initial commit

.gitignore

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+*~
+*.pyc
+*.so
+*_inner.c
+*_bind.cpp
+*.o
+build/*
+\#*\#
+*.DS_Store
+*.cmat

LICENSE.txt

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Makefile

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+# build extensions
+compile_cmd = python setup.py build_ext --inplace --force
+all: word2vec_inner.so cat2vec_bind.so sent2vec_bind.so
+
+word2vec_inner.so: word2vec_inner.pyx options.h options.c settings.py
+	$(compile_cmd)
+sent2vec_bind.so: sent2vec_bind.pyx sent2vec_calc.h sent2vec_calc.cpp options.h options.c settings.py
+	$(compile_cmd)
+cat2vec_bind.so: cat2vec_bind.pyx cat2vec_calc.h cat2vec_calc.cpp options.h options.c settings.py
+	$(compile_cmd)

README.md

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+# Category2Vec
+
+## Introduction
+Category2Vec is an implementation of the category vector models [Marui and Hagiwara 2015], and the paragraph vector models [Le and Mikolov 2014].
+These programs are based on word2vec [Mikolov et al. 2013a,b] in gensim project (https://radimrehurek.com/gensim/)[Rahurek 2013].
+
+After training, you can obtain distributed representations for categories, paragraphs, and words. You can also infer a category from a description.
+
+## Demo
+There are demo programs of category vector models(Category2Vec) and paragraph vector models(Sentence2Vec).
+To execute the demo programs,
+
+```
+    make # to build extensions (optional)
+    python demo_catvec.py  # for category vector models
+    python demo_sentvec.py # for paragraph vector models
+```
+
+
+## Usage
+### Download
+Clone the git repository as 
+
+```
+    git clone https://github.com/rakuten-nlp/category2vec.git
+```
+
+or download the zip archive from here: https://github.com/rakuten-nlp/category2vec/archive/master.zip
+
+### Dependency
+Category2Vec requires following python modules:
+
+* numpy
+* scipy
+* six
+* cython
+
+For Ubuntu, you can install them as follows:
+```
+    apt-get install python-dev python-pip python-numpy python-scipy
+    pip install six cython
+```
+
+To build extensions, it is recommended to get a BLAS library such as:
+
+* ATLAS
+* OpenBLAS
+
+For Ubuntu, you can use the provided ATLAS package as follows:
+```
+    apt-get install libatlas-base-dev
+```
+
+There is limitation in OpenBLAS package included in Ubuntu, we recommend you to install it from the source.
+```
+    apt-get install gfortran
+    git clone https://github.com/xianyi/OpenBLAS.git
+    cd OpenBLAS
+    #edit Makefile.rule (USE_THREAD=1 is recommended)
+    make
+    make install PREFIX=your_installation_directory # for example, PREFIX=/usr/local
+```
+
+
+### Build
+You don't have to build the extensions to use Category2Vec,
+but to pursue good performance, we recommend to build the extensions.
+GCC >= 4.6 is required.
+
+Before the compilation, edit `settings.py`.
+If you use scipy >= 0.15.1, you should use the option `use_blas = True`, and specify your BLAS library in `blas_include` and `blas_libs`.
+If you have an Intel CPU (>= Sandy Bridge), you can enable the option `use_avx = True`.
+
+After you editted the settings, type `make` to build extensions.
+
+#### Ubuntu
+This version has been tested under the following environments:
+
+* Ubuntu 12.04 / 14.04
+* Python 2.7.3 / 2.7.6 / 2.7.8 (anaconda)
+* Numpy 1.8.0 / 1.8.2 / 1.9.0 (anaconda) / 1.10.0
+* Scipy 0.9.0 / 0.13.3 / 0.14.0 (anaconda) / 0.15.0 / 0.16.0 (use_blas=True)
+* gcc 4.6.3 / 4.8.1 / 4.8.3
+* OpenBLAS 0.2.12 / ATLAS 3.10.1-4
+
+And under the following CPUs:
+
+* Haswell CPUs
+    * i7-4770K
+    * i7-5820K
+* Ivy Bridge CPUs
+    * Xeon E5-26xx v2 series
+* AMD CPUs
+    * Opteron 4100 series (use_avx=False)
+
+#### Mac OS X
+If you use `clang`, edit `settings.py` and specify `use_clang = True`.
+```
+    easy_install pip
+    pip install numpy scipy six cython # it's important to build cython using gcc
+    # you can install OpenBLAS using the instruction above 
+    # or you can install it from Homebrew as follows:
+    # brew install homebrew/science/openblas
+    make
+```
+
+If you want to use gcc, you can edit `settings.py` and specify `force_gcc = True` and `use_clang = False`.
+For Homebrew,
+```
+    brew install gcc49
+    brew install python
+    source ~/.bash_profile # to use brewed python
+    easy_install pip
+    pip install numpy scipy six cython # it's important to build cython using gcc
+    # you can install OpenBLAS using the instruction above 
+    # or you can install it from Homebrew as follows:
+    # brew install homebrew/science/openblas
+    make
+```
+If you get errors around AVX (e.g. no such instruction error), replace the native OS X assembler (usr/bin/as) by a script below.
+
+https://gist.github.com/xianyi/2957847
+
+Or you can specify `use_avx = False` to avoid this error.
+
+This version has been tested under Mac OS X 10.9.5, Python 2.7.9 (Numpy 1.9.2, Scipy 0.15.1) and apple llvm 6.0 / gcc 4.8.4 (brew gcc48) / gcc 4.9.2 (brew gcc49) with the above modification on the assembler.
+
+### Example
+
+
+## Terms and Conditions
+Distribution, modification, and academic/commercial use of Category2Vec is permitted, provided that
+you conform with GNU Lesser General Public License v3.0 https://www.gnu.org/licenses/lgpl.html
+
+If you are using Category2Vec for research purposes, please cite our paper on Category2Vec [Marui and Hagiwara 2015]
+
+Although we are accepting contributions (e.g., issues and pull
+requests) to this category2vec projects,
+our contribution policy has not been finalized yet.
+We will be announcing our official policy in near future, after which
+we can fully accept contributions.
+
+## FAQ (Frequently Asked Questions)
+Q. Is commercial use permitted?
+- A. Yes, as long as you follow the terms and conditions. See "Terms and Conditions" above for the details.
+
+## Acknowledgements
+The developers would like to thank Masato Hagiwara and Kaoru Yamada for their contribution to this project.
+
+## References
+[Marui and Hagiwara 2015] Junki Marui, and Masato Hagiwara. Category2Vec: 単語・段落・カテゴリに対するベクトル分散表現. 言語処理学会第21回年次大会(NLP2015).
+
+[Le and Mikolov 2014] Quoc Le, and Tomas Mikolov. Distributed Representations of Sentence and Documents. In Proceedings of ICML 2014.
+
+[Mikolov et al. 2013a] Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. Efficient Estimation of Word Representations 
+in Vector Space. In Proceedings of Workshop at ICLR, 2013.
+
+[Mikolov et al. 2013b] Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean. Distributed Representations 
+of Words and Phrases and their Compositionality. In Proceedings of NIPS, 2013.
+
+[Rahurek 2013] Radim Rehurek, Optimizing word2vec in gensim, http://radimrehurek.com/2013/09/word2vec-in-python-part-two-optimizing/
+
+---
+
+© 2015 Rakuten NLP Project. All Rights Reserved. / Sponsored by [Rakuten, Inc.](http://global.rakuten.com/corp/) and [Rakuten Institute of Technology](http://rit.rakuten.co.jp/).