Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:9100
loss:TripletLoss
text-embeddings-inference
Instructions to use Geo150/addresses-tuned-for-latam with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Geo150/addresses-tuned-for-latam with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Geo150/addresses-tuned-for-latam") sentences = [ "[START] Carrera", "[START] Cra 110 #46-101, Bogotá, Colombia", "[START] Librería Xecraido 120, Cali, Colombia", "[START] Carrera 120 #62-111, Cali, Colombia" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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