bert-base-indonesian-522M
| Property | Value |
|---|---|
| Author | cahya |
| Training Data Size | 522MB |
| Vocabulary Size | 32,000 tokens |
| Model Hub | Hugging Face |
What is bert-base-indonesian-522M?
bert-base-indonesian-522M is a BERT base model specifically pre-trained on Indonesian Wikipedia data using masked language modeling (MLM). This uncased model represents a significant contribution to Indonesian natural language processing, offering robust language understanding capabilities for various downstream tasks.
Implementation Details
The model utilizes the BERT base architecture and implements WordPiece tokenization with a 32,000 token vocabulary. It processes text in the format [CLS] Sentence A [SEP] Sentence B [SEP] and handles both PyTorch and TensorFlow implementations. Being uncased, it treats "indonesia" and "Indonesia" identically, simplifying text processing.
- Pre-trained on 522MB of Indonesian Wikipedia content
- Supports masked language modeling tasks
- Implements both PyTorch and TensorFlow interfaces
- Utilizes WordPiece tokenization
Core Capabilities
- Masked language modeling for Indonesian text
- Text feature extraction
- Sentence embedding generation
- Support for downstream tasks like text classification and generation
Frequently Asked Questions
Q: What makes this model unique?
This model is specifically designed for Indonesian language processing, trained on a substantial corpus of Indonesian Wikipedia data. Its uncased nature and specialized vocabulary make it particularly effective for Indonesian text analysis tasks.
Q: What are the recommended use cases?
The model is well-suited for various Indonesian language processing tasks, including text classification, masked language modeling, and feature extraction. It can be easily integrated into both PyTorch and TensorFlow workflows, making it versatile for different development environments.
