Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
dense
Generated from Trainer
dataset_size:5000
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use inesctec/CitiLink-mpnet-Entity-Linker-pt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use inesctec/CitiLink-mpnet-Entity-Linker-pt with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("inesctec/CitiLink-mpnet-Entity-Linker-pt") sentences = [ "CHEFIA INTERMEDIA GRAU 3 DA ÁREA FINANCEIRA", "rua 13 de dezembro, com o número 35 de policia", "Chefia Intermedia grau 3 da área financeira", "rua padre américo, bairro do património, bloco 3 – 1.º esq. (t2)" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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