Sentence Similarity
sentence-transformers
Safetensors
Model2Vec
English
static-embeddings
e-commerce
ecommerce
product-search
semantic-search
retrieval
shopify
woocommerce
esci
cpu
edge
no-gpu
Instructions to use albertobarnabo/ecommerce-product-search-embeddings-static with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use albertobarnabo/ecommerce-product-search-embeddings-static with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("albertobarnabo/ecommerce-product-search-embeddings-static") 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] - Model2Vec
How to use albertobarnabo/ecommerce-product-search-embeddings-static with Model2Vec:
from model2vec import StaticModel model = StaticModel.from_pretrained("albertobarnabo/ecommerce-product-search-embeddings-static") - Notebooks
- Google Colab
- Kaggle
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