pip install .

• Open Jupyter notebook
• If installation happened successfully, then for the first time, Universal Sentence Encoder model will be downloaded from tensorflow_hub.
• Click on the Terminal Icon which appears on the menu (to activate the extension)
• Type "help" to see a list of currently supported commands in the repo
• Watch Demo video for some examples

From a list of templates present at mopp/mopp_serverextension/data/ner_templates.csv, generate training data by running the following command:

cd scripts && python generate_training_data.py

This command will generate data for intent matching and NER(Named Entity Recognition).

Use the generated data to create a intent-matcher using faiss.

cd scripts && python create_intent_index.py

cd scripts && python train_spacy_ner.py

• Add more templates in ner_templates with a new intent_id
• Generate training data. Modify generate_training_data.py if different generation techniques are needed or if introducing a new entity.
• Train intent index
• Train NER model
• modify mopp/mopp_serverextension/__init__.py with new intent's condition and add actual code for the intent
• Reinstall plugin by running: pip install .

• Deepak Rawat
• Kartik Godawat