flask-nlp-question-answer

Trained question answer model integrated into Flask demo app

1. Training data set is provided by Google's Natural Questions
2. Data is cleaned and stored in MongoDB - notebooks: TFQA-bilstm-attn.ipynb
3. Model is parallel bi-directional lstm for both question and answer text; the second lstm layer is followed by a multihead attention layer
4. The parallel models are concatenated into dense layers with a sigmoid activation at the final layer
5. The Spacy large vocabulary model is used for token ID's and vectors for each word
6. Model is in notebooks: TFQA-bilstm-attn.ipynb
7. Weights from trained model are saved in file which is too large to upload but can be readily regenerated
8. A Flask demo app is included with Bootstrap 4 styling:
   • Weights from trained model are imported on demo app startup
   • Start page has input boxes for question and text corpus.
   • Text corpus is parsed into potential one, two and three sentence answers which are fed into the trained model
   • The highest three scores are listed as potential answers with scores in [0, 1)
   • Named entities are also displayed for the text corpus.