Instructions to use surajp/sanbert-from-scratch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use surajp/sanbert-from-scratch with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="surajp/sanbert-from-scratch")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("surajp/sanbert-from-scratch") model = AutoModelForMaskedLM.from_pretrained("surajp/sanbert-from-scratch") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 910152ed9f347b0563b40b76e847cb35379a229e0582081ec0855d231766fd17
- Size of remote file:
- 133 MB
- SHA256:
- 6619ba9029de2cfc64d36b316af5bfe0db59c8491c8d0841a6fae99d69d8eb6d
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