bucc-bitext-mining

<!-- 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;">BUCC.v2</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>

BUCC bitext mining dataset

| | |
|---------------|---------------------------------------------|
| Task category | t2t |
| Domains | Written |
| Reference | https://comparable.limsi.fr/bucc2018/bucc2018-task.html |

## 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(["BUCC.v2"])
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{zweigenbaum-etal-2017-overview,
abstract = {This paper presents the BUCC 2017 shared task on parallel sentence extraction from comparable corpora. It recalls the design of the datasets, presents their final construction and statistics and the methods used to evaluate system results. 13 runs were submitted to the shared task by 4 teams, covering three of the four proposed language pairs: French-English (7 runs), German-English (3 runs), and Chinese-English (3 runs). The best F-scores as measured against the gold standard were 0.84 (German-English), 0.80 (French-English), and 0.43 (Chinese-English). Because of the design of the dataset, in which not all gold parallel sentence pairs are known, these are only minimum values. We examined manually a small sample of the false negative sentence pairs for the most precise French-English runs and estimated the number of parallel sentence pairs not yet in the provided gold standard. Adding them to the gold standard leads to revised estimates for the French-English F-scores of at most +1.5pt. This suggests that the BUCC 2017 datasets provide a reasonable approximate evaluation of the parallel sentence spotting task.},
address = {Vancouver, Canada},
author = {Zweigenbaum, Pierre and
Sharoff, Serge and
Rapp, Reinhard},
booktitle = {Proceedings of the 10th Workshop on Building and Using Comparable Corpora},
doi = {10.18653/v1/W17-2512},
editor = {Sharoff, Serge and
Zweigenbaum, Pierre and
Rapp, Reinhard},
month = aug,
pages = {60--67},
publisher = {Association for Computational Linguistics},
title = {Overview of the Second {BUCC} Shared Task: Spotting Parallel Sentences in Comparable Corpora},
url = {https://aclanthology.org/W17-2512},
year = {2017},
}

@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("BUCC.v2")

desc_stats = task.metadata.descriptive_stats
```

```json
{
"test": {
"num_samples": 35000,
"number_of_characters": 6640032,
"unique_pairs": 34978,
"min_sentence1_length": 16,
"average_sentence1_length": 99.10931428571429,
"max_sentence1_length": 204,
"unique_sentence1": 34978,
"min_sentence2_length": 42,
"average_sentence2_length": 90.60588571428572,
"max_sentence2_length": 159,
"unique_sentence2": 25306,
"hf_subset_descriptive_stats": {
"de-en": {
"num_samples": 9580,
"number_of_characters": 1919197,
"unique_pairs": 9573,
"min_sentence1_length": 50,
"average_sentence1_length": 109.07974947807934,
"max_sentence1_length": 204,
"unique_sentence1": 9573,
"min_sentence2_length": 46,
"average_sentence2_length": 91.25396659707724,
"max_sentence2_length": 155,
"unique_sentence2": 9570
},
"fr-en": {
"num_samples": 9086,
"number_of_characters": 1677545,
"unique_pairs": 9081,
"min_sentence1_length": 43,
"average_sentence1_length": 99.31785163988553,
"max_sentence1_length": 174,
"unique_sentence1": 9081,
"min_sentence2_length": 42,
"average_sentence2_length": 85.3117983711204,
"max_sentence2_length": 159,
"unique_sentence2": 9076
},
"ru-en": {
"num_samples": 14435,
"number_of_characters": 2808206,
"unique_pairs": 14425,
"min_sentence1_length": 40,
"average_sentence1_length": 101.6593003117423,
"max_sentence1_length": 186,
"unique_sentence1": 14425,
"min_sentence2_length": 45,
"average_sentence2_length": 92.88216141323173,
"max_sentence2_length": 159,
"unique_sentence2": 14424
},
"zh-en": {
"num_samples": 1899,
"number_of_characters": 235084,
"unique_pairs": 1899,
"min_sentence1_length": 16,
"average_sentence1_length": 28.429699842022117,
"max_sentence1_length": 40,
"unique_sentence1": 1899,
"min_sentence2_length": 48,
"average_sentence2_length": 95.3638757240653,
"max_sentence2_length": 159,
"unique_sentence2": 1899
}
}
}
}
```

</details>

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

<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;">BUCC.v2</h1>
<div style="font-size: 1.5rem; color: #4a4a4a; margin-bottom: 5px; font-weight: 300;">一款 <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（Massive Text Embedding Benchmark，大规模文本嵌入基准）</a> 数据集</div>
<div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">大规模文本嵌入基准</div>
</div>

**BUCC 双语语料挖掘数据集**

| 任务类别 | 文本到文本（t2t） |
|----------------|-------------------|
| 领域 | 书面文本 |
| 参考链接 | https://comparable.limsi.fr/bucc2018/bucc2018-task.html |

## 如何在该任务上评估模型

您可通过以下代码在本数据集上评估嵌入模型：
python
import mteb

task = mteb.get_tasks(["BUCC.v2"])
evaluator = mteb.MTEB(task)

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

> 注：数据集自述文件需添加arXiv链接以实现数据集与论文的自动关联。
若需了解如何在`mteb`任务中运行模型，请访问其[GitHub仓库](https://github.com/embeddings-benchmark/mteb)。

## 引用规范

若您使用本数据集，请同时引用本数据集与[mteb](https://github.com/embeddings-benchmark/mteb)，因为本数据集作为[MMTEB（Massive Multilingual Text Embedding Benchmark，大规模多语言文本嵌入基准）贡献项目](https://github.com/embeddings-benchmark/mteb/tree/main/docs/mmteb)的一部分，经过了额外的预处理。
bibtex
@inproceedings{zweigenbaum-etal-2017-overview,
abstract = {This paper presents the BUCC 2017 shared task on parallel sentence extraction from comparable corpora. It recalls the design of the datasets, presents their final construction and statistics and the methods used to evaluate system results. 13 runs were submitted to the shared task by 4 teams, covering three of the four proposed language pairs: French-English (7 runs), German-English (3 runs), and Chinese-English (3 runs). The best F-scores as measured against the gold standard were 0.84 (German-English), 0.80 (French-English), and 0.43 (Chinese-English). Because of the design of the dataset, in which not all gold parallel sentence pairs are known, these are only minimum values. We examined manually a small sample of the false negative sentence pairs for the most precise French-English runs and estimated the number of parallel sentence pairs not yet in the provided gold standard. Adding them to the gold standard leads to revised estimates for the French-English F-scores of at most +1.5pt. This suggests that the BUCC 2017 datasets provide a reasonable approximate evaluation of the parallel sentence spotting task.},
address = {Vancouver, Canada},
author = {Zweigenbaum, Pierre and
Sharoff, Serge and
Rapp, Reinhard},
booktitle = {Proceedings of the 10th Workshop on Building and Using Comparable Corpora},
doi = {10.18653/v1/W17-2512},
editor = {Sharoff, Serge and
Zweigenbaum, Pierre and
Rapp, Reinhard},
month = aug,
pages = {60--67},
publisher = {Association for Computational Linguistics},
title = {Overview of the Second {BUCC} Shared Task: Spotting Parallel Sentences in Comparable Corpora},
url = {https://aclanthology.org/W17-2512},
year = {2017},
}

@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},
}

## 数据集统计信息

<details>
<summary> 数据集统计信息</summary>

以下代码展示了本任务的描述性统计数据。您也可以通过以下代码获取：
python
import mteb

task = mteb.get_task("BUCC.v2")

desc_stats = task.metadata.descriptive_stats

json
{
"test": {
"num_samples": 35000,
"number_of_characters": 6640032,
"unique_pairs": 34978,
"min_sentence1_length": 16,
"average_sentence1_length": 99.10931428571429,
"max_sentence1_length": 204,
"unique_sentence1": 34978,
"min_sentence2_length": 42,
"average_sentence2_length": 90.60588571428572,
"max_sentence2_length": 159,
"unique_sentence2": 25306,
"hf_subset_descriptive_stats": {
"de-en": {
"num_samples": 9580,
"number_of_characters": 1919197,
"unique_pairs": 9573,
"min_sentence1_length": 50,
"average_sentence1_length": 109.07974947807934,
"max_sentence1_length": 204,
"unique_sentence1": 9573,
"min_sentence2_length": 46,
"average_sentence2_length": 91.25396659707724,
"max_sentence2_length": 155,
"unique_sentence2": 9570
},
"fr-en": {
"num_samples": 9086,
"number_of_characters": 1677545,
"unique_pairs": 9081,
"min_sentence1_length": 43,
"average_sentence1_length": 99.31785163988553,
"max_sentence1_length": 174,
"unique_sentence1": 9081,
"min_sentence2_length": 42,
"average_sentence2_length": 85.3117983711204,
"max_sentence2_length": 159,
"unique_sentence2": 9076
},
"ru-en": {
"num_samples": 14435,
"number_of_characters": 2808206,
"unique_pairs": 14425,
"min_sentence1_length": 40,
"average_sentence1_length": 101.6593003117423,
"max_sentence1_length": 186,
"unique_sentence1": 14425,
"min_sentence2_length": 45,
"average_sentence2_length": 92.88216141323173,
"max_sentence2_length": 159,
"unique_sentence2": 14424
},
"zh-en": {
"num_samples": 1899,
"number_of_characters": 235084,
"unique_pairs": 1899,
"min_sentence1_length": 16,
"average_sentence1_length": 28.429699842022117,
"max_sentence1_length": 40,
"unique_sentence1": 1899,
"min_sentence2_length": 48,
"average_sentence2_length": 95.3638757240653,
"max_sentence2_length": 159,
"unique_sentence2": 1899
}
}
}
}

</details>

---
*本数据集卡片由[MTEB](https://github.com/embeddings-benchmark/mteb)自动生成*