khaze0911/banking77-distilbert
Text Classification β’ 67M β’ Updated β’ 21 β’ 1
Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
10K - 100K
ArXiv:
License:
Dataset Viewer
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Dataset composed of online banking queries annotated with their corresponding intents.
| Task category | t2c |
| Domains | Written |
| Reference | https://arxiv.org/abs/2003.04807 |
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(["Banking77Classification"])
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{casanueva-etal-2020-efficient,
address = {Online},
author = {Casanueva, I{\~n}igo and
Tem{\v{c}}inas, Tadas and
Gerz, Daniela and
Henderson, Matthew and
Vuli{\'c}, Ivan},
booktitle = {Proceedings of the 2nd Workshop on Natural Language Processing for Conversational AI},
doi = {10.18653/v1/2020.nlp4convai-1.5},
editor = {Wen, Tsung-Hsien and
Celikyilmaz, Asli and
Yu, Zhou and
Papangelis, Alexandros and
Eric, Mihail and
Kumar, Anuj and
Casanueva, I{\~n}igo and
Shah, Rushin},
month = jul,
pages = {38--45},
publisher = {Association for Computational Linguistics},
title = {Efficient Intent Detection with Dual Sentence Encoders},
url = {https://aclanthology.org/2020.nlp4convai-1.5},
year = {2020},
}
@article{enevoldsen2025mmtebmassivemultilingualtext,
title={MMTEB: Massive Multilingual Text Embedding Benchmark},
author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and MΓ‘rton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik KrzemiΕski 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 RystrΓΈm and Roman Solomatin and Γmer ΓaΔatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and RafaΕ PoΕwiata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and BjΓΆrn PlΓΌster and Jan Philipp Harries and LoΓ―c 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 Ε uppa 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 GΓΌnther and Mengzhou Xia and Weijia Shi and Xing Han LΓΉ 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("Banking77Classification")
desc_stats = task.metadata.descriptive_stats
{
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}
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