Instructions to use l3cube-pune/hindi-bert-scratch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use l3cube-pune/hindi-bert-scratch with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="l3cube-pune/hindi-bert-scratch")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("l3cube-pune/hindi-bert-scratch") model = AutoModelForMaskedLM.from_pretrained("l3cube-pune/hindi-bert-scratch", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a7d4d11d848d81c60174dc4b48b1318f3c959ee0f25e397e83923b2e1d510028
- Size of remote file:
- 504 MB
- SHA256:
- e83d5b5b809071165dbcb97716149a605fb853022abb02754ab6d5450e1c15c1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.