Rename to decision-models-evaluation-docs; public card

README.md

@@ -1,5 +1,5 @@
 ---
-pretty_name: Decision models × EvalExplorer
+pretty_name: Decision models × evaluation documents
 license: other
 language:
 - en
@@ -123,11 +123,18 @@ configs:
     path: taxonomy/train-*
 ---
 
-# Decision models × EvalExplorer
+# Decision models × evaluation documents
 
-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).
+1,420 publicly published international development evaluation reports (about 40 organisations), with document labels and excerpt tags. Built for [baobab-tech/decision-models-experiments](https://github.com/baobab-tech/decision-models-experiments): experiment [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).
+Reports were converted from PDF with Docling and labelled by an LLM ingestion pipeline. The dataset is built by [`build_evaluation_docs_dataset.py`](https://github.com/baobab-tech/decision-models-experiments/blob/main/experiments/common/build_evaluation_docs_dataset.py) from `baobabtech/evalexplorer-data` revision `3543e3e`; label names and definitions come from the pipeline's taxonomy and classification prompt.
+
+```python
+from datasets import load_dataset
+docs = load_dataset("baobabtech/decision-models-evaluation-docs", "documents", split="test")
+excerpts = load_dataset("baobabtech/decision-models-evaluation-docs", "excerpts", split="test").filter(lambda r: r["eval_sample"])
+taxonomy = load_dataset("baobabtech/decision-models-evaluation-docs", "taxonomy", split="train")
+```
 
 ## Configs
 
@@ -170,12 +177,12 @@ Splits are by document (80/10/10 on a hash of `document_id`), shared by both con
 
 ## 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.
+Labels are the 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.
+Earlier Baobab Tech classifier runs on `documents` test (134), mean field score 0–100: 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
+## Licence
 
-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.
+The reports are publicly published by their organisations, which keep their rights; check each report's terms before reusing its text beyond research. Labels, splits and taxonomy are released by Baobab Tech.