typed-extraction v1

README.md

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+---
+license: cc-by-sa-4.0
+language:
+- en
+task_categories:
+- token-classification
+- question-answering
+pretty_name: typed-extraction
+size_categories:
+- 10K<n<100K
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train.parquet
+  - split: test
+    path: data/test.parquet
+---
+
+# typed-extraction
+
+A benchmark for **typed extraction over a document**: one piece of text in, a
+named map of typed fields asked at once, and for each field either a typed value
+or `null` when the document does not say.
+
+It follows the System One extraction request shape (`state`, `fields`,
+`reference_date`; answers with `value`, `span`, `p_null`, `confidence`), so a
+case can be replayed against any API that implements it. It is the sibling of
+[typed-decisions](https://huggingface.co/datasets/LocalLLaMA/typed-decisions).
+It is an **independent** benchmark. It is not affiliated with any vendor of
+extraction models.
+
+## What is different about it
+
+- **The gold is fact, not a teacher's opinion.** Every answer is a human
+  annotation from an open dataset, the record a constructed document was
+  rendered from, or a Wikidata fact verified against the article text. No model
+  labelled anything, so there is no teacher ceiling and frontier models can be
+  ranked.
+- **Absent fields are first-class.** 31% of test fields have null gold, and the
+  headline reports the **hallucination rate** (a value given for an absent
+  field) separately from the **miss rate**.
+- **Typed answers.** Fields are `text`, `number`, `date`, `time`,
+  `datetime`, `duration` or `list`. A value has to be the right type, not a
+  string that happens to contain it. Numbers may be **computed** (totals,
+  counts, a deposit stated as "two months' rent"), and dates may be
+  **relative** ("delivered last Tuesday", resolved against `reference_date`).
+- **Held-out families.** Three constructed families (real-estate listings,
+  meeting notes, medical appointment letters) appear only in the test split, so
+  a score on them cannot come from memorised templates.
+- **Variation.** Constructed cases draw field names and instructions from
+  synonyms. They shuffle field order, ask for a plausible field the document
+  does not contain, and pad 30% of documents to 3–12k characters with decoy
+  values of the same shape.
+
+## Splits
+
+| | test | train |
+|---|---|---|
+| cases | 1,690 | 9,642 |
+| fields | 17,733 | 74,014 |
+| null gold | 31.1% | 19.7% |
+| held-out family fields | 2,380 | 0 |
+
+Test fields by family:
+
+| family | origin | cases | fields | what it tests |
+|---|---|---|---|---|
+| squad2 | converted | 200 | 1,908 | spans; unanswerable questions as null |
+| drop | converted | 198 | 1,684 | numbers, mostly computed |
+| cuad | converted | 102 | 4,182 | 41 clause lists per long contract (median ~26k chars) |
+| extractbench | converted | 30 | 2,115 | nested schemas on long PDFs (10-Q, credit agreements, papers…), test only |
+| wikipedia | distant | 300 | 964 | real articles, Wikidata values verified in the text |
+| claim_email | constructed | 80 | 617 | relative dates, computed totals and day counts |
+| invoice | constructed | 80 | 681 | due dates from payment terms, totals, line items |
+| lease | constructed | 80 | 770 | end dates, deposits stated as multiples of rent |
+| itinerary | constructed | 80 | 608 | datetimes, flight lists, journey minutes |
+| shipping_notice | constructed | 80 | 607 | business-day ETAs, unit conversion |
+| job_posting | constructed | 80 | 609 | salary ranges ("$85k–$110k"), skill lists |
+| bank_statement | constructed | 80 | 608 | closing balances, largest debit, counts |
+| real_estate_listing | constructed, **held out** | 100 | 961 | price per square foot, open-house datetimes |
+| meeting_notes | constructed, **held out** | 100 | 659 | durations, "same time next week" |
+| medical_appointment | constructed, **held out** | 100 | 760 | arrive-by times, absent copays and referrals |
+
+Test fields by type:
+
+| type | fields |
+|---|---|
+| list | 6,854 |
+| text | 5,278 |
+| number | 3,480 |
+| date | 1,119 |
+| duration | 442 |
+| datetime | 380 |
+| time | 180 |
+
+Present test gold is 8,192 verbatim spans, 2,020 normalised (written in another
+form), 1,926 computed and 86 relative.
+
+## Scoring
+
+`typed_extraction.metrics` (numpy only) scores each field exact (0/1) and
+partially:
+
+- **text:** SQuAD-normalised exact match; partial credit is token F1.
+- **number / duration:** relative tolerance 1e-3, or rounding at the field's
+  `decimals`.
+- **date / time / datetime:** equal ISO values.
+- **list:** multiset F1. A text item matches at token Jaccard ≥ 0.5 (CUAD's
+  convention), and exact means F1 = 1.
+- **null:** right only when the prediction is null (an empty list for lists).
+
+**Headline: the mean over families**, since families differ about 10× in field
+count. It is reported with hallucination rate, miss rate, `p_null` AUROC, ECE,
+and accuracy at 90% coverage (answering only the most confident 90%) for
+predictors that report confidence.
+
+## Gold quality
+
+200 test fields were hand-checked against their documents, weighted towards
+null and derived gold. **2 were wrong (1.0%, 95% CI 0.3–3.6%)**. Both were
+fixed, together with every recurring issue the audit found:
+
+- CUAD exact clause boundaries, by moving to Jaccard list matching;
+- honorific-only differences;
+- ExtractBench instructions that asked for 'NA' where the gold is null;
+- ambiguous slash dates;
+- two malformed ExtractBench fields.
+
+Null gold had no errors in the sample.
+
+**Known limitations:**
+
+- The Wikipedia slice is thin: mostly 3 fields per case, and its few nulls are
+  all death dates.
+- ExtractBench text comes from `pdftotext`. 5 of its 35 PDFs (image-only resumes)
+  are excluded, and its LLM-judged metrics are not reproduced, so its scores are
+  not comparable to the ExtractBench paper.
+- SQuAD 2.0 and DROP are in most models' pretraining data.
+- Itinerary durations use local clock times; the instructions say so.
+
+## Sources and licences
+
+| source | licence |
+|---|---|
+| SQuAD 2.0 | CC BY-SA 4.0 |
+| DROP | CC BY-SA 4.0 |
+| CUAD v1 | CC BY 4.0 |
+| ExtractBench, commit ca81763 | MIT |
+| Wikipedia text | CC BY-SA 4.0 |
+| Wikidata | CC0 |
+| constructed families | generated for this dataset |
+
+The dataset as a whole is released under **CC BY-SA 4.0**, as the share-alike
+sources require.
+
+## Format
+
+Each row is one case:
+
+| column | content |
+|---|---|
+| `id`, `family`, `origin`, `held_out` | identifiers |
+| `state` | the document |
+| `reference_date` | the date relative expressions resolve against, if any |
+| `fields` | JSON: name → `{type, instructions, options}` |
+| `gold` | JSON: name → `{value, source, alts, spans}` |
+
+`spans` are `[start, end)` character offsets into `state`.

data/test.parquet

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+oid sha256:852bb4251a91ae3f9f9a274949b27bf85b13ccd2302bcd1305150adff5ccc3c3
+size 5082283

data/train.parquet

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+size 11248679

stats.json

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+{
+ "test": {
+  "cases": 1690,
+  "fields": 17733,
+  "null_share": 0.311,
+  "by_family": {
+   "squad2": 200,
+   "drop": 198,
+   "cuad": 102,
+   "claim_email": 80,
+   "invoice": 80,
+   "lease": 80,
+   "itinerary": 80,
+   "shipping_notice": 80,
+   "job_posting": 80,
+   "bank_statement": 80,
+   "medical_appointment": 100,
+   "meeting_notes": 100,
+   "real_estate_listing": 100,
+   "wikipedia": 300,
+   "extractbench": 30
+  },
+  "fields_by_family": {
+   "squad2": 1908,
+   "drop": 1684,
+   "cuad": 4182,
+   "claim_email": 617,
+   "invoice": 681,
+   "lease": 770,
+   "itinerary": 608,
+   "shipping_notice": 607,
+   "job_posting": 609,
+   "bank_statement": 608,
+   "medical_appointment": 760,
+   "meeting_notes": 659,
+   "real_estate_listing": 961,
+   "wikipedia": 964,
+   "extractbench": 2115
+  },
+  "fields_by_origin": {
+   "converted": 9889,
+   "constructed": 6880,
+   "distant": 964
+  },
+  "fields_by_type": {
+   "text": 5278,
+   "number": 3480,
+   "list": 6854,
+   "date": 1119,
+   "duration": 442,
+   "datetime": 380,
+   "time": 180
+  },
+  "present_by_source": {
+   "span": 8192,
+   "computed": 1926,
+   "relative": 86,
+   "normalised": 2020
+  },
+  "held_out_fields": 2380
+ },
+ "train": {
+  "cases": 9642,
+  "fields": 74014,
+  "null_share": 0.197,
+  "by_family": {
+   "squad2": 3000,
+   "drop": 1492,
+   "cuad": 150,
+   "claim_email": 500,
+   "invoice": 500,
+   "lease": 500,
+   "itinerary": 500,
+   "shipping_notice": 500,
+   "job_posting": 500,
+   "bank_statement": 500,
+   "wikipedia": 1500
+  },
+  "fields_by_family": {
+   "squad2": 20054,
+   "drop": 12971,
+   "cuad": 8101,
+   "claim_email": 3800,
+   "invoice": 4297,
+   "lease": 4801,
+   "itinerary": 3801,
+   "shipping_notice": 3807,
+   "job_posting": 3803,
+   "bank_statement": 3815,
+   "wikipedia": 4764
+  },
+  "fields_by_origin": {
+   "converted": 41126,
+   "constructed": 28124,
+   "distant": 4764
+  },
+  "fields_by_type": {
+   "text": 37474,
+   "number": 17130,
+   "list": 11334,
+   "duration": 1513,
+   "date": 5563,
+   "datetime": 500,
+   "time": 500
+  },
+  "present_by_source": {
+   "span": 44482,
+   "computed": 9387,
+   "relative": 516,
+   "normalised": 5046
+  },
+  "held_out_fields": 0
+ }
+}