Datasets:
File size: 4,117 Bytes
dadb424 3b9c58d dadb424 3b9c58d dadb424 3b9c58d dadb424 3b9c58d dadb424 754e4df 3b9c58d dadb424 3b9c58d dadb424 3b9c58d dadb424 3b9c58d dadb424 3b9c58d c5a1de3 3b9c58d dadb424 3b9c58d 4e1dc85 3b9c58d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 | ---
dataset_info:
features:
- name: input
dtype: string
- name: output
list:
- name: dataset_mention
struct:
- name: dataset_name
dtype: string
- name: dataset_tag
struct:
- name: value
dtype: string
- name: confidence
dtype: string
- name: data_type
struct:
- name: value
dtype: string
- name: confidence
dtype: string
- name: acronym
dtype: string
- name: author
dtype: string
- name: producer
dtype: string
- name: publication_year
dtype: string
- name: reference_year
dtype: string
- name: reference_population
dtype: string
- name: geography
dtype: string
- name: description
dtype: string
- name: is_used
struct:
- name: value
dtype: bool
- name: confidence
dtype: string
- name: usage_context
struct:
- name: value
dtype: string
- name: confidence
dtype: string
splits:
- name: train
num_bytes: 474751
num_examples: 403
- name: test
num_bytes: 49767
num_examples: 46
- name: eval
num_bytes: 54930
num_examples: 51
download_size: 154032
dataset_size: 579448
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: eval
path: data/eval-*
license: mit
task_categories:
- token-classification
language:
- en
tags:
- GLiNER
- data-mentions
- World Bank
- PRWP
---
# World Bank PRWP - Refugee Data Manual Annotation
## Dataset Description
This dataset consists of manually annotated excerpts from text describing data sources. It is intended for training and evaluating Named Entity Recognition (NER) models (specifically designed for the 13-field GLiNER2 data-mention schema) to extract mentions of datasets, databases, surveys, censuses, and other data sources.
The dataset focuses on the PRWP (Poverty and Equity Global Practice) refugee data contexts from the World Bank.
### Organization
The dataset is divided into three splits:
- **train**: 403 multi-mention records used for training models.
- **eval**: 51 multi-mention records used for validation.
- **test**: 46 multi-mention records used to benchmark model performance.
### Data Instances
Each record corresponds to a text snippet (`input`) and contains a list of data mentions (`output`) complying with a strict 13-field JSON schema.
The schema enforces verbatim grounding, where each `dataset_name`, along with non-classification metadata fields (acronym, author, producer, description, etc.), must be an exact substring of the `input` text.
Features:
- `input`: The original text snippet.
- `output`: A list of objects containing:
- `dataset_name`: The verbatim mention of the data source.
- `dataset_tag`: Classification (named, descriptive, vague).
- `data_type`: Inferred type of data (survey, census, administrative, database, indicator, geospatial, microdata, report, other).
- `acronym`, `author`, `producer`, `description`, `geography`, `publication_year`, `reference_year`, `reference_population`: Contextual entity string metadata fields representing properties of the dataset.
- `is_used`: Indication if this data source was utilized in the research/analysis.
- `usage_context`: Role of the data source (primary, supporting, etc.).
### Quality Assurance
This ground-truth dataset underwent deep manual auditing and programmatic refinement:
- Corrected categorization and unified data tags.
- Verified 100% "verbatim" text grounding for spans.
- Dropped false-positive non-data mentions, such as bare years, general organization references, or methodology fragments.
- Detected and merged any duplicate entries or highly overlapping record snippets using Jaccard text similarity.
## Usage
This dataset is structurally aligned to be used directly to fine-tune GLiNER2 adapter modules or to evaluate general data-mention entity extraction pipelines.
|