Sentence Similarity
sentence-transformers
English
bert
ctranslate2
int8
float16
mteb
Sentence Transformers
Eval Results (legacy)
text-embeddings-inference
Instructions to use michaelfeil/ct2fast-e5-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use michaelfeil/ct2fast-e5-small with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("michaelfeil/ct2fast-e5-small") 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:
- 35234d83a89661623d1702a0f4639f133e7a5d3fab4d1a4f2b236f90ef4a0c18
- Size of remote file:
- 66.7 MB
- SHA256:
- 129a95bd0bfa92362778e4e9329003d15b6407344aa987d858b95809af7e0d49
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