Merge branch 'develop'

CHANGELOG.md

@@ -7,6 +7,18 @@ Most recent releases are shown at the top. Each release shows:
 - **Fixed**: Bug fixes that don't change documented behaviour
 
 
+## 0.17.3 (2020-06-26)
+
+### New:
+- N/A
+
+### Changed
+- N/A
+
+### Fixed:
+- Changed `setup.py` to open README file using `encoding="utf-8"` to prevent installation problems on Windows machines with `cp1252` encoding
+
+
 ## 0.17.2 (2020-06-25)
 
 ### New:

FAQ.md

@@ -123,10 +123,16 @@ by default.  Other data like pretrained word vectors are downloaded to the `<hom
 
 In some settings, it is necessary to either train models or make predictions in environments with no internet 
 access (e.g., behind a firewall, air-gapped networks).  Typically, it is sufficient to copy the above folders
-to the machine without internet access. 
+to the machine without internet access. For instance, if loading and using a `Predictor` instance as shown below,
+then all that is typically needed is a vocabulary file that is typically retrieved from the cache:
+```python
+p = ktrain.load_predictor('/tmp/mypred')
+p.predict(data)
+```
 
-However, due to a current bug in the `transformers` library, files from `<home_directory>/.cache/torch/transformers` are
-not loaded when there is no internet access.  To get around this, you can download the model files from [here]( https://huggingface.co/models) and point
+In some cases (e.g., when training a model on a system with no internet access or using pretrained model for question-answering),
+ due to a current bug in the `transformers` library, files from `<home_directory>/.cache/torch/transformers` may
+not load when there is no internet access even when present.  To get around this, you can download the model files from [here]( https://huggingface.co/models) and point
 *ktrain* to the folder.  There are typically three files you need, and it is important that the downloaded files are rennamed 
 to `tf_model.h5`, `config.json`, and `vocab.txt`.
 

examples/text/language_translation_example.ipynb

@@ -95,8 +95,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Some comments about traslations:\n",
-    "Notice in the example above that we supplied a document of **two** sentences as input.  The `translate` method can accept single sentences, paragraphs, or entire documents.  However, if the document is large (e.g., a book), we recommend that you break it up into smaller chunks (e.g., pages or paragraphs).  This is because *ktrain* tokenizes your document into individual sentences, which re joined together and fed to model as single batch when making a prediction. If the batch is too large for memory, the prediction will fail.\n",
+    "#### Some comments about translations:\n",
+    "Notice in the example above that we supplied a document of **two** sentences as input.  The `translate` method can accept single sentences, paragraphs, or entire documents.  However, if the document is large (e.g., a book), we recommend that you break it up into smaller chunks (e.g., pages or paragraphs).  This is because *ktrain* tokenizes your document into individual sentences, which are joined together and fed to model as single batch when generating the translation. If the batch is too large for memory, errors will occur.\n",
     "\n",
     "When instantiating the `EnglishTranslator`, pretrained models are automatically loaded, which may take a few seconds. Once instantiated, the `translate` method can be repeatedly invoked on different documents or sentences. Next, let us reinstantiate an `EnglishTranslator` object to translate Arabic.\n"
    ]

ktrain/version.py

@@ -1,2 +1,2 @@
 __all__ = ['__version__']
-__version__ = '0.17.2'
+__version__ = '0.17.3'

setup.py

@@ -1,7 +1,7 @@
 from distutils.core import setup
 import setuptools
 
-with open('README.md') as readme_file: 
+with open('README.md', encoding='utf-8') as readme_file: 
     readme_file.readline()
     readme = readme_file.read()
 exec(open('ktrain/version.py').read())