Merge branch 'develop'

CHANGELOG.md

@@ -6,6 +6,19 @@ Most recent releases are shown at the top. Each release shows:
 - **Changed**: Additional parameters, changes to inputs or outputs, etc
 - **Fixed**: Bug fixes that don't change documented behaviour
 
+## 0.21.3 (2020-09-08)
+
+### New:
+- N/A
+
+### Changed
+- added `TopicModel.get_word_weights` method to retrieve the word weights for a given topic
+- added `return_fig` option to `Learner.lr_plot` and `Learner.plot`, which allows the matplotlib `Figure` to be returned to user
+
+### Fixed:
+- N/A
+
+
 ## 0.21.2 (2020-09-03)
 
 ### New:

README.md

@@ -11,7 +11,7 @@
 
 ### News and Announcements
 - **2020-09-03:**
-  - As of v0.21.x, *ktrain* no longer installs TensorFlow 2 automatically, which allows *ktrain* to be used with any version of TensorFlow 2 installed by the user. See the [installation instructions](#installation) for more details.
+  - As of v0.21.x, *ktrain* no longer installs TensorFlow 2 automatically, which allows *ktrain* to be used with any version of TensorFlow 2 installed by the user. See the [installation instructions](https://github.com/amaiya/ktrain#installation) for more details.
 - **2020-08-24:**
   - ***ktrain*** **v0.20.x is released** and includes updates to `ZeroShotClassifier`. The `ZeroShotClassifier` allows documents to be classified into user-provided categories **without** training examples.  Updates include the ability to predict large sequences of documents (and topics) and the ability to customize inferences for different settings.  See the [example notebook](https://nbviewer.jupyter.org/github/amaiya/ktrain/blob/develop/examples/text/zero_shot_learning_with_nli.ipynb) for more information.
 ```python
@@ -83,7 +83,7 @@ print(translator.translate(src_text))
 
 - estimate an optimal learning rate for your model given your data using a Learning Rate Finder
 - utilize learning rate schedules such as the [triangular policy](https://arxiv.org/abs/1506.01186), the [1cycle policy](https://arxiv.org/abs/1803.09820), and [SGDR](https://arxiv.org/abs/1608.03983) to effectively minimize loss and improve generalization
-- build text classifiers for any language (e.g., [Chinese Sentiment Analysis with BERT](https://nbviewer.jupyter.org/github/amaiya/ktrain/blob/master/examples/text/ChineseHotelReviews-BERT.ipynb), [Arabic Sentiment Analysis with NBSVM](https://nbviewer.jupyter.org/github/amaiya/ktrain/blob/master/examples/text/ArabicHotelReviews-nbsvm.ipynb))
+- build text classifiers for any language (e.g., [Arabic Sentiment Analysis with BERT](https://nbviewer.jupyter.org/github/amaiya/ktrain/blob/develop/examples/text/ArabicHotelReviews-AraBERT.ipynb), [Chinese Sentiment Analysis with NBSVM](https://nbviewer.jupyter.org/github/amaiya/ktrain/blob/master/examples/text/ChineseHotelReviews-nbsvm.ipynb))
 - easily train NER models for any language (e.g., [Dutch NER](https://nbviewer.jupyter.org/github/amaiya/ktrain/blob/master/examples/text/CoNLL2002_Dutch-BiLSTM.ipynb) )
 - load and preprocess text and image data from a variety of formats 
 - inspect data points that were misclassified and [provide explanations](https://eli5.readthedocs.io/en/latest/) to help improve your model