---
license: mit
language:
- en
- code
tags:
- code-search
- embeddings
- onnx
- sentence-similarity
- cqs
library_name: sentence-transformers
pipeline_tag: sentence-similarity
base_model: nomic-ai/CodeRankEmbed
---

# CodeRankEmbed (ONNX export)

ONNX export of [nomic-ai/CodeRankEmbed](https://huggingface.co/nomic-ai/CodeRankEmbed) — a 137M-parameter code search embedder built on `Snowflake/snowflake-arctic-embed-m-long`. Exported for use with [cqs](https://github.com/jamie8johnson/cqs)'s ONNX Runtime embedding pipeline; no PyTorch dependency required.

This is a faithful conversion of the upstream weights — no fine-tuning, no quantization. License and behavior match the upstream model.

## Specs

- **Base:** `nomic-ai/CodeRankEmbed` (137M params, 768-dim, 8192 max seq)
- **Format:** ONNX (FP32)
- **Pooling:** Mean
- **Query prefix:** `Represent this query for searching relevant code: ` (required — see usage)
- **Document prefix:** none

## Production Eval (cqs v3.v2 fixture, 2026-05-01)

Run against cqs's production fixture (218 queries: 109 test + 109 dev) on the cqs codebase itself. Numbers are with cqs's full hybrid-search stack (dense + FTS + SPLADE blend, name-boost, type-boost, MMR-off):

| split | metric | BGE-large (1024-dim) | **CodeRankEmbed (768-dim)** | v9-200k (768-dim) |
|-------|--------|---------------------:|----------------------------:|------------------:|
| test  | R@1    | 43.1%                | 42.2%                       | 45.9%             |
| test  | R@5    | 69.7%                | **67.9%**                   | 70.6%             |
| test  | R@20   | **83.5%**            | 79.8%                       | 80.7%             |
| dev   | R@1    | 45.9%                | **47.7%**                   | 46.8%             |
| dev   | R@5    | **77.1%**            | 69.7%                       | 68.8%             |
| dev   | R@20   | **86.2%**            | 81.7%                       | 81.7%             |

**Verdict:** edges out BGE-large on dev R@1, otherwise close on test and behind on dev R@5/R@20. Best fit when you want a code-specialist embedder at 1/3 the BGE-large parameter count without trading off too much on diverse natural-language queries. cqs ships it as an opt-in preset (not the default) — set `CQS_EMBEDDING_MODEL=nomic-coderank` or use `cqs slot create coderank --model nomic-coderank`.

## Usage

### With cqs

```bash
# Full reindex with this model
export CQS_EMBEDDING_MODEL=nomic-coderank
cqs index --force

# Or, for slot-based comparisons:
cqs slot create coderank --model nomic-coderank
cqs index --slot coderank --force
```

cqs handles the query-prefix wiring automatically. Documents are encoded without a prefix per the upstream convention.

### Direct ONNX

```python
import onnxruntime as ort
from transformers import AutoTokenizer
import numpy as np

session = AutoTokenizer.from_pretrained("jamie8johnson/CodeRankEmbed-onnx")
ort_session = ort.InferenceSession("model.onnx")
tokenizer = AutoTokenizer.from_pretrained("nomic-ai/CodeRankEmbed")

# Query prefix is REQUIRED
query = "Represent this query for searching relevant code: find functions that validate email addresses"
code  = "def validate_email(addr): ..."   # no prefix on documents

q_inputs = tokenizer(query, return_tensors="np", padding=True, truncation=True, max_length=8192)
q_out = ort_session.run(None, dict(q_inputs))
# Mean-pool over the token dimension and L2-normalize for cosine similarity.
```

## License

MIT, inherited from the upstream `nomic-ai/CodeRankEmbed` model.

## Citation

Please cite the upstream model:

```
@misc{nomic-coderank-embed,
  author = {Nomic AI},
  title = {CodeRankEmbed},
  year = {2024},
  publisher = {HuggingFace},
  url = {https://huggingface.co/nomic-ai/CodeRankEmbed}
}
```