Voice49/dber

DB-ER — Dataset for Database Entity Recognition

Dataset Summary

DB-ER is a token-level dataset for Database Entity Recognition (DB-ER) in natural-language queries (NLQs) paired with SQL. The task is to tag each token as one of Table, Column, Value, or O (non-entity).
Each example includes: the NLQ, database identifier, a canonical dataset id, the paired SQL query, a tokenized question, a compact entity→token reverse index, an explicit entities table (typed schema/value items), and CoNLL-style DB‑ER tags.

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Fields

• question_id (int) — Example id
• db_id (str) — Database identifier
• dber_id (str) — Canonical id linking back to the source file/split (BIRD, SPIDER)
• question (str) — NLQ text
• SQL (str) — Paired SQL query
• tokens (List[str]) — Tokenized NLQ
• entities (List[Object]) — Typed DB items referenced in the SQL; each item has:
  • id (int) — Local entity id (unique within the example)
  • type ("table"|"column"|"value")
  • value (str) — Surface form from the DB schema or literal value
• entity_to_token (List[Object]) — Reverse index:
  • entity_id (int) — Refers to an entities[*].id
  • token_idxs (List[int]) — Token indices composing that entity in tokens
• dber_tags (List[str]) — CoNLL-style IOB2 tags over tokens

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Splits

Entity token prevalence is consistent across splits: ~29% entity vs. ~71% O.

Split	# Examples
human_train	500
human_test	500
synthetic_train	15,026

synthetic_train is produced via our auto-annotation pipeline, which aligns SQL-referenced entities to NLQ spans using string-similarity candidates (Jaccard 3-gram / Levenshtein) and a non-overlapping ILP selection objective. See Annotation below.

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Example

{
  "question_id": 13692,
  "db_id": "retail_complains",
  "dber_id": "bird:train.json:282",
  "question": "Among the clients born between 1980 and 2000, list the name of male clients who complained through referral.",
  "SQL": "SELECT T1.first, T1.middle, T1.last FROM client AS T1 INNER JOIN events AS T2 ON T1.client_id = T2.Client_ID WHERE T1.year BETWEEN 1980 AND 2000 AND T1.sex = 'Male' AND T2.`Submitted via` = 'Referral'",
  "tokens": ["Among","the","clients","born","between","1980","and","2000",",","list","the","name","of","male","clients","who","complained","through","referral","."],
  "entities": [
    {"id": 0, "type": "column", "value": "first"},
    {"id": 1, "type": "column", "value": "middle"},
    {"id": 2, "type": "column", "value": "last"},
    {"id": 3, "type": "table", "value": "client"},
    {"id": 4, "type": "table", "value": "events"},
    {"id": 5, "type": "column", "value": "client_id"},
    {"id": 6, "type": "column", "value": "year"},
    {"id": 7, "type": "value", "value": "1980"},
    {"id": 8, "type": "value", "value": "2000"},
    {"id": 9, "type": "column", "value": "sex"},
    {"id": 10, "type": "value", "value": "Male"},
    {"id": 11, "type": "column", "value": "Submitted via"},
    {"id": 12, "type": "value", "value": "Referral"}
  ]
  "entity_to_token": [
    ...,
    {"entity_id":3,"token_idxs":[2]},
    {"entity_id":5,"token_idxs":[14]},
    {"entity_id":7,"token_idxs":[5]},
    {"entity_id":8,"token_idxs":[7]},
    {"entity_id":10,"token_idxs":[13]},
    {"entity_id":12,"token_idxs":[18]},
    ...
  ],
  "dber_tags": ["O","O","B-TABLE","O","O","B-VALUE","O","B-VALUE","O","O","O","O","O","B-VALUE","B-COLUMN","O","O","O","B-VALUE","O"]
}

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Annotation

• Human: collaborative web UI with schema and SQL visible during labeling.
• Synthetic: for each NLQ–SQL pair, generate candidate spans with Jaccard/Levenshtein, then solve a non-overlapping ILP to select spans maximizing similarity. Hyperparameters are validated on human data.

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Data provenance

• Sources: text-to-SQL benchmarks BIRD (https://bird-bench.github.io/) and Spider (https://yale-lily.github.io/spider).
• Transform: NLQ–SQL pairs → DB-ER annotations via the synthetic pipeline; human annotations provide gold labels and validation.

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Release notes

• v1.1 (2025-08-26): HF Data Viewer compatibility update
• v1.0: Initial public release