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
| { | |
| "transformer_task": "feature-extraction", | |
| "modality_config": { | |
| "text": { | |
| "method": "forward", | |
| "method_output_name": "last_hidden_state" | |
| } | |
| }, | |
| "module_output_name": "token_embeddings" | |
| } |