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.

This project is not installable through PyPI yet. For now, you can install Sherlock by cloning this repository, navigating to the root directory of this repository and run pip install ..

A notebook can be found in notebooks/ which shows how to download the raw and preprocessed data files, and demonstrates the usage of Sherlock.

Data can be downloaded using the download_data() function in the helpers module. This will download 3.6GB of data into the data directory.

To use the pretrained model for generating predictions for a new dataset, features can be extracted using the features.preprocessing module. Please note that extracting features can take quite long due to the unoptimized code. With the resulting feature vectors, the pretrained Sherlock model can be deployed on the dataset.

To retrain Sherlock, you are currently restricted to using 78 classes to comply with the original model architecture. The code of the neural network behind Sherlock will be added soon.

Sherlock can be retrained by using the code in the deploy.train_sherlock module.

├── data   <- Placeholder directory to download data into.

├── docs   <- Files for https://sherlock.media.mit.edu landing page.

├── models  <- Trained models.
    ├── sherlock_model.json
    └── sherlock_weights.h5

├── notebooks   <- Notebooks demonstrating the deployment of Sherlock using this repository.
        └── retrain_sherlock.ipynb

├── sherlock  <- Package files.
    ├── deploy  <- Files and modules to (re)train models on new data and generate predictions.
        └── classes_sherlock.npy
        └── model_helpers.py
        └── predict_sherlock.py
        └── train_sherlock.py
    ├── features     <- Files to turn raw data, storing raw data columns, into features.
        ├── feature_column_identifiers   <- Directory with 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
        └── par_vec_trained_400.pkl
        └── paragraph_vectors.py
        └── preprocessing.py
        └── word_embeddings.py
    ├── helpers.py     <- Supportive modules.