t5-base-finetuned-sarcasm-twitter
| Property | Value |
|---|---|
| Parameter Count | 223M |
| Model Type | Text-to-Text Generation |
| Architecture | T5-base |
| Paper | Link to Paper |
| Author | mrm8488 |
What is t5-base-finetuned-sarcasm-twitter?
This model is a fine-tuned version of Google's T5-base architecture specifically optimized for detecting sarcasm in Twitter conversations. It treats sarcasm detection as a text-to-text generation task, leveraging contextual information from conversation threads to identify subtle forms of irony and sarcasm.
Implementation Details
The model was trained on the Twitter Sarcasm Dataset, comprising 4,050 training samples, 450 validation samples, and 500 test samples. It achieves impressive metrics with 83% accuracy across both sarcastic and non-sarcastic content.
- F1-score of 0.82 for sarcastic content detection
- F1-score of 0.83 for normal content classification
- Processes both response tweets and their conversation context
Core Capabilities
- Context-aware sarcasm detection in Twitter conversations
- Binary classification (derison/normal) through text generation
- Handles multi-turn conversation contexts
- Supports real-time inference on tweet streams
Frequently Asked Questions
Q: What makes this model unique?
This model's uniqueness lies in its approach to sarcasm detection as a text-to-text generation task, rather than traditional classification. It considers the full conversation context, making it more accurate in detecting subtle forms of sarcasm that depend on previous exchanges.
Q: What are the recommended use cases?
The model is ideal for social media analysis, sentiment analysis pipelines, automated content moderation, and research applications requiring sarcasm detection in conversational contexts. It's particularly effective for Twitter-style short-form content.
