Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:15178
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use stephanefschwarz/google-bert-MultipleNegativesRanking-loss with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use stephanefschwarz/google-bert-MultipleNegativesRanking-loss with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("stephanefschwarz/google-bert-MultipleNegativesRanking-loss") sentences = [ "hapvida assistencia medica sa - coleta e vida imagem centro sao jose dos campos", "sao jose imag", "parokia gracas", "igreja evangelica assembleia de deus - igreja evangelica assembleia de deusbairro saramandaia" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e10a1be9803f46ca30e088e773a11e96cc098e822826f959dea459189bdd6b44
- Size of remote file:
- 5.62 kB
- SHA256:
- 623aa587efe5d0a8773099f9d559518ff2f8fdc3bfcb58bb3d9f817c29ae94b6
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