Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:363
loss:CosineSimilarityLoss
text-embeddings-inference
Instructions to use antonypamo/RRFSAVANTMADE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use antonypamo/RRFSAVANTMADE with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("antonypamo/RRFSAVANTMADE") sentences = [ "\\section{5. Correspondencia con constantes físicas}", "\\section{7. Simulación Python – Malla Icosaédrica y Autovalores}", "\\textbf{Interpretación:} Cada modo $n$ representa un posible estado de partícula, resonancia o nodo cognitivo.", "rroc_V" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Commit History
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