twitter-roberta-base-irony
| Property | Value |
|---|---|
| Author | cardiffnlp |
| Framework | PyTorch, TensorFlow |
| Paper | TweetEval Benchmark Paper |
| Dataset | tweet_eval |
What is twitter-roberta-base-irony?
twitter-roberta-base-irony is a specialized language model built on RoBERTa architecture, specifically trained to detect irony in tweets. The model has been pre-trained on approximately 58 million tweets and fine-tuned using the TweetEval benchmark dataset, making it particularly effective for social media content analysis.
Implementation Details
The model is implemented using the transformers library and supports both PyTorch and TensorFlow frameworks. It processes text input through a specialized tokenizer that handles Twitter-specific content, including @mentions and URLs, converting them to standardized tokens (@user and http respectively).
- Built on RoBERTa-base architecture
- Supports binary classification: irony vs. non-irony
- Includes preprocessing capabilities for Twitter-specific content
- Integrated with TweetNLP Python library
Core Capabilities
- Accurate irony detection in social media text
- Robust handling of Twitter-specific content
- High-performance binary classification with probability scores
- Easy integration with popular deep learning frameworks
Frequently Asked Questions
Q: What makes this model unique?
This model is specifically optimized for detecting irony in social media content, with special preprocessing for Twitter-specific elements and extensive pre-training on tweet data. It's part of the comprehensive TweetEval benchmark, making it particularly reliable for social media analysis tasks.
Q: What are the recommended use cases?
The model is ideal for social media sentiment analysis, content moderation, brand monitoring, and research applications requiring irony detection in social media text. It's particularly effective for analyzing Twitter content and can be integrated into larger NLP pipelines.
