File size: 4,615 Bytes
8a732e6 933d37b 8a732e6 933d37b 9800f98 933d37b 9800f98 8695167 933d37b 8695167 933d37b 8695167 933d37b 8695167 933d37b 8695167 933d37b 8695167 933d37b 2d3f9da e088064 2d3f9da 933d37b 2d3f9da e088064 2d3f9da e088064 2d3f9da e088064 2d3f9da e088064 2d3f9da e088064 2d3f9da 892b195 2d3f9da 892b195 2d3f9da 2caeaaa 54d2e3e 3419aa6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 |
---
language:
- en
license: apache-2.0
size_categories:
- 10K<n<100K
task_categories:
- token-classification
pretty_name: buster
tags:
- finance
configs:
- config_name: default
data_files:
- split: FOLD_1
path: data/FOLD_1-*
- split: FOLD_2
path: data/FOLD_2-*
- split: FOLD_3
path: data/FOLD_3-*
- split: FOLD_4
path: data/FOLD_4-*
- split: FOLD_5
path: data/FOLD_5-*
- split: SILVER
path: data/SILVER-*
dataset_info:
features:
- name: document_id
dtype: string
- name: text
dtype: string
- name: tokens
sequence: string
- name: labels
sequence: string
splits:
- name: FOLD_1
num_bytes: 13597946
num_examples: 753
- name: FOLD_2
num_bytes: 13477878
num_examples: 759
- name: FOLD_3
num_bytes: 13602552
num_examples: 758
- name: FOLD_4
num_bytes: 13834760
num_examples: 755
- name: FOLD_5
num_bytes: 13632431
num_examples: 754
- name: SILVER
num_bytes: 111769291
num_examples: 6196
download_size: 47212151
dataset_size: 179914858
---
# Dataset Card for BUSTER
BUSiness Transaction Entity Recognition dataset.
BUSTER is an Entity Recognition (ER) benchmark for entities related to business transactions. It consists of a gold corpus of
3779 manually annotated documents on financial transactions that were randomly divided into 5 folds,
plus an additional silver corpus of 6196 automatically annotated documents that were created by the model-optimized RoBERTa system.
### Data Splits Statistics
<table border="1" cellspacing="0" cellpadding="5" style="border-collapse: collapse; width: 100%;">
<thead>
<tr>
<th></th>
<th></th>
<th colspan="6" style="text-align:center;">Gold</th>
<th>Silver</th>
</tr>
<tr>
<th></th>
<th></th>
<th>fold 1</th>
<th>fold 2</th>
<th>fold 3</th>
<th>fold 4</th>
<th>fold 5</th>
<th>Total</th>
<th>Total</th>
</tr>
</thead>
<tbody>
<tr>
<td></td>
<td>N. Docs</td>
<td>753</td>
<td>759</td>
<td>758</td>
<td>755</td>
<td>754</td>
<td>3779</td>
<td>6196</td>
</tr>
<tr>
<td></td>
<td>N. Tokens</td>
<td>685K</td>
<td>680K</td>
<td>687K</td>
<td>697K</td>
<td>688K</td>
<td>3437K</td>
<td>5647K</td>
</tr>
<tr>
<td></td>
<td>N. Annotations</td>
<td>4119</td>
<td>4267</td>
<td>4100</td>
<td>4103</td>
<td>4163</td>
<td>20752</td>
<td>33272</td>
</tr>
</tbody>
</table>
### Pre-print
You can find the pre-print [here](https://arxiv.org/abs/2402.09916).
### Citation Information
If you use BUSTER in your work, please cite us:
```
@inproceedings{zugarini-etal-2023-buster,
title = "{BUSTER}: a {``}{BUS}iness Transaction Entity Recognition{''} dataset",
author = "Zugarini, Andrea and
Zamai, Andrew and
Ernandes, Marco and
Rigutini, Leonardo",
editor = "Wang, Mingxuan and
Zitouni, Imed",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-industry.57",
doi = "10.18653/v1/2023.emnlp-industry.57",
pages = "605--611",
abstract = "Albeit Natural Language Processing has seen major breakthroughs in the last few years, transferring such advances into real-world business cases can be challenging. One of the reasons resides in the displacement between popular benchmarks and actual data. Lack of supervision, unbalanced classes, noisy data and long documents often affect real problems in vertical domains such as finance, law and health. To support industry-oriented research, we present BUSTER, a BUSiness Transaction Entity Recognition dataset. The dataset consists of 3779 manually annotated documents on financial transactions. We establish several baselines exploiting both general-purpose and domain-specific language models. The best performing model is also used to automatically annotate 6196 documents, which we release as an additional silver corpus to BUSTER.",
}
```
|