| --- |
| 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 |
| ``` |
|
|
