Datasets:

mteb
/

xnli

@@ -1,20 +1,26 @@
 ---
 language:
-- ar
-- bg
-- de
-- el
-- en
-- es
-- fr
-- hi
-- ru
-- sw
-- th
-- tr
-- ur
-- vi
-- zh
 paperswithcode_id: xnli
 pretty_name: Cross-lingual Natural Language Inference
 configs:
@@ -146,261 +152,742 @@ configs:
     split: train
   - path: validation/hi.parquet
     split: validation
 ---
-# Dataset Card for "xnli"
-## 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
-- **Homepage:** [https://www.nyu.edu/projects/bowman/xnli/](https://www.nyu.edu/projects/bowman/xnli/)
-- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-- **Size of downloaded dataset files:** 7.74 GB
-- **Size of the generated dataset:** 3.23 GB
-- **Total amount of disk used:** 10.97 GB
-### Dataset Summary
-XNLI is a subset of a few thousand examples from MNLI which has been translated
-into a 14 different languages (some low-ish resource). As with MNLI, the goal is
-to predict textual entailment (does sentence A imply/contradict/neither sentence
-B) and is a classification task (given two sentences, predict one of three
-labels).
-### Supported Tasks and Leaderboards
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-### Languages
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-## Dataset Structure
-### Data Instances
-#### all_languages
-- **Size of downloaded dataset files:** 483.96 MB
-- **Size of the generated dataset:** 1.61 GB
-- **Total amount of disk used:** 2.09 GB
-An example of 'train' looks as follows.
-```
-This example was too long and was cropped:
-{
-    "hypothesis": "{\"language\": [\"ar\", \"bg\", \"de\", \"el\", \"en\", \"es\", \"fr\", \"hi\", \"ru\", \"sw\", \"th\", \"tr\", \"ur\", \"vi\", \"zh\"], \"translation\": [\"احد اع...",
-    "label": 0,
-    "premise": "{\"ar\": \"واحدة من رقابنا ستقوم بتنفيذ تعليماتك كلها بكل دقة\", \"bg\": \"един от нашите номера ще ви даде инструкции .\", \"de\": \"Eine ..."
 }
-```
-#### ar
-- **Size of downloaded dataset files:** 483.96 MB
-- **Size of the generated dataset:** 109.32 MB
-- **Total amount of disk used:** 593.29 MB
-An example of 'validation' looks as follows.
-```
-{
-    "hypothesis": "اتصل بأمه حالما أوصلته حافلة المدرسية.",
-    "label": 1,
-    "premise": "وقال، ماما، لقد عدت للمنزل."
 }
-```
-#### bg
-- **Size of downloaded dataset files:** 483.96 MB
-- **Size of the generated dataset:** 128.32 MB
-- **Total amount of disk used:** 612.28 MB
-An example of 'train' looks as follows.
-```
-This example was too long and was cropped:
-{
-    "hypothesis": "\"губиш нещата на следното ниво , ако хората си припомнят .\"...",
-    "label": 0,
-    "premise": "\"по време на сезона и предполагам , че на твоето ниво ще ги загубиш на следващото ниво , ако те решат да си припомнят отбора на ..."
 }
 ```
-#### de
-- **Size of downloaded dataset files:** 483.96 MB
-- **Size of the generated dataset:** 86.17 MB
-- **Total amount of disk used:** 570.14 MB
-An example of 'train' looks as follows.
-```
-This example was too long and was cropped:
-{
-    "hypothesis": "Man verliert die Dinge auf die folgende Ebene , wenn sich die Leute erinnern .",
-    "label": 0,
-    "premise": "\"Du weißt , während der Saison und ich schätze , auf deiner Ebene verlierst du sie auf die nächste Ebene , wenn sie sich entschl..."
-}
-```
-#### el
-- **Size of downloaded dataset files:** 483.96 MB
-- **Size of the generated dataset:** 142.30 MB
-- **Total amount of disk used:** 626.26 MB
-An example of 'validation' looks as follows.
 ```
-This example was too long and was cropped:
 {
-    "hypothesis": "\"Τηλεφώνησε στη μαμά του μόλις το σχολικό λεωφορείο τον άφησε.\"...",
-    "label": 1,
-    "premise": "Και είπε, Μαμά, έφτασα στο σπίτι."
 }
 ```
-### Data Fields
-The data fields are the same among all splits.
-#### all_languages
-- `premise`: a multilingual `string` variable, with possible languages including `ar`, `bg`, `de`, `el`, `en`.
-- `hypothesis`: a multilingual `string` variable, with possible languages including `ar`, `bg`, `de`, `el`, `en`.
-- `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2).
-#### ar
-- `premise`: a `string` feature.
-- `hypothesis`: a `string` feature.
-- `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2).
-#### bg
-- `premise`: a `string` feature.
-- `hypothesis`: a `string` feature.
-- `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2).
-#### de
-- `premise`: a `string` feature.
-- `hypothesis`: a `string` feature.
-- `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2).
-#### el
-- `premise`: a `string` feature.
-- `hypothesis`: a `string` feature.
-- `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2).
-### Data Splits
-|    name     |train |validation|test|
-|-------------|-----:|---------:|---:|
-|all_languages|392702|      2490|5010|
-|ar           |392702|      2490|5010|
-|bg           |392702|      2490|5010|
-|de           |392702|      2490|5010|
-|el           |392702|      2490|5010|
-## Dataset Creation
-### Curation Rationale
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-### Source Data
-#### Initial Data Collection and Normalization
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-#### Who are the source language producers?
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-### Annotations
-#### Annotation process
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-#### Who are the annotators?
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-### Personal and Sensitive Information
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-## Considerations for Using the Data
-### Social Impact of Dataset
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-### Discussion of Biases
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-### Other Known Limitations
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-## Additional Information
-### Dataset Curators
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-### Licensing Information
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-### Citation Information
-```
-@InProceedings{conneau2018xnli,
-  author = {Conneau, Alexis
-                 and Rinott, Ruty
-                 and Lample, Guillaume
-                 and Williams, Adina
-                 and Bowman, Samuel R.
-                 and Schwenk, Holger
-                 and Stoyanov, Veselin},
-  title = {XNLI: Evaluating Cross-lingual Sentence Representations},
-  booktitle = {Proceedings of the 2018 Conference on Empirical Methods
-               in Natural Language Processing},
-  year = {2018},
-  publisher = {Association for Computational Linguistics},
-  location = {Brussels, Belgium},
-}
-```
-### Contributions
-Thanks to [@lewtun](https://github.com/lewtun), [@mariamabarham](https://github.com/mariamabarham), [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.

 ---
+annotations_creators:
+- expert-annotated
 language:
+- eng
+- fra
+- zho
+- deu
+- ara
+- ell
+- bul
+- swa
+- hin
+- tur
+- spa
+- rus
+- vie
+- tha
+license: unknown
+multilinguality: multilingual
+task_categories:
+- text-classification
+task_ids: []
 paperswithcode_id: xnli
 pretty_name: Cross-lingual Natural Language Inference
 configs:
     split: train
   - path: validation/hi.parquet
     split: 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;">XNLI</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>
+|               |                                             |
+|---------------|---------------------------------------------|
+| Task category | t2t                              |
+| Domains       | Non-fiction, Fiction, Government, Written                               |
+| Reference     | https://aclanthology.org/D18-1269/ |
+## 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(["XNLI"])
+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{conneau2018xnli,
+  author = {Conneau, Alexis
+and Rinott, Ruty
+and Lample, Guillaume
+and Williams, Adina
+and Bowman, Samuel R.
+and Schwenk, Holger
+and Stoyanov, Veselin},
+  booktitle = {Proceedings of the 2018 Conference on Empirical Methods
+in Natural Language Processing},
+  location = {Brussels, Belgium},
+  publisher = {Association for Computational Linguistics},
+  title = {XNLI: Evaluating Cross-lingual Sentence Representations},
+  year = {2018},
 }
+@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("XNLI")
+desc_stats = task.metadata.descriptive_stats
 ```
+```json
 {
+    "test": {
+        "num_samples": 19110,
+        "number_of_characters": 2907145,
+        "min_sentence1_length": 3,
+        "avg_sentence1_length": 103.23793825222397,
+        "max_sentence1_length": 401,
+        "unique_sentence1": 15328,
+        "min_sentence2_length": 2,
+        "avg_sentence2_length": 48.88895866038723,
+        "max_sentence2_length": 187,
+        "unique_sentence2": 19104,
+        "unique_labels": 2,
+        "labels": {
+            "0": {
+                "count": 9562
+            },
+            "1": {
+                "count": 9548
+            }
+        },
+        "hf_subset_descriptive_stats": {
+            "ar": {
+                "num_samples": 1365,
+                "number_of_characters": 179591,
+                "min_sentence1_length": 11,
+                "avg_sentence1_length": 89.57362637362637,
+                "max_sentence1_length": 242,
+                "unique_sentence1": 1095,
+                "min_sentence2_length": 8,
+                "avg_sentence2_length": 41.99487179487179,
+                "max_sentence2_length": 115,
+                "unique_sentence2": 1365,
+                "unique_labels": 2,
+                "labels": {
+                    "0": {
+                        "count": 683
+                    },
+                    "1": {
+                        "count": 682
+                    }
+                }
+            },
+            "bg": {
+                "num_samples": 1365,
+                "number_of_characters": 220646,
+                "min_sentence1_length": 14,
+                "avg_sentence1_length": 110.01611721611722,
+                "max_sentence1_length": 303,
+                "unique_sentence1": 1095,
+                "min_sentence2_length": 8,
+                "avg_sentence2_length": 51.62930402930403,
+                "max_sentence2_length": 150,
+                "unique_sentence2": 1365,
+                "unique_labels": 2,
+                "labels": {
+                    "0": {
+                        "count": 683
+                    },
+                    "1": {
+                        "count": 682
+                    }
+                }
+            },
+            "de": {
+                "num_samples": 1365,
+                "number_of_characters": 241224,
+                "min_sentence1_length": 3,
+                "avg_sentence1_length": 119.92600732600732,
+                "max_sentence1_length": 301,
+                "unique_sentence1": 1095,
+                "min_sentence2_length": 9,
+                "avg_sentence2_length": 56.794871794871796,
+                "max_sentence2_length": 187,
+                "unique_sentence2": 1365,
+                "unique_labels": 2,
+                "labels": {
+                    "0": {
+                        "count": 683
+                    },
+                    "1": {
+                        "count": 682
+                    }
+                }
+            },
+            "el": {
+                "num_samples": 1365,
+                "number_of_characters": 240222,
+                "min_sentence1_length": 13,
+                "avg_sentence1_length": 119.05421245421246,
+                "max_sentence1_length": 344,
+                "unique_sentence1": 1095,
+                "min_sentence2_length": 13,
+                "avg_sentence2_length": 56.93260073260073,
+                "max_sentence2_length": 172,
+                "unique_sentence2": 1365,
+                "unique_labels": 2,
+                "labels": {
+                    "0": {
+                        "count": 683
+                    },
+                    "1": {
+                        "count": 682
+                    }
+                }
+            },
+            "en": {
+                "num_samples": 1365,
+                "number_of_characters": 212223,
+                "min_sentence1_length": 19,
+                "avg_sentence1_length": 105.67032967032966,
+                "max_sentence1_length": 268,
+                "unique_sentence1": 1095,
+                "min_sentence2_length": 9,
+                "avg_sentence2_length": 49.8043956043956,
+                "max_sentence2_length": 137,
+                "unique_sentence2": 1365,
+                "unique_labels": 2,
+                "labels": {
+                    "0": {
+                        "count": 683
+                    },
+                    "1": {
+                        "count": 682
+                    }
+                }
+            },
+            "es": {
+                "num_samples": 1365,
+                "number_of_characters": 232207,
+                "min_sentence1_length": 11,
+                "avg_sentence1_length": 115.43296703296703,
+                "max_sentence1_length": 385,
+                "unique_sentence1": 1094,
+                "min_sentence2_length": 8,
+                "avg_sentence2_length": 54.68205128205128,
+                "max_sentence2_length": 163,
+                "unique_sentence2": 1365,
+                "unique_labels": 2,
+                "labels": {
+                    "0": {
+                        "count": 683
+                    },
+                    "1": {
+                        "count": 682
+                    }
+                }
+            },
+            "fr": {
+                "num_samples": 1365,
+                "number_of_characters": 245259,
+                "min_sentence1_length": 9,
+                "avg_sentence1_length": 121.0967032967033,
+                "max_sentence1_length": 327,
+                "unique_sentence1": 1095,
+                "min_sentence2_length": 10,
+                "avg_sentence2_length": 58.58021978021978,
+                "max_sentence2_length": 169,
+                "unique_sentence2": 1365,
+                "unique_labels": 2,
+                "labels": {
+                    "0": {
+                        "count": 683
+                    },
+                    "1": {
+                        "count": 682
+                    }
+                }
+            },
+            "hi": {
+                "num_samples": 1365,
+                "number_of_characters": 211312,
+                "min_sentence1_length": 16,
+                "avg_sentence1_length": 104.63443223443224,
+                "max_sentence1_length": 401,
+                "unique_sentence1": 1095,
+                "min_sentence2_length": 9,
+                "avg_sentence2_length": 50.17289377289377,
+                "max_sentence2_length": 162,
+                "unique_sentence2": 1365,
+                "unique_labels": 2,
+                "labels": {
+                    "0": {
+                        "count": 683
+                    },
+                    "1": {
+                        "count": 682
+                    }
+                }
+            },
+            "ru": {
+                "num_samples": 1365,
+                "number_of_characters": 222797,
+                "min_sentence1_length": 11,
+                "avg_sentence1_length": 110.76923076923077,
+                "max_sentence1_length": 306,
+                "unique_sentence1": 1095,
+                "min_sentence2_length": 8,
+                "avg_sentence2_length": 52.452014652014654,
+                "max_sentence2_length": 167,
+                "unique_sentence2": 1365,
+                "unique_labels": 2,
+                "labels": {
+                    "0": {
+                        "count": 683
+                    },
+                    "1": {
+                        "count": 682
+                    }
+                }
+            },
+            "sw": {
+                "num_samples": 1365,
+                "number_of_characters": 210103,
+                "min_sentence1_length": 10,
+                "avg_sentence1_length": 104.43956043956044,
+                "max_sentence1_length": 266,
+                "unique_sentence1": 1094,
+                "min_sentence2_length": 2,
+                "avg_sentence2_length": 49.48205128205128,
+                "max_sentence2_length": 146,
+                "unique_sentence2": 1365,
+                "unique_labels": 2,
+                "labels": {
+                    "0": {
+                        "count": 683
+                    },
+                    "1": {
+                        "count": 682
+                    }
+                }
+            },
+            "th": {
+                "num_samples": 1365,
+                "number_of_characters": 192788,
+                "min_sentence1_length": 12,
+                "avg_sentence1_length": 96.6923076923077,
+                "max_sentence1_length": 262,
+                "unique_sentence1": 1095,
+                "min_sentence2_length": 6,
+                "avg_sentence2_length": 44.544322344322346,
+                "max_sentence2_length": 129,
+                "unique_sentence2": 1365,
+                "unique_labels": 2,
+                "labels": {
+                    "0": {
+                        "count": 683
+                    },
+                    "1": {
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 }
 ```
+</details>
+---
+*This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*