twitter-xlm-roberta-base-sentiment-finetunned
| Property | Value |
|---|---|
| Developer | CitizenLab |
| Base Architecture | XLM-RoBERTa |
| Task | Text Classification |
| Languages Supported | 10 (en, nl, fr, pt, it, es, de, da, pl, af) |
| Downloads | 82,879 |
What is twitter-xlm-roberta-base-sentiment-finetunned?
This is a sophisticated multilingual sentiment analysis model built on the XLM-RoBERTa architecture, fine-tuned specifically for sentiment classification across 10 different languages. Based on the Cardiff NLP Group's work, it excels at detecting positive, negative, and neutral sentiments in text content with impressive accuracy scores.
Implementation Details
The model demonstrates strong performance metrics with an overall accuracy of 80% and particularly excels at identifying positive (F1: 0.85) and neutral (F1: 0.86) sentiments. It's implemented using the Transformers library and can be easily deployed using the Pipeline API.
- Multilingual support for 10 different languages
- Built on XLM-RoBERTa architecture
- Fine-tuned on sentiment classification tasks
- Simple integration with Transformers pipeline
Core Capabilities
- Sentiment classification with three categories: Positive, Negative, and Neutral
- High confidence scoring (demonstrated by 0.98+ confidence in example predictions)
- Cross-lingual sentiment analysis
- Efficient processing of social media content
Frequently Asked Questions
Q: What makes this model unique?
The model's key strength lies in its multilingual capabilities combined with high accuracy across different sentiment categories. It's particularly notable for its strong performance in neutral and positive sentiment detection, making it ideal for social media analysis across multiple languages.
Q: What are the recommended use cases?
This model is particularly well-suited for: 1) Social media sentiment analysis across multiple languages, 2) Content moderation systems, 3) Customer feedback analysis, and 4) Cross-lingual sentiment monitoring in international markets.
