IndoBERT Base P2
| Property | Value |
|---|---|
| Parameter Count | 124.5M |
| Training Data | Indo4B (23.43 GB) |
| License | MIT |
| Paper | ArXiv Link |
What is indobert-base-p2?
IndoBERT Base P2 is a state-of-the-art language model specifically designed for Indonesian language processing. It represents the second phase of the base architecture variant, trained on the extensive Indo4B dataset comprising 23.43 GB of text. This model is part of the broader IndoBERT family, which aims to advance natural language understanding for Indonesian text.
Implementation Details
The model is implemented using the BERT architecture and can be easily loaded using the Hugging Face transformers library. It utilizes both Masked Language Modeling (MLM) and Next Sentence Prediction (NSP) objectives during training, making it suitable for various downstream tasks.
- Built on BERT base architecture with 124.5M parameters
- Trained on Indo4B dataset using MLM and NSP objectives
- Supports PyTorch and TensorFlow frameworks
- Implements uncased tokenization
Core Capabilities
- Feature extraction for Indonesian text
- Contextual embeddings generation
- Support for masked language modeling
- Next sentence prediction
- Transfer learning for downstream Indonesian NLP tasks
Frequently Asked Questions
Q: What makes this model unique?
IndoBERT-base-p2 is specifically optimized for Indonesian language processing, trained on one of the largest Indonesian text datasets (Indo4B). Its phase 2 training ensures refined feature extraction capabilities while maintaining a balanced parameter count for practical applications.
Q: What are the recommended use cases?
The model is well-suited for various Indonesian NLP tasks including text classification, named entity recognition, sentiment analysis, and question answering. It's particularly effective for tasks requiring deep contextual understanding of Indonesian text.
