fix typo

freenlpc/freenlpc.py

@@ -83,12 +83,12 @@ def __init_api(self):
                         }
 
 
-    def classification(self, text: str, lables: list, multiclass: bool =True):
+    def classification(self, text: str, labels: list, multiclass: bool =True):
         """perform classification on a piece of text.
 
         Args:
             text (str): The block of text you want to analyze. 2,500 tokens maximum.
-            lables (list): A list of labels you want to use to classify your text. 25 labels maximum.
+            labels (list): A list of labels you want to use to classify your text. 25 labels maximum.
             multiclass (bool, optional): Whether multiple labels should be applied to your text,
                 meaning that the model will calculate an independent score for each label. Defaults to True.
 
@@ -98,7 +98,7 @@ def classification(self, text: str, lables: list, multiclass: bool =True):
         while True:
             try:
                 sleep(1)
-                response = self.__models[self.classification.__name__].classification(text, lables, multiclass)
+                response = self.__models[self.classification.__name__].classification(text, labels, multiclass)
                 result = []
                 for i in range(len(response['labels'])):
                     info = {}
@@ -111,12 +111,12 @@ def classification(self, text: str, lables: list, multiclass: bool =True):
             except requests.exceptions.HTTPError:
                 self.__init_api()
 
-    def classification2(self, text: str, lables: list, multiclass: bool =True):
+    def classification2(self, text: str, labels: list, multiclass: bool =True):
         """perform classification on a piece of text.
 
         Args:
             text (str): The block of text you want to analyze. 2,500 tokens maximum.
-            lables (list): A list of labels you want to use to classify your text. 25 labels maximum.
+            labels (list): A list of labels you want to use to classify your text. 25 labels maximum.
             multiclass (bool, optional): Whether multiple labels should be applied to your text,
                 meaning that the model will calculate an independent score for each label. Defaults to True.
 

setup.py

@@ -1,6 +1,6 @@
 from setuptools import setup, find_packages
 
-VERSION = '0.1.5'
+VERSION = '0.1.6'
 DESCRIPTION = 'A wrapper for nlpcloud free-tier services with no requests per minute limits.'
 LONG_DESCRIPTION = """# freenlpc
 a wrapper for nlpcloud free-tier.