Question Answer System Training With Distilbert Base Uncased
Question Answering system built on Pegasus+SQuAD for accurate responses. Optimized for high accuracy and user experience across applications
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Project Outcomes
- Discover how to adjust and adapt pre-trained models like DistilBERT to question answering workloads.
- Get acquainted with one of the most used datasets in NLP: SQuAD (Stanford Question Answering Data set).
- Learn how to extract features from text data necessary for natural language processing models.
- Learn how to set up training loops with Hugging Face's Trainer API.
- Discover the essential skills for metrics such as accuracy and F1 score when testing a model.
- Share a deployed and trained model with the rest of the community through the Hugging Face Hub.
- Deal with big data using concepts of batch and pad.
- Learn how to use Google Colab's free GPU so that models train more quickly.
- Create a realistic artificial intelligence applied tool, something capable of answering questions based on a given context.
- The question-answering system built using DistilBERT can be deployed in customer service applications.