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")# pip install -U transformers accelerate # 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
Download training_args.bin from NLPC-UOM/SinBERT-large: direct link, hf CLI and curl.
- Browser
- Download file 2.86 kB
-
https://huggingface.co/NLPC-UOM/SinBERT-large/resolve/main/training_args.bin
- Command line
-
hf download hf://NLPC-UOM/SinBERT-large/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/NLPC-UOM/SinBERT-large/resolve/main/training_args.bin
2.86 kB
- Xet hash:
- fbe45cc0384ddef3887b4a7d6a821e0cc881ddbb94820d89f50dd286050117a3
- Size of remote file:
- 2.86 kB
- SHA256:
- 32b53913f43a6274c8bdf6d147cb45bfdbb00d834d020b0e8c13e25adc86d592
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