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: 408 Bytes
2dd6583 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | {
"add_prefix_space": true,
"backend": "tokenizers",
"bos_token": "<s>",
"clean_up_tokenization_spaces": true,
"cls_token": "<s>",
"eos_token": "</s>",
"is_local": false,
"local_files_only": false,
"mask_token": "<mask>",
"model_max_length": 512,
"pad_token": "<pad>",
"sep_token": "</s>",
"sp_model_kwargs": {},
"tokenizer_class": "XLMRobertaTokenizer",
"unk_token": "<unk>"
}
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