Sentence Similarity
sentence-transformers
TensorBoard
Safetensors
modernbert
feature-extraction
dense
Generated from Trainer
dataset_size:1175405
loss:CosineSimilarityLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use erickfmm/mrbert-es-sbert-ft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use erickfmm/mrbert-es-sbert-ft with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("erickfmm/mrbert-es-sbert-ft") sentences = [ "El camino de Santiago articula la península ibérica con Europa.", "Y un millon de euros y de pesetas tampoco son lo mismo.", "Asimismo, en los montes puede haber matorral de coscoja y, también, lentisco, romero, enebro o brezo.", "El país fue el noveno mayor importador de petróleo del mundo en 2013 ." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.models.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
| }, | |
| { | |
| "idx": 2, | |
| "name": "2", | |
| "path": "2_Normalize", | |
| "type": "sentence_transformers.models.Normalize" | |
| } | |
| ] |