xlm roberta base indonesian ner EU AI Act Compliance Profile

About this Model

Fine-tuned XLM-RoBERTa Base model for Named Entity Recognition (NER) on Indonesian news articles, with built-in rule-based post-processing for Indonesian text.

Model Performance

| Metric | Score | |--------|-------| | F1 | 0.9120 | | Precision | 0.8928 | | Recall | 0.9320 | | Accuracy | 0.9779 |

Evaluated on held-out test set

Supported Entities

The model recognizes 9 entity types commonly found in Indonesian news:

• PER - Person names
• ORG - Organizations
• GPE - Geopolitical entities (countries, cities, states)
• LOC - Locations (non-GPE)
• DATE - Dates and time periods
• EVENT - Named events
• FAC - Facilities
• MONEY - Monetary values
• LAW - Laws and regulations

Quick Start

from transformers import pipeline

# Load the NER pipeline
ner = pipeline("token-classification", model="khoirif/xlm-roberta-base-indonesian-ner", aggregation_strategy="simple")

# Run inference
text = "Gubernur Jawa Barat meresmikan proyek senilai Rp 10 miliar."
results = ner(text)

# Display results
for entity in results:
    print(f"{entity['word']} -> {entity['entity_group']} (score: {entity['score']:.2f})")

Use Cases

Recommended for:

• News article analysis and information extraction
• Entity-based search and retrieval systems
• Financial and regulatory document processing
• Indonesian-language knowledge graphs

Limitations:

• Optimized for formal Indonesian news text
• Not designed for informal language or slang
• Single language inference only

Training Details

Hyperparameters

Learning rate: 3e-5
Train batch size: 4
Eval batch size: 8
Gradient accumulation steps: 4
Effective batch size: 16 (4 × 4)
Epochs: 10
Weight decay: 0.02
LR scheduler: Linear
Warmup ratio: 0.03
Max gradient norm: 0.5
Label smoothing: 0.05
Mixed precision: FP16
Optimizer: AdamW (default)

Training Progress

| Epoch | Train Loss | Val Loss | Precision | Recall | F1 | Accuracy | |-------|-

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