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coldchair16/
CPRetriever-Code

Name: CPRetriever-Code
Availability: InStock

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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 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.

For the training pipeline, see the full project: 👉 CPRet on GitHub

📦 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