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
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license: mit
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# # Fast-Inference with Ctranslate2
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Speedup inference while reducing memory by 2x-4x using int8 inference in C++ on CPU or GPU.
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# # Hugging Face Inference Endpoints -compatible version of michaelfeil/ct2fast-e5-small-v2
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Duplicate of michaelfeil/ct2fast-e5-small-v2, modified to run on Hugging Face Inference Endpoints.
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Creates symbolic links so that ctranslate2 reads the repository model without downloading from HF.
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# # Fast-Inference with Ctranslate2
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Speedup inference while reducing memory by 2x-4x using int8 inference in C++ on CPU or GPU.
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