README.md

---
annotations_creators:
- expert-annotated
language:
- nld
license: cc-by-nc-sa-4.0
multilinguality: monolingual
source_datasets:
- clips/mteb-nl-bbsard
task_categories:
- text-retrieval
task_ids: []
dataset_info:
- config_name: corpus
  features:
  - name: id
    dtype: string
  - name: text
    dtype: string
  - name: title
    dtype: string
  splits:
  - name: test
    num_bytes: 21656574
    num_examples: 22415
  download_size: 8789748
  dataset_size: 21656574
- config_name: qrels
  features:
  - name: query-id
    dtype: string
  - name: corpus-id
    dtype: string
  - name: score
    dtype: int64
  splits:
  - name: test
    num_bytes: 24338
    num_examples: 1059
  download_size: 6847
  dataset_size: 24338
- config_name: queries
  features:
  - name: id
    dtype: string
  - name: text
    dtype: string
  splits:
  - name: test
    num_bytes: 23204
    num_examples: 222
  download_size: 13641
  dataset_size: 23204
configs:
- config_name: corpus
  data_files:
  - split: test
    path: corpus/test-*
- config_name: qrels
  data_files:
  - split: test
    path: qrels/test-*
- config_name: queries
  data_files:
  - split: test
    path: queries/test-*
tags:
- mteb
- text
---
<!-- adapted from https://github.com/huggingface/huggingface_hub/blob/v0.30.2/src/huggingface_hub/templates/datasetcard_template.md -->

<div align="center" style="padding: 40px 20px; background-color: white; border-radius: 12px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.05); max-width: 600px; margin: 0 auto;">
  <h1 style="font-size: 3.5rem; color: #1a1a1a; margin: 0 0 20px 0; letter-spacing: 2px; font-weight: 700;">bBSARDNLRetrieval</h1>
  <div style="font-size: 1.5rem; color: #4a4a4a; margin-bottom: 5px; font-weight: 300;">An <a href="https://github.com/embeddings-benchmark/mteb" style="color: #2c5282; font-weight: 600; text-decoration: none;" onmouseover="this.style.textDecoration='underline'" onmouseout="this.style.textDecoration='none'">MTEB</a> dataset</div>
  <div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">Massive Text Embedding Benchmark</div>
</div>

Building on the Belgian Statutory Article Retrieval Dataset (BSARD) in French, we introduce the bilingual version of this dataset, bBSARD. The dataset contains parallel Belgian statutory articles in both French and Dutch, along with legal questions from BSARD and their Dutch translation.

|               |                                             |
|---------------|---------------------------------------------|
| Task category | t2t                              |
| Domains       | Legal, Written                               |
| Reference     | https://aclanthology.org/2025.regnlp-1.3.pdf |

Source datasets:
- [clips/bBSARD](https://huggingface.co/datasets/clips/bBSARD)


## How to evaluate on this task

You can evaluate an embedding model on this dataset using the following code:

```python
import mteb

task = mteb.get_task("bBSARDNLRetrieval")
evaluator = mteb.MTEB([task])

model = mteb.get_model(YOUR_MODEL)
evaluator.run(model)
```

<!-- Datasets want link to arxiv in readme to autolink dataset with paper -->
To learn more about how to run models on `mteb` task check out the [GitHub repository](https://github.com/embeddings-benchmark/mteb).

## Citation

If you use this dataset, please cite the dataset as well as MTEB-NL, as this dataset includes additional processing.

```bibtex

@article{lotfi2025bilingual,
  author = {Lotfi, Ehsan and Banar, Nikolay and Yuzbashyan, Nerses and Daelemans, Walter},
  journal = {COLING 2025},
  pages = {10},
  title = {Bilingual BSARD: Extending Statutory Article Retrieval to Dutch},
  year = {2025},
}


@misc{banar2025mtebnle5nlembeddingbenchmark,
      title={MTEB-NL and E5-NL: Embedding Benchmark and Models for Dutch}, 
      author={Nikolay Banar and Ehsan Lotfi and Jens Van Nooten and Cristina Arhiliuc and Marija Kliocaite and Walter Daelemans},
      year={2025},
      eprint={2509.12340},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2509.12340}, 
}
```

# Dataset Statistics
<details>
  <summary> Dataset Statistics</summary>

The following code contains the descriptive statistics from the task. These can also be obtained using:

```python
import mteb

task = mteb.get_task("bBSARDNLRetrieval")

desc_stats = task.metadata.descriptive_stats
```

```json
{
    "test": {
        "num_samples": 22637,
        "number_of_characters": 21218611,
        "documents_text_statistics": {
            "total_text_length": 21197901,
            "min_text_length": 7,
            "average_text_length": 945.7015837608744,
            "max_text_length": 37834,
            "unique_texts": 22415
        },
        "documents_image_statistics": null,
        "queries_text_statistics": {
            "total_text_length": 20710,
            "min_text_length": 22,
            "average_text_length": 93.28828828828829,
            "max_text_length": 250,
            "unique_texts": 222
        },
        "queries_image_statistics": null,
        "relevant_docs_statistics": {
            "num_relevant_docs": 1059,
            "min_relevant_docs_per_query": 1,
            "average_relevant_docs_per_query": 4.77027027027027,
            "max_relevant_docs_per_query": 57,
            "unique_relevant_docs": 491
        },
        "top_ranked_statistics": null
    }
}
```

</details>

---