---
license: apache-2.0
language:
- en
- code
library_name: transformers
tags:
- code
- embeddings
- retrieval
- code-search
- semantic-search
- feature-extraction
- sentence-transformers
datasets:
- code-rag-bench/cornstack
- bigcode/stackoverflow
- code_search_net
pipeline_tag: feature-extraction
base_model: Qwen/Qwen2.5-Coder-0.5B
model-index:
- name: CodeCompass-Embed
  results:
  - task:
      type: retrieval
      name: Code Retrieval
    dataset:
      type: CoIR-Retrieval/codetrans-dl
      name: CodeTrans-DL
    metrics:
    - type: ndcg@10
      value: 0.3305
      name: NDCG@10
  - task:
      type: retrieval
      name: Code Retrieval
    dataset:
      type: CoIR-Retrieval/CodeSearchNet-python
      name: CodeSearchNet Python
    metrics:
    - type: ndcg@10
      value: 0.9228
      name: NDCG@10
---

# CodeCompass-Embed

**CodeCompass-Embed** is a code embedding model fine-tuned from [Qwen2.5-Coder-0.5B](https://huggingface.co/Qwen/Qwen2.5-Coder-0.5B) for semantic code search and retrieval tasks.

## Model Highlights

- 🏆 #1 on CodeTrans-DL (code translation between frameworks)
- 🥇 #4 on CodeSearchNet-Python (natural language to code search)
- ⚡ 494M parameters, 896-dim embeddings
- 🔄 Bidirectional attention (converted from causal LLM)
- 🎯 Mean pooling with L2 normalization
- 📏 Trained at 512 tokens, extrapolates to longer sequences via RoPE

## Model Details

| Property | Value |
|----------|-------|
| Base Model | Qwen2.5-Coder-0.5B |
| Parameters | 494M |
| Embedding Dimension | 896 |
| Max Sequence Length | 512 (training) / 32K (inference) |
| Pooling | Mean |
| Normalization | L2 |
| Attention | Bidirectional (all 24 layers) |

## Benchmark Results (CoIR)

Evaluated on the [CoIR Benchmark](https://github.com/CoIR-team/coir) (NDCG@10). Sorted by CSN-Python.

| Model | Params | CSN-Python | CodeTrans-DL | Text2SQL | SO-QA | CF-ST | Apps |
|-------|--------|------------|--------------|----------|-------|-------|------|
| SFR-Embedding-Code | 400M | 0.9505 | 0.2683 | 0.9949 | 0.9107 | 0.7258 | 0.2212 |
| Jina-Code-v2 | 161M | 0.9439 | 0.2739 | 0.5169 | 0.8874 | 0.6975 | 0.1538 |
| CodeRankEmbed | 137M | 0.9378 | 0.2604 | 0.7686 | 0.8990 | 0.7166 | 0.1993 |
| **CodeCompass-Embed** | **494M** | **0.9228** | **0.3305** | **0.5673** | **0.6480** | **0.4080** | **0.1277** |
| Snowflake-Arctic-Embed-L | 568M | 0.9146 | 0.1958 | 0.5401 | 0.8718 | 0.6503 | 0.1435 |
| BGE-M3 | 568M | 0.8976 | 0.2194 | 0.5728 | 0.8501 | 0.6437 | 0.1445 |
| BGE-Base-en-v1.5 | 109M | 0.8944 | 0.2125 | 0.5265 | 0.8581 | 0.6423 | 0.1415 |
| CodeT5+-110M | 110M | 0.8702 | 0.1794 | 0.3275 | 0.8147 | 0.5804 | 0.1179 |

*CodeCompass-Embed ranks #1 on CodeTrans-DL and #4 on CSN-Python.*

## Usage

```python
import torch
import torch.nn.functional as F
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("faisalmumtaz/codecompass-embed", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("faisalmumtaz/codecompass-embed")

# Enable bidirectional attention
for layer in model.layers:
    layer.self_attn.is_causal = False

model.eval()

def encode(texts, is_query=False):
    if is_query:
        texts = [f"Instruct: Find the most relevant code snippet given the following query:
Query: {t}" for t in texts]
    
    inputs = tokenizer(texts, padding=True, truncation=True, max_length=512, return_tensors="pt")
    
    with torch.no_grad():
        outputs = model(**inputs, output_hidden_states=True)
        hidden = outputs.hidden_states[-1]
        mask = inputs["attention_mask"].unsqueeze(-1).float()
        embeddings = (hidden * mask).sum(1) / mask.sum(1).clamp(min=1e-9)
        embeddings = F.normalize(embeddings, p=2, dim=-1)
    
    return embeddings

query_emb = encode(["sort a list"], is_query=True)
code_embs = encode(["def sort(lst): return sorted(lst)"])
similarity = (query_emb @ code_embs.T).item()
```

## Instruction Templates

| Task | Template |
|------|----------|
| NL to Code | `Instruct: Find the most relevant code snippet given the following query:
Query: {q}` |
| Code to Code | `Instruct: Find an equivalent code snippet given the following code snippet:
Query: {q}` |
| Tech Q&A | `Instruct: Find the most relevant answer given the following question:
Query: {q}` |
| Text to SQL | `Instruct: Given a natural language question and schema, find the corresponding SQL query:
Query: {q}` |

Documents do not need instruction prefixes.

## Training

- **Data**: 8.8M samples from CoRNStack, StackOverflow, CodeSearchNet
- **Loss**: InfoNCE (τ=0.05) with 7 hard negatives per sample
- **Batch Size**: 1024 (via GradCache)
- **Steps**: 950
- **Hardware**: NVIDIA H100

## Limitations

- Weaker on Q&A style tasks (StackOverflow-QA, CodeFeedback)
- Trained on Python/JavaScript/Java/Go/PHP/Ruby

## Citation

```bibtex
@misc{codecompass2026,
  author = {Faisal Mumtaz},
  title = {CodeCompass-Embed: A Code Embedding Model for Semantic Code Search},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/faisalmumtaz/codecompass-embed}
}
```

## License

Apache 2.0