Datasets:
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license: cc-by-4.0
task_categories:
- token-classification
tags:
- ner
- dataset-mention
- data-use
size_categories:
- 1K-10K
configs:
- config_name: default
data_files:
- split: train
path: "data/train.jsonl"
- split: validation
path: "data/validation.jsonl"
- split: holdout
path: "data/holdout.jsonl"
---
# Dataset Card for Datause Dataset
Combined data-mention extraction dataset for the GLiNER2 data-use swarm, with
three splits: `train`, `validation`, `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 two fields:
* `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`).
* `entity_descriptions`: Definitions for the three categories.
Example:
```json
{
"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"]
},
"entity_descriptions": {
"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.",
"descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.",
"vague_data": "A data mention that only generally references data, information, or statistics."
}
}
}
```
## 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("ai4data/datause-dataset") # DatasetDict: train / validation / holdout
```
|