Datasets:

LocalLLaMA
/

datainventor-typed-extraction-expand

license: cc-by-sa-4.0
language:
  - en
task_categories:
  - token-classification
  - question-answering
tags:
  - synthetic
  - datainventor
  - expand
size_categories:
  - n<1K
configs:
  - config_name: like-yours
    data_files:
      - split: train
        path: data/like-yours.jsonl
  - config_name: related-tasks
    data_files:
      - split: train
        path: data/related-tasks.jsonl
pretty_name: 'DataInventor Expand: typed extraction'

DataInventor Expand: typed extraction

Synthetic typed-extraction cases in the format of LocalLLaMA/typed-extraction: a document (state), a map of typed fields to extract (fields), and the gold answer for each (gold: value, source, alts and character spans). It was grown with the Expand mode of DataInventor, which takes a small dataset you own and writes more rows in the same schema. 167 rows in total; each growth mode is a separate config.

Seed

16 rows of the train split of LocalLLaMA/typed-extraction (9,642 cases): four each from the squad2, drop, lease and wikipedia families. Released under CC BY-SA 4.0 like the seed.

Engine

openai/gpt-oss-20b on DeepInfra through the Hugging Face router (reasoning effort low). Each row is written in two calls: the input fields first, then the output fields given that input and three full seed rows as examples.

Checks applied to every row

Schema: the seed's columns and types; n_fields is recomputed from fields; fields and gold must have the same keys.
Entry structure, measured from the seed: every fields entry has name, type, instructions and options, with type one of the seed's (text, number, date, duration); every gold entry has value, source, alts and spans, with source one of the seed's (span, computed, normalised). Flat or partial entries are rejected; extra keys are dropped.
Values fit their field type: numbers for number and duration, ISO dates for date (or null).
Spans: a span answer must appear in the document (text exactly or ignoring case, numbers as written, with or without thousands separators), and its character offsets are recomputed from the document; the model's offsets are never kept. Empty strings become null.
Text: degenerate text, prompt leaks, placeholders, tables and code fences (the seed has none), and length bounds from the seed (in like-yours; per-task bounds from the plan in related-tasks).
Duplicates: exact and near-duplicates of seed rows and of other new rows, and 13-gram overlap with six public benchmarks.

Rows that fail any check are rejected and counted by reason; the counts are below.

Configs

`like-yours` (92 rows)

More data like yours: new rows on the seed's own topics, three rotating seed rows shown as examples for each.

Plan: Historical events, Sports statistics, Real estate contracts, Wikipedia city profiles, Film metadata, Medical statistics, War history, Business contracts, Government appointments, Economic data, Demographics, Legal documents.
Attempts: 159. Kept by the run: 100. Rejected during the run:
- span not in the document: 30
- malformed entry in gold: 8
- entry value not in the seed (fields.type): 3
- rule broken: 3
- too short: 2
- entry value not in the seed (gold.source): 2
- broken JSON: 1
- missing a field: 1
- malformed entry in fields: 1
Removed after the run, by a check added after it finished or by hand review: 8
- span not in the document: 7
- removed after hand review: 1
Near-duplicates: mean highest word-3-gram overlap with a seed row 0.051, with another new row 0.065; no row at 0.5 or above.
Topic spread (topic labels from the seed, one model call per 25 rows): 2.78 bits in the seed, 3.17 bits in the new rows.
Time 2.9 min; endpoint cost $0.0412.

`related-tasks` (75 rows)

Related tasks: six other tasks from the same domain, written in the same fields and language.

Plan: Event Timeline, Key Figures, Statistical Summary, Location Mapping, Cause and Effect, Source Attribution.
Attempts: 380. Kept by the run: 78. Rejected during the run:
- span not in the document: 143
- malformed entry in gold: 62
- malformed entry in fields: 51
- entry value not in the seed (fields.type): 22
- rule broken: 10
- broken JSON: 10
- entry value not in the seed (gold.source): 3
- missing a field: 1
Removed after the run, by a check added after it finished or by hand review: 3
- span not in the document: 3
Near-duplicates: mean highest word-3-gram overlap with a seed row 0.007, with another new row 0.015; no row at 0.5 or above.
Topic spread (topic labels from the seed, one model call per 25 rows): 2.83 bits in the seed, 2.92 bits in the new rows.
Time 5.6 min; endpoint cost $0.0876.

Columns

id, family, origin, held_out, state, reference_date, fields (JSON string), gold (JSON string), n_fields, plus expand_mode and expand_topic. family, origin and held_out copy the seed's values so the rows load alongside it; they do NOT mean a row came from SQuAD 2.0, DROP or Wikipedia: every row here is synthetic.

Limits

The checks prove that every span is in its document and every value has the right type. They do not prove that the right span was chosen or that a computed or normalised value is right. A 15-row read per config found one wrong computed answer in like-yours (a question about a war the document does not mention, answered with a number instead of null; that row was removed) and fields that repeat the same question. Expect similar errors in rows nobody read.
Documents are invented and some contain false history or garbled facts (for example, a misdated battle); the gold answers follow the document, not the world.
reference_date copies seed values and does not always fit the document.
An outside-domain config was generated and left out: in a 15-row read, 2 rows filled a review date the document never gives.
Every row was written by a language model and has not been checked by a person beyond a 15-row read per config. The checks catch format faults, not wrong facts.
Synthetic data repeats the habits and blind spots of the model that wrote it. Read a sample before you train on it.

Make your own with DataInventor. License: CC-BY-SA-4.0.