Model Card: Fake-news-generator

Model Purpose

Summary

Intended Uses

1. Research on AI Fake News Generation:
   • Understanding the capabilities and limitations of AI models in generating deceptive content.
   • Exploring potential mitigation strategies and ethical considerations.
2. Educational Purposes:
   • Increasing awareness of the challenges posed by AI-generated fake content.
   • Promoting responsible AI development and usage.
3. Testing Fake News Detection Systems:
   • Evaluating the effectiveness of automatic fake news detection systems against AI-generated content.

Factors

• Training Data:
  • Initially trained on XSUM BBC news summarization data.
  • Fine-tuned end-to-end to generate full articles from short text summaries.
• Generation Process:
  • Content is generated token-by-token based on the provided summary prompt.
  • No ground-truth real/fake labels or classifier included in the training data.
• Output Characteristics:
  • Outputs are raw model decodes without post-processing.

Caveats and Recommendations

• Not Intended for Malicious Uses:
  • This model is explicitly not intended for creating or disseminating malicious or harmful content.
• Ethical Considerations:
  • Users are strongly advised to exercise caution and ethical responsibility when using or sharing outputs from this model.
• Limitation on Real/Fake Labels:
  • The model lacks ground-truth labels for distinguishing between real and fake news.
• Limited Post-Processing:
  • Generated outputs are presented without additional post-processing to emphasize raw model capabilities.

Training procedure

Training hyperparameters

• learning_rate: 5e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 10

Training results

Framework versions

• Transformers 4.36.0
• Pytorch 2.1.2+cu121
• Datasets 2.16.1
• Tokenizers 0.15.0