Sentence Similarity
sentence-transformers
PyTorch
English
bert
mteb
Sentence Transformers
Eval Results (legacy)
text-embeddings-inference
Instructions to use cgldo/semanticClone with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use cgldo/semanticClone with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("cgldo/semanticClone") 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:
- a5a90aaea79c9ccca5a930674d2d5279fb74731fb5234f4dc32ccde61c52ef80
- Size of remote file:
- 670 MB
- SHA256:
- 65a6da76608a92fb42fbee99ffe472357416caf215be2d4cfe4c3fdf6f040f4a
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