robust-swedish-sentiment-multiclass
| Property | Value |
|---|---|
| Parameter Count | 370M |
| License | Apache 2.0 |
| Language | Swedish |
| Downloads | 17,457 |
| Author | KBLab |
What is robust-swedish-sentiment-multiclass?
The robust-swedish-sentiment-multiclass is a sophisticated sentiment analysis model developed by the National Library of Sweden/KBLab. It represents a significant advancement in Swedish language processing, built upon the Megatron-BERT-large-165K architecture and fine-tuned specifically for multi-label sentiment classification tasks.
Implementation Details
The model is implemented using PyTorch and Transformers frameworks, utilizing Safetensors for efficient tensor operations. It has been trained on an extensive dataset of approximately 75,000 Swedish texts, spanning multiple linguistic domains to ensure robust performance across different contexts.
- Based on Megatron-BERT architecture with 370M parameters
- Supports multi-label classification for sentiment analysis
- Implements efficient tensor operations using I64 and F32 formats
- Provides inference endpoints for practical applications
Core Capabilities
- Multi-label sentiment classification for Swedish text
- Cross-domain sentiment analysis
- High-performance text processing with large-scale parameter optimization
- Production-ready with inference endpoint support
Frequently Asked Questions
Q: What makes this model unique?
This model stands out due to its robust training on diverse Swedish text sources, making it particularly effective for real-world applications. Its multi-label classification capability and large parameter count (370M) enable nuanced sentiment analysis across various domains.
Q: What are the recommended use cases?
The model is ideal for sentiment analysis of Swedish text in various contexts, including social media monitoring, customer feedback analysis, and research applications requiring nuanced sentiment classification. It's particularly suitable for projects requiring cross-domain sentiment analysis in Swedish.
