Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
token-pruning
text-embeddings-inference
Instructions to use jangedoo/multilingual-e5-small-en-pruned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use jangedoo/multilingual-e5-small-en-pruned with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("jangedoo/multilingual-e5-small-en-pruned") 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
- Xet hash:
- 58cedf6a5f0421aed26d6136bb7bdce553daf670be888e75f359f60f8279de67
- Size of remote file:
- 17.1 MB
- SHA256:
- 6040ba36e3e2f7b2fa6ae076b69d024a08666bea4c345105a32e542900fcc7e7
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