Instructions to use surajp/sanbert-from-indicbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use surajp/sanbert-from-indicbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="surajp/sanbert-from-indicbert")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("surajp/sanbert-from-indicbert") model = AutoModelForMaskedLM.from_pretrained("surajp/sanbert-from-indicbert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 26595d8a6776ce32fd6b3587376c2f88481c604f3392ef34b7f081c97fd4fb1c
- Size of remote file:
- 133 MB
- SHA256:
- 9657fa5e150284d088742b9f199ebf4421d5fa3f4a32b561e4c020205c8f4e86
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