---
dataset_info:
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  - name: corpus-id
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configs:
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    path: corpus/NanoCodeSearchNetGo-*
  - split: NanoCodeSearchNetJava
    path: corpus/NanoCodeSearchNetJava-*
  - split: NanoCodeSearchNetJavaScript
    path: corpus/NanoCodeSearchNetJavaScript-*
  - split: NanoCodeSearchNetPHP
    path: corpus/NanoCodeSearchNetPHP-*
  - split: NanoCodeSearchNetPython
    path: corpus/NanoCodeSearchNetPython-*
  - split: NanoCodeSearchNetRuby
    path: corpus/NanoCodeSearchNetRuby-*
- config_name: qrels
  data_files:
  - split: NanoCodeSearchNetGo
    path: qrels/NanoCodeSearchNetGo-*
  - split: NanoCodeSearchNetJava
    path: qrels/NanoCodeSearchNetJava-*
  - split: NanoCodeSearchNetJavaScript
    path: qrels/NanoCodeSearchNetJavaScript-*
  - split: NanoCodeSearchNetPHP
    path: qrels/NanoCodeSearchNetPHP-*
  - split: NanoCodeSearchNetPython
    path: qrels/NanoCodeSearchNetPython-*
  - split: NanoCodeSearchNetRuby
    path: qrels/NanoCodeSearchNetRuby-*
- config_name: queries
  data_files:
  - split: NanoCodeSearchNetGo
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  - split: NanoCodeSearchNetJava
    path: queries/NanoCodeSearchNetJava-*
  - split: NanoCodeSearchNetJavaScript
    path: queries/NanoCodeSearchNetJavaScript-*
  - split: NanoCodeSearchNetPHP
    path: queries/NanoCodeSearchNetPHP-*
  - split: NanoCodeSearchNetPython
    path: queries/NanoCodeSearchNetPython-*
  - split: NanoCodeSearchNetRuby
    path: queries/NanoCodeSearchNetRuby-*
---


# NanoCodeSearchNet

A tiny, evaluation-ready slice of [CodeSearchNet](https://huggingface.co/datasets/code-search-net/code_search_net) (test set) that mirrors the spirit of [NanoBEIR](https://huggingface.co/collections/zeta-alpha-ai/nanobeir): same task, same style, but dramatically smaller so you can iterate and benchmark in minutes instead of hours.

Evaluation can be performed during and after training by integrating with Sentence Transformer's Evaluation module (InformationRetrievalEvaluator).

## NanoCodeSearchNet Evaluation (NDCG@10)

| Model | Avg | Go | Java | JavaScript | PHP | Python | Ruby |
|---|---:|---:|---:|---:|---:|---:|---:|
| [multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) | **0.7351** | 0.6706 | 0.7899 | 0.6582 | 0.6651 | 0.9258 | 0.7008 |
| [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | **0.7769** | 0.7459 | 0.8304 | 0.7016 | 0.7069 | 0.9513 | 0.7251 |
| [e5-small-v2](https://huggingface.co/intfloat/e5-small-v2) | **0.7371** | 0.7137 | 0.7758 | 0.6126 | 0.6561 | 0.9582 | 0.7060 |
| [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) | **0.7541** | 0.7097 | 0.8124 | 0.6715 | 0.7065 | 0.9386 | 0.6860 |
| [bge-m3](https://huggingface.co/BAAI/bge-m3) | **0.7094** | 0.6680 | 0.7050 | 0.6154 | 0.6238 | 0.9779 | 0.6662 |
| [gte-multilingual-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-base) | **0.8112** | 0.7789 | 0.8666 | 0.7344 | 0.7991 | 0.9652 | 0.7231 |
| [nomic-embed-text-v2-moe](https://huggingface.co/nomic-ai/nomic-embed-text-v2-moe) | **0.7824** | 0.7635 | 0.8343 | 0.6519 | 0.7470 | 0.9852 | 0.7122 |
| [paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) | **0.4651** | 0.3978 | 0.4608 | 0.3269 | 0.2183 | 0.9236 | 0.4631 |

Notes:
- The above results were computed with `nano_code_search_net_eval.py`.
  - https://huggingface.co/datasets/hotchpotch/NanoCodeSearchNet/blob/main/nano_code_search_net_eval.py
  

## What this dataset is

- A collection of 6 programming-language subsets (`corpus`, `queries`, `qrels`) published on the Hugging Face Hub under `hotchpotch/NanoCodeSearchNet`.
- Each subset contains **50 test queries** and a **corpus of up to 10,000 code snippets**.
- Queries are function docstrings, and positives are the corresponding function bodies from the same source row.
- Query IDs are `q-<docid>`, where `docid` is the `func_code_url` when available.
- Built from the CodeSearchNet `test` split (`refs/convert/parquet`) with deterministic sampling (seed=42).
- License: **Other** (see CodeSearchNet and upstream repository licenses).

## Subset names

- Split names:
  - `NanoCodeSearchNetGo`
  - `NanoCodeSearchNetJava`
  - `NanoCodeSearchNetJavaScript`
  - `NanoCodeSearchNetPHP`
  - `NanoCodeSearchNetPython`
  - `NanoCodeSearchNetRuby`
- Config names: `corpus`, `queries`, `qrels`

## Usage

```python
from datasets import load_dataset

split = "NanoCodeSearchNetPython"
queries = load_dataset("hotchpotch/NanoCodeSearchNet", "queries", split=split)
corpus  = load_dataset("hotchpotch/NanoCodeSearchNet", "corpus",  split=split)
qrels   = load_dataset("hotchpotch/NanoCodeSearchNet", "qrels",   split=split)

print(queries[0]["text"])
```

### Example eval code

```bash
python ./nano_code_search_net_eval.py \
  --model-path intfloat/multilingual-e5-small \
  --query-prompt "query: " \
  --corpus-prompt "passage: "
```

For models that require `trust_remote_code`, add `--trust-remote-code` (e.g., `BAAI/bge-m3`).

## Why Nano?

- **Fast eval loops**: 50 queries × 10k docs fits comfortably on a single GPU/CPU run.
- **Reproducible**: deterministic sampling and stable IDs.
- **Drop-in**: BEIR/NanoBEIR-style schemas, so existing IR loaders need minimal tweaks.

### Upstream sources

- Original data: **CodeSearchNet** — [CodeSearchNet Challenge: Evaluating the State of Semantic Code Search: 1909.09436](https://huggingface.co/papers/1909.09436).
- Base dataset: [code-search-net/code_search_net](https://huggingface.co/datasets/code-search-net/code_search_net) (Hugging Face Hub).
- Inspiration: **NanoBEIR** (lightweight evaluation subsets).

## License

Other. This dataset is derived from CodeSearchNet and ultimately from open-source GitHub repositories. Please respect original repository licenses and attribution requirements.

## Author

- Yuichi Tateno