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:
- c4ce8a3e450c07cc07e759e33a6532d6feac11b2dfa47f6de457ec40886278e3
- Size of remote file:
- 266 MB
- SHA256:
- d2b1a7535aa31664f43a282b7ec537685916aab41d667570c05141852d796852
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