Feature Extraction
ONNX
sentence-transformers
multilingual
onnxruntime
bert
e5
int8
text-embeddings-inference
Instructions to use orca-zhang/multilingual-e5-small-int8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use orca-zhang/multilingual-e5-small-int8 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("orca-zhang/multilingual-e5-small-int8") 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 orca-zhang/multilingual-e5-small-int8: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/orca-zhang/multilingual-e5-small-int8/resolve/main/tokenizer.json
- Command line
-
hf download hf://orca-zhang/multilingual-e5-small-int8/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/orca-zhang/multilingual-e5-small-int8/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- 6af26b4ae9e339ce7e39303028626f589021baa47bca077ce1854e8b54a9bc97
- Size of remote file:
- 17.1 MB
- SHA256:
- 0b44a9d7b51c3c62626640cda0e2c2f70fdacdc25bbbd68038369d14ebdf4c39
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