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sidharthms committed Jul 5, 2018
1 parent a58893f commit 9b709226095cc7119c65599283950f319a1a535f
Showing with 18 additions and 19 deletions.
  1. +1 −1 .travis.yml
  2. +8 −8 README.rst
  3. +7 −7 examples/getting_started.ipynb
  4. +2 −3 examples/question_answering.ipynb
@@ -39,7 +39,7 @@ before_install:

install:
- conda install --yes python=$PYTHON_VERSION pip scikit-learn nose
- pip install --process-dependency-links git+https://github.com/sidharthms/deepmatcher | cat
- pip install --process-dependency-links git+https://github.com/anhaidgroup/deepmatcher | cat
- python -m nltk.downloader perluniprops nonbreaking_prefixes punkt

script:
@@ -2,8 +2,8 @@
DeepMatcher
##################

.. image:: https://travis-ci.org/sidharthms/deepmatcher.svg?branch=master
:target: https://travis-ci.org/sidharthms/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
@@ -125,11 +125,11 @@ and Han Li, under the supervision of Prof. AnHai Doan and Prof. Theodoros Rekats
.. _`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/sidharthms/deepmatcher/blob/master/examples/getting_started.ipynb
.. _`Data Processing`: https://nbviewer.jupyter.org/github/sidharthms/deepmatcher/blob/master/examples/data_processing.ipynb
.. _`Matching Models`: https://nbviewer.jupyter.org/github/sidharthms/deepmatcher/blob/master/examples/matching_models.ipynb
.. _`End to End Entity Matching`: https://nbviewer.jupyter.org/github/sidharthms/deepmatcher/blob/master/examples/end_to_end_em.ipynb
.. _`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://deepmatcher.github.io/docs/
.. _`Question Answering with DeepMatcher`: https://nbviewer.jupyter.org/github/sidharthms/deepmatcher/blob/master/examples/question_answering.ipynb
.. _`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/sidharthms/deepmatcher/issues
.. _`file GitHub issues`: https://github.com/anhaidgroup/deepmatcher/issues
@@ -9,7 +9,7 @@
"source": [
"# Getting started with DeepMatcher\n",
"\n",
"Note: you can run **[this notebook live in Google Colab](https://colab.research.google.com/github/sidharthms/deepmatcher/blob/master/examples/getting_started.ipynb)** and use free GPUs provided by Google.\n",
"Note: you can run **[this notebook live in Google Colab](https://colab.research.google.com/github/anhaidgroup/deepmatcher/blob/master/examples/getting_started.ipynb)** and use free GPUs provided by Google.\n",
"\n",
"This tutorial describes how to effortlessly perform entity matching using deep neural networks. Specifically, we will see how to match pairs of tuples (also called data records or table rows) to determine if they refer to the same real world entity. To do so, we will need labeled examples as input, i.e., tuple pairs which have been annotated as matches or non-matches. This will be used to train our neural network using supervised learning. At the end of this tutorial, you will have a trained neural network as output which you can easily apply to unlabeled tuple pairs to make predictions."
]
@@ -226,10 +226,10 @@
"outputs": [],
"source": [
"!mkdir -p sample_data/itunes-amazon\n",
"!wget -qnc -P sample_data/itunes-amazon https://raw.githubusercontent.com/sidharthms/deepmatcher/master/examples/sample_data/itunes-amazon/train.csv\n",
"!wget -qnc -P sample_data/itunes-amazon https://raw.githubusercontent.com/sidharthms/deepmatcher/master/examples/sample_data/itunes-amazon/validation.csv\n",
"!wget -qnc -P sample_data/itunes-amazon https://raw.githubusercontent.com/sidharthms/deepmatcher/master/examples/sample_data/itunes-amazon/test.csv\n",
"!wget -qnc -P sample_data/itunes-amazon https://raw.githubusercontent.com/sidharthms/deepmatcher/master/examples/sample_data/itunes-amazon/unlabeled.csv"
"!wget -qnc -P sample_data/itunes-amazon https://raw.githubusercontent.com/anhaidgroup/deepmatcher/master/examples/sample_data/itunes-amazon/train.csv\n",
"!wget -qnc -P sample_data/itunes-amazon https://raw.githubusercontent.com/anhaidgroup/deepmatcher/master/examples/sample_data/itunes-amazon/validation.csv\n",
"!wget -qnc -P sample_data/itunes-amazon https://raw.githubusercontent.com/anhaidgroup/deepmatcher/master/examples/sample_data/itunes-amazon/test.csv\n",
"!wget -qnc -P sample_data/itunes-amazon https://raw.githubusercontent.com/anhaidgroup/deepmatcher/master/examples/sample_data/itunes-amazon/unlabeled.csv"
]
},
{
@@ -512,7 +512,7 @@
"* **Label column (required for train, validation, test):** Column containing the labels (match or non-match) for each tuple pair. Expected to be named \"label\" by default\n",
"* **ID column (required):** Column containing a unique ID for each tuple pair. This is for evaluation convenience. Expected to be named \"id\" by default.\n",
"\n",
"More details on what data processing involves and ways to customize it are described in **[this notebook](https://nbviewer.jupyter.org/github/sidharthms/deepmatcher/blob/master/examples/data_processing.ipynb)**. \n",
"More details on what data processing involves and ways to customize it are described in **[this notebook](https://nbviewer.jupyter.org/github/anhaidgroup/deepmatcher/blob/master/examples/data_processing.ipynb)**. \n",
"\n",
"### Processing labeled data\n",
"In order to process our train, validation and test CSV files we call `dm.data.process` in the following code snippet which will load and process the CSV files and return three processed `MatchingDataset` objects respectively. These dataset objects will later be used for training and evaluation. The basic parameters to `dm.data.process` are as follows:\n",
@@ -858,7 +858,7 @@
"* **attention:** This model considers the **alignment of words** present in each attribute value pair to determine a match or non-match. It does not take word order into account.\n",
"* **hybrid:** This model considers the **alignment of sequences of words** present in each attribute value pair to determine a match or non-match. This is the default.\n",
"\n",
"`deepmatcher` is highly customizable and allows you to tune almost every aspect of the neural network model for your application scenario. **[This tutorial](https://nbviewer.jupyter.org/github/sidharthms/deepmatcher/blob/master/examples/matching_models.ipynb)** discusses the structure of `MatchingModel`s and how they can be customized.\n",
"`deepmatcher` is highly customizable and allows you to tune almost every aspect of the neural network model for your application scenario. **[This tutorial](https://nbviewer.jupyter.org/github/anhaidgroup/deepmatcher/blob/master/examples/matching_models.ipynb)** discusses the structure of `MatchingModel`s and how they can be customized.\n",
"\n",
"For this tutorial, let's create a `hybrid` model for entity matching:"
]
@@ -6,7 +6,7 @@
"source": [
"# Question Answering with DeepMatcher\n",
"\n",
"Note: you can run **[this notebook live in Google Colab](https://colab.research.google.com/github/sidharthms/deepmatcher/blob/master/examples/question_answering.ipynb)**.\n",
"Note: you can run **[this notebook live in Google Colab](https://colab.research.google.com/github/anhaidgroup/deepmatcher/blob/master/examples/question_answering.ipynb)**.\n",
"\n",
"DeepMatcher can be easily be used for text matching tasks such Question Answering, Text Entailment, etc. In this tutorial we will see how to use DeepMatcher for Answer Selection, a major sub-task of Question Answering. Specifically, we will look at [WikiQA](https://aclweb.org/anthology/D15-1237), a benchmark dataset for Answer Selection. There are three main steps in this tutorial:\n",
"\n",
@@ -26,8 +26,7 @@
"try:\n",
" import deepmatcher\n",
"except:\n",
" !pip install -qqq http://download.pytorch.org/whl/cu80/torch-0.3.1-cp36-cp36m-linux_x86_64.whl\n",
" !pip install -qqq --process-dependency-links git+https://github.com/sidharthms/deepmatcher"
" !pip install -qqq deepmatcher"
]
},
{

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