Datasets:
metadata
annotations_creators:
- expert-annotated
language:
- cat
- spa
license: cc-by-sa-4.0
multilinguality: multilingual
task_categories:
- text-classification
task_ids: []
dataset_info:
- config_name: catalan
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 1260596
num_examples: 6028
- name: test
num_bytes: 420682
num_examples: 2010
- name: validation
num_bytes: 424788
num_examples: 2010
download_size: 1452948
dataset_size: 2106066
- config_name: spanish
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 1368273
num_examples: 6046
- name: test
num_bytes: 455394
num_examples: 2016
- name: validation
num_bytes: 458715
num_examples: 2015
download_size: 1577104
dataset_size: 2282382
configs:
- config_name: catalan
data_files:
- split: train
path: catalan/train-*
- split: test
path: catalan/test-*
- split: validation
path: catalan/validation-*
- config_name: spanish
data_files:
- split: train
path: spanish/train-*
- split: test
path: spanish/test-*
- split: validation
path: spanish/validation-*
tags:
- mteb
- text
This dataset contains two corpora in Spanish and Catalan that consist of annotated Twitter messages for automatic stance detection. The data was collected over 12 days during February and March of 2019 from tweets posted in Barcelona, and during September of 2018 from tweets posted in the town of Terrassa, Catalonia. Each corpus is annotated with three classes: AGAINST, FAVOR and NEUTRAL, which express the stance towards the target - independence of Catalonia.
| Task category | t2c |
| Domains | Social, Government, Written |
| Reference | https://aclanthology.org/2020.lrec-1.171/ |
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task = mteb.get_tasks(["CataloniaTweetClassification"])
evaluator = mteb.MTEB(task)
model = mteb.get_model(YOUR_MODEL)
evaluator.run(model)
To learn more about how to run models on mteb task check out the GitHub repitory.
Citation
If you use this dataset, please cite the dataset as well as mteb, as this dataset likely includes additional processing as a part of the MMTEB Contribution.
@inproceedings{zotova-etal-2020-multilingual,
author = {Zotova, Elena and
Agerri, Rodrigo and
Nu{\~n}ez, Manuel and
Rigau, German},
booktitle = {Proceedings of the Twelfth 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
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios},
isbn = {979-10-95546-34-4},
month = may,
pages = {1368--1375},
publisher = {European Language Resources Association},
title = {Multilingual Stance Detection in Tweets: The {C}atalonia Independence Corpus},
year = {2020},
}
@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
Dataset Statistics
The following code contains the descriptive statistics from the task. These can also be obtained using:
import mteb
task = mteb.get_task("CataloniaTweetClassification")
desc_stats = task.metadata.descriptive_stats
{
"validation": {
"num_samples": 4025,
"number_of_characters": 814740,
"number_texts_intersect_with_train": 5,
"min_text_length": 17,
"average_text_length": 202.4198757763975,
"max_text_length": 956,
"unique_text": 4025,
"unique_labels": 3,
"labels": {
"1": {
"count": 1545
},
"0": {
"count": 1676
},
"2": {
"count": 804
}
},
"hf_subset_descriptive_stats": {
"spanish": {
"num_samples": 2015,
"number_of_characters": 424553,
"number_texts_intersect_with_train": 5,
"min_text_length": 17,
"average_text_length": 210.69627791563275,
"max_text_length": 956,
"unique_text": 2015,
"unique_labels": 3,
"labels": {
"1": {
"count": 782
},
"0": {
"count": 856
},
"2": {
"count": 377
}
}
},
"catalan": {
"num_samples": 2010,
"number_of_characters": 390187,
"number_texts_intersect_with_train": 0,
"min_text_length": 26,
"average_text_length": 194.1228855721393,
"max_text_length": 753,
"unique_text": 2010,
"unique_labels": 3,
"labels": {
"1": {
"count": 763
},
"2": {
"count": 427
},
"0": {
"count": 820
}
}
}
}
},
"test": {
"num_samples": 4026,
"number_of_characters": 807122,
"number_texts_intersect_with_train": 4,
"min_text_length": 21,
"average_text_length": 200.47739692001988,
"max_text_length": 911,
"unique_text": 4026,
"unique_labels": 3,
"labels": {
"0": {
"count": 1581
},
"1": {
"count": 1611
},
"2": {
"count": 834
}
},
"hf_subset_descriptive_stats": {
"spanish": {
"num_samples": 2016,
"number_of_characters": 421522,
"number_texts_intersect_with_train": 1,
"min_text_length": 21,
"average_text_length": 209.08829365079364,
"max_text_length": 911,
"unique_text": 2016,
"unique_labels": 3,
"labels": {
"0": {
"count": 829
},
"1": {
"count": 807
},
"2": {
"count": 380
}
}
},
"catalan": {
"num_samples": 2010,
"number_of_characters": 385600,
"number_texts_intersect_with_train": 0,
"min_text_length": 26,
"average_text_length": 191.8407960199005,
"max_text_length": 781,
"unique_text": 2010,
"unique_labels": 3,
"labels": {
"1": {
"count": 804
},
"2": {
"count": 454
},
"0": {
"count": 752
}
}
}
}
},
"train": {
"num_samples": 12074,
"number_of_characters": 2421991,
"number_texts_intersect_with_train": null,
"min_text_length": 16,
"average_text_length": 200.59557727348022,
"max_text_length": 938,
"unique_text": 12070,
"unique_labels": 3,
"labels": {
"0": {
"count": 4836
},
"2": {
"count": 2388
},
"1": {
"count": 4850
}
},
"hf_subset_descriptive_stats": {
"spanish": {
"num_samples": 6046,
"number_of_characters": 1266286,
"number_texts_intersect_with_train": null,
"min_text_length": 16,
"average_text_length": 209.44194508766125,
"max_text_length": 938,
"unique_text": 6043,
"unique_labels": 3,
"labels": {
"0": {
"count": 2420
},
"2": {
"count": 1111
},
"1": {
"count": 2515
}
}
},
"catalan": {
"num_samples": 6028,
"number_of_characters": 1155705,
"number_texts_intersect_with_train": null,
"min_text_length": 30,
"average_text_length": 191.72279362972793,
"max_text_length": 828,
"unique_text": 6028,
"unique_labels": 3,
"labels": {
"0": {
"count": 2416
},
"1": {
"count": 2335
},
"2": {
"count": 1277
}
}
}
}
}
}
This dataset card was automatically generated using MTEB