Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
semantic-similarity-classification
Size:
10K - 100K
ArXiv:
License:
metadata
annotations_creators:
- derived
language:
- ces
- deu
- eng
- fra
- hin
- jpn
- kor
- msa
- nld
- por
- spa
- tur
- zho
license: cc-by-nc-sa-4.0
multilinguality: multilingual
source_datasets:
- BASF-AI/PubChemWikiMultilingualPC
task_categories:
- text-classification
task_ids:
- semantic-similarity-classification
dataset_info:
- config_name: cs
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: labels
dtype: int64
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- config_name: fr
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- config_name: ms
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- config_name: nl
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- config_name: pt
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- config_name: tr
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- config_name: zh
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configs:
- config_name: cs
data_files:
- split: test
path: cs/test-*
- config_name: de
data_files:
- split: test
path: de/test-*
- config_name: es
data_files:
- split: test
path: es/test-*
- config_name: fr
data_files:
- split: test
path: fr/test-*
- config_name: hi
data_files:
- split: test
path: hi/test-*
- config_name: ja
data_files:
- split: test
path: ja/test-*
- config_name: ko
data_files:
- split: test
path: ko/test-*
- config_name: ms
data_files:
- split: test
path: ms/test-*
- config_name: nl
data_files:
- split: test
path: nl/test-*
- config_name: pt
data_files:
- split: test
path: pt/test-*
- config_name: tr
data_files:
- split: test
path: tr/test-*
- config_name: zh
data_files:
- split: test
path: zh/test-*
tags:
- mteb
- text
ChemTEB evaluates the performance of text embedding models on chemical domain data.
| Task category | t2t |
| Domains | Chemistry |
| Reference | https://arxiv.org/abs/2412.00532 |
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("PubChemWikiPairClassification")
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.
@article{kasmaee2024chemteb,
author = {Kasmaee, Ali Shiraee and Khodadad, Mohammad and Saloot, Mohammad Arshi and Sherck, Nick and Dokas, Stephen and Mahyar, Hamidreza and Samiee, Soheila},
journal = {arXiv preprint arXiv:2412.00532},
title = {ChemTEB: Chemical Text Embedding Benchmark, an Overview of Embedding Models Performance \\& Efficiency on a Specific Domain},
year = {2024},
}
@article{kim2023pubchem,
author = {Kim, Sunghwan and Chen, Jie and Cheng, Tiejun and Gindulyte, Asta and He, Jia and He, Siqian and Li, Qingliang and Shoemaker, Benjamin A and Thiessen, Paul A and Yu, Bo and others},
journal = {Nucleic acids research},
number = {D1},
pages = {D1373--D1380},
publisher = {Oxford University Press},
title = {PubChem 2023 update},
volume = {51},
year = {2023},
}
@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("PubChemWikiPairClassification")
desc_stats = task.metadata.descriptive_stats
{}
This dataset card was automatically generated using MTEB