• Ubuntu
• macOS

pip uninstall mopp

For Mac and other Ubuntu installations not having a nvidia GPU, we need to explicitly set an environment variable at time of install.

export JUPYTER_TEXT2CODE_MODE="cpu"

sudo apt-get install libopenblas-dev libomp-dev

git clone https://github.com/deepklarity/jupyter-text2code.git
cd jupyter-text2code
pip install .
jupyter nbextension enable jupyter-text2code/main

pip uninstall jupyter-text2code

• Start Jupyter notebook server by running the following command: jupyter notebook
• If you don't see Nbextensions tab in Jupyter notebook run the following command:jupyter contrib nbextension install --user
• You can open the sample notebooks/ctds.ipynb notebook for testing
• 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

We have published CPU and GPU images to docker hub with all dependencies pre-installed.

The plugin now supports pandas commands + quick snippet insertion of available snippets from awesome-notebooks. With this change, we can now get snippets for most popular integrations from within the jupyter tab. eg:

• Get followers count from twitter
• Get stats about a story from instagram The detailed training steps are available in scripts README where we also evaluated performance of different models and ended up selecting SentenceTransformers paraphrase-MiniLM-L6-v2

• 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 jupyter_text2code/jupyter_text2code_serverextension/__init__.py with new intent's condition and add actual code for the intent
• Reinstall plugin by running: pip install .

• Publish Docker image
• Refactor code and make it mode modular, remove duplicate code, etc
• Add support for more commands
• Improve intent detection and NER
• Add support for Windows
• 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

• Deepak Rawat
• Kartik Godawat
• Abdullah Meda