Instructions to use LightEmbed/LaBSE-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LightEmbed/LaBSE-onnx with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="LightEmbed/LaBSE-onnx")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("LightEmbed/LaBSE-onnx") model = AutoModel.from_pretrained("LightEmbed/LaBSE-onnx", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from LightEmbed/LaBSE-onnx: direct link, hf CLI and curl.
- Browser
- Download file 13.6 MB
-
https://huggingface.co/LightEmbed/LaBSE-onnx/resolve/main/tokenizer.json
- Command line
-
hf download hf://LightEmbed/LaBSE-onnx/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/LightEmbed/LaBSE-onnx/resolve/main/tokenizer.json
13.6 MB
- Xet hash:
- f7c27219fb0ce445969a1876fcda99323f065c2d8d820594c538c31f55ed2c82
- Size of remote file:
- 13.6 MB
- SHA256:
- 09216b42d2697b7b4a26ac05ff09ba8bf52dc19b896c5ceee8bbff9f39055322
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