remove unnecessary messages

requirements.txt

@@ -5,5 +5,6 @@ scipy
 torch==1.3.0
 transformers==2.0.0
 tensorflow-gpu
+scikit-learn
 
-# visdom
+# visdom

textattack.egg-info/PKG-INFO

@@ -26,6 +26,14 @@ Description: # TextAttack
         nltk.download('stopwords')
         ```
         
+        ### Common Errors
+        
+        #### Errors regarding GCC
+        If you see an error that GCC is incompatible, make sure your system has an up-to-date version of the GCC compiler. On distributed systems with a `module` system, typing `module load gcc` may be sufficient.
+        
+        #### Errors regarding Java
+        Using the LanguageTool constraint relies on Java 8 internally (it's not ideal, we know). Please install Java 8 if you're interested in using the LanguageTool grammaticality constraint. If your system has the `module` command, try typing `module load java8`.
+        
         ## Features
         
         ### Models

textattack.egg-info/SOURCES.txt

@@ -1,6 +1,7 @@
 README.md
 setup.py
 textattack/__init__.py
+textattack/run_attack.py
 textattack/tokenized_text.py
 textattack/utils.py
 textattack.egg-info/PKG-INFO
@@ -26,7 +27,12 @@ textattack/constraints/syntax/__init__.py
 textattack/constraints/syntax/language_tool.py
 textattack/datasets/__init__.py
 textattack/datasets/dataset.py
-textattack/datasets/yelp_sentiment.py
+textattack/datasets/classification/__init__.py
+textattack/datasets/classification/ag_news.py
+textattack/datasets/classification/imdb_sentiment.py
+textattack/datasets/classification/kaggle_fake_news.py
+textattack/datasets/classification/movie_review_sentiment.py
+textattack/datasets/classification/yelp_sentiment.py
 textattack/models/__init__.py
 textattack/models/bert_for_sentiment_classification.py
 textattack/models/infer_sent.py
@@ -34,5 +40,4 @@ textattack/models/lstm_for_sentiment_classification.py
 textattack/transformations/__init__.py
 textattack/transformations/transformation.py
 textattack/transformations/word_swap.py
-textattack/transformations/word_swap_counterfit.py
 textattack/transformations/word_swap_embedding.py

textattack.egg-info/requires.txt

@@ -1,6 +1,8 @@
+language_check
 nltk
 numpy<1.17
 scipy
 torch==1.3.0
 transformers==2.0.0
 tensorflow-gpu
+scikit-learn

textattack/models/infer_sent.py

@@ -134,13 +134,13 @@ class InferSent(nn.Module):
         assert hasattr(self, 'w2v_path'), 'w2v path not set'
         word_dict = self.get_word_dict(sentences, tokenize)
         self.word_vec = self.get_w2v(word_dict)
-        print('Vocab size : %s' % (len(self.word_vec)))
+        # print('Vocab size : %s' % (len(self.word_vec)))
 
     # build w2v vocab with k most frequent words
     def build_vocab_k_words(self, K):
         assert hasattr(self, 'w2v_path'), 'w2v path not set'
         self.word_vec = self.get_w2v_k(K)
-        print('Vocab size : %s' % (K))
+        # print('Vocab size : %s' % (K))
 
     def update_vocab(self, sentences, tokenize=True):
         assert hasattr(self, 'w2v_path'), 'warning : w2v path not set'

textattack/tokenized_text.py

@@ -11,8 +11,6 @@ class TokenizedText:
     
     def words(self):
         """ Returns the distinct words from self.text. 
-        
-            @TODO Should we ever consider case when substituting words?
         """
         return self.raw_words