README.md

---
language:
- id
license: apache-2.0
task_categories:
- token-classification
pretty_name: Indo-NER (GLiNER Auto-Tagged)
size_categories:
- 100K<n<1M
tags:
- ner
- gliner
- named-entity-recognition
- indonesian
- synthetic
---

# Indo-NER: Indonesian Named Entity Recognition Dataset (Silver Standard)

## Dataset Summary
**Indo-NER** is a large-scale Indonesian Named Entity Recognition (NER) dataset automatically annotated using a zero-shot multilingual NER model. The dataset is designed to support research, benchmarking, and experimentation in Indonesian NLP, particularly for entity extraction tasks.

This dataset is **silver standard**, meaning annotations are machine-generated and may contain noise. It is suitable for pretraining, bootstrapping, and research use cases, but human validation is recommended for production-critical systems.

---

## Data Fields

Each data sample contains the following fields:

- **text**: Indonesian input sentence or paragraph
- **entities**: List of extracted entity objects, each consisting of:
  - **start**: Start character index of the entity span
  - **end**: End character index of the entity span
  - **label**: Short entity code (e.g., `PER`, `ORG`, `LOC`)
  - **original_english**: Source English sentence (if translated or available)

---

## Label Scheme (19 Classes)

| Code | Description | Examples |
|-----|------------|----------|
| PER | Person | Jokowi, Lionel Messi, Einstein |
| ORG | Organization | Google, OpenAI, PBB |
| NOR | Political Organization | Partai Golkar, Democrats, Nazi |
| LOC | Location (Geographical) | Gunung Merapi, Asia, Sungai Musi |
| GPE | Geopolitical Entity | Indonesia, Jakarta, Jawa Barat |
| FAC | Facility | Bandara Soetta, Jalan Tol |
| DAT | Date | 17 Agustus 1945, Tahun 2024 |
| TIM | Time | Pukul 07.00, Siang hari |
| CRD | Cardinal Number | Satu, Dua, 100 |
| ORD | Ordinal Number | Pertama, Ke-10 |
| QTY | Quantity | 10 kg, 3 liter |
| PRC | Percent | 50%, Sepuluh persen |
| MON | Money | Rp 50.000, 100 Dolar AS |
| EVT | Event | Perang Dunia II, G20 Summit |
| PRD | Product | iPhone 15, Windows 11 |
| WOA | Work of Art | Harry Potter, Mona Lisa |
| LAW | Law | UUD 1945, UU Cipta Kerja |
| LAN | Language | Bahasa Indonesia, English |
| REG | Religion | Islam, Kristen, Hindu |

---

## Creation Methodology

### Source Data

The text corpus is derived from large-scale Indonesian NER task collections, ensuring diverse sentence structures and real-world contexts.

### Annotation Process (Auto-Tagging)

- **Model**: GLiNER Large v2.5
- **Approach**: Zero-shot multilingual NER
- **Confidence Threshold**: 0.3 (balanced recall and precision)
- **Processing**: Batch inference on NVIDIA T4 GPU
- **Label Mapping**: Natural language prompts (e.g., *"political organization"*) mapped to standardized short labels (e.g., `NOR`)

---

## Usage

Load the dataset using the Hugging Face `datasets` library:

```python
from datasets import load_dataset

dataset = load_dataset("treamyracle/indo-ner")

# View the first training example
print(dataset["train"][0])
````

---

## Limitations

This dataset is **silver standard** and auto-generated, which implies:

* Possible boundary inaccuracies
* Potential hallucinated entities
* Label noise in ambiguous contexts

Human validation or post-processing is recommended for downstream or production use.

---

## Citation

If you use this dataset, please cite the GLiNER authors and this repository:

```bibtex
@misc{indo-ner-2024,
  author       = {treamyracle},
  title        = {Indo-NER: GLiNER Auto-Tagged Dataset},
  year         = {2024},
  publisher    = {Hugging Face},
  journal      = {Hugging Face Repository},
  howpublished = {\url{https://huggingface.co/datasets/treamyracle/indo-ner}}
}
```

---