Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
topic-classification
Languages:
English
Size:
10K - 100K
ArXiv:
License:
metadata
annotations_creators:
- derived
language:
- eng
license: unknown
multilinguality: monolingual
task_categories:
- text-classification
task_ids:
- topic-classification
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 466788625
num_examples: 25000
- name: validation
num_bytes: 95315107
num_examples: 5000
- name: test
num_bytes: 38487994
num_examples: 2048
download_size: 249805146
dataset_size: 600591726
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
tags:
- mteb
- text
Classification Dataset of Patents and Abstract
| Task category | t2c |
| Domains | Legal, Written |
| Reference | https://aclanthology.org/P19-1212.pdf |
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(["PatentClassification"])
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{sharma-etal-2019-bigpatent,
abstract = {Most existing text summarization datasets are compiled from the news domain, where summaries have a flattened discourse structure. In such datasets, summary-worthy content often appears in the beginning of input articles. Moreover, large segments from input articles are present verbatim in their respective summaries. These issues impede the learning and evaluation of systems that can understand an article{'}s global content structure as well as produce abstractive summaries with high compression ratio. In this work, we present a novel dataset, BIGPATENT, consisting of 1.3 million records of U.S. patent documents along with human written abstractive summaries. Compared to existing summarization datasets, BIGPATENT has the following properties: i) summaries contain a richer discourse structure with more recurring entities, ii) salient content is evenly distributed in the input, and iii) lesser and shorter extractive fragments are present in the summaries. Finally, we train and evaluate baselines and popular learning models on BIGPATENT to shed light on new challenges and motivate future directions for summarization research.},
address = {Florence, Italy},
author = {Sharma, Eva and
Li, Chen and
Wang, Lu},
booktitle = {Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics},
doi = {10.18653/v1/P19-1212},
editor = {Korhonen, Anna and
Traum, David and
M{\`a}rquez, Llu{\'\i}s},
month = jul,
pages = {2204--2213},
publisher = {Association for Computational Linguistics},
title = {{BIGPATENT}: A Large-Scale Dataset for Abstractive and Coherent Summarization},
url = {https://aclanthology.org/P19-1212},
year = {2019},
}
@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
Dataset Statistics
The following code contains the descriptive statistics from the task. These can also be obtained using:
import mteb
task = mteb.get_task("PatentClassification")
desc_stats = task.metadata.descriptive_stats
{
"test": {
"num_samples": 2048,
"number_of_characters": 38376596,
"number_texts_intersect_with_train": 9,
"min_text_length": 2168,
"average_text_length": 18738.572265625,
"max_text_length": 226050,
"unique_text": 2048,
"unique_labels": 9,
"labels": {
"7": {
"count": 424
},
"0": {
"count": 309
},
"6": {
"count": 453
},
"2": {
"count": 161
},
"1": {
"count": 266
},
"8": {
"count": 206
},
"4": {
"count": 64
},
"5": {
"count": 147
},
"3": {
"count": 18
}
}
},
"train": {
"num_samples": 25000,
"number_of_characters": 465511243,
"number_texts_intersect_with_train": null,
"min_text_length": 1551,
"average_text_length": 18620.44972,
"max_text_length": 331797,
"unique_text": 24950,
"unique_labels": 9,
"labels": {
"6": {
"count": 5408
},
"0": {
"count": 3614
},
"7": {
"count": 5321
},
"8": {
"count": 2562
},
"2": {
"count": 2099
},
"4": {
"count": 705
},
"1": {
"count": 3357
},
"3": {
"count": 204
},
"5": {
"count": 1730
}
}
}
}
This dataset card was automatically generated using MTEB