Codify enables data scientists to perform all the tedious and time-consuming tasks such as EDA (exploratory data analysis), data cleaning, data pre-processing, data visualization, modeling, and evaluation in the data-science life cycle, by only conveying the logic of the task in natural language (English) and the system will automatically give out all the relevant python code snippets.
Codify
Codify stands for a smart intelligent system that can code like a human being for a data science application. It enables data scientists to perform all the tedious and time-consuming tasks such as EDA (exploratory data analysis), data cleaning, data pre-processing, data visualization, modeling, and evaluation in the data-science life cycle, by only conveying the logic of the task in natural language (English query) and the system will automatically give out all the relevant python code snippets, or in other words the user just needs to type what they want in the form of a natural language query (English), and our system will automatically give out all the relevant code snippets in python for it.
Click here to read our paper, published on SSRN
[Dataset Stats]()
| Parameter | Statistics | | -------- | ----------- | | Total Number of Users Queries | 525 | | Total Number of Unique Intents | 20 | | Total Number of Unique Entity | 10 | | Total Number of Unique Python Code Snippets | 100 |
Intent Classification
Comparison among Several Approachs for Intent Classification.
| Sr. No | Method | Accuracy | Paper | Year | | ------ | ---------------------------- | -------- | --------------------------------------------------------------------------- | -------- | | 1. | Sum of Word Embedding (Citation Word Embedding) | 88.60% | Paper | Jan 2021 | | 3. | Facebook InferSent| 87.34% | Paper | Jul 2018 | | 2. | Semantic Subword Hashing| 78.48% | Paper | Sep 2019 | | 4. | TF-IDF (Citation Word Embedding) | 74.68% | Paper |Jan 2021 |
Custom-NER-using-Spacy
Custom Named Entity Recognition annotated using NER Annotated by tecoholic and Spacy for training the modelGet Started
Download glove embeddings folder and place it inside "./codify/intentwordemb" folder
Download entity recognition model folder and place it inside "./codify/models" folder
Activate your environment
conda activate codify_envRun Python Server
python server.pyRun React App
cd client/npm startCredits
- NER Annotator - https://github.com/tecoholic/ner-annotator
spacy- https://github.com/explosion/spaCy- Custom NER for Extracting Disease Entities Blog - https://medium.com/analytics-vidhya/custom-ner-for-extracting-disease-entities-c620aca2e1bb
