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 modules.json from dominguesm/CodeRankEmbed-Model2Vec: direct link, hf CLI and curl.
- Browser
- Download file 278 Bytes
-
https://huggingface.co/dominguesm/CodeRankEmbed-Model2Vec/resolve/main/modules.json
- Command line
-
hf download hf://dominguesm/CodeRankEmbed-Model2Vec/modules.json
-
curl -L -o modules.json https://huggingface.co/dominguesm/CodeRankEmbed-Model2Vec/resolve/main/modules.json
278 Bytes
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": ".", | |
| "type": "sentence_transformers.models.StaticEmbedding" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Normalize", | |
| "type": "sentence_transformers.models.Normalize" | |
| } | |
| ] |