release 1.0.8

.travis.yml

@@ -25,10 +25,8 @@ install:
   - pip install -U numpy
   - pip install -U scipy
   - pip install google-compute-engine
-  #- if [[ $TRAVIS_PYTHON_VERSION == '3.7' ]]; then pip install -U theano; else pip install -U tensorflow; fi
   - pip install -U tensorflow
   - pip install -U .
-  #- if [[ $TRAVIS_PYTHON_VERSION == '3.7' ]]; then switch_kerasbackend theano; fi
   - python -m spacy download en
 script:
   - python setup.py test

README.md

@@ -1,6 +1,6 @@
 # Short Text Mining in Python
 
-[![Build Status](https://travis-ci.org/stephenhky/PyShortTextCategorization.svg?branch=py3test)](https://travis-ci.org/stephenhky/PyShortTextCategorization)
+[![Build Status](https://travis-ci.org/stephenhky/PyShortTextCategorization.svg?branch=master)](https://travis-ci.org/stephenhky/PyShortTextCategorization)
 [![GitHub release](https://img.shields.io/github/release/stephenhky/PyShortTextCategorization.svg?maxAge=3600)](https://github.com/stephenhky/PyShortTextCategorization/releases)
 
 ## Introduction
@@ -14,6 +14,7 @@ of these representations, including topic modeling and word-embedding algorithms
 
 Since release 1.0.0, `shorttext` runs on Python 2.7, 3.5, and 3.6.
 Since release 1.0.7, it runs on Python 3.7 as well, but the backend for `keras` cannot be `TensorFlow`.
+Since release 1.0.8, it runs on Python 3.7 with 'TensorFlow' being the backend for `keras`.
 
 Characteristics:
 
@@ -84,6 +85,7 @@ If you would like to contribute, feel free to submit the pull requests. You can
 
 ## News
 
+* 02/14/2019: `shorttext` 1.0.8 released.
 * 01/30/2019: `shorttext` 1.0.7 released.
 * 01/29/2019: `shorttext` 1.0.6 released.
 * 01/13/2019: `shorttext` 1.0.5 released.

apidocs/source/conf.py

@@ -60,7 +60,7 @@
 # The short X.Y version.
 version = '1.0'
 # The full version, including alpha/beta/rc tags.
-release = '1.0.7'
+release = '1.0.8'
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.

docs/conf.py

@@ -58,7 +58,7 @@
 # The short X.Y version.
 version = u'1.0'
 # The full version, including alpha/beta/rc tags.
-release = u'1.0.7'
+release = u'1.0.8'
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.

docs/faq.rst

@@ -15,6 +15,7 @@ Ans: Yes. Besides Word2Vec, you can use FastText and Poincaré embedding. See: :
 **Q3. Can this package work on Python 3?**
 
 Ans: Since release 1.0.0, this package can be run in Python 2.7, 3.5, and 3.6. (Before that, it operates only under Python 2.7.)
+     Since release 1.0.7, this package can also be run in Python 3.7 as well.
 
 
 

docs/intro.rst

@@ -8,7 +8,8 @@ representation of the texts and documents are needed before they are put into
 any classification algorithm. In this package, it facilitates various types
 of these representations, including topic modeling and word-embedding algorithms.
 
-Since release 1.0.0, `shorttext` runs on Python 2.7, 3.5, and 3.6.
+Since release 1.0.0, `shorttext` runs on Python 2.7, 3.5, and 3.6. Since release 1.0.7,
+it runs also in Python 3.7.
 
 Characteristics:
 

docs/news.rst

@@ -1,6 +1,7 @@
 News
 ====
 
+* 02/14/2019: `shorttext` 1.0.8 released.
 * 01/30/2019: `shorttext` 1.0.7 released.
 * 01/29/2019: `shorttext` 1.0.6 released.
 * 01/13/2019: `shorttext` 1.0.5 released.
@@ -46,6 +47,11 @@ What's New
 Release 1.0.7 (January 30, 2019)
 --------------------------------
 
+* Compatibility with Python 3.7 with TensorFlow as the backend.
+
+Release 1.0.7 (January 30, 2019)
+--------------------------------
+
 * Compatibility with Python 3.7 with Theano as the backend;
 * Minor documentation changes.
 

setup.py

@@ -20,7 +20,7 @@ def package_description():
 
 
 setup(name='shorttext',
-      version="1.0.8a01",
+      version="1.0.8",
       description="Short Text Mining",
       long_description=package_description(),
       classifiers=[

shorttext/__init__.py

@@ -8,7 +8,7 @@
 from . import metrics
 from . import spell
 
-__version__ = '1.0.7'
+__version__ = '1.0.8'
 
 
 # to include BERT: refer to: