# dm_shit

**Repository Path**: zand0630/dm_shit

## Basic Information

- **Project Name**: dm_shit
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: BSD-3-Clause
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-01-19
- **Last Updated**: 2025-01-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

DeepMatcher
=============

.. image:: https://travis-ci.org/anhaidgroup/deepmatcher.svg?branch=master
    :target: https://travis-ci.org/anhaidgroup/deepmatcher

.. image:: https://img.shields.io/badge/License-BSD%203--Clause-blue.svg
    :target: https://opensource.org/licenses/BSD-3-Clause

DeepMatcher is a Python package for performing entity and text matching using deep learning.
It provides built-in neural networks and utilities that enable you to train and apply
state-of-the-art deep learning models for entity matching in less than 10 lines of code.
The models are also easily customizable - the modular design allows any subcomponent to be
altered or swapped out for a custom implementation.

As an example, given labeled tuple pairs such as the following:

.. image:: https://raw.githubusercontent.com/anhaidgroup/deepmatcher/master/docs/source/_static/match_input_ex.png

DeepMatcher uses labeled tuple pairs and trains a neural network to perform matching, i.e., to
predict match / non-match labels. The trained network can then be used to obtain labels for
unlabeled tuple pairs.

Paper and Data
****************

For details on the architecture of the models used, take a look at our paper `Deep
Learning for Entity Matching`_ (SIGMOD '18). All public datasets used in
the paper can be downloaded from the `datasets page <Datasets.md>`__.

Quick Start: DeepMatcher in 30 seconds
******************************************

There are four main steps in using DeepMatcher:

1. Data processing: Load and process labeled training, validation and test CSV data.

.. code-block:: python

   import deepmatcher as dm
   train, validation, test = dm.data.process(path='data_directory',
       train='train.csv', validation='validation.csv', test='test.csv')

2. Model definition: Specify neural network architecture. Uses the built-in hybrid
   model (as discussed in section 4.4 of `our paper
   <http://pages.cs.wisc.edu/~anhai/papers1/deepmatcher-sigmod18.pdf>`__) by default. Can
   be customized to your heart's desire.

.. code-block:: python

   model = dm.MatchingModel()

3. Model training: Train neural network.

.. code-block:: python

   model.run_train(train, validation, best_save_path='best_model.pth')

4. Application: Evaluate model on test set and apply to unlabeled data.

.. code-block:: python

   model.run_eval(test)

   unlabeled = dm.data.process_unlabeled(path='data_directory/unlabeled.csv', trained_model=model)
   model.run_prediction(unlabeled)

Installation
**************

We currently support only Python versions 3.5+. Installing using pip is recommended:

.. code-block::

   pip install deepmatcher

Tutorials
**********

**Using DeepMatcher:**

1. `Getting Started`_: A more in-depth guide to help you get familiar with the basics of
   using DeepMatcher.
2. `Data Processing`_: Advanced guide on what data processing involves and how to
   customize it.
3. `Matching Models`_: Advanced guide on neural network architecture for entity matching
   and how to customize it.

**Entity Matching Workflow:**

`End to End Entity Matching`_: A guide to develop a complete entity
matching workflow. The tutorial discusses how to use DeepMatcher with `Magellan`_ to
perform blocking, sampling, labeling and matching to obtain matching tuple pairs from two
tables.

**DeepMatcher for other matching tasks:**

`Question Answering with DeepMatcher`_: A tutorial on how to use DeepMatcher for question
answering. Specifically, we will look at `WikiQA`_, a benchmark dataset for the task of
Answer Selection.

API Reference
***************

API docs `are here`_.

Support
**********

Take a look at `the FAQ <FAQ.md>`__ for common issues. If you run into any issues or have questions not answered in the FAQ,
please `file GitHub issues`_ and we will address them asap.

The Team
**********

DeepMatcher was developed by University of Wisconsin-Madison grad students Sidharth Mudgal
and Han Li, under the supervision of Prof. AnHai Doan and Prof. Theodoros Rekatsinas.

.. _`Deep Learning for Entity Matching`: http://pages.cs.wisc.edu/~anhai/papers1/deepmatcher-sigmod18.pdf
.. _`Prof. AnHai Doan's data repository`: https://sites.google.com/site/anhaidgroup/useful-stuff/data
.. _`Magellan`: https://sites.google.com/site/anhaidgroup/projects/magellan
.. _`Getting Started`: https://nbviewer.jupyter.org/github/anhaidgroup/deepmatcher/blob/master/examples/getting_started.ipynb
.. _`Data Processing`: https://nbviewer.jupyter.org/github/anhaidgroup/deepmatcher/blob/master/examples/data_processing.ipynb
.. _`Matching Models`: https://nbviewer.jupyter.org/github/anhaidgroup/deepmatcher/blob/master/examples/matching_models.ipynb
.. _`End to End Entity Matching`: https://nbviewer.jupyter.org/github/anhaidgroup/deepmatcher/blob/master/examples/end_to_end_em.ipynb
.. _`are here`: https://anhaidgroup.github.io/deepmatcher/html/
.. _`Question Answering with DeepMatcher`: https://nbviewer.jupyter.org/github/anhaidgroup/deepmatcher/blob/master/examples/question_answering.ipynb
.. _`WikiQA`: https://aclweb.org/anthology/D15-1237
.. _`file GitHub issues`: https://github.com/anhaidgroup/deepmatcher/issues