ner-laporgub-jateng

27
license:mit
by
Mozkyy
Other
OTHER
New
27 downloads
Early-stage
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Quick Summary

AI model with specialized capabilities.

Training Data Analysis

🔵 Good (6.0/10)

Researched training datasets used by ner-laporgub-jateng with quality assessment

Specialized For

general
multilingual

Training Datasets (1)

c4
🔵 6/10
general
multilingual
Key Strengths
  • •Scale and Accessibility: 750GB of publicly available, filtered text
  • •Systematic Filtering: Documented heuristics enable reproducibility
  • •Language Diversity: Despite English-only, captures diverse writing styles
Considerations
  • •English-Only: Limits multilingual applications
  • •Filtering Limitations: Offensive content and low-quality text remain despite filtering

Explore our comprehensive training dataset analysis

View All Datasets

Code Examples

Intended Usepythontransformers
from transformers import AutoTokenizer, TFAutoModelForTokenClassification, pipeline

# Load model
model_name = "Mozkyy/ner-laporgub-jateng" 
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = TFAutoModelForTokenClassification.from_pretrained(model_name)

# Create pipeline
nlp = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple")

# Test
text = "Jalan rusak di Desa Karanganyar Kabupaten Demak sangat parah"
results = nlp(text)

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

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