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
Download 1_Pooling/config.json from Mateo-Rua/embeddings-productos-ecommerce-es: direct link, hf CLI and curl.
- Browser
- Download file 90 Bytes
-
https://huggingface.co/Mateo-Rua/embeddings-productos-ecommerce-es/resolve/main/1_Pooling/config.json
- Command line
-
hf download hf://Mateo-Rua/embeddings-productos-ecommerce-es/1_Pooling/config.json
-
curl -L -o config.json https://huggingface.co/Mateo-Rua/embeddings-productos-ecommerce-es/resolve/main/1_Pooling/config.json
90 Bytes
| { | |
| "embedding_dimension": 384, | |
| "pooling_mode": "mean", | |
| "include_prompt": true | |
| } |