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
 
 
+## 0.16.2 (2020-06-07)
+
+### New:
+- N/A
+
+### Changed
+- default model for summarization changed to `facebook/bart-large-cnn` due to breaking change in v2.11
+- added `device` argument to `TransformerSummarizer` constructor to control PyTorch device
+
+### Fixed:
+- require `transformers>=2.11.0` due to breaking changes in 2.11 related to `bart` models
+
+
 ## 0.16.1 (2020-06-05)
 
 ### New:

FAQ.md

@@ -307,7 +307,7 @@ smaller sample sizes (e.g., 500, 1000) may be sufficient for your use case.
 Examples include:
 
 - **medical informatics:**  analyzing doctors' written analyses of patients and medical imagery
-- **finance:** analyzing financial and stock-related news stories
+- **finance:**  financial crime analytics, mining stock-related news stories
 - **insurance:** detecting fraud in insurance claims
 - **social science:** making sense of text-based responses in surveys and emotion-classification from text data
 - **linguistics:** detecting sarcasm in the news

ktrain/core.py

@@ -1335,7 +1335,7 @@ def load_predictor(fpath, batch_size=U.DEFAULT_BS):
             #warnings.warn('could not load .preproc file as %s - attempting to load as %s' % (os.path.join(fpath, U.PREPROC_NAME), preproc_name))
             with open(preproc_name, 'rb') as f: preproc = pickle.load(f)
         except:
-            raise Exception('Could not find a .preproc file in either the post v0.16.x loction (%s) or pre v0.16.x location (%s)' % (os.path.join(fpath. U.PRERPC_NAME), fpath+'.preproc'))
+            raise Exception('Could not find a .preproc file in either the post v0.16.x loction (%s) or pre v0.16.x location (%s)' % (os.path.join(fpath. U.PREPROC_NAME), fpath+'.preproc'))
 
     # load the model
     model = _load_model(fpath, preproc=preproc)

ktrain/text/summarization/core.py

@@ -6,21 +6,23 @@ class TransformerSummarizer():
     interface to Transformer-based text summarization
     """
 
-    def __init__(self, model_name='bart-large-cnn'):
+    def __init__(self, model_name='facebook/bart-large-cnn', device=None):
         """
         interface to BART-based text summarization using transformers library
 
         Args:
-          model_name(str): name of BART model
+          model_name(str): name of BART model for summarization
+          device(str): device to use (e.g., 'cuda', 'cpu')
         """
-        if model_name.split('-')[0] != 'bart': 
+        if 'bart' not in model_name:
             raise ValueError('TransformerSummarizer currently only accepts BART models')
         try:
             import torch
         except ImportError:
             raise Exception('TransformerSummarizer requires PyTorch to be installed.')
+        self.torch_device = device
+        if self.torch_device is None: self.torch_device = 'cuda' if torch.cuda.is_available() else 'cpu'
         from transformers import BartTokenizer, BartForConditionalGeneration
-        self.torch_device = 'cuda' if torch.cuda.is_available() else 'cpu'
         self.tokenizer = BartTokenizer.from_pretrained(model_name)
         self.model = BartForConditionalGeneration.from_pretrained(model_name).to(self.torch_device)
 

ktrain/version.py

@@ -1,2 +1,2 @@
 __all__ = ['__version__']
-__version__ = '0.16.1'
+__version__ = '0.16.2'

setup.py

@@ -39,7 +39,7 @@
           'seqeval',
           'packaging',
           'tensorflow_datasets',
-          'transformers>=2.7.0',
+          'transformers>=2.11.0',
           'ipython',
           'syntok',
           'whoosh'