Merge pull request #338 from BrikerMan/develop

Release v1.1.1

.editorconfig

@@ -0,0 +1,32 @@
+# EditorConfig is awesome: http://EditorConfig.org
+
+# top-most EditorConfig file
+root = true
+
+# Unix-style newlines with a newline ending every file
+[*]
+end_of_line = lf
+insert_final_newline = true
+trim_trailing_whitespace = true
+
+# Matches multiple files with brace expansion notation
+# Set default charset
+[*.{js,py}]
+charset = utf-8
+
+# 4 space indentation
+[*.py]
+indent_style = space
+indent_size = 4
+max_line_length = 120
+
+# 2 space indentation
+[*.{html, yml, json, rst}]
+indent_style = space
+indent_size = 2
+max_line_length = 120
+
+# Tab indentation (no size specified)
+[Makefile]
+indent_style = tab
+tab_width = 4

.gitignore

@@ -9,6 +9,7 @@ __pycache__/
 # Distribution / packaging
 .Python
 build/
+_build/
 develop-eggs/
 dist/
 downloads/
@@ -105,7 +106,7 @@ venv.bak/
 # mypy
 .mypy_cache/
 
-
+docs/README.rst
 .idea
 .vscode
 venv-tf/*

.readthedocs.yml

@@ -0,0 +1,23 @@
+# .readthedocs.yml
+# Read the Docs configuration file
+# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details
+
+# Required
+version: 2
+
+# Build documentation in the docs/ directory with Sphinx
+sphinx:
+  configuration: docs/conf.py
+
+# Build documentation with MkDocs
+#mkdocs:
+#  configuration: mkdocs.yml
+
+# Optionally build your docs in additional formats such as PDF and ePub
+formats: all
+
+# Optionally set the version of Python and requirements required to build your docs
+python:
+  version: 3.7
+  install:
+    - requirements: ./requirements.dev.txt

.travis.yml

@@ -24,7 +24,6 @@ env:
 
 python:
   - "3.6"
-  - "3.7"
 
 cache: pip
 
@@ -35,23 +34,25 @@ stages:
   - Lint
   - name: Test
     if: (branch = master) OR (type IN (pull_request))
-  - name: Document
-    if: branch = master AND (NOT (type IN (pull_request)))
 
 install:
   - pip install jieba
   - pip install python-coveralls
   - pip install -e .
   - pip install tensorflow==1.14.0
-  - pip install coverage
-  - pip install nose
+  - pip install pytest-cov
+  - pip install pytest
   - python -c "import kashgari;print(f'kashgari version {kashgari.__version__}')"
   - git fetch --unshallow --quiet
   - export PYTHONPATH=`pwd`
 
-script: nosetests --with-coverage --cover-html --cover-html-dir=htmlcov
-  --cover-xml --cover-xml-file=coverage.xml --with-xunit
-  --cover-package="kashgari"
+#script: nosetests --with-coverage --cover-html --cover-html-dir=htmlcov
+#  --cover-xml --cover-xml-file=coverage.xml --with-xunit
+#  --cover-package="kashgari"
+#  $TEST_FILE
+
+script: pytest --doctest-modules --junitxml=test-reports/junit.xml
+  --cov=kashgari --cov-report=xml:coverage.xml --cov-report=html:htmlcov
   $TEST_FILE
 
 after_script:
@@ -68,13 +69,3 @@ jobs:
         - pip install flake8 flake8-builtins
       script:
         - flake8 kashgari
-
-    - stage: Document
-      python: "3.6"
-      install:
-        - echo -e "machine github.com\n login ${GITHUB_TOKEN}" > ~/.netrc
-        - pip install mkdocs mkdocs-material pymdown-extensions
-      script:
-        - cp README.md mkdocs/docs/index.md
-        - cd mkdocs
-        - mkdocs gh-deploy --force --clean

README.md

@@ -29,9 +29,8 @@
     <a href="#overview">Overview</a> |
     <a href="#performance">Performance</a> |
     <a href="#quick-start">Quick start</a> |
-    <a href="https://kashgari.bmio.net/">Documentation</a> |
-    <a href="https://kashgari-zh.bmio.net/">中文文档</a> |
-    <a href="https://kashgari.bmio.net/about/contributing/">Contributing</a>
+    <a href="https://kashgari.readthedocs.io/">Documentation</a> |
+    <a href="https://kashgari.readthedocs.io/about/contributing/">Contributing</a>
 </h4>
 
 <!-- markdownlint-enable -->
@@ -46,8 +45,8 @@ Kashgari is a simple and powerful NLP Transfer learning framework, build a state
 - **Human-friendly**. Kashgari's code is straightforward, well documented and tested, which makes it very easy to understand and modify.
 - **Powerful and simple**. Kashgari allows you to apply state-of-the-art natural language processing (NLP) models to your text, such as named entity recognition (NER), part-of-speech tagging (PoS) and classification.
 - **Built-in transfer learning**. Kashgari built-in pre-trained BERT and Word2vec embedding models, which makes it very simple to transfer learning to train your model.
-- **Fully scalable**. Kashgari provides a simple, fast, and scalable environment for fast experimentation, train your models and experiment with new approaches using different embeddings and model structure. 
-- **Production Ready**. Kashgari could export model with `SavedModel` format for tensorflow serving, you could directly deploy it on the cloud. 
+- **Fully scalable**. Kashgari provides a simple, fast, and scalable environment for fast experimentation, train your models and experiment with new approaches using different embeddings and model structure.
+- **Production Ready**. Kashgari could export model with `SavedModel` format for tensorflow serving, you could directly deploy it on the cloud.
 
 ## Our Goal
 
@@ -65,10 +64,10 @@ Kashgari is a simple and powerful NLP Transfer learning framework, build a state
 
 Here is a set of quick tutorials to get you started with the library:
 
-- [Tutorial 1: Text Classification](https://kashgari.bmio.net/tutorial/text-classification/)
-- [Tutorial 2: Text Labeling](https://kashgari.bmio.net/tutorial/text-labeling/)
-- [Tutorial 3: Text Scoring](https://kashgari.bmio.net/tutorial/text-scoring/)
-- [Tutorial 4: Language Embedding](https://kashgari.bmio.net/embeddings/)
+- [Tutorial 1: Text Classification](./docs/tutorial/text-classification.md)
+- [Tutorial 2: Text Labeling](./docs/tutorial/text-labeling.md)
+- [Tutorial 3: Text Scoring](./docs/tutorial/text-scoring.md)
+- [Tutorial 4: Language Embedding](./docs/embeddings/index.md)
 
 There are also articles and posts that illustrate how to use Kashgari:
 

mkdocs/docs/FAQ.md → docs/FAQ.md

@@ -1,4 +1,4 @@
-# FAQ
+# Frequently Asked Questions
 
 ## How can I run Keras on GPU
 

docs/_static/css/extra.css

@@ -0,0 +1,24 @@
+body,
+h1,
+h2,
+h3,
+h4,
+h5,
+h6,
+input {
+    font-family: "Roboto", "Helvetica Neue", Helvetica, Arial, "Microsoft YaHei", "微软正黑体", "Microsoft JhengHei", sans-serif;
+}
+
+@media only screen and (min-width: 100em) {
+    html {
+        font-size: 125%;
+    }
+}
+
+.wy-side-nav-search>div.version {
+    margin-top: 5px;
+}
+
+.wy-nav-content {
+    max-width: none;
+}