Part 2: Pre-training RoBERTa from Scratch with Curriculum Learning and Bias-Aware Loss
📰 Medium · NLP
Learn to pre-train RoBERTa from scratch using curriculum learning and bias-aware loss to achieve equitable representations across demographic groups
Action Steps
- Implement curriculum learning to gradually increase the difficulty of training data
- Design a bias-aware loss function to penalize biased predictions
- Pre-train RoBERTa from scratch using the proposed approach
- Evaluate the model's performance on a held-out test set
- Fine-tune the model on a specific downstream task to assess its adaptability
Who Needs to Know This
NLP engineers and data scientists on a team can benefit from this approach to improve their model's language understanding and fairness, and apply it to various applications such as text classification and sentiment analysis
Key Insight
💡 Curriculum learning and bias-aware loss can help improve the fairness and language understanding of pre-trained models like RoBERTa
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🤖 Train RoBERTa from scratch with curriculum learning & bias-aware loss for equitable representations! 📚
Key Takeaways
Learn to pre-train RoBERTa from scratch using curriculum learning and bias-aware loss to achieve equitable representations across demographic groups
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