Add dataset card

README.md

@@ -1,34 +1,28 @@
 ---
 annotations_creators:
-- crowdsourced
+- human-annotated
 language_creators:
 - crowdsourced
 - found
 - machine-generated
 language:
-- de
-- en
-- es
-- fr
-- it
-- nl
-- pl
-- pt
-- ru
-- zh
-license:
-- other
-multilinguality:
-- multilingual
+- eng
+- deu
+- spa
+- fra
+- ita
+- nld
+- pol
+- por
+- rus
+- cmn
+license: unknown
+multilinguality: translated
 size_categories:
 - 10K<n<100K
-source_datasets:
-- extended|other-sts-b
 task_categories:
-- text-classification
-task_ids:
-- text-scoring
-- semantic-similarity-scoring
+- sentence-similarity
+task_ids: []
 pretty_name: STSb Multi MT
 configs:
 - config_name: default
@@ -119,174 +113,455 @@ configs:
     split: train
   - path: dev/pl.parquet
     split: dev
+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 STSb Multi MT
-
-## Table of Contents
-- [Dataset Description](#dataset-description)
-  - [Dataset Summary](#dataset-summary)
-  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
-  - [Languages](#languages)
-- [Dataset Structure](#dataset-structure)
-  - [Data Instances](#data-instances)
-  - [Data Fields](#data-fields)
-  - [Data Splits](#data-splits)
-- [Dataset Creation](#dataset-creation)
-  - [Curation Rationale](#curation-rationale)
-  - [Source Data](#source-data)
-  - [Annotations](#annotations)
-  - [Personal and Sensitive Information](#personal-and-sensitive-information)
-- [Considerations for Using the Data](#considerations-for-using-the-data)
-  - [Social Impact of Dataset](#social-impact-of-dataset)
-  - [Discussion of Biases](#discussion-of-biases)
-  - [Other Known Limitations](#other-known-limitations)
-- [Additional Information](#additional-information)
-  - [Dataset Curators](#dataset-curators)
-  - [Licensing Information](#licensing-information)
-  - [Citation Information](#citation-information)
-  - [Contributions](#contributions)
-
-## Dataset Description
+<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;">STSBenchmarkMultilingualSTS</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>
 
-- **Repository**: https://github.com/PhilipMay/stsb-multi-mt
-- **Homepage (original dataset):** https://ixa2.si.ehu.es/stswiki/index.php/STSbenchmark
-- **Paper about original dataset:** https://arxiv.org/abs/1708.00055
-- **Leaderboard:** https://ixa2.si.ehu.eus/stswiki/index.php/STSbenchmark#Results
-- **Point of Contact:** [Open an issue on GitHub](https://github.com/PhilipMay/stsb-multi-mt/issues/new)
+Semantic Textual Similarity Benchmark (STSbenchmark) dataset, but translated using DeepL API.
 
-### Dataset Summary
+|               |                                             |
+|---------------|---------------------------------------------|
+| Task category | t2t                              |
+| Domains       | News, Social, Web, Spoken, Written                               |
+| Reference     | https://github.com/PhilipMay/stsb-multi-mt/ |
 
-> STS Benchmark comprises a selection of the English datasets used in the STS tasks organized
-> in the context of SemEval between 2012 and 2017. The selection of datasets include text from
-> image captions, news headlines and user forums. ([source](https://ixa2.si.ehu.es/stswiki/index.php/STSbenchmark))
 
-These are different multilingual translations and the English original of the [STSbenchmark dataset](https://ixa2.si.ehu.es/stswiki/index.php/STSbenchmark). Translation has been done with [deepl.com](https://www.deepl.com/). It can be used to train [sentence embeddings](https://github.com/UKPLab/sentence-transformers) like [T-Systems-onsite/cross-en-de-roberta-sentence-transformer](https://huggingface.co/T-Systems-onsite/cross-en-de-roberta-sentence-transformer).
+## How to evaluate on this task
 
+You can evaluate an embedding model on this dataset using the following code:
 
-**Examples of Use**
-
-Load German dev Dataset:
 ```python
-from datasets import load_dataset
-dataset = load_dataset("stsb_multi_mt", name="de", split="dev")
-```
+import mteb
 
-Load English train Dataset:
-```python
-from datasets import load_dataset
-dataset = load_dataset("stsb_multi_mt", name="en", split="train")
-```
+task = mteb.get_tasks(["STSBenchmarkMultilingualSTS"])
+evaluator = mteb.MTEB(task)
 
-### Supported Tasks and Leaderboards
+model = mteb.get_model(YOUR_MODEL)
+evaluator.run(model)
+```
 
-[More Information Needed]
+<!-- 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). 
 
-### Languages
+## Citation
 
-Available languages are: de, en, es, fr, it, nl, pl, pt, ru, zh
+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).
 
-## Dataset Structure
+```bibtex
 
-### Data Instances
+@inproceedings{huggingface:dataset:stsb_multi_mt,
+  author = {Philip May},
+  title = {Machine translated multilingual STS benchmark dataset.},
+  url = {https://github.com/PhilipMay/stsb-multi-mt},
+  year = {2021},
+}
 
-This dataset provides pairs of sentences and a score of their similarity.
 
-score | 2 example sentences | explanation
-------|---------|------------
-5 | *The bird is bathing in the sink.<br/>Birdie is washing itself in the water basin.* | The two sentences are completely equivalent, as they mean the same thing.
-4 | *Two boys on a couch are playing video games.<br/>Two boys are playing a video game.* | The two sentences are mostly equivalent, but some unimportant details differ.
-3 | *John said he is considered a witness but not a suspect.<br/>“He is not a suspect anymore.” John said.* | The two sentences are roughly equivalent, but some important information differs/missing.
-2 | *They flew out of the nest in groups.<br/>They flew into the nest together.* | The two sentences are not equivalent, but share some details.
-1 | *The woman is playing the violin.<br/>The young lady enjoys listening to the guitar.* | The two sentences are not equivalent, but are on the same topic.
-0 | *The black dog is running through the snow.<br/>A race car driver is driving his car through the mud.* | The two sentences are completely dissimilar.
+@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},
+}
 
-An example:
-```
-{
-    "sentence1": "A man is playing a large flute.",
-    "sentence2": "A man is playing a flute.",
-    "similarity_score": 3.8
+@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},
 }
 ```
 
-### Data Fields
-
-- `sentence1`: The 1st sentence as a `str`.
-- `sentence2`: The 2nd sentence as a `str`.
-- `similarity_score`: The similarity score as a `float` which is `<= 5.0` and `>= 0.0`.
-
-### Data Splits
-
-- train with 5749 samples
-- dev with 1500 samples
-- test with 1379 sampples
-
-## Dataset Creation
-
-### Curation Rationale
-
-[More Information Needed]
-
-### Source Data
-
-#### Initial Data Collection and Normalization
-
-[More Information Needed]
-
-#### Who are the source language producers?
+# Dataset Statistics
+<details>
+  <summary> Dataset Statistics</summary>
 
-[More Information Needed]
+The following code contains the descriptive statistics from the task. These can also be obtained using:
 
-### Annotations
-
-#### Annotation process
-
-[More Information Needed]
-
-#### Who are the annotators?
-
-[More Information Needed]
-
-### Personal and Sensitive Information
-
-[More Information Needed]
-
-## Considerations for Using the Data
-
-### Social Impact of Dataset
-
-[More Information Needed]
-
-### Discussion of Biases
-
-[More Information Needed]
-
-### Other Known Limitations
-
-[More Information Needed]
-
-## Additional Information
-
-### Dataset Curators
-
-[More Information Needed]
-
-### Licensing Information
-
-See [LICENSE](https://github.com/PhilipMay/stsb-multi-mt/blob/main/LICENSE) and [download at original dataset](https://ixa2.si.ehu.eus/stswiki/index.php/STSbenchmark).
+```python
+import mteb
 
-### Citation Information
+task = mteb.get_task("STSBenchmarkMultilingualSTS")
 
+desc_stats = task.metadata.descriptive_stats
 ```
-@InProceedings{huggingface:dataset:stsb_multi_mt,
-title = {Machine translated multilingual STS benchmark dataset.},
-author={Philip May},
-year={2021},
-url={https://github.com/PhilipMay/stsb-multi-mt}
+
+```json
+{
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+        "max_sentence1_length": 274,
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+        "min_sentence2_length": 3,
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+        "max_sentence2_length": 281,
+        "unique_sentence2": 14605,
+        "min_score": 0.0,
+        "avg_score": 2.3639075540602206,
+        "max_score": 5.0,
+        "hf_subset_descriptive_stats": {
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+                "avg_score": 2.3639075540602206,
+                "max_score": 5.0
+            },
+            "de": {
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+                "max_score": 5.0
+            },
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+                "max_score": 5.0
+            },
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+                "max_score": 5.0
+            }
+        }
+    },
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+            },
+            "pt": {
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+                "avg_score": 2.6079166059890806,
+                "max_score": 5.0
+            },
+            "ru": {
+                "num_samples": 1379,
+                "number_of_characters": 156178,
+                "unique_pairs": 1376,
+                "min_sentence1_length": 10,
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+                "max_sentence1_length": 263,
+                "unique_sentence1": 1240,
+                "min_sentence2_length": 10,
+                "average_sentence2_len": 56.44525018129079,
+                "max_sentence2_length": 269,
+                "unique_sentence2": 1321,
+                "min_score": 0.0,
+                "avg_score": 2.6079166059890806,
+                "max_score": 5.0
+            },
+            "zh": {
+                "num_samples": 1379,
+                "number_of_characters": 49316,
+                "unique_pairs": 1372,
+                "min_sentence1_length": 3,
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+                "max_sentence1_length": 96,
+                "unique_sentence1": 1242,
+                "min_sentence2_length": 3,
+                "average_sentence2_len": 17.80565627266135,
+                "max_sentence2_length": 131,
+                "unique_sentence2": 1320,
+                "min_score": 0.0,
+                "avg_score": 2.6079166059890806,
+                "max_score": 5.0
+            }
+        }
+    }
 }
 ```
 
-### Contributions
+</details>
 
-Thanks to [@PhilipMay](https://github.com/PhilipMay) for adding this dataset.
+---
+*This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*