bert-base-indonesian-1.5G
| Property | Value |
|---|---|
| License | MIT |
| Training Data | Wikipedia (522MB) + Indonesian Newspapers (1GB) |
| Framework Support | PyTorch, TensorFlow |
| Vocabulary Size | 32,000 tokens |
What is bert-base-indonesian-1.5G?
bert-base-indonesian-1.5G is a BERT base model specifically trained for the Indonesian language. It's an uncased model pre-trained on a substantial corpus of Indonesian text, including Wikipedia and newspaper content, using masked language modeling (MLM) objectives. This model represents a significant contribution to Indonesian natural language processing, offering robust language understanding capabilities.
Implementation Details
The model utilizes the BERT base architecture and has been trained on 1.5GB of Indonesian text data. It implements WordPiece tokenization with a 32,000 token vocabulary and follows the standard BERT input format: [CLS] Sentence A [SEP] Sentence B [SEP]. The model supports both PyTorch and TensorFlow implementations, making it versatile for different development environments.
- Uncased tokenization for improved generalization
- Masked Language Modeling (MLM) pre-training objective
- Supports both sentence-level and pair-wise inputs
- Compatible with standard transformer pipelines
Core Capabilities
- Masked token prediction for fill-in-the-blank tasks
- Feature extraction for downstream NLP tasks
- Text classification and generation support
- Cross-framework compatibility (PyTorch/TensorFlow)
Frequently Asked Questions
Q: What makes this model unique?
This model is specifically optimized for Indonesian language processing, trained on a diverse dataset of Indonesian text sources. Its uncased nature and substantial training data make it particularly effective for general Indonesian NLP tasks.
Q: What are the recommended use cases?
The model excels in masked language modeling tasks, text classification, and feature extraction for Indonesian text. It's particularly suitable for applications requiring Indonesian language understanding, including text completion, classification, and as a foundation for fine-tuning on specific downstream tasks.
