Instructions to use l3cube-pune/hing-mbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use l3cube-pune/hing-mbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="l3cube-pune/hing-mbert")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("l3cube-pune/hing-mbert") model = AutoModelForMaskedLM.from_pretrained("l3cube-pune/hing-mbert") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a29849a87aa685cfe444cf04a3948f4e6ca8fc4c15f04f13e09f848bcdd9eebf
- Size of remote file:
- 712 MB
- SHA256:
- 84f625a31d99207d8e8450d14eae1b6caa7f9418cee59db4dc3389b11a304eda
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