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
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Download README.md from dominguesm/CodeRankEmbed-Model2Vec: direct link, hf CLI and curl.
- Browser
- Download file 161 Bytes
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https://huggingface.co/dominguesm/CodeRankEmbed-Model2Vec/resolve/main/README.md
- Command line
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hf download hf://dominguesm/CodeRankEmbed-Model2Vec/README.md
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curl -L -o README.md https://huggingface.co/dominguesm/CodeRankEmbed-Model2Vec/resolve/main/README.md
161 Bytes
metadata
language:
- code
license: mit
library_name: model2vec
tags:
- model2vec
- embeddings
- code
- retrieval
- static-embeddings
WIP