--- license: cc-by-sa-4.0 language: - en task_categories: - token-classification - question-answering pretty_name: typed-extraction size_categories: - 10K | # | Model | Kind | Headline ↑ | Fields with a value ↑ | Hallucination ↓ | Miss ↓ | Computed ↑ | `p_null` AUROC ↑ | |---|---|---|---|---|---|---|---|---| | 1 | **[ScaleDown Extract](https://openrouter.ai/scaledown/extract)** | extraction API | **0.782** | 0.723 | 0.117 | 0.200 | 0.570 | – | | 2 | [UltraBERT-150M clairic-extraction](https://huggingface.co/tasksource/UltraBERT-150M-clairic-extraction) | span extractor, open 150M encoder | **0.616** | 0.548 | 0.141 | 0.297 | 0.013 | 0.815 | | 3 | [Schematron V2 Small](https://openrouter.ai/inference-net/schematron-v2-small) | extraction LLM | **0.562** | 0.529 | 0.445 | 0.303 | 0.134 | – | | – | Always null (answers nothing) | reference | **0.199** | 0.000 | 0.000 | 1.000 | 0.000 | – | | – | Regex rules | reference | **0.129** | 0.152 | 0.993 | 0.000 | 0.026 | 0.503 | ### Fitted or fine-tuned on `train` These models were trained on this benchmark's `train` split, so they are not zero-shot and their scores are not comparable with the table above. The three test-only families never appear in `train`, so they still test transfer. | # | Model | Kind | Headline ↑ | Fields with a value ↑ | Hallucination ↓ | Miss ↓ | Computed ↑ | `p_null` AUROC ↑ | |---|---|---|---|---|---|---|---|---| | 1 | **[adaptive-classifier + MiniLM-L6](https://huggingface.co/adaptive-classifier/typed-extraction-minilm-l6)** | candidate classifier, trained on `train` | **0.315** | 0.166 | 0.109 | 0.600 | 0.005 | 0.789 | | 2 | [adaptive-classifier + bge-small](https://huggingface.co/adaptive-classifier/typed-extraction-bge-small) | candidate classifier, trained on `train` | **0.291** | 0.143 | 0.103 | 0.600 | 0.008 | 0.788 | | – | Always null (answers nothing) | reference | **0.199** | 0.000 | 0.000 | 1.000 | 0.000 | – | | – | Regex rules | reference | **0.129** | 0.152 | 0.993 | 0.000 | 0.026 | 0.503 | ### Notes on the rows - **ScaleDown Extract** is the bar. It returns values that are not in the text (it does the arithmetic), and its weak spots are ExtractBench's long nested schemas and relative dates. - **The adaptive-classifier baselines** pick one of 40 candidate spans per field with a single yes/no classifier, so they rarely hallucinate but cannot compute, and for fields that have a value the right answer is among their candidates only 49% of the time. That ceiling, not the classifier, is what holds them back. They are published as reproducible open baselines, not as competitive extractors. - **UltraBERT-150M clairic-extraction** (submitted by sileod, [discussion #1](https://huggingface.co/datasets/LocalLLaMA/typed-extraction/discussions/1)) is the first open-weight row. It tags answer spans per field with no LLM and maps them to types in plain code, so it rarely computes values (0.013). Its confidence is the raw span probability (ECE 0.42), but it ranks nulls well. We re-scored the published predictions with the unmodified `score.py` and got the same numbers. Its training data includes the SQuAD 2.0, DROP and CUAD train splits; 23 test documents share text with it, and dropping them leaves the headline at 0.616. - **Always null** answers nothing; it scores 0.199 because 31% of fields are absent. A model below it is doing more harm than good. - Remote models were called once, at temperature 0, with responses cached. LLMs got a JSON schema with one nullable property per field; documents longer than their context were cut (5 cases for Schematron), and that is counted. - Rows for LFM2.5 and Clairic will be added when their runs finish. ### Scoring your model ```bash pip install numpy pyarrow huggingface_hub huggingface-cli download LocalLLaMA/typed-extraction --repo-type dataset --local-dir typed-extraction python typed-extraction/score.py predictions.jsonl --by-family ``` `predictions.jsonl` has one line per test case, `{"id": ..., "answers": {"": {"value": ..., "p_null": ..., "confidence": ...}}}`, with `null` (or `[]` for lists) for fields the document does not state. `p_null` and `confidence` are optional and enable the calibration metrics. Please report the headline, hallucination and miss together; any one alone can be gamed. To be added to the leaderboard, open a discussion on this dataset with the scorer's output and a link to the model, and say whether it was zero-shot or trained on `train`. ## 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`.