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:
- 981cba0fe7fe0a7c618c778d18fa52a88396e1180cdb58aa82ae0d90dd470cb6
- Size of remote file:
- 471 MB
- SHA256:
- 11c9183ed003a224fd99cd9eb0eee0dfd08ddd8fa7969546085cf0457bb27e7b
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