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Datasets:
tasksource
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procedural-extraction

Tasks:
Token Classification
Question Answering
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
100K - 1M
ArXiv:
Tags:
tasksource
jev
extraction
clairic
procedural
Synthetic
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pandas
Polars
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metadata 
pretty_name: procedural-extraction
language:
  - en
license: mit
task_categories:
  - token-classification
  - question-answering
tags:
  - tasksource
  - jev
  - extraction
  - clairic
  - procedural
  - synthetic
configs:
  - config_name: record_field_extraction
    data_files:
      - split: train
        path: record_field_extraction/train-*.parquet
      - split: validation
        path: record_field_extraction/validation-*.parquet
      - split: test
        path: record_field_extraction/test-*.parquet
  - config_name: state_update_extraction
    data_files:
      - split: train
        path: state_update_extraction/train-*.parquet
      - split: validation
        path: state_update_extraction/validation-*.parquet
      - split: test
        path: state_update_extraction/test-*.parquet
  - config_name: conditional_list_extraction
    data_files:
      - split: train
        path: conditional_list_extraction/train-*.parquet
      - split: validation
        path: conditional_list_extraction/validation-*.parquet
      - split: test
        path: conditional_list_extraction/test-*.parquet
  - config_name: log_query_extraction
    data_files:
      - split: train
        path: log_query_extraction/train-*.parquet
      - split: validation
        path: log_query_extraction/validation-*.parquet
      - split: test
        path: log_query_extraction/test-*.parquet
  - config_name: typed_mention_extraction
    data_files:
      - split: train
        path: typed_mention_extraction/train-*.parquet
      - split: validation
        path: typed_mention_extraction/validation-*.parquet
      - split: test
        path: typed_mention_extraction/test-*.parquet
  - config_name: business_document_extraction
    data_files:
      - split: train
        path: business_document_extraction/train-*.parquet
      - split: validation
        path: business_document_extraction/validation-*.parquet
      - split: test
        path: business_document_extraction/test-*.parquet
  - config_name: nested_structure_extraction
    data_files:
      - split: train
        path: nested_structure_extraction/train-*.parquet
      - split: validation
        path: nested_structure_extraction/validation-*.parquet
      - split: test
        path: nested_structure_extraction/test-*.parquet
  - config_name: html_extraction
    data_files:
      - split: train
        path: html_extraction/train-*.parquet
      - split: validation
        path: html_extraction/validation-*.parquet
      - split: test
        path: html_extraction/test-*.parquet
  - config_name: date_range_extraction
    data_files:
      - split: train
        path: date_range_extraction/train-*.parquet
      - split: validation
        path: date_range_extraction/validation-*.parquet
      - split: test
        path: date_range_extraction/test-*.parquet
  - config_name: spec_match_extraction
    data_files:
      - split: train
        path: spec_match_extraction/train-*.parquet
      - split: validation
        path: spec_match_extraction/validation-*.parquet
      - split: test
        path: spec_match_extraction/test-*.parquet
  - config_name: lookalike_field_extraction
    data_files:
      - split: train
        path: lookalike_field_extraction/train-*.parquet
      - split: validation
        path: lookalike_field_extraction/validation-*.parquet
      - split: test
        path: lookalike_field_extraction/test-*.parquet
  - config_name: structure_lookup_extraction
    data_files:
      - split: train
        path: structure_lookup_extraction/train-*.parquet
      - split: validation
        path: structure_lookup_extraction/validation-*.parquet
      - split: test
        path: structure_lookup_extraction/test-*.parquet
  - config_name: relational_span_extraction
    data_files:
      - split: train
        path: relational_span_extraction/train-*.parquet
      - split: validation
        path: relational_span_extraction/validation-*.parquet
      - split: test
        path: relational_span_extraction/test-*.parquet

procedural-extraction

Procedurally generated extraction requests: one text and several typed fields to fill from it in one pass, as extraction models (e.g. Cloudflare's Clairic) are asked. Each field is text (one span) or list (every matching span) and is answered with character spans of the text or null. Answers are computed by construction, so spans are exact and labels noise-free (each generator samples a latent structure, such as a fact store, a merged configuration tree or a DOM, and a solver computes the answer; independent readers of the rendered texts recompute every answer in reasoning-core's tests); nulls are earned (the text mentions a negated, proposed, former or other entity's value, not nothing). The tasks are generated by reasoning-core (commit 388d64a, see provenance.json) and are a procedural component of the extraction config of tasksource-jev-typed-decisions.

config what is extracted
record_field_extraction A person's city, employer, job title, phone, email and birth date, from prose about several people with near-miss names; negated, former, hoped-for and relatives' values as confusers; "same city/company as X" chains
state_update_extraction The value in force (or before the last change) of plan slots, from a timestamped chat of statements, proposals approved/rejected/pending, corrections and undos, out of order from level 2
conditional_list_extraction Every order id or product meeting conjunctive, negated or numeric conditions after later updates, or the heaviest such order; prose, bullets or a table
log_query_extraction Every request id or user of log lines matching filters on level, exact service (auth vs auth-proxy), status, latency and user, or the latest/slowest match; plain, logfmt or JSON lines
typed_mention_extraction Named-entity style: every mention of each requested type, where one name (Jordan) is a person, place, organization or product by context; companies by a defined current role among former and unrelated relations; codes by a stated format among near-miss strings
business_document_extraction The value in force on a date in an invoice, purchase order, contract or insurance policy: numbered amendments override printed values and each other, dated windows, schedule references that chain, asked keys left out while their near-miss key is stated (subtotal vs total)
nested_structure_extraction Path, any-depth and predicate-filter queries on a JSON, YAML or XML configuration whose override layers deep-merge or replace whole blocks
html_extraction Fields of the main item in raw HTML: scope (nested and related item cards), visibility (hidden, template, struck prices), dt/dd, label-for, attributes and spec tables with rowspan/colspan
date_range_extraction Every invoice, shipment, contract or ticket whose date of one role (issued vs due) falls in a range, before/after a date, in a month or on a weekday, or the earliest/latest; dates ISO, worded, day/month or month/day numeric under a stated section convention, year-less under a stated year, or relative to another record
spec_match_extraction Every laptop, e-bike or monitor meeting conjunctive spec requirements, or the best such one; specs and thresholds in mixed exact units (g/kg, GB/TB, min/h, Wh/kWh, in/cm) and a stated number convention (1.234,5), near-miss products failing one requirement, unstated specs not qualifying
lookalike_field_extraction Fields asked by role (issue vs delivery date, subtotal vs tax, sender vs approver, order vs tracking number, billing vs return address, phones, weights, durations) from a form, sections, table, prose, email, log or YAML-like record; an asked role that is not stated is null although same-typed values of other roles are, or shows a placeholder (pending, N/A); a conditional or policy value of the role (a late fee that may apply) is not the answer
structure_lookup_extraction A value by its place: a section path (Billing > Contact > Phone) in an outline whose labels recur under other sections, or a cell of one of several same-column tables picked by row and column, a condition on another column or a row position; Markdown, ASCII, CSV, aligned or transposed tables; null when the place exists only elsewhere or the cell is empty
relational_span_extraction Every mention of an event's direct or indirect causes or effects in a narrative with temporal-only and ruled-out look-alikes, or of what follows from facts and if-then rules (some conjunctive), what triggers an event and what it triggers

Levels 0 to 4 are drawn uniformly; level is a column. Mean per-field score by level (0 / 1 / 2 / 3 / 4):

config train validation test DeepSeek-v4-flash GLiNER-large
record_field_extraction 10,000 500 500 1.00 / 0.96 / 0.81 / 0.72 / 0.62 0.80 / 0.59 / 0.36 / 0.45 / 0.38
state_update_extraction 10,000 500 500 0.84 / 0.76 / 0.77 / 0.68 / 0.51 0.58 / 0.46 / 0.37 / 0.36 / 0.29
conditional_list_extraction 10,000 500 500 0.88 / 0.79 / 0.70 / 0.64 / 0.52 0.29 / 0.17 / 0.12 / 0.05 / 0.10
log_query_extraction 10,000 500 500 0.86 / 0.76 / 0.68 / 0.63 / 0.59 0.31 / 0.16 / 0.09 / 0.24 / 0.15
typed_mention_extraction 10,000 500 500 0.85 / 0.80 / 0.79 / 0.69 / 0.66 0.40 / 0.42 / 0.49 / 0.34 / 0.39
business_document_extraction 10,000 500 500 0.70 / 0.77 / 0.76 / 0.81 / 0.75 0.53 / 0.49 / 0.45 / 0.40 / 0.30
nested_structure_extraction 10,000 500 500 0.93 / 0.88 / 0.94 / 0.86 / 0.86 0.18 / 0.20 / 0.20 / 0.20 / 0.18
html_extraction 10,000 500 500 0.98 / 0.96 / 0.94 / 0.98 / 0.90 0.35 / 0.31 / 0.30 / 0.27 / 0.39
date_range_extraction 10,000 500 500 0.75 / 0.62 / 0.60 / 0.44 / 0.44 0.16 / 0.03 / 0.03 / 0.00 / 0.02
spec_match_extraction 10,000 500 500 0.81 / 0.53 / 0.27 / 0.25 / 0.17 0.11 / 0.08 / 0.04 / 0.03 / 0.04
lookalike_field_extraction 10,000 500 500 0.93 / 0.96 / 0.89 / 0.85 / 0.80 0.37 / 0.35 / 0.23 / 0.22 / 0.09
structure_lookup_extraction 10,000 500 500 1.00 / 0.99 / 0.97 / 0.97 / 0.91 0.27 / 0.12 / 0.07 / 0.07 / 0.09
relational_span_extraction 10,000 500 500 0.83 / 0.85 / 0.85 / 0.84 / 0.73 0.16 / 0.11 / 0.09 / 0.07 / 0.07

Schema

state (the text), fields ([{name, type, instruction}]), answers (one per field: {null, values: [{text, start, end}]}, state[start:end] == text), metadata (JSON: level, state seed), level.

Citation

If you use tasksource, its standardized task annotations, or datasets derived from them in published research, please cite the tasksource paper (Sileo, LREC-COLING 2024). The procedural data is generated with Reasoning Core; please also cite the Reasoning Core paper (Sileo et al., 2026).

@inproceedings{sileo-2024-tasksource,
    title = "tasksource: A Large Collection of {NLP} tasks with a Structured Dataset Preprocessing Framework",
    author = "Sileo, Damien",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.1361",
    doi = "10.63317/2h4pkr685mkq",
    pages = "15655--15684",
}

@article{sileo2026reasoning,
  title={Reasoning Core: Designing Broad Procedural Data for Completion-Supervised Reasoning Training},
  author={Sileo, Damien and Lacombe, Valentin and Kachler, Dimitri},
  journal={arXiv preprint arXiv:2608.05148},
  year={2026},
  url={https://arxiv.org/abs/2608.05148}
}