Instructions to use r1ck/vi-sentence-embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use r1ck/vi-sentence-embedding with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("r1ck/vi-sentence-embedding") model = AutoModelForMaskedLM.from_pretrained("r1ck/vi-sentence-embedding") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 376d9e0cc3bced4d31c928678db2fee5a7c0d387509fdf49553f0af475ddec1d
- Size of remote file:
- 540 MB
- SHA256:
- 6fb20a954ff535734026b488d2916ddeda7109e8a867e57da48639f12da34994
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