Sentence Similarity
sentence-transformers
Safetensors
Spanish
bert
public-procurement
semantic-search
cpv
text-embeddings-inference
Instructions to use hsilvosa/openplacsp-e5-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use hsilvosa/openplacsp-e5-small with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("hsilvosa/openplacsp-e5-small") 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 hsilvosa/openplacsp-e5-small: direct link, hf CLI and curl.
- Browser
- Download file 321 Bytes
-
https://huggingface.co/hsilvosa/openplacsp-e5-small/resolve/main/1_Pooling/config.json
- Command line
-
hf download hf://hsilvosa/openplacsp-e5-small/1_Pooling/config.json
-
curl -L -o config.json https://huggingface.co/hsilvosa/openplacsp-e5-small/resolve/main/1_Pooling/config.json
321 Bytes
| { | |
| "word_embedding_dimension": 384, | |
| "pooling_mode_cls_token": false, | |
| "pooling_mode_mean_tokens": true, | |
| "pooling_mode_max_tokens": false, | |
| "pooling_mode_mean_sqrt_len_tokens": false, | |
| "pooling_mode_weightedmean_tokens": false, | |
| "pooling_mode_lasttoken": false, | |
| "include_prompt": true | |
| } |