distilbert-base-fallacy-classification
| Property | Value |
|---|---|
| License | MIT |
| Base Model | DistilBERT-base-uncased |
| Training Data | Logical Fallacy Dataset |
| Languages | English |
| Downloads | 13,794 |
What is distilbert-base-fallacy-classification?
This model is a specialized implementation of DistilBERT fine-tuned for detecting and classifying logical fallacies in text. It can identify 14 distinct types of logical fallacies, including ad hominem, circular reasoning, false causality, and more. The model leverages the efficiency of DistilBERT's architecture while maintaining high accuracy in fallacy detection.
Implementation Details
The model was trained using carefully selected hyperparameters, including a learning rate of 2e-5, batch size of 16, and running for 8 epochs with 122 batches per epoch. It implements a text classification pipeline that can process input text and return confidence scores for each fallacy type.
- Built on DistilBERT's efficient architecture
- Fine-tuned on the comprehensive Logical Fallacy Dataset
- Outputs probability scores for 14 different fallacy types
- Supports both pipeline and detailed classification approaches
Core Capabilities
- Accurate classification of 14 distinct logical fallacy types
- High-confidence scoring (example shows 95%+ accuracy for circular reasoning)
- Efficient processing through DistilBERT architecture
- Easy integration through Hugging Face Transformers library
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its specialized focus on logical fallacy detection, offering comprehensive coverage of 14 different fallacy types with high accuracy. It's built on the efficient DistilBERT architecture, making it both powerful and practical for real-world applications.
Q: What are the recommended use cases?
The model is ideal for applications in argument analysis, educational tools, content moderation, and research in digital humanities. It can be used to analyze debates, academic writing, social media discussions, and any text where identifying logical fallacies is important.
