IndoBERT Base Model (Phase 1)
| Property | Value |
|---|---|
| Parameter Count | 124.5M |
| Training Data | Indo4B (23.43 GB) |
| License | MIT |
| Paper | View Paper |
What is indobert-base-p1?
IndoBERT base-p1 is a state-of-the-art language model specifically designed for Indonesian language processing. It's built on the BERT architecture and trained on a massive 23.43GB dataset called Indo4B. This model represents the first phase of the base architecture series in the IndoBERT family.
Implementation Details
The model implements a masked language modeling (MLM) objective combined with next sentence prediction (NSP). It utilizes the BERT architecture with 124.5M parameters, making it a balanced choice between computational efficiency and performance.
- Built on PyTorch framework
- Supports feature extraction capabilities
- Implements transformer architecture
- Uses uncased tokenization
Core Capabilities
- Contextual word embeddings for Indonesian text
- Next sentence prediction for text coherence
- Masked language modeling for bidirectional context understanding
- Supports both inference and fine-tuning tasks
Frequently Asked Questions
Q: What makes this model unique?
This model is specifically trained on Indonesian language data, making it highly effective for Indonesian NLP tasks. It's part of a larger family of IndoBERT models, offering different sizes and capabilities for various use cases.
Q: What are the recommended use cases?
The model is ideal for Indonesian language processing tasks including text classification, named entity recognition, question answering, and general language understanding tasks. It's particularly suitable for applications requiring deep understanding of Indonesian language context.
