Instructions to use NLPC-UOM/SinBERT-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NLPC-UOM/SinBERT-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="NLPC-UOM/SinBERT-large")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("NLPC-UOM/SinBERT-large") model = AutoModelForMaskedLM.from_pretrained("NLPC-UOM/SinBERT-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c953b59873be2bd8a8744d99ddb339dda6b265b51be834dd61708cce57cd0bfe
- Size of remote file:
- 504 MB
- SHA256:
- 743ae3b83ab0862404e189e68bfcd18ad63e37288548e55a713117951c29ef81
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