Instructions to use dominguesm/CodeRankEmbed-Model2Vec with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Model2Vec
How to use dominguesm/CodeRankEmbed-Model2Vec with Model2Vec:
from model2vec import StaticModel model = StaticModel.from_pretrained("dominguesm/CodeRankEmbed-Model2Vec") embeddings = model.encode(["It's dangerous to go alone!", "It's a secret to everybody."]) print(embeddings.shape) - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from dominguesm/CodeRankEmbed-Model2Vec: direct link, hf CLI and curl.
- Browser
- Download file 64.3 MB
-
https://huggingface.co/dominguesm/CodeRankEmbed-Model2Vec/resolve/main/model.safetensors
- Command line
-
hf download hf://dominguesm/CodeRankEmbed-Model2Vec/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/dominguesm/CodeRankEmbed-Model2Vec/resolve/main/model.safetensors
64.3 MB
- Xet hash:
- 91f65c21d3cc341a0fb0b3fac08cfcbf9c1dcffce95fb47ec8b347916760336d
- Size of remote file:
- 64.3 MB
- SHA256:
- ca6159081a6e96cebe4ad878e5e8437bfccc761e8db16223370149cd2faa6c0b
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