Instructions to use l3cube-pune/hing-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use l3cube-pune/hing-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="l3cube-pune/hing-bert")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("l3cube-pune/hing-bert") model = AutoModelForMaskedLM.from_pretrained("l3cube-pune/hing-bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 89cf59c49b140678851fb4f243f8a2761db9f7263dd6a06b8b9a0623559e8c17
- Size of remote file:
- 438 MB
- SHA256:
- e655f85fd6167a64ed85d3f034fedc21c54f2f91eee314890988b5a5edff75dd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.