Token Classification
GLiNER2
Safetensors
GLiNER
English
ner
named-entity-recognition
world-bank
datasets
data-use
lora
adapter
forced-displacement
refugees
fcv
Instructions to use ai4data/datause-extraction with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER2
How to use ai4data/datause-extraction with GLiNER2:
from gliner2 import AutoExtractor extractor = AutoExtractor.from_pretrained("ai4data/datause-extraction") # Extract entities text = "Apple CEO Tim Cook announced iPhone 15 in Cupertino yesterday." result = extractor.extract_entities(text, ["company", "person", "product", "location"]) print(result) - GLiNER
How to use ai4data/datause-extraction with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("ai4data/datause-extraction") text = "Cristiano Ronaldo dos Santos Aveiro was born on 5 February 1985 in Funchal, Madeira, Portugal." labels = ["person", "date", "location"] entities = model.predict_entities(text, labels) for entity in entities: print(entity["text"], "=>", entity["label"]) - Notebooks
- Google Colab
- Kaggle
fix: correct program name to AI for Data—Data for AI (World Bank & UNHCR)
Browse files
README.md
CHANGED
|
@@ -35,7 +35,7 @@ model-index:
|
|
| 35 |
|
| 36 |
A fine-tuned [GLiNER2](https://huggingface.co/fastino/gliner2-large-v1) adapter for extracting structured dataset mentions from research documents and policy papers.
|
| 37 |
|
| 38 |
-
Developed
|
| 39 |
|
| 40 |
## Overview
|
| 41 |
|
|
@@ -161,7 +161,7 @@ If you use this model, please cite:
|
|
| 161 |
```bibtex
|
| 162 |
@misc{ai4data-datause-extraction,
|
| 163 |
title={Dataset Use Extraction Model},
|
| 164 |
-
author={
|
| 165 |
year={2025},
|
| 166 |
publisher={Hugging Face},
|
| 167 |
url={https://huggingface.co/ai4data/datause-extraction}
|
|
@@ -172,4 +172,4 @@ If you use this model, please cite:
|
|
| 172 |
|
| 173 |
- **Library**: [ai4data](https://github.com/rafmacalaba/monitoring_of_datause)
|
| 174 |
- **Base model**: [fastino/gliner2-large-v1](https://huggingface.co/fastino/gliner2-large-v1)
|
| 175 |
-
- **
|
|
|
|
| 35 |
|
| 36 |
A fine-tuned [GLiNER2](https://huggingface.co/fastino/gliner2-large-v1) adapter for extracting structured dataset mentions from research documents and policy papers.
|
| 37 |
|
| 38 |
+
Developed as part of the **AI for Data—Data for AI** program, a collaboration between the **World Bank** and **UNHCR**, to monitor and measure data use across development research.
|
| 39 |
|
| 40 |
## Overview
|
| 41 |
|
|
|
|
| 161 |
```bibtex
|
| 162 |
@misc{ai4data-datause-extraction,
|
| 163 |
title={Dataset Use Extraction Model},
|
| 164 |
+
author={AI for Data—Data for AI},
|
| 165 |
year={2025},
|
| 166 |
publisher={Hugging Face},
|
| 167 |
url={https://huggingface.co/ai4data/datause-extraction}
|
|
|
|
| 172 |
|
| 173 |
- **Library**: [ai4data](https://github.com/rafmacalaba/monitoring_of_datause)
|
| 174 |
- **Base model**: [fastino/gliner2-large-v1](https://huggingface.co/fastino/gliner2-large-v1)
|
| 175 |
+
- **Program**: [AI for Data—Data for AI](https://www.worldbank.org/en/programs/ai4data) (World Bank & UNHCR)
|