README.md

---
annotations_creators:
- human-annotated
language:
- jpn
license: cc-by-sa-4.0
multilinguality: monolingual
task_categories:
- sentence-similarity
task_ids:
- semantic-similarity-scoring
dataset_info:
  features:
  - name: sentence1
    dtype: string
  - name: sentence2
    dtype: string
  - name: score
    dtype: float64
  splits:
  - name: train
    num_bytes: 1901818
    num_examples: 12451
  - name: validation
    num_bytes: 225466
    num_examples: 1457
  download_size: 920917
  dataset_size: 2127284
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
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;">JSTS</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>

Japanese Semantic Textual Similarity Benchmark dataset construct from YJ Image Captions Dataset (Miyazaki and Shimizu, 2016) and annotated by crowdsource annotators.

|               |                                             |
|---------------|---------------------------------------------|
| Task category | t2t                              |
| Domains       | Web, Written                               |
| Reference     | https://aclanthology.org/2022.lrec-1.317.pdf#page=2.00 |


## 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_tasks(["JSTS"])
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 repitory](https://github.com/embeddings-benchmark/mteb). 

## Citation

If you use this dataset, please cite the dataset as well as [mteb](https://github.com/embeddings-benchmark/mteb), as this dataset likely includes additional processing as a part of the [MMTEB Contribution](https://github.com/embeddings-benchmark/mteb/tree/main/docs/mmteb).

```bibtex

@inproceedings{kurihara-etal-2022-jglue,
  abstract = {To develop high-performance natural language understanding (NLU) models, it is necessary to have a benchmark to evaluate and analyze NLU ability from various perspectives. While the English NLU benchmark, GLUE, has been the forerunner, benchmarks are now being released for languages other than English, such as CLUE for Chinese and FLUE for French; but there is no such benchmark for Japanese. We build a Japanese NLU benchmark, JGLUE, from scratch without translation to measure the general NLU ability in Japanese. We hope that JGLUE will facilitate NLU research in Japanese.},
  address = {Marseille, France},
  author = {Kurihara, Kentaro  and
Kawahara, Daisuke  and
Shibata, Tomohide},
  booktitle = {Proceedings of the Thirteenth Language Resources and Evaluation Conference},
  editor = {Calzolari, Nicoletta  and
B{\'e}chet, Fr{\'e}d{\'e}ric  and
Blache, Philippe  and
Choukri, Khalid  and
Cieri, Christopher  and
Declerck, Thierry  and
Goggi, Sara  and
Isahara, Hitoshi  and
Maegaard, Bente  and
Mariani, Joseph  and
Mazo, H{\'e}l{\`e}ne  and
Odijk, Jan  and
Piperidis, Stelios},
  month = jun,
  pages = {2957--2966},
  publisher = {European Language Resources Association},
  title = {{JGLUE}: {J}apanese General Language Understanding Evaluation},
  url = {https://aclanthology.org/2022.lrec-1.317},
  year = {2022},
}


@article{enevoldsen2025mmtebmassivemultilingualtext,
  title={MMTEB: Massive Multilingual Text Embedding Benchmark},
  author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Loïc Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff},
  publisher = {arXiv},
  journal={arXiv preprint arXiv:2502.13595},
  year={2025},
  url={https://arxiv.org/abs/2502.13595},
  doi = {10.48550/arXiv.2502.13595},
}

@article{muennighoff2022mteb,
  author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils},
  title = {MTEB: Massive Text Embedding Benchmark},
  publisher = {arXiv},
  journal={arXiv preprint arXiv:2210.07316},
  year = {2022}
  url = {https://arxiv.org/abs/2210.07316},
  doi = {10.48550/ARXIV.2210.07316},
}
```

# 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("JSTS")

desc_stats = task.metadata.descriptive_stats
```

```json
{
    "validation": {
        "num_samples": 1457,
        "number_of_characters": 67518,
        "unique_pairs": 1456,
        "min_sentence1_length": 12,
        "average_sentence1_len": 23.3452299245024,
        "max_sentence1_length": 79,
        "unique_sentence1": 1403,
        "min_sentence2_length": 8,
        "average_sentence2_len": 22.99519560741249,
        "max_sentence2_length": 77,
        "unique_sentence2": 1434,
        "min_score": 0.0,
        "avg_score": 2.2719286174379807,
        "max_score": 5.0
    }
}
```

</details>

---
*This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*