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README.md

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 ---
+pretty_name: Decision models × EvalExplorer
+license: other
+language:
+- en
 dataset_info:
 - config_name: documents
   features:
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   - split: all
     path: taxonomy/all-*
 ---
+
+# Decision models × EvalExplorer
+
+Evaluation data for [baobab-tech/decision-models-experiments](https://github.com/baobab-tech/decision-models-experiments), experiments [01](https://github.com/baobab-tech/decision-models-experiments/tree/main/experiments/01-many-option-classification) (zero-shot many-option classification) and [02](https://github.com/baobab-tech/decision-models-experiments/tree/main/experiments/02-fine-tuning) (fine-tuning).
+
+International development evaluation reports from [EvalExplorer](https://www.evalexplorer.ai), copied from `baobabtech/evalexplorer-data` at revision `3543e3e` by [`build_evalexplorer_dataset.py`](https://github.com/baobab-tech/decision-models-experiments/blob/main/experiments/common/build_evalexplorer_dataset.py). Taxonomy labels and definitions come from the EvalExplorer codebase (`lib/taxonomy.ts`, `lib/geography.ts`, the pipeline classification prompt).
+
+## Configs
+
+| Config | Rows (train / validation / test) | One row per |
+|---|---|---|
+| `documents` | 1,148 / 138 / 134 | report: first pages + document labels |
+| `excerpts` | 157,302 / 18,000 / 15,834 | finding, recommendation or methodology excerpt + tags |
+| `taxonomy` | 334 (`all`) | label code |
+
+Splits are by document (80/10/10 on a hash of `document_id`), shared by both configs.
+
+### `documents`
+
+| Column | Contents |
+|---|---|
+| `document_id`, `title` | Report ID and title |
+| `text` | First pages, exactly as the `classify_codes` config fed them to classifiers: 2 pages for reports under 10 pages, otherwise 5; cut at 24,000 characters. Median 1,935 tokens |
+| `n_chars`, `truncated` | Length before truncation; whether it was cut (92 of 1,420) |
+| `evaluation_approach` | One of 6 codes or null |
+| `evaluation_type` | One of 4 codes or null |
+| `temporality` | `baseline`, `midterm`, `endline` or null |
+| `themes` | 1–4 of 18 codes |
+| `countries` | ISO 3166-1 alpha-2 codes (145 distinct) |
+| `regions` | Region codes (10 distinct) |
+| `label_source` | `ai`, or `manual` for the 36 hand-corrected documents |
+
+### `excerpts`
+
+| Column | Contents |
+|---|---|
+| `excerpt_id`, `document_id`, `type`, `section_category`, `page` | Identity and position |
+| `text` | Verbatim excerpt; median 34 words, p90 107 |
+| `themes` (22 codes), `regions` (17), `countries` (198) | Findings and recommendations |
+| `methods` (24 codes) | Methodology excerpts |
+| `eval_sample` | `true` for the fixed experiment-01 test sample: 300 findings, 150 recommendations, 150 methodology, seed 0 |
+
+### `taxonomy`
+
+`field`, `code`, `label`, `definition` (document fields only), `region` (countries only), `in_documents`, `in_excerpts`. The two flags mark the codes that occur in gold labels; experiments ask over those sets.
+
+## Labels
+
+Labels are the EvalExplorer ingestion pipeline's LLM output (Gemini 2.5 Flash, gpt-oss-120b, Qwen 3 235B as fallbacks), treated as gold; 36 document classifications were corrected by hand. Codes seen in fewer than 20 documents were dropped from labels upstream. Experiment 01 uses an LLM judge on disagreements because gold is model output.
+
+## Baselines on the same test split
+
+Earlier EvalExplorer runs on `documents` test (134), mean field score 0–100 ([results](https://huggingface.co/datasets/baobabtech/evalexplorer-classify-experiments)): Qwen3.5-4B 67.1 zero-shot / 84.7 SFT; Gemma 4 E4B 72.3 / 83.0; GLiNER2.5 base 45.4 / 57.8.
+
+## Licence and access
+
+Private. The reports are published by about 40 development organisations; their rights have not been reviewed for redistribution. Releasing this dataset publicly needs that review first, or a release of labels, IDs and source links without the text.