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Datasets:
PITTI
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buster-expanded-ner

Tasks:
Token Classification
Modalities:
Text
Formats:
json
Sub-tasks:
named-entity-recognition
Languages:
English
Size:
1K - 10K
Tags:
finance
named-entity-recognition
sec-filings
bioes
Libraries:
Datasets
pandas
Polars
License:
Dataset card Data Studio Files
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metadata 
annotations_creators:
  - expert-generated
  - machine-generated
language:
  - en
license: apache-2.0
multilinguality:
  - monolingual
pretty_name: BUSTER Expanded NER
size_categories:
  - 1K<n<10K
source_datasets:
  - extended|expertai/BUSTER
task_categories:
  - token-classification
task_ids:
  - named-entity-recognition
tags:
  - finance
  - named-entity-recognition
  - sec-filings
  - bioes
configs:
  - config_name: default
    data_files:
      - split: FOLD_1
        path: data/FOLD_1.jsonl.gz
      - split: FOLD_2
        path: data/FOLD_2.jsonl.gz
      - split: FOLD_3
        path: data/FOLD_3.jsonl.gz
      - split: FOLD_4
        path: data/FOLD_4.jsonl.gz
      - split: FOLD_5
        path: data/FOLD_5.jsonl.gz

BUSTER Expanded NER

BUSTER Expanded NER is a derived annotation layer over the 3,779 manually annotated English documents in the gold corpus of expertai/BUSTER. It replaces BUSTER's transaction-role ontology with four general named-entity labels and materially expands mention coverage within each document.

The release is designed as a tokenizer-neutral source for training flat NER models. It stores exact character spans rather than tokenizer-specific tags, so BIOES labels can be produced after choosing a tokenizer.

Annotation scope: this version annotates safe repeated occurrences of already identified entity surfaces and adds a precision-first set of omitted entities. It is substantially more complete than the original transaction-role layer, but it is not guaranteed to be exhaustive. An unlabelled phrase is not always a reliable negative.

What changed from BUSTER

  • The five original company roles (buyer, seller, acquired company, legal advisor, and generic advisor) are mapped to organization by default.
  • Reviewed investment funds and investment firms are distinguished from other organizations with the fund label.
  • Reviewed person, omitted organization/fund, and named event mentions are added.
  • Once an entity surface is known in a document, safe non-overlapping occurrences of that surface are projected across the document. Matching is case-aware, tolerates whitespace variation, and filters URL, domain, identifier, overlap, and known-longer-name cases.
  • BUSTER's Generic_Info.ANNUAL_REVENUES label is outside this release's NER ontology and is not included.
  • The original manually annotated folds are retained. The automatically annotated BUSTER silver corpus is not included.

These changes create a different task from the original BUSTER benchmark. Scores on this dataset should not be compared directly with scores reported for BUSTER's transaction-role ontology.

Labels

Label Meaning
person A named person.
organization A company, university, agency, NGO, accelerator, association, or other organization.
fund An investment fund or investment firm, including reviewed venture-capital and private-equity entities. This more specific label takes precedence over organization.
event A named gathering at which people or organizations attend, meet, pitch, present, or exhibit.

Dataset statistics

Counts below are entity mentions, not unique names.

Split Documents Organization Fund Person Event All mentions
FOLD_1 753 12,913 368 879 8 14,168
FOLD_2 759 13,250 324 808 13 14,395
FOLD_3 758 12,696 311 876 7 13,890
FOLD_4 755 13,116 346 955 1 14,418
FOLD_5 754 12,814 431 910 3 14,158
Total 3,779 64,789 1,780 4,428 32 71,029

The five folds are inherited cross-validation partitions, not a prescribed train/validation/test division. For example, use one fold for evaluation and train on the other four.

Loading the dataset

Replace the repository name below after uploading:

from datasets import concatenate_datasets, load_dataset

dataset = load_dataset("YOUR_USERNAME/buster-expanded-ner")

held_out = "FOLD_5"
train = concatenate_datasets(
    dataset[fold] for fold in dataset if fold != held_out
)
validation = dataset[held_out]

Each compressed file is newline-delimited JSON and can also be read directly:

import gzip
import json

with gzip.open("data/FOLD_1.jsonl.gz", "rt", encoding="utf-8") as handle:
    first_document = json.loads(next(handle))

Data format

Each row contains one complete document:

{
  "schema_version": 1,
  "dataset": "buster",
  "document_id": "...",
  "split": "FOLD_1",
  "language": "en",
  "text": "Alice met Acme Ventures.",
  "entities": [
    {
      "start": 0,
      "end": 5,
      "label": "person",
      "text": "Alice",
      "source_label": "MODEL_REVIEWED_TEXT_MENTION",
      "annotation_source": "review_config:buster_person_additions.json",
      "override": null,
      "override_origin": null,
      "span_addition_origin": null,
      "review_confirmation": null
    },
    {
      "start": 10,
      "end": 23,
      "label": "fund",
      "text": "Acme Ventures",
      "source_label": "Parties.BUYING_COMPANY",
      "annotation_source": null,
      "override": "fund",
      "override_origin": "review_fragment:...",
      "span_addition_origin": null,
      "review_confirmation": null
    }
  ],
  "unresolved_entities": [],
  "training_ready": true,
  "metadata": {
    "source_dataset": "expertai/BUSTER",
    "annotation_scope": "..."
  }
}

Offsets use the half-open convention: text[start:end]. Entities are sorted by offset, do not overlap, and satisfy text[start:end] == entity["text"]. Provenance fields on an entity are nullable when they do not apply. The upload files make those optional keys explicit so the Hugging Face JSON loader infers a stable list<struct> schema; all canonical values are preserved.

Producing BIOES tags

BIOES tags are deliberately not stored because they depend on tokenization. For a chosen fast tokenizer:

  1. tokenize text with return_offsets_mapping=True;
  2. align each entity's [start, end) span to complete model tokens;
  3. assign S-label to a one-token span or B-label, I-label, and E-label to a multi-token span;
  4. keep non-entity tokens as O and mask special tokens and padding from the loss; and
  5. split long documents into windows without cutting an entity span.

Reject or explicitly handle any entity that does not align to complete tokens. Do not silently turn a truncated or partially aligned entity into O.

Annotation process

The original BUSTER BIO annotations were aligned back to the unmodified source text and converted into character spans. Fund candidates and missing entities were reviewed in versioned batches. Person additions use strong name, honorific, or role evidence, and named-event additions use participation, presentation, speaker, venue, or date evidence. The reviews favor precision over recall.

After reviewed changes are applied, known surfaces are projected only within their source document. Projection never crosses documents and never overwrites an existing overlapping span. The canonical build reports 45,800 added same-document surface occurrences and no unresolved alignments in this release. All 3,779 rows have training_ready: true.

The augmentation includes model-assisted review followed by deterministic validation. It should not be interpreted as a new, fully human-annotated gold standard.

Intended uses

This dataset is suitable for:

  • training or cross-validating finance-domain flat NER models;
  • deriving tokenizer-specific BIO/BIOES supervision;
  • studying organization-versus-fund classification; and
  • pretraining before fine-tuning on a smaller, exhaustively annotated corpus.

It should not be used as a drop-in replacement for the original BUSTER task or as evidence that unannotated text contains no entity.

Limitations and responsible use

  • Omitted people, organizations, funds, and especially events can still remain in the text. The 32 event mentions are too sparse for broad event coverage.
  • Surface projection improves recall for known names but can propagate an incorrect source or review decision to repeated occurrences.
  • Fund-versus-organization boundaries can be context-dependent and reflect the review policy used for this release.
  • Noise in the original BUSTER spans and source documents may remain.
  • The source consists of English financial-transaction documents collected from SEC EDGAR, so models may not generalize to other languages, periods, jurisdictions, industries, or document genres.
  • Documents can be long (up to about 9,900 characters in this release), so fixed-context models require a span-safe chunking strategy.
  • The documents are public filings but can contain names and business contact details. Users remain responsible for appropriate handling and downstream use.

License

This derived release follows the upstream BUSTER dataset's Apache License 2.0. See LICENSE and NOTICE. Users should also review the upstream dataset card.

Citation

Please cite the original BUSTER paper when using this dataset:

@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},
  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}
}

When the uploaded dataset has a stable owner, repository URL, and release tag, add a citation for this derived annotation release as well.