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 tokenizer.json from dominguesm/CodeRankEmbed-Model2Vec: direct link, hf CLI and curl.
- Browser
- Download file 1.04 MB
-
https://huggingface.co/dominguesm/CodeRankEmbed-Model2Vec/resolve/main/tokenizer.json
- Command line
-
hf download hf://dominguesm/CodeRankEmbed-Model2Vec/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/dominguesm/CodeRankEmbed-Model2Vec/resolve/main/tokenizer.json
1.04 MB
File too large to display, you can check the raw version instead.