Number parser: a hyphen joined to a word is not a minus sign; rescored baselines

README.md

@@ -30,6 +30,76 @@ case can be replayed against any API that implements it. It is the sibling of
 It is an **independent** benchmark. It is not affiliated with any vendor of
 extraction models.
 
+![Headline against hallucination rate, and the ranked headline, for every scored model](https://huggingface.co/datasets/LocalLLaMA/typed-extraction/resolve/main/assets/leaderboard.png)
+
+## Leaderboard
+
+`test` split: 1,690 cases, 17,733 fields. This table lists models scored
+zero-shot: they never saw `train`. Models fitted or trained on `train` are in the
+second table below; the two tables are not comparable.
+
+**Headline** is exact match averaged over the 15 families; **fields with a
+value** is the same over fields whose gold is not null; **computed** is exact
+match on the 1,926 fields whose answer has to be worked out (totals, counts, day
+spans). Every model sees the same request: the document and the fields with
+their instructions, nothing else.
+
+<!-- LEADERBOARD:BEGIN -->
+| # | 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 | [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 |
+<!-- LEADERBOARD:END -->
+
+### 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.
+- **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": {"<field>": {"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
@@ -115,55 +185,6 @@ 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.
 
-## Leaderboard
-
-Test split, 1,690 cases, 17,733 fields. **headline** is exact match averaged
-over the 15 families; **present** is the same over fields that have a value;
-**computed** is exact match on the 1,926 fields whose answer has to be worked out
-(totals, counts, day spans). Every model sees the same request: the document and
-the fields with their instructions, nothing else.
-
-<!-- LEADERBOARD:BEGIN -->
-| model | kind | headline | present | hallucination ↓ | miss ↓ | computed | `p_null` AUROC |
-|---|---|---|---|---|---|---|---|
-| [ScaleDown Extract](https://openrouter.ai/scaledown/extract) | extraction API | **0.782** | 0.723 | 0.117 | 0.200 | 0.570 | – |
-| [Schematron V2 Small](https://openrouter.ai/inference-net/schematron-v2-small) | extraction LLM | **0.562** | 0.529 | 0.445 | 0.303 | 0.134 | – |
-| [adaptive-classifier + MiniLM-L6](https://huggingface.co/adaptive-classifier/typed-extraction-minilm-l6) | candidate classifier | **0.315** | 0.166 | 0.109 | 0.600 | 0.005 | 0.789 |
-| [adaptive-classifier + bge-small](https://huggingface.co/adaptive-classifier/typed-extraction-bge-small) | candidate classifier | **0.291** | 0.143 | 0.103 | 0.600 | 0.008 | 0.788 |
-| always null | floor | **0.199** | 0.000 | 0.000 | 1.000 | 0.000 | – |
-| regex rules | floor | **0.129** | 0.152 | 0.993 | 0.000 | 0.026 | 0.503 |
-<!-- LEADERBOARD:END -->
-
-- **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.
-- **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": {"<field>": {"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.
-
 ## Gold quality
 
 200 test fields were hand-checked against their documents, weighted towards