Sentence Similarity
sentence-transformers
ONNX
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:2347
loss:CosineSimilarityLoss
text-embeddings-inference
Instructions to use jacgandres/fine-tunning-embeddings with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use jacgandres/fine-tunning-embeddings with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("jacgandres/fine-tunning-embeddings") sentences = [ "Calibrador", "griego químico, de quimos, jugo.) adj. Perteneciente a la química. I. El que profesa la química. (R. A. E.) R R asp atubos o diablo.", "Hayes, que dice: la máxima efi- ciencia con el desperdicio mínimo en la producción de recursos na- turales irrenovables. (Day.)", "con rosca cónica exterior, que sirve para probar el roscado y dimensiones de las cajas en las juntas de caja y espiga. (M. J. Zevada.) C ALIBRADOR DE ESPIGAS." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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