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NanoCodeSearchNet

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metadata
dataset_info:
  - config_name: corpus
    features:
      - name: _id
        dtype: string
      - name: text
        dtype: string
    splits:
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        num_examples: 10000
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      - name: NanoCodeSearchNetRuby
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  - config_name: qrels
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      - name: corpus-id
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      - name: text
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      - name: NanoCodeSearchNetRuby
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configs:
  - config_name: corpus
    data_files:
      - split: NanoCodeSearchNetGo
        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
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  - config_name: queries
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      - 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 (test set) that mirrors the spirit of 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 0.7351 0.6706 0.7899 0.6582 0.6651 0.9258 0.7008
multilingual-e5-large 0.7769 0.7459 0.8304 0.7016 0.7069 0.9513 0.7251
e5-small-v2 0.7371 0.7137 0.7758 0.6126 0.6561 0.9582 0.7060
e5-large-v2 0.7541 0.7097 0.8124 0.6715 0.7065 0.9386 0.6860
bge-m3 0.7094 0.6680 0.7050 0.6154 0.6238 0.9779 0.6662
gte-multilingual-base 0.8112 0.7789 0.8666 0.7344 0.7991 0.9652 0.7231
nomic-embed-text-v2-moe 0.7824 0.7635 0.8343 0.6519 0.7470 0.9852 0.7122
paraphrase-multilingual-MiniLM-L12-v2 0.4651 0.3978 0.4608 0.3269 0.2183 0.9236 0.4631

Notes:

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 

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 

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

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