distilbert-base-indonesian
| Property | Value |
|---|---|
| Model Type | DistilBERT |
| Language | Indonesian |
| Vocabulary Size | 32,000 tokens |
| Training Data | 522MB Wikipedia + 1GB newspapers |
| Author | cahya |
| Model Link | Hugging Face |
What is distilbert-base-indonesian?
distilbert-base-indonesian is a compressed version of BERT specifically designed for Indonesian language processing. This uncased model has been distilled from a larger BERT architecture while maintaining strong performance on Indonesian text analysis tasks. The model leverages knowledge distillation techniques to create a lighter, faster version of BERT without significant compromise in capability.
Implementation Details
The model uses WordPiece tokenization with a 32,000 token vocabulary and follows the standard DistilBERT architecture. Input sequences are structured as [CLS] Sentence A [SEP] Sentence B [SEP], making it suitable for various NLP tasks. The model was trained on a substantial corpus of Indonesian text, including Wikipedia articles and newspaper content, all preprocessed to lowercase format.
- Efficient masked language modeling capabilities
- Supports both PyTorch and TensorFlow implementations
- Optimized for Indonesian language understanding
- Maintains BERT-like performance with reduced parameters
Core Capabilities
- Masked Language Modeling (MLM)
- Feature extraction for downstream tasks
- Text classification support
- Text generation applications
- Sentence embedding generation
Frequently Asked Questions
Q: What makes this model unique?
This model stands out as a specialized Indonesian language model that offers the benefits of BERT while being more computationally efficient through distillation. It's particularly valuable for applications requiring Indonesian language understanding with limited computational resources.
Q: What are the recommended use cases?
The model is well-suited for various Indonesian NLP tasks, including text classification, masked language modeling, and feature extraction for downstream applications. It's particularly effective for tasks requiring understanding of Indonesian language context and semantics while maintaining computational efficiency.
