Datasets:
language:
- afr
- amh
- arb
- arq
- ary
- eng
- es
- hau
- hin
- ind
- kin
- mar
- pan
- tel
dataset_info:
- config_name: afr
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: float64
splits:
- name: test
num_bytes: 65243
num_examples: 375
- name: dev
num_bytes: 66249
num_examples: 375
download_size: 95864
dataset_size: 131492
- config_name: amh
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: float64
splits:
- name: train
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num_examples: 992
- name: test
num_bytes: 36637
num_examples: 171
- name: dev
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num_examples: 95
download_size: 153682
dataset_size: 265610
- config_name: arb
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: float64
splits:
- name: test
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num_examples: 595
- name: dev
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num_examples: 32
download_size: 72348
dataset_size: 116319
- config_name: arq
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: float64
splits:
- name: train
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num_examples: 1261
- name: test
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num_examples: 583
- name: dev
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num_examples: 97
download_size: 149472
dataset_size: 261529
- config_name: ary
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: float64
splits:
- name: train
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num_examples: 924
- name: test
num_bytes: 175568
num_examples: 426
- name: dev
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num_examples: 71
download_size: 274828
dataset_size: 586104
- config_name: eng
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: float64
splits:
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num_examples: 5500
- name: test
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num_examples: 2600
- name: dev
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num_examples: 250
download_size: 868674
dataset_size: 1256319
- config_name: esp
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: float64
splits:
- name: train
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num_examples: 1562
- name: test
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num_examples: 600
- name: dev
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num_examples: 140
download_size: 323584
dataset_size: 468916
- config_name: hau
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: float64
splits:
- name: train
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num_examples: 1736
- name: test
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num_examples: 603
- name: dev
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num_examples: 212
download_size: 328542
dataset_size: 594948
- config_name: hin
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: float64
splits:
- name: test
num_bytes: 377385
num_examples: 968
- name: dev
num_bytes: 113047
num_examples: 288
download_size: 217493
dataset_size: 490432
- config_name: ind
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: float64
splits:
- name: test
num_bytes: 68185
num_examples: 360
- name: dev
num_bytes: 26579
num_examples: 144
download_size: 68263
dataset_size: 94764
- config_name: kin
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: float64
splits:
- name: train
num_bytes: 234520
num_examples: 778
- name: test
num_bytes: 67211
num_examples: 222
- name: dev
num_bytes: 30758
num_examples: 102
download_size: 219256
dataset_size: 332489
- config_name: mar
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: float64
splits:
- name: train
num_bytes: 555224
num_examples: 1155
- name: test
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num_examples: 298
- name: dev
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num_examples: 293
download_size: 381039
dataset_size: 841063
- config_name: pan
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: float64
splits:
- name: test
num_bytes: 307401
num_examples: 634
- name: dev
num_bytes: 117984
num_examples: 242
download_size: 166402
dataset_size: 425385
- config_name: tel
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: float64
splits:
- name: train
num_bytes: 561688
num_examples: 1146
- name: test
num_bytes: 145249
num_examples: 297
- name: dev
num_bytes: 64775
num_examples: 130
download_size: 347275
dataset_size: 771712
configs:
- config_name: afr
data_files:
- split: test
path: afr/test-*
- split: dev
path: afr/dev-*
- config_name: amh
data_files:
- split: train
path: amh/train-*
- split: test
path: amh/test-*
- split: dev
path: amh/dev-*
- config_name: arb
data_files:
- split: test
path: arb/test-*
- split: dev
path: arb/dev-*
- config_name: arq
data_files:
- split: train
path: arq/train-*
- split: test
path: arq/test-*
- split: dev
path: arq/dev-*
- config_name: ary
data_files:
- split: train
path: ary/train-*
- split: test
path: ary/test-*
- split: dev
path: ary/dev-*
- config_name: eng
data_files:
- split: train
path: eng/train-*
- split: test
path: eng/test-*
- split: dev
path: eng/dev-*
- config_name: esp
data_files:
- split: train
path: esp/train-*
- split: test
path: esp/test-*
- split: dev
path: esp/dev-*
- config_name: hau
data_files:
- split: train
path: hau/train-*
- split: test
path: hau/test-*
- split: dev
path: hau/dev-*
- config_name: hin
data_files:
- split: test
path: hin/test-*
- split: dev
path: hin/dev-*
- config_name: ind
data_files:
- split: test
path: ind/test-*
- split: dev
path: ind/dev-*
- config_name: kin
data_files:
- split: train
path: kin/train-*
- split: test
path: kin/test-*
- split: dev
path: kin/dev-*
- config_name: mar
data_files:
- split: train
path: mar/train-*
- split: test
path: mar/test-*
- split: dev
path: mar/dev-*
- config_name: pan
data_files:
- split: test
path: pan/test-*
- split: dev
path: pan/dev-*
- config_name: tel
data_files:
- split: train
path: tel/train-*
- split: test
path: tel/test-*
- split: dev
path: tel/dev-*
task_categories:
- text-classification
- sentence-similarity
Dataset Description
- Homepage: https://github.com/semantic-textual-relatedness/Semantic_Relatedness_SemEval2024
- Repository: GitHub
- Paper: SemRel2024: A Collection of Semantic Textual Relatedness Datasets for 14 Languages
- Leaderboard: N/A
- Point of Contact: Nedjma Ousidhoum, Shamsuddeen Hassan Muhammad
Dataset Summary
SemRel2024 is a collection of Semantic Textual Relatedness (STR) datasets for 14 languages, including African and Asian languages. The dataset is designed for the SemEval-2024 Task 1: Semantic Textual Relatedness for African and Asian Languages. The task aims to evaluate the ability of systems to measure the semantic relatedness between two text segments, such as sentences or phrases.
Supported Tasks and Leaderboards
The SemRel2024 dataset can be used for the Semantic Textual Relatedness task, which involves predicting the degree of semantic relatedness between two text segments on a scale, typically from 0 (not related at all) to 5 (highly related).
SemEval-2024 Task 1: Semantic Textual Relatedness for African and Asian Languages
Languages
The SemRel2024 dataset covers the following 14 languages:
- Afrikaans (afr)
- Algerian Arabic (arq)
- Amharic (amh)
- English (eng)
- Hausa (hau)
- Indonesian (ind)
- Hindi (hin)
- Kinyarwanda (kin)
- Marathi (mar)
- Modern Standard Arabic (arb)
- Moroccan Arabic (ary)
- Punjabi (pan)
- Spanish (esp)
- Telugu (tel)
Dataset Structure
Data Instances
Each instance in the dataset consists of two text segments and a relatedness score indicating the degree of semantic relatedness between them.
{ "text1": "string", "text2": "string", "score": float }
- text1: a string feature representing the first text segment.
- text2: a string feature representing the second text segment.
- score: a float value representing the semantic relatedness score between text1 and text2, typically ranging from 0 (not related at all) to 5 (highly related).
Citation Information
If you use the SemRel2024 dataset in your research, please cite the following papers:
@misc{ousidhoum2024semrel2024, title={SemRel2024: A Collection of Semantic Textual Relatedness Datasets for 14 Languages}, author={Nedjma Ousidhoum and Shamsuddeen Hassan Muhammad and Mohamed Abdalla and Idris Abdulmumin and Ibrahim Said Ahmad and Sanchit Ahuja and Alham Fikri Aji and Vladimir Araujo and Abinew Ali Ayele and Pavan Baswani and Meriem Beloucif and Chris Biemann and Sofia Bourhim and Christine De Kock and Genet Shanko Dekebo and Oumaima Hourrane and Gopichand Kanumolu and Lokesh Madasu and Samuel Rutunda and Manish Shrivastava and Thamar Solorio and Nirmal Surange and Hailegnaw Getaneh Tilaye and Krishnapriya Vishnubhotla and Genta Winata and Seid Muhie Yimam and Saif M. Mohammad}, year={2024}, eprint={2402.08638}, archivePrefix={arXiv}, primaryClass={cs.CL} }
@inproceedings{ousidhoum-etal-2024-semeval, title = "{S}em{E}val-2024 Task 1: Semantic Textual Relatedness for African and Asian Languages", author = "Ousidhoum, Nedjma and Muhammad, Shamsuddeen Hassan and Abdalla, Mohamed and Abdulmumin, Idris and Ahmad,Ibrahim Said and Ahuja, Sanchit and Aji, Alham Fikri and Araujo, Vladimir and Beloucif, Meriem and De Kock, Christine and Hourrane, Oumaima and Shrivastava, Manish and Solorio, Thamar and Surange, Nirmal and Vishnubhotla, Krishnapriya and Yimam, Seid Muhie and Mohammad, Saif M.", booktitle = "Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)", year = "2024", publisher = "Association for Computational Linguistics" }