Sentence Similarity
sentence-transformers
ONNX
English
bert
feature-extraction
text-embeddings-inference
Instructions to use dplotnikov/go_inference_sbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use dplotnikov/go_inference_sbert with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("dplotnikov/go_inference_sbert") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ccd3979b0d8398c510cda101423a9eabd7a767161564febd6980249f4090f12d
- Size of remote file:
- 16.6 MB
- SHA256:
- a5faaf78a37590d3fe640f887620e74f6022d34550172b91ad2131bf0ad77d64
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