Add dataset card

README.md

@@ -1,17 +1,23 @@
 ---
-annotations_creators: []
+annotations_creators:
+- human-annotated
 language_creators: []
 language:
-- de
-- en
-- fr
-- ru
-- zh
-license:
-- cc-by-sa-4.0
-multilinguality:
-- monolingual
-- multilingual
+- deu
+- eng
+- fra
+- eng
+- rus
+- eng
+- cmn
+- eng
+license: unknown
+multilinguality: translated
+source_datasets:
+- mteb/bucc-bitext-mining
+task_categories:
+- translation
+task_ids: []
 pretty_name: MTEB Benchmark
 configs:
 - config_name: default
@@ -34,28 +40,178 @@ configs:
   data_files:
   - path: test/zh-en.jsonl.gz
     split: test
+tags:
+- mteb
+- text
 ---
+<!-- adapted from https://github.com/huggingface/huggingface_hub/blob/v0.30.2/src/huggingface_hub/templates/datasetcard_template.md -->
 
-# Dataset Card for MTEB Benchmark
+<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>
 
-## Dataset Description
+BUCC bitext mining dataset
 
-- **Homepage:** https://github.com/embeddings-benchmark/mteb-draft
-- **Repository:** https://github.com/embeddings-benchmark/mteb-draft
-- **Paper:** soon
-- **Leaderboard:** https://docs.google.com/spreadsheets/d/14P8bdEzsIgTGGlp9oOlMw-THrQbn2fYfZEkZV4NUBos
-- **Point of Contact:** [email protected]
+|               |                                             |
+|---------------|---------------------------------------------|
+| Task category | t2t                              |
+| Domains       | Written                               |
+| Reference     | https://comparable.limsi.fr/bucc2018/bucc2018-task.html |
 
-### Dataset Summary
 
-MTEB is a heterogeneous benchmark that has been built from diverse tasks:
-* BitextMining: [BUCC](https://comparable.limsi.fr/bucc2018/bucc2018-task.html), [Tatoeba](https://github.com/facebookresearch/LASER/tree/main/data/tatoeba/v1)
-* Classification: [AmazonCounterfactualClassification](https://arxiv.org/abs/2104.06893), [AmazonPolarityClassification](https://dl.acm.org/doi/10.1145/2507157.2507163), [AmazonReviewsClassification](https://arxiv.org/abs/2010.02573), [Banking77Classification](https://arxiv.org/abs/2003.04807), [EmotionClassification](https://www.aclweb.org/anthology/D18-1404), [ImdbClassification](http://www.aclweb.org/anthology/P11-1015), [MassiveIntentClassification](https://arxiv.org/abs/2204.08582#:~:text=MASSIVE%20contains%201M%20realistic%2C%20parallel,diverse%20languages%20from%2029%20genera.), [MassiveScenarioClassification](https://arxiv.org/abs/2204.08582#:~:text=MASSIVE%20contains%201M%20realistic%2C%20parallel,diverse%20languages%20from%2029%20genera.), [MTOPDomainClassification](https://arxiv.org/pdf/2008.09335.pdf), [MTOPIntentClassification](https://arxiv.org/pdf/2008.09335.pdf), [ToxicConversationsClassification](https://www.kaggle.com/competitions/jigsaw-unintended-bias-in-toxicity-classification/overview), [TweetSentimentExtractionClassification](https://www.kaggle.com/competitions/tweet-sentiment-extraction/overview)
-* Clustering: [ArxivClusteringP2P](https://www.kaggle.com/Cornell-University/arxiv), [ArxivClusteringS2S](https://www.kaggle.com/Cornell-University/arxiv), [BiorxivClusteringP2P](https://api.biorxiv.org/), [BiorxivClusteringS2S](https://api.biorxiv.org/), [MedrxivClusteringP2P](https://api.biorxiv.org/), [MedrxivClusteringS2S](https://api.biorxiv.org/), [RedditClustering](https://arxiv.org/abs/2104.07081), [RedditClusteringP2P](https://huggingface.co/datasets/sentence-transformers/reddit-title-body), [StackExchangeClustering](https://arxiv.org/abs/2104.07081), [StackExchangeClusteringP2P](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_title_body_jsonl), [TwentyNewsgroupsClustering](https://scikit-learn.org/0.19/datasets/twenty_newsgroups.html)
-* Pair Classification: [SprintDuplicateQuestions](https://www.aclweb.org/anthology/D18-1131/), [TwitterSemEval2015](https://alt.qcri.org/semeval2015/task1/), [TwitterURLCorpus](https://languagenet.github.io/)
-* Reranking: [AskUbuntuDupQuestions](https://github.com/taolei87/askubuntu), [MindSmallReranking](https://www.microsoft.com/en-us/research/uploads/prod/2019/03/nl4se18LinkSO.pdf), [SciDocs](https://allenai.org/data/scidocs), [StackOverflowDupQuestions](https://www.microsoft.com/en-us/research/uploads/prod/2019/03/nl4se18LinkSO.pdf)
-* Retrieval: [ArguAna](http://argumentation.bplaced.net/arguana/data), [ClimateFEVER](https://www.sustainablefinance.uzh.ch/en/research/climate-fever.html), [CQADupstackRetrieval](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/), [DBPedia](https://github.com/iai-group/DBpedia-Entity/), [FEVER](https://fever.ai/), [FiQA2018](https://sites.google.com/view/fiqa/), [HotpotQA](https://hotpotqa.github.io/), [MSMARCO](https://microsoft.github.io/msmarco/), [MSMARCOv2](https://microsoft.github.io/msmarco/TREC-Deep-Learning.html), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/), [NQ](https://ai.google.com/research/NaturalQuestions/), [QuoraRetrieval](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs), [SCIDOCS](https://allenai.org/data/scidocs), [SciFact](https://github.com/allenai/scifact), [Touche2020](https://webis.de/events/touche-20/shared-task-1.html), [TRECCOVID](https://ir.nist.gov/covidSubmit/index.html)
-* STS: [BIOSSES](https://tabilab.cmpe.boun.edu.tr/BIOSSES/DataSet.html), [SICK-R](https://www.aclweb.org/anthology/S14-2001.pdf), [STS12](https://www.aclweb.org/anthology/S12-1051.pdf), [STS13](https://www.aclweb.org/anthology/S13-1004/), [STS14](http://alt.qcri.org/semeval2014/task10/), [STS15](http://alt.qcri.org/semeval2015/task2/), [STS16](http://alt.qcri.org/semeval2016/task1/), [STS17](http://alt.qcri.org/semeval2016/task1/), [STS22](https://competitions.codalab.org/competitions/33835), [STSBenchmark](http://ixa2.si.ehu.es/stswiki/index.php/STSbenchmark)
-* Summarization: [SummEval](https://tabilab.cmpe.boun.edu.tr/BIOSSES/DataSet.html)
+## How to evaluate on this task
 
-All these datasets have been preprocessed and can be used for your experiments.
+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)*