Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
topic-classification
Languages:
English
Size:
10K - 100K
ArXiv:
License:
metadata
annotations_creators:
- expert-annotated
language:
- eng
license: unknown
multilinguality: monolingual
source_datasets:
- cardiffnlp/tweet_topic_single
task_categories:
- text-classification
task_ids:
- topic-classification
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: test_2020
num_bytes: 66224
num_examples: 376
- name: test_2021
num_bytes: 306974
num_examples: 1693
- name: train_2020
num_bytes: 511564
num_examples: 2858
- name: train_2021
num_bytes: 273056
num_examples: 1516
- name: train_all
num_bytes: 784620
num_examples: 4374
- name: validation_2020
num_bytes: 62470
num_examples: 352
- name: validation_2021
num_bytes: 32937
num_examples: 189
- name: train_random
num_bytes: 514173
num_examples: 2830
- name: validation_random
num_bytes: 62046
num_examples: 354
- name: test_coling2022_random
num_bytes: 610185
num_examples: 3399
- name: train_coling2022_random
num_bytes: 645545
num_examples: 3598
- name: test_coling2022
num_bytes: 613161
num_examples: 3399
- name: train_coling2022
num_bytes: 642569
num_examples: 3598
- name: train
num_bytes: 273056
num_examples: 1516
download_size: 3667198
dataset_size: 5398580
configs:
- config_name: default
data_files:
- split: test_2020
path: data/test_2020-*
- split: test_2021
path: data/test_2021-*
- split: train_2020
path: data/train_2020-*
- split: train_2021
path: data/train_2021-*
- split: train_all
path: data/train_all-*
- split: validation_2020
path: data/validation_2020-*
- split: validation_2021
path: data/validation_2021-*
- split: train_random
path: data/train_random-*
- split: validation_random
path: data/validation_random-*
- split: test_coling2022_random
path: data/test_coling2022_random-*
- split: train_coling2022_random
path: data/train_coling2022_random-*
- split: test_coling2022
path: data/test_coling2022-*
- split: train_coling2022
path: data/train_coling2022-*
- split: train
path: data/train-*
tags:
- mteb
- text
Topic classification dataset on Twitter with 6 labels. Each instance of TweetTopic comes with a timestamp which distributes from September 2019 to August 2021. Tweets were preprocessed before the annotation to normalize some artifacts, converting URLs into a special token {{URL}} and non-verified usernames into {{USERNAME}}. For verified usernames, we replace its display name (or account name) with symbols {@}.
| Task category | t2c |
| Domains | Social, News, Written |
| Reference | https://arxiv.org/abs/2209.09824 |
Source datasets:
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task = mteb.get_task("TweetTopicSingleClassification")
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 repository.
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{dimosthenis-etal-2022-twitter,
address = {Gyeongju, Republic of Korea},
author = {Antypas, Dimosthenis and
Ushio, Asahi and
Camacho-Collados, Jose and
Neves, Leonardo and
Silva, Vitor and
Barbieri, Francesco},
booktitle = {Proceedings of the 29th International Conference on Computational Linguistics},
month = oct,
publisher = {International Committee on Computational Linguistics},
title = {{T}witter {T}opic {C}lassification},
year = {2022},
}
@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, Loïc 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("TweetTopicSingleClassification")
desc_stats = task.metadata.descriptive_stats
{}
This dataset card was automatically generated using MTEB