Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:867042
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use agentlans/multilingual-e5-small-aligned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use agentlans/multilingual-e5-small-aligned with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("agentlans/multilingual-e5-small-aligned") sentences = [ "An air strike.", "מר פרקינסון היה מזועזע אם היה יודע איך מר פוקס מתנהג.", "Sonia: Jangan berkata begitu.", "En luftattack." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download sentencepiece.bpe.model from agentlans/multilingual-e5-small-aligned: direct link, hf CLI and curl.
- Browser
- Download file 5.07 MB
-
https://huggingface.co/agentlans/multilingual-e5-small-aligned/resolve/main/sentencepiece.bpe.model
- Command line
-
hf download hf://agentlans/multilingual-e5-small-aligned/sentencepiece.bpe.model
-
curl -L -o sentencepiece.bpe.model https://huggingface.co/agentlans/multilingual-e5-small-aligned/resolve/main/sentencepiece.bpe.model
5.07 MB
- Xet hash:
- 8b03b9e079abc849bdd27d0942fa6a77f9e7836db188512be97e4b3d52f415a8
- Size of remote file:
- 5.07 MB
- SHA256:
- cfc8146abe2a0488e9e2a0c56de7952f7c11ab059eca145a0a727afce0db2865
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