Sentence Similarity
sentence-transformers
English
bert
ctranslate2
int8
float16
mteb
Sentence Transformers
Eval Results (legacy)
text-embeddings-inference
Instructions to use michaelfeil/ct2fast-e5-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use michaelfeil/ct2fast-e5-large with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("michaelfeil/ct2fast-e5-large") 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:
- 3c10003292a29f133a21bdf7dd971d6394daaa47b44e78cbaeac63eff7afe64f
- Size of remote file:
- 670 MB
- SHA256:
- 86051c2d239ce41cb82c1ab7d6100db24aabcd903fa98b86415d5181c1d4a679
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