Instructions to use NLPC-UOM/SinBERT-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NLPC-UOM/SinBERT-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="NLPC-UOM/SinBERT-small")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("NLPC-UOM/SinBERT-small") model = AutoModelForMaskedLM.from_pretrained("NLPC-UOM/SinBERT-small") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a981704b0a74619bc7e49cefb404b8a53e0878f99988def05d92ffe235b2fbf1
- Size of remote file:
- 2.86 kB
- SHA256:
- 9e37dbfc9e6d0c9ae4747840eb0b3efaafc6a0b0d2364f6c74296f5100cb7d19
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