Datasets:

baobabtech
/

decision-models-evaluation-docs

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: experiment 01 (zero-shot many-option classification) and 02 (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 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).

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). 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.