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
TerminalIcon 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_templateswith a new intent_id - Generate training data. Modify
generate_training_data.pyif different generation techniques are needed or if introducing a new entity. - Train intent index
- Train NER model
- modify
mopp/mopp_serverextension/__init__.pywith new intent's condition and add actual code for the intent - Reinstall plugin by running:
pip install .
- Refactor code and make it mode modular, remove duplicate code, etc
- Add support for more commands
- Improve intent detection and NER
- Explore sentence Paraphrasing to generate higher-quality training data
- Gather real-world variable names, library names as opposed to randomly generating them
- Try NER with a transformer-based model
- With enough data, train a language model to directly do English->code like GPT-3 does, instead of having separate stages in the pipeline
- Create a survey to collect linguistic data
- Add Speech2Code support