Sherlock: data and deployment scripts.

Sherlock is a deep-learning approach to semantic data type detection which is important for, among others, data cleaning and schema matching. This repository provides data and scripts to guide the deployment of Sherlock.

Material to be added: data, code of model, benchmark and experiments. More details about this repository follow.

Project Organization

├── docs               <- Files for https://sherlock.media.mit.edu landing page.
 
├── data  
    ├── processed      <- Example of preprocessed data set (features and labels).
    └── raw            <- Raw data example corresponding to preprocessed data.
 
├── notebooks          <- Notebooks demonstrating the deployment of Sherlock using this repository.
        └── retrain_sherlock.ipynb
 
├── src                
    ├── deploy         <- Scripts to (re)train models on new data, and generate predictions.
        └── classes_sherlock.npy
        └── predict_sherlock.py
        └── train_sherlock.py
    ├── features       <- Scripts to turn raw data, storing raw data columns, into features.
        └── bag_of_characters.py
        └── bag_of_words.py
        └── build_features.py
        └── par_vec_trained_400.pkl
        └── paragraph_vectors.py
        └── word_embeddings.py
    ├── models         <- Trained models.
        ├── sherlock_model.json
        └── sherlock_weights.h5

└── requirements.txt  <- Dependencies for reproducing the work, and using the provided scripts.

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