README.md

---
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
configs:
- config_name: corpus
  data_files:
  - split: train
    path: corpus/train-*
- config_name: default
  data_files:
  - split: test
    path: data/test-*
- config_name: queries
  data_files:
  - split: train
    path: queries/train-*
dataset_info:
- config_name: corpus
  features:
  - name: _id
    dtype: string
  - name: title
    dtype: string
  - name: text
    dtype: string
  splits:
  - name: train
    num_bytes: 338
    num_examples: 5
  download_size: 1765
  dataset_size: 338
- config_name: default
  features:
  - name: query-id
    dtype: string
  - name: corpus-id
    dtype: string
  - name: score
    dtype: int64
  splits:
  - name: test
    num_bytes: 150
    num_examples: 5
  download_size: 1551
  dataset_size: 150
- config_name: queries
  features:
  - name: _id
    dtype: string
  - name: text
    dtype: string
  splits:
  - name: train
    num_bytes: 453
    num_examples: 5
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  dataset_size: 453
---

# 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.