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---
license: cc-by-nc-4.0
---
# CPRetriever-Code
**CPRetriever-Code** is a code embedding model trained via contrastive learning for **code-related retrieval tasks** in competitive programming. It achieves strong performance on tasks such as:
* **Text-to-Code** retrieval (problem description → relevant code)
* **Code-to-Code** retrieval (find alternate solutions to the same problem)
This model is part of the [CPRet](https://github.com/coldchair/CPRet) suite for competitive programming retrieval research.
## 🔧 Usage
You can load this model using the `sentence-transformers` library:
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("coldchair16/CPRetriever-Code")
embeddings = model.encode([
"def mex_query(arr):\n n = len(arr)\n seen = set()\n for i in range(n):\n seen.add(arr[i])\n i = 0\n while True:\n if i not in seen:\n return i\n i += 1"
])
```
## 💡 Applications
This model is optimized for **code-level semantic retrieval** in competitive programming settings:
* **Text-to-Code**: Retrieve relevant code snippets given a natural language problem description.
* **Code-to-Code**: Retrieve alternative implementations of the same problem.
It is particularly effective for analyzing programming contest submissions, searching solution variants, and building educational tools for code understanding.
## 📚 Training and Evaluation
CPRetriever-Code is trained via **contrastive learning** using positive and hard negative code pairs derived from [CPRet-data](https://huggingface.co/datasets/coldchair16/CPRet-data).
For the training pipeline, see the full project:
👉 [CPRet on GitHub](https://github.com/coldchair/CPRet?tab=readme-ov-file)
## 📦 Model Card
* Architecture: `Salesforce/SFR-Embedding-Code-2B_R` (encoder backbone)
* Training: Contrastive objective on code/code and text/code pairs
* Format: Compatible with `sentence-transformers`
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