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-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use michaelfeil/ct2fast-e5-small-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("michaelfeil/ct2fast-e5-small-v2") 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:
- cc00c994d28d7c9656a214c56773724edd846d4a4b53532cb2673b04dd87e624
- Size of remote file:
- 66.7 MB
- SHA256:
- e0abf9d784a0af48d6b84f95eafee9b4e6ab47ec5a992583fea422ae264f9d4b
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