English Sarcasm Detector
| Property | Value |
|---|---|
| Author | helinivan |
| Base Model | bert-base-uncased |
| Performance | F1: 92.38%, Accuracy: 92.42% |
| Downloads | 12,072 |
What is english-sarcasm-detector?
The English Sarcasm Detector is a specialized text classification model designed to identify sarcasm in news article headlines. Built on the BERT architecture, this model has been fine-tuned using a comprehensive dataset from Kaggle's News Headlines Dataset for Sarcasm Detection. It performs binary classification, determining whether a given headline is sarcastic (1) or not sarcastic (0) with impressive accuracy.
Implementation Details
The model utilizes the bert-base-uncased architecture as its foundation and implements a straightforward yet effective preprocessing pipeline that includes lowercase conversion and punctuation removal. The implementation supports text sequences up to 256 tokens and returns both classification results and confidence scores.
- Built on BERT architecture with fine-tuning for sarcasm detection
- Preprocessing includes text normalization and punctuation removal
- Returns binary classification with confidence scores
- Achieves 92.75% precision and 92.38% recall
Core Capabilities
- Binary classification of sarcastic vs non-sarcastic text
- Specialized in processing news headlines
- High-confidence predictions with probability scores
- Efficient preprocessing pipeline
- Support for standard transformer-based implementations
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its specialized focus on news headline sarcasm detection and its impressive performance metrics, achieving over 92% F1 score. It's particularly notable for its balance of precision and recall, making it highly reliable for practical applications.
Q: What are the recommended use cases?
The model is ideal for: analyzing news headlines for sarcastic content, content moderation systems, social media analysis, and automated content categorization. It's particularly useful for media organizations and content aggregators needing to automatically detect and flag sarcastic headlines.
