Sentence Similarity
sentence-transformers
Safetensors
multilingual
bert
feature-extraction
dense
Generated from Trainer
dataset_size:74864
loss:CoSENTLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use Antix5/product-embed-multi-e5-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Antix5/product-embed-multi-e5-small with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Antix5/product-embed-multi-e5-small") sentences = [ "Légumes mijotés Jardinière et haricots blancs", "AMSCAN GOLD PLSTC FORKS | PARTY SUPPLY | 240 CT.", "辣椒酱", "Pizza de verduras brasadas" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 95b6f9f4bd916e1539f8958c5d6089b29b29ef87dc55871ff2aa075ce29b5329
- Size of remote file:
- 17.1 MB
- SHA256:
- 2b95ee17661f8dfbbceaba374f6d277a6b5d8e1898c070a16331622024f58c67
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