txlm-roberta-hindi-sentiment
| Property | Value |
|---|---|
| License | MIT |
| Base Model | Twitter-XLM-RoBERTa-base |
| Training Dataset Size | 6807 examples |
| Performance | 0.89 F1-score |
What is txlm-roberta-hindi-sentiment?
txlm-roberta-hindi-sentiment is a specialized sentiment analysis model designed specifically for Hindi language text in Devanagari script. It's built upon the Twitter-XLM-RoBERTa-base architecture and has been fine-tuned using a substantial dataset of Hindi language examples. This model represents a significant advancement in Hindi language sentiment analysis, offering robust performance with a weighted average macro F1-score of 0.89.
Implementation Details
The model is implemented using PyTorch and leverages the transformer architecture from the XLM-RoBERTa family. It was trained on a carefully curated dataset comprising 6807 training examples, 1634 testing examples, and 635 validation examples. The implementation employs native PyTorch modules for fine-tuning, ensuring optimal performance and compatibility.
- Built on Twitter-XLM-RoBERTa-base architecture
- Trained with PyTorch framework
- Specialized for Hindi language processing
- Supports Devanagari script analysis
Core Capabilities
- Accurate sentiment classification of Hindi text
- Processing of Devanagari script
- High-performance scoring (0.89 F1-score)
- Efficient text classification for Hindi content
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its specialized focus on Hindi language sentiment analysis, utilizing the robust XLM-RoBERTa architecture while achieving impressive performance metrics. Its extensive training on a diverse Hindi dataset makes it particularly effective for real-world applications.
Q: What are the recommended use cases?
The model is ideal for sentiment analysis of Hindi text content, particularly useful for social media monitoring, customer feedback analysis, and public opinion tracking in Hindi-speaking regions. It's especially suitable for applications requiring accurate sentiment classification of Devanagari script text.
