Sentence Similarity
ONNX
sentence-transformers
light-embed
bert
feature-extraction
text-embeddings-inference
Instructions to use LightEmbed/sbert-LaBSE-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LightEmbed/sbert-LaBSE-onnx with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LightEmbed/sbert-LaBSE-onnx") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from LightEmbed/sbert-LaBSE-onnx: direct link, hf CLI and curl.
- Browser
- Download file 13.6 MB
-
https://huggingface.co/LightEmbed/sbert-LaBSE-onnx/resolve/main/tokenizer.json
- Command line
-
hf download hf://LightEmbed/sbert-LaBSE-onnx/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/LightEmbed/sbert-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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