Update README.md

README.md

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 ---
-dataset_info:
-  features:
-  - name: text
-    dtype: string
-  - name: original_english
-    dtype: string
-  - name: entities
-    list:
-    - name: end
-      dtype: int64
-    - name: label
-      dtype: string
-    - name: start
-      dtype: int64
-  splits:
-  - name: train
-    num_bytes: 55713554
-    num_examples: 131767
-  download_size: 27701087
-  dataset_size: 55713554
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
+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}}
+}
+```
+
+---
+
+## Installation on Hugging Face
+
+1. Open your dataset repository:
+   [https://huggingface.co/datasets/treamyracle/indo-ner](https://huggingface.co/datasets/treamyracle/indo-ner)
+2. Click **Create README.md** (if not present) or open **README.md** and click **Edit**
+3. Delete all existing content
+4. Paste this entire Markdown file
+5. Click **Commit changes**
+
+Hugging Face will automatically render metadata, tags, and sections to improve dataset discoverability.
+
+---
+
+**Optional Variants Available**:
+
+* Academic-style README
+* Short / minimal README
+* Paper / thesis / benchmark-ready README
+
+Just ask and it will be generated in a single copy-paste block.
+
+```