Twitter XLM-RoBERTa Base Sentiment Multilingual
| Property | Value |
|---|---|
| Author | CardiffNLP |
| Downloads | 2.5M+ |
| Task | Text Classification |
| Accuracy | 69.31% |
What is twitter-xlm-roberta-base-sentiment-multilingual?
This model is a sophisticated multilingual sentiment analysis tool built on the XLM-RoBERTa architecture, specifically fine-tuned for analyzing sentiment in tweets across multiple languages. Developed by CardiffNLP, it represents a significant advancement in multilingual social media text analysis, achieving consistent performance metrics of 69.31% across different evaluation criteria.
Implementation Details
The model is implemented using the TweetNLP framework and can be easily deployed using PyTorch. It's built upon the cardiffnlp/twitter-xlm-roberta-base architecture and has been fine-tuned on the cardiffnlp/tweet_sentiment_multilingual dataset.
- Micro F1 Score: 0.693
- Macro F1 Score: 0.693
- Simple installation via pip install tweetnlp
- Maximum sequence length of 128 tokens
Core Capabilities
- Multilingual sentiment analysis of social media text
- Handles various tweet formats including mentions and URLs
- Real-time classification with simple API integration
- Robust performance across different languages
Frequently Asked Questions
Q: What makes this model unique?
This model's uniqueness lies in its multilingual capabilities combined with social media-specific optimization. With over 2.5 million downloads, it has proven its reliability in real-world applications, particularly for sentiment analysis of tweets across different languages.
Q: What are the recommended use cases?
The model is ideally suited for sentiment analysis of multilingual social media content, social listening applications, brand monitoring across different languages, and large-scale social media analytics projects.
