Erlangshen-Roberta-330M-Sentiment
| Property | Value |
|---|---|
| Parameter Count | 326M |
| License | Apache 2.0 |
| Paper | Link |
| Architecture | RoBERTa |
| Language | Chinese |
What is Erlangshen-Roberta-330M-Sentiment?
Erlangshen-Roberta-330M-Sentiment is a sophisticated Chinese language model based on RoBERTa-wwm-ext-large, specifically fine-tuned for sentiment analysis tasks. The model has been trained on a diverse collection of 8 Chinese sentiment analysis datasets, encompassing 227,347 samples, making it particularly robust for understanding and analyzing Chinese text sentiment.
Implementation Details
The model builds upon the chinese-roberta-wwm-ext-large architecture and demonstrates impressive performance metrics across various benchmarks. It achieves 97.9% accuracy on ASAP-SENT, 97.51% on ASAP-ASPECT, and 96.66% on ChnSentiCorp, showcasing its superior sentiment analysis capabilities.
- Based on RoBERTa architecture with whole word masking
- 326M parameters for deep language understanding
- Optimized for Chinese sentiment analysis tasks
- Implements PyTorch framework with Transformers library support
Core Capabilities
- High-accuracy sentiment classification for Chinese text
- Robust performance across multiple sentiment analysis benchmarks
- Easy integration with standard NLP pipelines
- Supports inference endpoints for production deployment
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its specialized fine-tuning on Chinese sentiment analysis tasks, leveraging a large-scale dataset of over 227,000 samples across 8 different domains. Its performance metrics consistently exceed those of smaller models in the same category.
Q: What are the recommended use cases?
The model is ideal for Chinese text sentiment analysis applications, including social media monitoring, customer feedback analysis, and opinion mining. It's particularly effective for applications requiring nuanced understanding of Chinese language sentiment expression.
