README.md

metadata

dataset_info:
  features:
    - name: id
      dtype: string
    - name: category
      dtype: string
    - name: detailed_category
      dtype: string
    - name: contains_domain_knowledge
      dtype: bool
    - name: db
      dtype: string
    - name: question
      dtype: string
    - name: sql
      dtype: string
    - name: tables
      dtype: string
    - name: join_keys
      dtype: string
    - name: column_mapping
      dtype: string
    - name: domain_knowledge
      dtype: string
    - name: sub_questions
      dtype: string
    - name: sub_sqls
      dtype: string
  splits:
    - name: dw
      num_bytes: 29632309
      num_examples: 5787
    - name: nova
      num_bytes: 4728511
      num_examples: 1053
    - name: neutron
      num_bytes: 4656231
      num_examples: 1017
    - name: dw_real
      num_bytes: 306012
      num_examples: 121
  download_size: 8347129
  dataset_size: 39323063
configs:
  - config_name: default
    data_files:
      - split: dw
        path: data/dw-*
      - split: nova
        path: data/nova-*
      - split: neutron
        path: data/neutron-*
      - split: dw_real
        path: data/dw_real-*
license: mit

Dataset Card for beaver-query

Homepage and leaderboard | Github repository | Paper

Beaver is a holistic framework for evaluating performance on complex, private‑enterprise text‑to‑SQL tasks. This repository includes questions and corresponding annotations. We reserve a portion of the full question set as a private, hidden test set. Each sample contains:

• id: ID of the question
• category: one of real, complex query, domain-specific query, domain-specific complex query.
  • real indicates the query originates from actual query logs. All other categories refer to queries synthesized from templates derived from real queries.
  • complex query: queries with high structural complexity (e.g., many joins, nesting) but no domain-specific knowledge
  • domain-specific query: queries with low structural complexity but requiring domain-specific knowledge
  • domain-specific complex query: queries with both high complexity and domain knowledge
• detailed_category: one of real, base, cte, nested, cte-nested, nested-cte. A base query is not treated as a complex query, while a cte, nested, cte-nested, nested-cte query is considered a complex query.
  • real indicates the query originates from actual query logs. All other categories refer to queries synthesized from templates derived from real queries.
  • base indicates queries synthesized from base templates
  • cte indicates queries synthesized from Common-Table-Expression (CTE) templates
  • nested indicates queries synthesized from nesting templates
  • cte-nested indicates queries synthesized from nesting templates, followed by CTE templates
  • nested-cte indicates queries synthesized from CTE templates, followed by nesting templates
• contains_domain_knowledge: whether the question includes domain knowledge
• db: the ID of the referenced database
• question: the natural language question user query
• sql: the SQL statement whose execution answers the question
• tables: the tables used in the SQL statement
• join_keys: the join keys used in the SQL statement
• column_mapping: mappings from phrases in the question to specific table columns
• domain_knowledge: domain‑specific formatting rules or predicate logic
• sub_questions: a decomposition of the question into multiple sub-steps
• sub_sqls: the SQL statements corresponding to each sub‑step

Getting started

from datasets import load_dataset
import json

domain = 'dw'
data = load_dataset('beaverbench/beaver-query')
json_fields = ['tables', 'join_keys', 'column_mapping', 'domain_knowledge', 'sub_questions', 'sub_sqls']
for sample in data[domain]:
    sample = {k: (json.loads(v) if k in json_fields else v)  for k, v in sample.items()}
    # print(json.dumps(sample, indent=2))

Citation

@article{chen2024beaver,
  title={BEAVER: an enterprise benchmark for text-to-sql},
  author={Chen, Peter Baile and Yang, Devin and Li, Weiyue and Wenz, Fabian and Zhang, Yi and Tatbul, Nesime and Cafarella, Michael and Demiralp, {\c{C}}a{\u{g}}atay and Stonebraker, Michael},
  journal={arXiv preprint arXiv:2409.02038},
  year={2024}
}