sherlock-project's Introduction

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: code of model and experiments.

Project Organization

├── docs   <- Files for https://sherlock.media.mit.edu landing page.
 
├── data   <- Placeholder directory to download data into.
 
├── 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.
        ├── feature_column_identifiers   <- directory to hold feature names categorized by feature set.
           └── char_col.tsv
           └── par_col.tsv
           └── rest_col.tsv
           └── word_col.tsv
        └── 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 <- Project dependencies.

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