Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
text-embeddings-inference
Instructions to use smart-tribune/sentence-transformers-multilingual-e5-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use smart-tribune/sentence-transformers-multilingual-e5-small with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("smart-tribune/sentence-transformers-multilingual-e5-small") 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 smart-tribune/sentence-transformers-multilingual-e5-small: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/smart-tribune/sentence-transformers-multilingual-e5-small/resolve/main/tokenizer.json
- Command line
-
hf download hf://smart-tribune/sentence-transformers-multilingual-e5-small/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/smart-tribune/sentence-transformers-multilingual-e5-small/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- b93141528b8377b33b7e585d8e805816218d5cd23eb984ecd1b214a55dbca6ee
- Size of remote file:
- 17.1 MB
- SHA256:
- 642eede941e22781e8b3c9a2dfe2015027b2fd855410b16cd66f07b0f3c76641
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