Add the extraction config

README.md

@@ -146,16 +146,16 @@ dataset_info:
     dtype: string
   splits:
   - name: train
-    num_bytes: 1067215371
-    num_examples: 498522
+    num_bytes: 1148802962
+    num_examples: 552917
   - name: validation
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-    num_examples: 35743
+    num_bytes: 94080807
+    num_examples: 44205
   - name: test
-    num_bytes: 81350588
-    num_examples: 38574
-  download_size: 834331789
-  dataset_size: 1232023864
+    num_bytes: 89896380
+    num_examples: 45112
+  download_size: 904654720
+  dataset_size: 1332780149
 - config_name: filtered-full
   features:
   - name: state

extraction/README.md

@@ -1,38 +0,0 @@
-## 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: 498,522 train, 35,743 validation, 38,574 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) 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.
-  Other span tagging (skills, keyphrases, aspect terms, toxic spans, metaphors, learner errors) asks for
-  every span of its kind.
-- 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 and raw HTML pages, at difficulty levels 0 to 4;
-  spans and nulls exact by construction.
-  Entity texts are the source tokens joined by spaces.
-- 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)`.

extraction/build-manifest.json

@@ -1,1807 +0,0 @@
-{
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