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
| { | |
| "model_type": "SentenceTransformer", | |
| "__version__": { | |
| "sentence_transformers": "5.1.2", | |
| "transformers": "4.57.6", | |
| "pytorch": "2.6.0+cu118" | |
| }, | |
| "prompts": { | |
| "query": "", | |
| "document": "" | |
| }, | |
| "default_prompt_name": null, | |
| "similarity_fn_name": "cosine" | |
| } |