Learning to Skim Text Transformer Replication

• Fine-tuned an NLP model that surpassed previous state-of-the-art performance in text skimming tasks.
• Achieved significant improvements over the proposed LSTM-jump model:
  • Increased accuracy by 5%
  • Doubled computational speed
• Authored and presented a detailed technical report to the professor and class, demonstrating strong communication and presentation skills.

Technologies

• Developed the project using a comprehensive tech stack:
  • Python
  • Google Colab
  • PyTorch
  • Scikit-learn
  • NumPy
  • Pandas
  • Hugging Face Transformers
  • SQL
  • Git
  • TensorFlow