mteb/banking77
Banking77Classification An MTEB dataset Massive Text Embedding Benchmark 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 =… See the full description on the dataset page: https://huggingface.co/datasets/mteb/banking77.
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<div align="center" style="padding: 40px 20px; background-color: white; border-radius: 12px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.05); max-width: 600px; margin: 0 auto;"> <h1 style="font-size: 3.5rem; color: #1a1a1a; margin: 0 0 20px 0; letter-spacing: 2px; font-weight: 700;">Banking77Classification</h1> <div style="font-size: 1.5rem; color: #4a4a4a; margin-bottom: 5px; font-weight: 300;">An <a href="https://github.com/embeddings-benchmark/mteb" style="color: #2c5282; font-weight: 600; text-decoration: none;" onmouseover="this.style.textDecoration='underline'" onmouseout="this.style.textDecoration='none'">MTEB</a> dataset</div> <div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">Massive Text Embedding Benchmark</div> </div>
Dataset composed of online banking queries annotated with their corresponding intents.
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)<!-- Datasets want link to arxiv in readme to autolink dataset with paper --> 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 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, 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
<details> <summary> Dataset Statistics</summary>
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{
"test": {
"num_samples": 3080,
"number_of_characters": 167036,
"number_texts_intersect_with_train": 0,
"min_text_length": 13,
"average_text_length": 54.23246753246753,
"max_text_length": 368,
"unique_text": 3080,
"unique_labels": 77,
"labels": {
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}
},
"train": {
"num_samples": 10003,
"number_of_characters": 594916,
"number_texts_intersect_with_train": null,
"min_text_length": 13,
"average_text_length": 59.47375787263821,
"max_text_length": 433,
"unique_text": 10003,
"unique_labels": 77,
"labels": {
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}
}
}
}</details>
This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)