Sentence Similarity
sentence-transformers
PyTorch
xlm-roberta
feature-extraction
text-embeddings-inference
Instructions to use embaas/sentence-transformers-multilingual-e5-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use embaas/sentence-transformers-multilingual-e5-large with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("embaas/sentence-transformers-multilingual-e5-large") 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 embaas/sentence-transformers-multilingual-e5-large: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/embaas/sentence-transformers-multilingual-e5-large/resolve/main/tokenizer.json
- Command line
-
hf download hf://embaas/sentence-transformers-multilingual-e5-large/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/embaas/sentence-transformers-multilingual-e5-large/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- 212bfaaf7beffc70a3f2b8fbd9929f72e086ac0287cc39a5d98486b405c9209a
- Size of remote file:
- 17.1 MB
- SHA256:
- 46afe88da5fd71bdbab5cfab5e84c1adce59c246ea5f9341bbecef061891d0a7
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