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:
- d297b7b5274b9bd58e95603eee0d0d4bae80d7dfd445509e360aad93d882c627
- Size of remote file:
- 90.9 MB
- SHA256:
- 1e37eb5e5d1e70307991eb44c6f0e55ad54884faa727f66e97f25c70271523c9
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