Instructions to use minishlab/potion-code-16M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Model2Vec
How to use minishlab/potion-code-16M with Model2Vec:
from model2vec import StaticModel model = StaticModel.from_pretrained("minishlab/potion-code-16M") 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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Browse files
README.md
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@@ -57,9 +57,9 @@ Results on the [CoIR benchmark](https://github.com/CoIR-team/coir) (NDCG@10, `mt
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| CodeRankEmbed | 137M | - | - | - | - | - | - | - | - | - | - | - |
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| BM25 | — | 4.76 | 32.45 | 59.69 | 67.85 | 33.00 | 47.29 | 32.97 | 15.53 | 69.54 | 28.07 | 39.11 |
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| **potion-code-16M** | **16M** |
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*Results for CodeRankEmbed
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## Model Details
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| CodeRankEmbed | 137M | - | - | - | - | - | - | - | - | - | - | - |
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| BM25 | — | 4.76 | 32.45 | 59.69 | 67.85 | 33.00 | 47.29 | 32.97 | 15.53 | 69.54 | 28.07 | 39.11 |
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| **potion-code-16M** | **16M** | **3.97** | **68.46** | **36.26** | **50.27** | **47.54** | **39.76** | **31.72** | **21.37** | **57.47** | **43.34** | **40.02** |
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*Results for CodeRankEmbed coming soon.*
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## Model Details
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