Instructions to use l3cube-pune/hindi-marathi-dev-bert-scratch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use l3cube-pune/hindi-marathi-dev-bert-scratch with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="l3cube-pune/hindi-marathi-dev-bert-scratch")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("l3cube-pune/hindi-marathi-dev-bert-scratch") model = AutoModelForMaskedLM.from_pretrained("l3cube-pune/hindi-marathi-dev-bert-scratch", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d4e55f71d15faa48d534425ab8d704ebbed103f24e560ffe1525a2fad04302f3
- Size of remote file:
- 3.39 MB
- SHA256:
- 29db45b3ec0fa774065ed9e3e9ae6882f023a74469da34e3ac9bf5e14844f34a
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