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
- Xet hash:
- ce96d480a336b6eccb1656e01d310f7258937f745985352fb9299b76b1765651
- Size of remote file:
- 5.75 kB
- SHA256:
- 00ea65e1b89d8fe4bdf5692d46d06c17b8190938fa4f084b8bc610a5acfdeab5
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