Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
Generated from Trainer
dataset_size:4173
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use adriansanz/sitges2608 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use adriansanz/sitges2608 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("adriansanz/sitges2608") sentences = [ "Les respectives convocatòries determinaran les parades disponibles així com les seves característiques i descripció.", "Quin és l'objectiu secundari dels ajuts per a la creació de noves empreses?", "Qui és responsable de la resolució de la situació en un domini particular?", "Quin és el paper de les convocatòries?" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download 1_Pooling/config.json from adriansanz/sitges2608: direct link, hf CLI and curl.
- Browser
- Download file 296 Bytes
-
https://huggingface.co/adriansanz/sitges2608/resolve/main/1_Pooling/config.json
- Command line
-
hf download hf://adriansanz/sitges2608/1_Pooling/config.json
-
curl -L -o config.json https://huggingface.co/adriansanz/sitges2608/resolve/main/1_Pooling/config.json
296 Bytes
| { | |
| "word_embedding_dimension": 768, | |
| "pooling_mode_cls_token": true, | |
| "pooling_mode_mean_tokens": false, | |
| "pooling_mode_max_tokens": false, | |
| "pooling_mode_mean_sqrt_len_tokens": false, | |
| "pooling_mode_weightedmean_tokens": false, | |
| "pooling_mode_lasttoken": false, | |
| "include_prompt": true | |
| } |