Sentence Similarity
sentence-transformers
ONNX
Safetensors
Spanish
bert
embeddings
contrastive-learning
e-commerce
semantic-search
product-recommendation
spanish
text-embeddings-inference
Instructions to use Mateo-Rua/embeddings-productos-ecommerce-es with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Mateo-Rua/embeddings-productos-ecommerce-es with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Mateo-Rua/embeddings-productos-ecommerce-es") sentences = [ "Esa es una persona feliz", "Ese es un perro feliz", "Esa es una persona muy feliz", "Hoy es un día soleado" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
File size: 429 Bytes
2dd6583 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | [
{
"idx": 0,
"name": "0",
"path": "",
"type": "sentence_transformers.base.modules.transformer.Transformer"
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{
"idx": 1,
"name": "1",
"path": "1_Pooling",
"type": "sentence_transformers.sentence_transformer.modules.pooling.Pooling"
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{
"idx": 2,
"name": "2",
"path": "2_Normalize",
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