Megatron-BERT-large Swedish Cased (165k)
| Property | Value |
|---|---|
| Parameter Count | 340M |
| Training Data | 70GB Swedish text |
| Training Steps | 165,000 |
| Model Type | BERT-large |
| HuggingFace | Link |
What is megatron-bert-large-swedish-cased-165k?
This is a large-scale Swedish language model based on the BERT architecture, trained using the Megatron-LM framework. It represents a significant checkpoint (165k steps) in the training process of a planned 500k-step training regime. The model was trained on approximately 70GB of Swedish text data, primarily consisting of OSCAR corpus and Swedish newspaper text from the National Library of Sweden.
Implementation Details
The model follows the BERT-large architecture with 340M parameters and was trained using RoBERTa's hyperparameter settings. Training utilized a substantial batch size of 8,000, leveraging the computational resources provided by the HPC RIVR consortium and EuroHPC JU on the Vega HPC system.
- Architecture: BERT-large configuration
- Training Framework: Megatron-LM
- Dataset: 70GB Swedish text corpus
- Batch Size: 8,000
- Training Progress: 165,000 steps out of planned 500,000
Core Capabilities
- Swedish language understanding and processing
- Cased text handling
- Large-scale language representation
- Advanced contextual embeddings for Swedish text
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for being one of the largest Swedish language models, trained using the Megatron-LM framework with BERT-large architecture. It's specifically optimized for Swedish language processing with a significant amount of training data.
Q: What are the recommended use cases?
The model is well-suited for Swedish natural language processing tasks, including text classification, named entity recognition, and question answering. It's particularly valuable for applications requiring deep understanding of Swedish language context.
