Datasets:

Shuu12121
/

owl_code_search_hard_negative_datasets-Pre_kd

language:
  - code
license: apache-2.0
task_categories:
  - feature-extraction
  - sentence-similarity
tags:
  - code-search
  - hard-negatives
  - knowledge-distillation
  - contrastive-learning
  - sentence-transformers
  - colbert
pretty_name: Owl Code Search Hard Negative Datasets (Pre-KD)
size_categories:
  - 1M<n<10M
dataset_info:
  - config_name: documents_go
    features:
      - name: document_id
        dtype: string
      - name: document
        dtype: string
      - name: split
        dtype: string
    splits:
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    download_size: 234362060
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  - config_name: documents_java
    features:
      - name: document_id
        dtype: string
      - name: document
        dtype: string
      - name: split
        dtype: string
    splits:
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  - config_name: documents_javascript
    features:
      - name: document_id
        dtype: string
      - name: document
        dtype: string
      - name: split
        dtype: string
    splits:
      - name: train
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        num_examples: 129007
    download_size: 36381974
    dataset_size: 103608078
  - config_name: documents_php
    features:
      - name: document_id
        dtype: string
      - name: document
        dtype: string
      - name: split
        dtype: string
    splits:
      - name: train
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        num_examples: 424463
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    dataset_size: 223866454
  - config_name: documents_python
    features:
      - name: document_id
        dtype: string
      - name: document
        dtype: string
      - name: split
        dtype: string
    splits:
      - name: train
        num_bytes: 1076037918
        num_examples: 776900
    download_size: 335083048
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  - config_name: documents_ruby
    features:
      - name: document_id
        dtype: string
      - name: document
        dtype: string
      - name: split
        dtype: string
    splits:
      - name: train
        num_bytes: 63811504
        num_examples: 104899
    download_size: 15337714
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  - config_name: documents_rust
    features:
      - name: document_id
        dtype: string
      - name: document
        dtype: string
      - name: split
        dtype: string
    splits:
      - name: train
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        num_examples: 381521
    download_size: 76499102
    dataset_size: 239736870
  - config_name: documents_typescript
    features:
      - name: document_id
        dtype: string
      - name: document
        dtype: string
      - name: split
        dtype: string
    splits:
      - name: train
        num_bytes: 265760164
        num_examples: 328457
    download_size: 77031340
    dataset_size: 265760164
  - config_name: queries_go
    features:
      - name: query_id
        dtype: string
      - name: query
        dtype: string
      - name: split
        dtype: string
    splits:
      - name: train
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        num_examples: 1361475
    download_size: 79920936
    dataset_size: 244424686
  - config_name: queries_java
    features:
      - name: query_id
        dtype: string
      - name: query
        dtype: string
      - name: split
        dtype: string
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        num_examples: 1281018
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  - config_name: queries_javascript
    features:
      - name: query_id
        dtype: string
      - name: query
        dtype: string
      - name: split
        dtype: string
    splits:
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    download_size: 14437070
    dataset_size: 40990466
  - config_name: queries_php
    features:
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        dtype: string
      - name: query
        dtype: string
      - name: split
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  - config_name: queries_python
    features:
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      - name: query
        dtype: string
      - name: split
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  - config_name: queries_ruby
    features:
      - name: query_id
        dtype: string
      - name: query
        dtype: string
      - name: split
        dtype: string
    splits:
      - name: train
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  - config_name: queries_rust
    features:
      - name: query_id
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      - name: query
        dtype: string
      - name: split
        dtype: string
    splits:
      - name: train
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        num_examples: 381521
    download_size: 30435878
    dataset_size: 78330830
  - config_name: queries_typescript
    features:
      - name: query_id
        dtype: string
      - name: query
        dtype: string
      - name: split
        dtype: string
    splits:
      - name: train
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        num_examples: 328457
    download_size: 30449400
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  - config_name: scores_go
    features:
      - name: query_id
        dtype: string
      - name: document_ids
        sequence: string
      - name: scores
        sequence: float64
      - name: split
        dtype: string
    splits:
      - name: train
        num_bytes: 1049496498
        num_examples: 1361475
    download_size: 556865396
    dataset_size: 1049496498
  - config_name: scores_java
    features:
      - name: query_id
        dtype: string
      - name: document_ids
        sequence: string
      - name: scores
        sequence: float64
      - name: split
        dtype: string
    splits:
      - name: train
        num_bytes: 1072717102
        num_examples: 1281018
    download_size: 525421824
    dataset_size: 1072717102
  - config_name: scores_javascript
    features:
      - name: query_id
        dtype: string
      - name: document_ids
        sequence: string
      - name: scores
        sequence: float64
      - name: split
        dtype: string
    splits:
      - name: train
        num_bytes: 129963078
        num_examples: 129007
    download_size: 50592770
    dataset_size: 129963078
  - config_name: scores_php
    features:
      - name: query_id
        dtype: string
      - name: document_ids
        sequence: string
      - name: scores
        sequence: float64
      - name: split
        dtype: string
    splits:
      - name: train
        num_bytes: 335188010
        num_examples: 424463
    download_size: 172108596
    dataset_size: 335188010
  - config_name: scores_python
    features:
      - name: query_id
        dtype: string
      - name: document_ids
        sequence: string
      - name: scores
        sequence: float64
      - name: split
        dtype: string
    splits:
      - name: train
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        num_examples: 776900
    download_size: 324380260
    dataset_size: 696396830
  - config_name: scores_ruby
    features:
      - name: query_id
        dtype: string
      - name: document_ids
        sequence: string
      - name: scores
        sequence: float64
      - name: split
        dtype: string
    splits:
      - name: train
        num_bytes: 83655522
        num_examples: 104899
    download_size: 40260810
    dataset_size: 83655522
  - config_name: scores_rust
    features:
      - name: query_id
        dtype: string
      - name: document_ids
        sequence: string
      - name: scores
        sequence: float64
      - name: split
        dtype: string
    splits:
      - name: train
        num_bytes: 314038494
        num_examples: 381521
    download_size: 163389490
    dataset_size: 314038494
  - config_name: scores_typescript
    features:
      - name: query_id
        dtype: string
      - name: document_ids
        sequence: string
      - name: scores
        sequence: float64
      - name: split
        dtype: string
    splits:
      - name: train
        num_bytes: 337032714
        num_examples: 328457
    download_size: 132654824
    dataset_size: 337032714
configs:
  - config_name: documents_go
    data_files:
      - split: train
        path: documents_go/train-*
  - config_name: documents_java
    data_files:
      - split: train
        path: documents_java/train-*
  - config_name: documents_javascript
    data_files:
      - split: train
        path: documents_javascript/train-*
  - config_name: documents_php
    data_files:
      - split: train
        path: documents_php/train-*
  - config_name: documents_python
    data_files:
      - split: train
        path: documents_python/train-*
  - config_name: documents_ruby
    data_files:
      - split: train
        path: documents_ruby/train-*
  - config_name: documents_rust
    data_files:
      - split: train
        path: documents_rust/train-*
  - config_name: documents_typescript
    data_files:
      - split: train
        path: documents_typescript/train-*
  - config_name: queries_go
    data_files:
      - split: train
        path: queries_go/train-*
  - config_name: queries_java
    data_files:
      - split: train
        path: queries_java/train-*
  - config_name: queries_javascript
    data_files:
      - split: train
        path: queries_javascript/train-*
  - config_name: queries_php
    data_files:
      - split: train
        path: queries_php/train-*
  - config_name: queries_python
    data_files:
      - split: train
        path: queries_python/train-*
  - config_name: queries_ruby
    data_files:
      - split: train
        path: queries_ruby/train-*
  - config_name: queries_rust
    data_files:
      - split: train
        path: queries_rust/train-*
  - config_name: queries_typescript
    data_files:
      - split: train
        path: queries_typescript/train-*
  - config_name: scores_go
    data_files:
      - split: train
        path: scores_go/train-*
  - config_name: scores_java
    data_files:
      - split: train
        path: scores_java/train-*
  - config_name: scores_javascript
    data_files:
      - split: train
        path: scores_javascript/train-*
  - config_name: scores_php
    data_files:
      - split: train
        path: scores_php/train-*
  - config_name: scores_python
    data_files:
      - split: train
        path: scores_python/train-*
  - config_name: scores_ruby
    data_files:
      - split: train
        path: scores_ruby/train-*
  - config_name: scores_rust
    data_files:
      - split: train
        path: scores_rust/train-*
  - config_name: scores_typescript
    data_files:
      - split: train
        path: scores_typescript/train-*

Owl Code Search Hard Negative Datasets

Knowledge Distillation (KD) ベースのハードネガティブ付きコード検索データセットです。
コード検索モデルShuu12121/CodeSearch-ModernBERT-Crow-v3-large-len1024-Plusを教師モデルとして、各コメントと説明コメントのペアのデータセットから各クエリに対する関数の類似度スコアを計算し、ハードネガティブ（正解に類似しているが不正解の文書）を付与しています。

概要

目的: コード検索モデルの Contrastive Learning / Knowledge Distillation ファインチューニング
言語: Go, Java, JavaScript, PHP, Python, Ruby, Rust, TypeScript（8言語）
総サンプル数: 4,787,740
データサイズ: 8.73 GB（展開後） / 3.37 GB（ダウンロード時）
フォーマット: Per-language config 形式（scores_{lang}, queries_{lang}, documents_{lang}）

データ構造

各言語ごとに 3 つの config が存在します：

`queries_{lang}`

各クエリ（自然言語による検索文）を格納。

カラム	型	説明
`query_id`	`string`	クエリの一意識別子
`query`	`string`	自然言語のクエリテキスト（docstring / コメント）
`split`	`string`	元データの分割情報

`documents_{lang}`

各文書（ソースコード）を格納。

カラム	型	説明
`document_id`	`string`	文書の一意識別子
`document`	`string`	ソースコード本文
`split`	`string`	元データの分割情報

`scores_{lang}`

教師モデルによる類似度スコアを格納。各クエリに対して、スコア順にソートされた文書 ID リストとスコアリストを保持。

カラム	型	説明
`query_id`	`string`	対応するクエリの ID
`document_ids`	`list[string]`	スコア順にソートされた文書 ID のリスト
`scores`	`list[float64]`	対応する類似度スコアのリスト
`split`	`string`	元データの分割情報

スコアの解釈:

scores[0] / document_ids[0] が正例（実際のペアだったもの）

score[0] = -1 は正解が上位32件に検索結果が含まれていなかった場合

言語別統計

言語	クエリ数	文書数	スコア数
Go	1,361,475	1,361,475	1,361,475
Java	1,281,018	1,281,018	1,281,018
JavaScript	129,007	129,007	129,007
PHP	424,463	424,463	424,463
Python	776,900	776,900	776,900
Ruby	104,899	104,899	104,899
Rust	381,521	381,521	381,521
TypeScript	328,457	328,457	328,457
合計	4,787,740	4,787,740	4,787,740

注意点

全データをメモリに載せようとするとOOMになる可能性があります！！

使い方

基本的な読み込み

from datasets import load_dataset

# Python の scores を読み込む
scores = load_dataset(
    "Shuu12121/owl_code_search_hard_negative_datasets-Pre_kd",
    name="scores_python",
    split="train",
)

# Python の queries を読み込む
queries = load_dataset(
    "Shuu12121/owl_code_search_hard_negative_datasets-Pre_kd",
    name="queries_python",
    split="train",
)

# Python の documents を読み込む
documents = load_dataset(
    "Shuu12121/owl_code_search_hard_negative_datasets-Pre_kd",
    name="documents_python",
    split="train",
)

ハードネガティブの抽出

# クエリ・文書テキストの辞書を構築
query_texts = dict(zip(queries["query_id"], queries["query"]))
doc_texts = dict(zip(documents["document_id"], documents["document"]))

# 閾値の設定
nv_threshold = 0.99  # positive スコアの 99% 未満をネガティブとする

# 1 サンプルの処理例
sample = scores[0]
query_text = query_texts[sample["query_id"]]
positive_doc = doc_texts[sample["document_ids"][0]]  # scores[0] が正例
positive_score = sample["scores"][0]

hard_negatives = []
for doc_id, score in zip(sample["document_ids"][1:], sample["scores"][1:]):
    if score < nv_threshold * positive_score and score != -1:
        hard_negatives.append(doc_texts[doc_id])

print(f"Query: {query_text[:100]}...")
print(f"Positive: {positive_doc[:100]}...")
print(f"Hard negatives: {len(hard_negatives)}")

作成に使用されたプログラム

リポジトリはこちら