Sentence Similarity
sentence-transformers
ONNX
bert
mteb
Sentence Transformers
Eval Results (legacy)
text-embeddings-inference
Instructions to use nilay-sam-23/multilingual-e5-small-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use nilay-sam-23/multilingual-e5-small-onnx with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("nilay-sam-23/multilingual-e5-small-onnx") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from nilay-sam-23/multilingual-e5-small-onnx: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/nilay-sam-23/multilingual-e5-small-onnx/resolve/main/tokenizer.json
- Command line
-
hf download hf://nilay-sam-23/multilingual-e5-small-onnx/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/nilay-sam-23/multilingual-e5-small-onnx/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- 77dc3d4fce0d076c99c0646439de3c9ecd4de828e88bd2425b832cb769a45340
- Size of remote file:
- 17.1 MB
- SHA256:
- ef04f2b385d1514f500e779207ace0f53e30895ce37563179e29f4022d28ca38
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