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deepmatcher-0.1.2.post2
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مشاهده بیشترتوضیحات
A deep learning package for entity matching
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | deepmatcher-0.1.2.post2 |
| نام | deepmatcher |
| نسخه کتابخانه | 0.1.2.post2 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Sidharth Mudgal, Han Li |
| ایمیل نویسنده | uwmagellan@gmail.com |
| آدرس صفحه اصلی | http://deepmatcher.ml |
| آدرس اینترنتی | https://pypi.org/project/deepmatcher/ |
| مجوز | BSD |
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
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.5 | Python |
نحوه نصب
نصب پکیج whl deepmatcher-0.1.2.post2:
pip install deepmatcher-0.1.2.post2.whl
نصب پکیج tar.gz deepmatcher-0.1.2.post2:
pip install deepmatcher-0.1.2.post2.tar.gz