Instructions to use aisingapore/sealion-bert-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aisingapore/sealion-bert-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="aisingapore/sealion-bert-base", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("aisingapore/sealion-bert-base", trust_remote_code=True) model = AutoModelForMaskedLM.from_pretrained("aisingapore/sealion-bert-base", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.model from aisingapore/sealion-bert-base: direct link, hf CLI and curl.
- Browser
- Download file 4.84 MB
-
https://huggingface.co/aisingapore/sealion-bert-base/resolve/main/tokenizer.model
- Command line
-
hf download hf://aisingapore/sealion-bert-base/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/aisingapore/sealion-bert-base/resolve/main/tokenizer.model
4.84 MB
- Xet hash:
- adb5c79e2449474bbc039ccd234e39da2b8869702bdf2983c0bb8941ee229092
- Size of remote file:
- 4.84 MB
- SHA256:
- 170a13cb9d99871ae000c2b45c6b9e5765ce0701cf2d027964e14d00d37e75e7
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