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README.md

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+---
+annotations_creators:
+  - derived
+language:
+- ja
+license: cc-by-sa-4.0
+multilinguality: monolingual
+task_categories:
+- text-retrieval
+task_ids:
+- document-retrieval
+tags:
+- mteb
+- text
+- code
+- japanese
+- sample
+---
+
+# JapaneseCode1Retrieval-sample
+
+A sample dataset for Japanese-English code retrieval evaluation. This dataset contains Japanese natural language descriptions paired with Python code snippets.
+
+## Task category
+
+Retrieval
+
+## Domains
+
+Programming, Code Generation
+
+## Dataset Structure
+
+The dataset follows the standard MTEB retrieval format:
+
+- `corpus/corpus-00000-of-00001.parquet`: 5 Python code documents with fields `_id`, `title`, `text`
+- `queries/queries-00000-of-00001.parquet`: 5 Japanese queries with fields `_id`, `text`  
+- `data/test-00000-of-00001.parquet`: 5 relevance judgments with fields `query-id`, `corpus-id`, `score`
+
+## Usage
+
+You can evaluate an embedding model on this sample dataset using the following code:
+
+```python
+import mteb
+
+# Load the sample dataset
+task = mteb.get_task("JapaneseCode1Retrieval-sample")
+evaluator = mteb.MTEB(tasks=[task])
+
+# Run evaluation with your model
+model = mteb.get_model("your-model-name")
+results = evaluator.run(model)
+```
+
+## Sample Content
+
+This sample dataset contains:
+- 5 Japanese natural language queries describing programming tasks
+- 5 corresponding Python code snippets
+- 5 relevance judgments connecting queries to code
+
+The data has been slightly modified for demonstration purposes.
+
+## License
+
+Please refer to the CC BY-SA 4.0 license terms.

corpus/corpus-00000-of-00001.parquet

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+size 2442

create_sample_dataset.py

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+#!/usr/bin/env python3
+
+import pandas as pd
+import random
+from pathlib import Path
+
+def create_sample_dataset():
+    # Load original data
+    base_path = Path("/Users/fodizoltan/Projects/toptal/voyageai/tmp/rteb/data/JapaneseCode1Retrieval")
+    
+    corpus_df = pd.read_parquet(base_path / "corpus" / "corpus-00000-of-00001.parquet")
+    queries_df = pd.read_parquet(base_path / "queries" / "queries-00000-of-00001.parquet")
+    qrels_df = pd.read_parquet(base_path / "data" / "test-00000-of-00001.parquet")
+    
+    print(f"Loaded {len(corpus_df)} corpus docs, {len(queries_df)} queries, {len(qrels_df)} qrels")
+    
+    # Get first 5 queries
+    first_5_queries = queries_df.head(5).copy()
+    print("First 5 queries:")
+    for _, row in first_5_queries.iterrows():
+        print(f"  {row['_id']}: {row['text']}")
+    
+    # Get related corpus documents from qrels
+    first_5_query_ids = first_5_queries['_id'].tolist()
+    related_qrels = qrels_df[qrels_df['query-id'].isin(first_5_query_ids)].copy()
+    related_corpus_ids = related_qrels['corpus-id'].unique()
+    related_corpus = corpus_df[corpus_df['_id'].isin(related_corpus_ids)].copy()
+    
+    print(f"Found {len(related_corpus)} related corpus documents")
+    print(f"Found {len(related_qrels)} qrels")
+    
+    # Tweak the data
+    tweaked_queries = tweak_queries(first_5_queries)
+    tweaked_corpus = tweak_corpus(related_corpus)
+    
+    return tweaked_corpus, tweaked_queries, related_qrels
+
+def tweak_queries(queries_df):
+    """Tweak Japanese queries by changing a few words/characters"""
+    tweaked = queries_df.copy()
+    
+    # Simple tweaks for Japanese text
+    tweaks = {
+        'リスト': 'リスト配列',
+        '要素': 'エレメント', 
+        '整数': '数値',
+        '文字列': 'ストリング',
+        '変数': 'パラメータ',
+        'する': 'を行う',
+        'の': 'が持つ'
+    }
+    
+    for idx, row in tweaked.iterrows():
+        text = row['text']
+        # Apply random tweaks
+        for old, new in tweaks.items():
+            if old in text and random.random() < 0.3:  # 30% chance to apply each tweak
+                text = text.replace(old, new, 1)  # Replace only first occurrence
+        tweaked.at[idx, 'text'] = text
+    
+    return tweaked
+
+def tweak_corpus(corpus_df):
+    """Tweak Python code by changing variable names and some function calls"""
+    tweaked = corpus_df.copy()
+    
+    # Simple code tweaks
+    code_tweaks = {
+        'x': 'data',
+        'i': 'idx', 
+        'd': 'digit',
+        'enumerate': 'enumerate_items',
+        'sum': 'total',
+        'len': 'length',
+        'str': 'string',
+        'int': 'integer',
+        'list': 'array'
+    }
+    
+    for idx, row in tweaked.iterrows():
+        text = row['text']
+        # Apply random tweaks
+        for old, new in code_tweaks.items():
+            if old in text and random.random() < 0.4:  # 40% chance to apply each tweak
+                # Only replace whole words to avoid breaking code
+                import re
+                pattern = r'\b' + re.escape(old) + r'\b'
+                if re.search(pattern, text):
+                    text = re.sub(pattern, new, text, count=1)
+        tweaked.at[idx, 'text'] = text
+    
+    return tweaked
+
+def save_sample_dataset(corpus_df, queries_df, qrels_df):
+    """Save sample dataset in MTEB format"""
+    sample_path = Path("/Users/fodizoltan/Projects/toptal/voyageai/tmp/rteb/data/JapaneseCode1Retrieval-sample")
+    sample_path.mkdir(exist_ok=True)
+    
+    # Create subdirectories
+    (sample_path / "corpus").mkdir(exist_ok=True)
+    (sample_path / "queries").mkdir(exist_ok=True)
+    (sample_path / "data").mkdir(exist_ok=True)
+    
+    # Save parquet files
+    corpus_df.to_parquet(sample_path / "corpus" / "corpus-00000-of-00001.parquet", index=False)
+    queries_df.to_parquet(sample_path / "queries" / "queries-00000-of-00001.parquet", index=False)
+    qrels_df.to_parquet(sample_path / "data" / "test-00000-of-00001.parquet", index=False)
+    
+    print(f"Sample dataset saved to {sample_path}")
+    print(f"  - Corpus: {len(corpus_df)} documents")
+    print(f"  - Queries: {len(queries_df)} queries")
+    print(f"  - Qrels: {len(qrels_df)} relevance judgments")
+
+if __name__ == "__main__":
+    corpus, queries, qrels = create_sample_dataset()
+    save_sample_dataset(corpus, queries, qrels)

data/test-00000-of-00001.parquet

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+size 2256

queries/queries-00000-of-00001.parquet

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