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
Download model.bin from michaelfeil/ct2fast-e5-small: direct link, hf CLI and curl.
- Browser
- Download file 133 MB
-
https://huggingface.co/michaelfeil/ct2fast-e5-small/resolve/ac4c16d2b9b9d16d656735ef18d88bfc3354db46/model.bin
- Command line
-
hf download hf://michaelfeil/ct2fast-e5-small@ac4c16d2b9b9d16d656735ef18d88bfc3354db46/model.bin
-
curl -L -o model.bin https://huggingface.co/michaelfeil/ct2fast-e5-small/resolve/ac4c16d2b9b9d16d656735ef18d88bfc3354db46/model.bin
133 MB
- Xet hash:
- f8dff26c07864f3e9113d29ea477b80fa56fefeaac6f443133e7d57afe4a5b87
- Size of remote file:
- 133 MB
- SHA256:
- 738ee67946d5969622bf1777a455b1b6eef99d62ce859cd781e1633bcc0b4260
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.