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semeval-2025-task11-track-a

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 ---
language:
- af
- am
- ar
- de
- en
- es
- ha
- hi
- ig
- mr
- om
- pcm
- pt
- ro
- ru
- rw
- so
- su
- sv
- sw
- ti
- tt
- uk
- vmw
- yo
- zh
license: cc-by-4.0
configs:
- config_name: afr
data_files:
- split: train
path: afr/train-*
- split: dev
path: afr/dev-*
- split: test
path: afr/test-*
- config_name: amh
data_files:
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path: amh/train-*
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path: amh/dev-*
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- config_name: arq
data_files:
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path: arq/train-*
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path: arq/dev-*
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- config_name: ary
data_files:
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path: ary/train-*
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path: ary/dev-*
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- config_name: chn
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- config_name: deu
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- name: fear
dtype: int64
- name: joy
dtype: int64
- name: sadness
dtype: int64
- name: surprise
dtype: int64
- name: emotions
sequence: string
splits:
- name: train
num_bytes: 353251.50760392024
num_examples: 1551
- name: dev
num_bytes: 57972.19962379497
num_examples: 258
- name: test
num_bytes: 175367.05823607268
num_examples: 777
download_size: 187109
dataset_size: 586590.765463788
- config_name: yor
features:
- name: id
dtype: string
- name: text
dtype: string
- name: anger
dtype: int64
- name: disgust
dtype: int64
- name: fear
dtype: int64
- name: joy
dtype: int64
- name: sadness
dtype: int64
- name: surprise
dtype: int64
- name: emotions
sequence: string
splits:
- name: train
num_bytes: 681449.7167962149
num_examples: 2992
- name: dev
num_bytes: 111675.1287326593
num_examples: 497
- name: test
num_bytes: 338546.44447118277
num_examples: 1500
download_size: 395234
dataset_size: 1131671.2900000568
---
# SemEval 2025 Task 11 - Track A Dataset
This dataset contains the data for SemEval 2025 Task 11: Bridging the Gap in Text-Based Emotion Detection - Track A, organized as language-specific configurations.
## Dataset Description
The dataset is a multi-language, multi-label emotion classification dataset with separate configurations for each language.
- **Total languages**: 26 standard ISO codes
- **Total examples**: 115159
- **Splits**: train, dev, test
## Language Configurations
Each language is available as a separate configuration with the following statistics:
| ISO Code | Original Code(s) | Train Examples | Dev Examples | Test Examples | Total |
|----------|------------------|---------------|-------------|--------------|-------|
| af | afr | 1222 | 98 | 1065 | 2385 |
| am | amh | 3549 | 592 | 1774 | 5915 |
| ar | arq, ary | 2509 | 367 | 1714 | 4590 |
| de | deu | 2603 | 200 | 2604 | 5407 |
| en | eng | 2768 | 116 | 2767 | 5651 |
| es | esp | 1996 | 184 | 1695 | 3875 |
| ha | hau | 2145 | 356 | 1080 | 3581 |
| hi | hin | 2556 | 100 | 1010 | 3666 |
| ig | ibo | 2880 | 479 | 1444 | 4803 |
| mr | mar | 2415 | 100 | 1000 | 3515 |
| om | orm | 3442 | 574 | 1721 | 5737 |
| pcm | pcm | 3728 | 620 | 1870 | 6218 |
| pt | ptbr, ptmz | 3772 | 457 | 3002 | 7231 |
| ro | ron | 1241 | 123 | 1119 | 2483 |
| ru | rus | 2679 | 199 | 1000 | 3878 |
| rw | kin | 2451 | 407 | 1231 | 4089 |
| so | som | 3392 | 566 | 1696 | 5654 |
| su | sun | 924 | 199 | 926 | 2049 |
| sv | swe | 1187 | 200 | 1188 | 2575 |
| sw | swa | 3307 | 551 | 1656 | 5514 |
| ti | tir | 3681 | 614 | 1840 | 6135 |
| tt | tat | 1000 | 200 | 1000 | 2200 |
| uk | ukr | 2466 | 249 | 2234 | 4949 |
| vmw | vmw | 1551 | 258 | 777 | 2586 |
| yo | yor | 2992 | 497 | 1500 | 4989 |
| zh | chn | 2642 | 200 | 2642 | 5484 |
## Features
- **id**: Unique identifier for each example
- **text**: Text content to classify
- **anger**, **disgust**, **fear**, **joy**, **sadness**, **surprise**: Presence of emotion
- **emotions**: List of emotions present in the text
## Usage
```python
from datasets import load_dataset
# Load all data for a specific language
eng_dataset = load_dataset("YOUR_USERNAME/semeval-2025-task11-track-a", "eng")
# Or load a specific split for a language
eng_train = load_dataset("YOUR_USERNAME/semeval-2025-task11-track-a", "eng", split="train")
```
## Citation
If you use this dataset, please cite the following papers:
```
@misc{{muhammad2025brighterbridginggaphumanannotated,
title={{BRIGHTER: BRIdging the Gap in Human-Annotated Textual Emotion Recognition Datasets for 28 Languages}},
author={{Shamsuddeen Hassan Muhammad and Nedjma Ousidhoum and Idris Abdulmumin and Jan Philip Wahle and Terry Ruas and Meriem Beloucif and Christine de Kock and Nirmal Surange and Daniela Teodorescu and Ibrahim Said Ahmad and David Ifeoluwa Adelani and Alham Fikri Aji and Felermino D. M. A. Ali and Ilseyar Alimova and Vladimir Araujo and Nikolay Babakov and Naomi Baes and Ana-Maria Bucur and Andiswa Bukula and Guanqun Cao and Rodrigo Tufiño and Rendi Chevi and Chiamaka Ijeoma Chukwuneke and Alexandra Ciobotaru and Daryna Dementieva and Murja Sani Gadanya and Robert Geislinger and Bela Gipp and Oumaima Hourrane and Oana Ignat and Falalu Ibrahim Lawan and Rooweither Mabuya and Rahmad Mahendra and Vukosi Marivate and Andrew Piper and Alexander Panchenko and Charles Henrique Porto Ferreira and Vitaly Protasov and Samuel Rutunda and Manish Shrivastava and Aura Cristina Udrea and Lilian Diana Awuor Wanzare and Sophie Wu and Florian Valentin Wunderlich and Hanif Muhammad Zhafran and Tianhui Zhang and Yi Zhou and Saif M. Mohammad}},
year={{2025}},
eprint={{2502.11926}},
archivePrefix={{arXiv}},
primaryClass={{cs.CL}},
url={{https://arxiv.org/abs/2502.11926}},
}}
```
```
@misc{{muhammad2025semeval2025task11bridging,
title={{SemEval-2025 Task 11: Bridging the Gap in Text-Based Emotion Detection}},
author={{Shamsuddeen Hassan Muhammad and Nedjma Ousidhoum and Idris Abdulmumin and Seid Muhie Yimam and Jan Philip Wahle and Terry Ruas and Meriem Beloucif and Christine De Kock and Tadesse Destaw Belay and Ibrahim Said Ahmad and Nirmal Surange and Daniela Teodorescu and David Ifeoluwa Adelani and Alham Fikri Aji and Felermino Ali and Vladimir Araujo and Abinew Ali Ayele and Oana Ignat and Alexander Panchenko and Yi Zhou and Saif M. Mohammad}},
year={{2025}},
eprint={{2503.07269}},
archivePrefix={{arXiv}},
primaryClass={{cs.CL}},
url={{https://arxiv.org/abs/2503.07269}},
}}
```
## License
This dataset is licensed under CC-BY 4.0.