README.md

metadata

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: 13597346
      num_examples: 753
    - name: FOLD_2
      num_bytes: 13477158
      num_examples: 759
    - name: FOLD_3
      num_bytes: 13601972
      num_examples: 758
    - name: FOLD_4
      num_bytes: 13834440
      num_examples: 755
    - name: FOLD_5
      num_bytes: 13631391
      num_examples: 754
    - name: SILVER
      num_bytes: 111769291
      num_examples: 6196
  download_size: 47212140
  dataset_size: 179911598

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

		Gold						Silver
		fold 1	fold 2	fold 3	fold 4	fold 5	Total	Total
	N. Docs	753	759	758	755	754	3779	6196
	N. Tokens	685K	680K	687K	697K	688K	3437K	5647K
	N. Annotations	4119	4267	4100	4103	4163	20752	33272

Pre-print

You can find the pre-print here.

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.",
}