Merge branch 'develop'

CHANGELOG.md

@@ -7,6 +7,19 @@ Most recent releases are shown at the top. Each release shows:
 - **Fixed**: Bug fixes that don't change documented behaviour
 
 
+## 0.13.2 (2020-04-09)
+
+### New:
+- N/A
+
+### Changed
+- `TransformerSummarizer` accepts BART `model_name` as parameter
+
+
+### Fixed:
+- N/A
+
+
 
 ## 0.13.1 including 0.13.0 (2020-04-09)
 

README.md

@@ -41,9 +41,7 @@ learner.fit(0.01, 1, cycle_len=5)
 
 *ktrain* is a lightweight wrapper for the deep learning library [TensorFlow Keras](https://www.tensorflow.org/guide/keras/overview) (and other libraries) to help build, train, and deploy neural networks.  With only a few lines of code, ktrain allows you to easily and quickly:
 
-- 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
-- employ fast and easy-to-use pre-canned models for  `text`, `vision`, and `graph` data:
+- employ fast, accurate, and easy-to-use pre-canned models for  `text`, `vision`, and `graph` data:
   - `text` data:
      - **Text Classification**: [BERT](https://arxiv.org/abs/1810.04805), [DistilBERT](https://arxiv.org/abs/1910.01108), [NBSVM](https://www.aclweb.org/anthology/P12-2018), [fastText](https://arxiv.org/abs/1607.01759), and other models <sub><sup>[[example notebook](https://nbviewer.jupyter.org/github/amaiya/ktrain/blob/master/examples/text/IMDb-BERT.ipynb)]</sup></sub>
      - **Text Regression**: [BERT](https://arxiv.org/abs/1810.04805), [DistilBERT](https://arxiv.org/abs/1910.01108), Embedding-based linear text regression, [fastText](https://arxiv.org/abs/1607.01759), and other models <sub><sup>[[example notebook](https://nbviewer.jupyter.org/github/amaiya/ktrain/blob/master/examples/text/text_regression_example.ipynb)]</sup></sub>
@@ -58,6 +56,8 @@ learner.fit(0.01, 1, cycle_len=5)
   - `graph` data:
     - **node classification** with graph neural networks ([GraphSAGE](https://cs.stanford.edu/people/jure/pubs/graphsage-nips17.pdf)) <sub><sup>[[example notebook](https://nbviewer.jupyter.org/github/amaiya/ktrain/blob/master/examples/graphs/pubmed_node_classification-GraphSAGE.ipynb)]</sup></sub>
     - **link prediction** with graph neural networks ([GraphSAGE](https://cs.stanford.edu/people/jure/pubs/graphsage-nips17.pdf)) <sub><sup>[[example notebook](https://nbviewer.jupyter.org/github/amaiya/ktrain/blob/develop/examples/graphs/cora_link_prediction-GraphSAGE.ipynb)]</sup></sub>
+- 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))
 - 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 

ktrain/text/summarization/core.py

@@ -3,18 +3,27 @@
 
 class TransformerSummarizer():
     """
-    NER preprocessing base class
+    interface to Transformer-based text summarization
     """
 
-    def __init__(self):
+    def __init__(self, model_name='bart-large-cnn'):
+        """
+        interface to BART-based text summarization using transformers library
+
+        Args:
+          model_name(str): name of BART model
+        """
+        if model_name.split('-')[0] != 'bart': 
+            raise ValueError('TransformerSummarizer currently only accepts BART models')
         try:
             import torch
         except ImportError:
             raise Exception('TransformerSummarizer requires PyTorch to be installed.')
         from transformers import BartTokenizer, BartForConditionalGeneration
         self.torch_device = 'cuda' if torch.cuda.is_available() else 'cpu'
-        self.tokenizer = BartTokenizer.from_pretrained('bart-large-cnn')
-        self.model = BartForConditionalGeneration.from_pretrained('bart-large-cnn').to(self.torch_device)
+        self.tokenizer = BartTokenizer.from_pretrained(model_name)
+        self.model = BartForConditionalGeneration.from_pretrained(model_name).to(self.torch_device)
+
 
     def summarize(self, doc):
         """

ktrain/version.py

@@ -1,2 +1,2 @@
 __all__ = ['__version__']
-__version__ = '0.13.1'
+__version__ = '0.13.2'