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 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 Training Progress Epoch Train Loss Val Loss Precision Recall F1 Accuracy 1 0.4338 0.4000 0.8563 0.8939 0.8747 0.9781 2 0.3984 0.3939 0.8620 0.9190 0.8896 0.9801 3 0.3823 0.3894 0.8749 0.9210 0.8973 0.9812 4 0.3702 0.3…
We use cookies for essential functionality and analytics. You can accept or reject analytics cookies.Cookie policy