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")# 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
- Xet hash:
- 1f5646a440f44d51dca9fd6cf1cbf3a061673be2f8c8e0da956af3c368045876
- Size of remote file:
- 504 MB
- SHA256:
- f5bb6e7695379411c4a3f2e5d4737fc6b0633315fa813f59987475389ce915ff
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