Merge branch 'develop'

CHANGELOG.md

@@ -7,10 +7,12 @@ Most recent releases are shown at the top. Each release shows:
 - **Fixed**: Bug fixes that don't change documented behaviour
 
 
-## 0.13.0 (2020-04-08)
+
+## 0.13.1 including 0.13.0 (2020-04-09)
 
 ### New:
--  support for link prediction with graph neural networks
+- support for link prediction with graph neural networks
+- text summarization with pretrained BART (included in 0.13.1 but not in 0.13.0)
 - `bigru` method now selects pretrained word vectors based on detected language
 
 ### Changed

README.md

@@ -7,8 +7,17 @@
 
 
 ### News and Announcements
-- **2020-04-08:**  
-  - ***ktrain*** **v0.13.x is released** and includes support for **link prediction** using graph neural networks.  See [example notebook](https://nbviewer.jupyter.org/github/amaiya/ktrain/blob/develop/examples/graphs/cora_link_prediction-GraphSAGE.ipynb) on citation prediction.
+- **2020-04-09:**  
+  - ***ktrain*** **v0.13.x is released** and includes support for:
+    - **link prediction** using graph neural networks - [see example link prediction notebook](https://nbviewer.jupyter.org/github/amaiya/ktrain/blob/develop/examples/graphs/cora_link_prediction-GraphSAGE.ipynb) on citation prediction
+    - **text summarization** with pretrained BART - [see example summarization notebook](https://nbviewer.jupyter.org/github/amaiya/ktrain/blob/master/examples/text/text_summarization_with_bart.ipynb) <sub><sup>(Summarization included in v0.13.1, but not v0.13.0.)</sup></sub>
+```python
+# text summarization with BART
+from ktrain import text
+ts = text.TransformerSummarizer()
+ts.summarize(some_long_document)
+```
+
 - **2020-03-31:**  
   - ***ktrain*** **v0.12.x is released** and now includes BERT embeddings (i.e., BERT, DistilBert, and Albert) that can be used for downstream tasks like building sequence-taggers (i.e., NER) 
       for any language such as English, Chinese, Russian, Arabic, Dutch, etc.  See [this English NER example notebook](https://nbviewer.jupyter.org/github/amaiya/ktrain/blob/develop/examples/text/CoNLL2003-BiLSTM.ipynb) or the [Dutch NER notebook](https://nbviewer.jupyter.org/github/amaiya/ktrain/blob/develop/examples/text/CoNLL2002_Dutch-BiLSTM.ipynb) for examples on how to use this feature.
@@ -43,6 +52,7 @@ learner.fit(0.01, 1, cycle_len=5)
      - **Unsupervised Topic Modeling** with [LDA](http://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf)  <sub><sup>[[example notebook](https://nbviewer.jupyter.org/github/amaiya/ktrain/blob/master/examples/text/20newsgroups-topic_modeling.ipynb)]</sup></sub>
      - **Document Similarity with One-Class Learning**:  given some documents of interest, find and score new documents that are semantically similar to them using [One-Class Text Classification](https://en.wikipedia.org/wiki/One-class_classification) <sub><sup>[[example notebook](https://nbviewer.jupyter.org/github/amaiya/ktrain/blob/master/examples/text/20newsgroups-document_similarity_scorer.ipynb)]</sup></sub>
      - **Document Recommendation Engine**:  given text from a sample document, recommend documents that are semantically-related to it from a larger corpus  <sub><sup>[[example notebook](https://nbviewer.jupyter.org/github/amaiya/ktrain/blob/master/examples/text/20newsgroups-recommendation_engine.ipynb)]</sup></sub>
+     - **Text Summarization**:  text summarization with a pretrained BART model - no training required <sub><sup>[[example notebook](https://nbviewer.jupyter.org/github/amaiya/ktrain/blob/master/examples/text/text_summarization_with_bart.ipynb)]</sup></sub>
   - `vision` data:
     - **image classification** (e.g., [ResNet](https://arxiv.org/abs/1512.03385), [Wide ResNet](https://arxiv.org/abs/1605.07146), [Inception](https://www.cs.unc.edu/~wliu/papers/GoogLeNet.pdf)) <sub><sup>[[example notebook](https://nbviewer.jupyter.org/github/amaiya/ktrain/blob/master/examples/vision/dogs_vs_cats-ResNet50.ipynb)]</sup></sub>
   - `graph` data:

examples/README.md

@@ -9,6 +9,7 @@ This directory contains various example notebooks using *ktrain*.  The directory
   - [document similarity with one-class learning](#docsim): given a sample of interesting documents, find and score new documents that are semantically similar to it using One-Class text classification
   - [document recommender system](#docrec):  given text from a sample document, recommend documents that are semantically similar to it from a larger corpus 
   - [Shallow NLP](#shallownlp):  a small collection of miscellaneous text utilities amenable to being used on machines with only a CPU available (no GPU required)
+  - [Text Summarization](#bart):  an example of text summarization using a pretrained BART model
 - `vision`:  
   - [image classification](#imageclass):  models for image datasetsimage classification examples using various models and datasets
 - `graphs`: 
@@ -118,7 +119,9 @@ The objective of the CoNLL2003 task is to classify sequences of words as belongi
 
 
 #### [20 News Groups](http://qwone.com/~jason/20Newsgroups/): recommend posts from 20newsgroups
-- [20newsgroups-recommendation_engine.ipynb](https://github.com/amaiya/ktrain/tree/master/examples/text):  given text from a sample document, recommend documents that are semantically similar to it from a larger corpu
+- [20newsgroups-recommendation_engine.ipynb](https://github.com/amaiya/ktrain/tree/master/examples/text):  given text from a sample document, recommend documents that are semantically similar to it from a larger corpus
+
+### <a name="bart"></a>Text Summarization with pretrained BART: [text_summarization_with_bart.ipynb](https://github.com/amaiya/ktrain/tree/master/examples/text)
 
 
 ## Vision Data