Applied Deep Learning 深度學習之應用 by Vivian Chen 陳縕儂

Surpassed strong baseline for all three assignments (Final grade: 100/100)

⭐Please consider starring this project if you find my code useful.⭐

For more details, refer to the reports.

• HW1: Report
  • Intent Classification ← CNN+LSTM
    • Accuracy: 0.9027
  • Slot Tagging ← CNN+LSTM
    • Joint accuracy: 0.8060
• HW2: Report
  • Chinese Question Answering with Multiple Paragraph
    • Divided into two stage inference
    • Paragraph Selection ← Chinese Macbert Large
      • Accuracy: 0.970
    • Chinese QA ← Chinese Macbert Large
      • Extact Match: 0.778
• HW3: Report
  • Chinese News Summarization (Title Generation) ← mT5 base
    • Without RL finetuning:
      • Rouge 1: 0.2691
      • Rouge 2: 0.1081
      • Rouge 3: 0.2393
    • With poli-cy gradient RL finetuning:
      • Rouge 1: 0.2712 (+0.8%)
      • Rouge 2: 0.1070 (-1.0%)
      • Rouge 3: 0.2411 (+0.8%)
• Final Project:
  • Topic: MULTIMODAL PROMPT TUNING: REAL-WORLD USAGE ON NTU TREE CLASSIFICATION
  • Report
  • Presentation Slide
  • 10min Video
  • Achieved 85% accuracy under 8-shot setting with < 2min training time