| --- |
| pretty_name: Decision models × evaluation documents |
| license: other |
| task_categories: |
| - text-classification |
| size_categories: |
| - 100K<n<1M |
| tags: |
| - international-development |
| - evaluation |
| - multi-label |
| - decision-models |
| language: |
| - en |
| dataset_info: |
| - config_name: documents |
| features: |
| - name: document_id |
| dtype: string |
| - name: title |
| dtype: string |
| - name: text |
| dtype: string |
| - name: n_chars |
| dtype: int64 |
| - name: truncated |
| dtype: bool |
| - name: evaluation_approach |
| dtype: string |
| - name: evaluation_type |
| dtype: string |
| - name: temporality |
| dtype: string |
| - name: themes |
| list: string |
| - name: countries |
| list: string |
| - name: evaluation_approach_pipeline |
| dtype: string |
| - name: evaluation_type_pipeline |
| dtype: string |
| - name: temporality_pipeline |
| dtype: string |
| - name: themes_pipeline |
| list: string |
| - name: countries_pipeline |
| list: string |
| - name: regions_pipeline |
| list: string |
| - name: label_source_pipeline |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 13068338 |
| num_examples: 1148 |
| - name: validation |
| num_bytes: 1610901 |
| num_examples: 138 |
| - name: test |
| num_bytes: 1547444 |
| num_examples: 134 |
| download_size: 8048167 |
| dataset_size: 16226683 |
| - config_name: excerpts |
| features: |
| - name: excerpt_id |
| dtype: large_string |
| - name: document_id |
| dtype: large_string |
| - name: type |
| dtype: large_string |
| - name: section_category |
| dtype: large_string |
| - name: page |
| dtype: int32 |
| - name: text |
| dtype: large_string |
| - name: themes |
| list: string |
| - name: regions |
| list: string |
| - name: countries |
| list: string |
| - name: methods |
| list: string |
| - name: eval_sample |
| dtype: bool |
| - name: themes_glm |
| list: string |
| - name: themes_deepseek |
| list: string |
| - name: themes_qwen |
| list: string |
| - name: themes_majority |
| list: string |
| - name: regions_glm |
| list: string |
| - name: regions_deepseek |
| list: string |
| - name: regions_qwen |
| list: string |
| - name: regions_majority |
| list: string |
| - name: countries_glm |
| list: string |
| - name: countries_deepseek |
| list: string |
| - name: countries_qwen |
| list: string |
| - name: countries_majority |
| list: string |
| - name: methods_glm |
| list: string |
| - name: methods_deepseek |
| list: string |
| - name: methods_qwen |
| list: string |
| - name: methods_majority |
| list: string |
| splits: |
| - name: train |
| num_bytes: 105407893 |
| num_examples: 157302 |
| - name: validation |
| num_bytes: 12401757 |
| num_examples: 18000 |
| - name: test |
| num_bytes: 11036653 |
| num_examples: 15834 |
| download_size: 52684858 |
| dataset_size: 128846303 |
| - config_name: taxonomy |
| features: |
| - name: field |
| dtype: large_string |
| - name: code |
| dtype: large_string |
| - name: label |
| dtype: large_string |
| - name: definition |
| dtype: large_string |
| - name: definition_excerpts |
| dtype: large_string |
| - name: region |
| dtype: large_string |
| - name: in_documents |
| dtype: bool |
| - name: in_excerpts |
| dtype: bool |
| splits: |
| - name: train |
| num_bytes: 38866 |
| num_examples: 334 |
| download_size: 19512 |
| dataset_size: 38866 |
| configs: |
| - config_name: documents |
| data_files: |
| - split: train |
| path: documents/train-* |
| - split: validation |
| path: documents/validation-* |
| - split: test |
| path: documents/test-* |
| - config_name: excerpts |
| data_files: |
| - split: train |
| path: excerpts/train-* |
| - split: validation |
| path: excerpts/validation-* |
| - split: test |
| path: excerpts/test-* |
| - config_name: taxonomy |
| data_files: |
| - split: train |
| path: taxonomy/train-* |
| --- |
| |
| # Decision models × evaluation documents |
|
|
| 1,420 publicly published international development evaluation reports (about 40 organisations), with document labels and excerpt tags. Document labels come in two sets: silver labels from GLM-5.3-Flash (default columns) and the original pipeline labels (`*_pipeline`). 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). |
|
|
| 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 `5315eab`; label names and definitions come from the pipeline's taxonomy and its document and excerpt prompts (eval-explorer commit `6ae90f7` for the excerpt prompts). |
|
|
| ```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 |
|
|
| | 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 (`train`) | 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`, `evaluation_type`, `temporality` | Silver labels from GLM-5.3-Flash (high reasoning effort, codes with one-line definitions, full first pages): one code or null. Test nulls: 24 / 10 / 37 of 134 | |
| | `themes`, `countries` | Silver labels: themes (1–4 of 18 codes) and ISO 3166-1 alpha-2 countries | |
| | `*_pipeline` | The ingestion pipeline's labels (Gemini 2.5 Flash, gpt-oss-120b, Qwen 3 235B): `evaluation_approach_pipeline`, `evaluation_type_pipeline`, `temporality_pipeline`, `themes_pipeline`, `countries_pipeline`, `regions_pipeline` | |
| | `label_source_pipeline` | `ai`, or `manual` for the 36 documents whose pipeline labels were corrected by hand | |
|
|
| ### `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) | Pipeline labels, findings and recommendations. Tagged with the whole section as input | |
| | `methods` (24 codes) | Pipeline labels, methodology excerpts | |
| | `eval_sample` | `true` for the fixed experiment-01 test sample: 300 findings, 150 recommendations, 150 methodology, seed 0 | |
| | `<field>_glm`, `<field>_deepseek`, `<field>_qwen` | `eval_sample` rows only: labels from GLM-5.3-Flash, DeepSeek-V4.1-Flash and Qwen3.8-Flash-Next, given the excerpt alone, with classify-only prompts rebuilt from the pipeline's ([prompts](https://github.com/baobab-tech/decision-models-experiments/blob/main/experiments/common/prompts/excerpt-tagging.md)). Generated 2026-10-03 | |
| | `<field>_majority` | `eval_sample` rows only: labels chosen by at least 2 of the 3 LLMs. The experiment-01 reference | |
|
|
| ### `taxonomy` |
|
|
| `field`, `code`, `label`, `definition` (document fields only, from the document classification prompt), `definition_excerpts` (themes and methods, from the excerpt extract-and-classify prompts; longer theme definitions), `region` (countries only), `in_documents`, `in_excerpts`. The two flags mark the codes that occur in the silver or pipeline labels; experiments ask over those sets. |
|
|
| ## Labels |
|
|
| - **Silver (default):** GLM-5.3-Flash relabelled every document through HF Inference Providers. It returns `null` more often than the pipeline (test: approach 18%, temporality 28%). |
| - **Pipeline (`*_pipeline`):** the ingestion pipeline's LLM output; 36 document classifications corrected by hand. |
| - Pipeline labels score 76.2 mean field score against silver on test (exact match 9.0%). |
| - Excerpt tags are pipeline output only. |
| - Codes seen in fewer than 20 documents were dropped upstream. |
| |
| ## Baselines on the same test split |
| |
| Earlier Baobab Tech classifier runs on `documents` test (134), mean field score 0–100, against pipeline labels / against silver: |
| |
| | Model | Zero-shot | Fine-tuned (pipeline / silver) | |
| |---|---|---| |
| | Gemma 4 26B-A4B | 70.1 / 72.9 | 84.4 / 80.3 | |
| | Qwen3.5 4B | 67.1 / 66.2 | 84.7 / 77.8 | |
| | Gemma 4 E4B | 72.3 / 72.1 | 83.0 / 76.8 | |
| | GLiNER2.5 base | 45.4 / 45.2 | 58.4 / 57.2 | |
| |
| ## Licence |
| |
| 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. No labels in this dataset are human gold labels except the 36 hand-corrected documents (`label_source_pipeline == "manual"`); all others are LLM output. |
| |
