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 1_Pooling/config.json from LightEmbed/LaBSE-onnx: direct link, hf CLI and curl.
- Browser
- Download file 190 Bytes
-
https://huggingface.co/LightEmbed/LaBSE-onnx/resolve/main/1_Pooling/config.json
- Command line
-
hf download hf://LightEmbed/LaBSE-onnx/1_Pooling/config.json
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curl -L -o config.json https://huggingface.co/LightEmbed/LaBSE-onnx/resolve/main/1_Pooling/config.json
190 Bytes
| { | |
| "word_embedding_dimension": 768, | |
| "pooling_mode_cls_token": true, | |
| "pooling_mode_mean_tokens": false, | |
| "pooling_mode_max_tokens": false, | |
| "pooling_mode_mean_sqrt_len_tokens": false | |
| } |