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:
- 25f67033e4c7233ea2deee962725b11a7397ec65107821a6c3ba7e98f1435cad
- Size of remote file:
- 652 MB
- SHA256:
- 96cbff2b6d425df20fd0c89526bf92c623c15bcbc2abbd177c0290114f829424
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