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README.md

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+---
+license: cc-by-4.0
+task_categories:
+- token-classification
+tags:
+- ner
+- dataset-mention
+- data-use
+size_categories:
+- 1K-10K
+---
+
+# Dataset Card for Datause Dataset
+
+Combined data-mention extraction dataset for the GLiNER2 data-use swarm. A single
+JSONL file (`datause-dataset.jsonl`) carries all three splits, tagged with a
+top-level `split` field: `train`, `validation`, or `holdout`.
+
+## Dataset Summary
+The dataset is designed to teach Named Entity Recognition (NER) models to extract
+references to datasets, databases, and surveys from PDF-extracted text.
+
+### Splits
+| split | records | notes |
+|---|---|---|
+| `train` | 1,779 | `v12-rerun` training split (70% positive + 120 pinned hard negatives) |
+| `validation` | 415 | `v12-rerun` validation split (early stopping) |
+| `holdout` | 1,149 | canonical `holdout_v10` prose-only evaluation benchmark |
+
+## Schema (GLiNER2 Flat-NER Format)
+Each record has three fields:
+* `split`: one of `train`, `validation`, `holdout`.
+* `input`: The raw text paragraph containing potential data mentions.
+* `output`: 
+    * `entities`: Dictionaries containing lists of span strings extracted for three categories:
+        * `named_data`: Proper name of a dataset (e.g. `National Education Outcomes Registry (NEOR)`).
+        * `descriptive_data`: Described data source (e.g. `school enrolment and retention indicators`).
+        * `vague_data`: General data references (e.g. `monitoring data`).
+
+Example:
+```json
+{
+  "split": "train",
+  "input": "We use data from the Demographic and Health Surveys (DHS) 2020 and MICS 2019 to analyze child nutrition.",
+  "output": {
+    "entities": {
+      "named_data": ["Demographic and Health Surveys (DHS)", "MICS"],
+      "descriptive_data": [],
+      "vague_data": ["data"]
+    }
+  }
+}
+```
+
+## Source Documents
+* **World Bank Group Project Appraisal Documents (PADs)** — social protection, education, agriculture, water.
+* **UNHCR Refugee Operational Reports** — livelihood surveys, MSNAs, Protection Briefs.
+* **Synthetic Target Sets** — 27 contexts balancing underrepresented classes.
+* **Layout Hard Negatives** — 120 curated tables/indices/footers with empty entities.
+
+## Loading
+```python
+from datasets import load_dataset
+ds = load_dataset("json", data_files="https://huggingface.co/datasets/ai4data/datause-dataset/resolve/main/datause-dataset.jsonl", split="train")
+splits = {s: ds.filter(lambda x: x["split"] == s) for s in ("train", "validation", "holdout")}
+```