.78 MB
- Xet hash:
- 2d895dab916f842a075055476d1a71d67d92defee7fd576c5dd6197193a01b19
- Size of remote file:
- 1.78 MB
- SHA256:
- 1dd5fb361036cf34afcd3279e70a5ef345c89a051906f213628ba75efa37ab94
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