Text2Code for Jupyter notebook

Blog post with more details:

Data analysis made easy: Text2Code for Jupyter notebook

Demo Video:

Text2Code for Jupyter notebook

Jupyter plugin Installation:

pip install .

Model training:

Generate training data:

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).

Create intent index faiss

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

cd scripts && python create_intent_index.py

Train NER model

cd scripts && python train_spacy_ner.py

Steps to add more intents:

• 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 .

Authored By:

• Deepak Rawat
• Kartik Godawat