Document the extraction config

extraction/README.md

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+## Extraction config
+
+`load_dataset("tasksource/tasksource-jev-typed-decisions", "extraction")`: one text and several typed fields to fill from it, answered in one
+pass, as extraction models (e.g. Cloudflare's Clairic) are asked. Rows: 584,912 train, 45,705 validation, 46,612 test.
+
+- `state`: the text. `fields`: `[{name, type, instruction}]`; `type` is `text` (one span; the instruction is
+  a question) or `list` (every span of a kind, e.g. every person).
+- `answers`: one per field, in order: `{null, values: [{text, start, end}]}`, character offsets into
+  `state` (`state[start:end] == text`, checked for every span). `null` means the text does not hold the
+  answer (an unanswerable question, an absent entity type). Several values of a `text` field are
+  alternative gold spans; of a `list` field, every item.
+- Extractive question answering (SQuAD 2.0, AdversarialQA, Natural Questions, HotpotQA, Quoref, MuSiQue,
+  DROP single-span, DuoRC, ConcurrentQA, FairytaleQA, QuAC with history, windowed CUAD, TyDi English,
+  Qasper spans, PolicyQA, SubjQA, COVID-QA, TimeQA, TAT-QA text spans, ROPES, QAConv and MultiDoc2Dial
+  grounding, MultiSpanQA, QA-SRL) groups the questions asked about one context, up to 8 fields per row.
+  Named entities (CoNLL-2003, OntoNotes, WNUT-17, Few-NERD, NCBI-disease, JNLPBA, acronyms, FiNER-139,
+  MAVEN event triggers, SLURP slots, EBM-NLP PICO spans, and astronomy, biomedical, financial, software,
+  legal, GUM, Twitter and English opinion-target sources) ask for every entity type of the dataset, or
+  the types present plus absent ones up to 8. Entity texts are the source tokens joined by spaces.
+  Other span tagging (skills, keyphrases, aspect terms, toxic spans, metaphors, learner errors, BioScope and
+  ConanDoyle-neg negation and speculation scopes, AbstRCT claims and premises, GUM elementary discourse units)
+  asks for every span of its kind. Event arguments (PHEE adverse drug events, RAMS and CASIE) ask for every
+  argument of each role of a trigger given by its character offsets.
+- Structural extraction uses English UD EWT/GUM dependency subtrees (`ud/...`), PreCo coreference
+  mention sets (`preco/mentions`), human-revised Re-DocRED relation targets (`redocred/entities`),
+  and CoNLL-2000 phrase chunks. UD fields select a unique head and keep full contiguous subtrees;
+  a missing dependent is null and a non-projective positive field is omitted.
+- Procedural extraction ([procedural-extraction](https://huggingface.co/datasets/tasksource/procedural-extraction),
+  sources `procedural/...`): person records with near-miss names and confusers, plan slots in force after a chat
+  of proposals and undos, conditional order lists after updates, log queries, typed mentions, business documents
+  with amendments, nested configurations with override layers, raw HTML pages, records dated in mixed formats
+  within a range, products matching specs in mixed units and number styles, fields asked by role among
+  same-typed look-alikes and conditional or policy values, values located by section path or table cell, row and condition, and the causes, effects
+  or consequences of an event in narratives and rule sets, at difficulty levels 0 to 4;
+  spans and nulls exact by construction.
+- Mostly English, with a small multilingual share: TyDi QA (Arabic, Bengali, Finnish, Korean, Russian, Swahili),
+  UNER (Portuguese, Chinese), MasakhaNER 2 (Swahili) and MASSIVE slots (Hindi), at most 1,600 training rows per
+  language.
+- Native splits, except those without released labels; evaluation texts that also occur in training are dropped. Each source is capped at
+  10,000 training rows (a suite of configs, such as `swde/...`, shares 20,000) and 500 per evaluation split; at most 20% of an entity source's
+  texts mention none of its entity types. `license`/`license_use` per row;
+  `metadata` keeps source question ids or entity types. See [build-manifest.json](build-manifest.json).
+
+The same question answering sources load in SQuAD format for `AutoModelForQuestionAnswering` with
+`tasksource.load_task(id, extractive=True)`.