Rename utils/utils_target_classifier.py to utils/target_classifier.py

utils/{utils_target_classifier.py → target_classifier.py}

@@ -10,8 +10,8 @@ from transformers import pipeline
 
 ## Labels dictionary ###
 _lab_dict = {
-            'NEGATIVE':'NO TARGET INFO',
-            'TARGET':'TARGET',
+            '0':'NO',
+            '1':'YES',
             }
 
 @st.cache_resource
@@ -53,9 +53,8 @@ def target_classification(haystack_doc:pd.DataFrame,
                         )->Tuple[DataFrame,Series]:
     """
     Text-Classification on the list of texts provided. Classifier provides the 
-    most appropriate label for each text. these labels are in terms of if text 
-    belongs to which particular Sustainable Devleopment Goal (SDG).
-    Params
+    most appropriate label for each text. There labels indicate whether the paragraph
+    references a specific action, target or measure in the paragraph.
     ---------
     haystack_doc: List of haystack Documents. The output of Preprocessing Pipeline 
     contains the list of paragraphs in different format,here the list of 
@@ -70,7 +69,7 @@ def target_classification(haystack_doc:pd.DataFrame,
     x: Series object with the unique SDG covered in the document uploaded and 
     the number of times it is covered/discussed/count_of_paragraphs. 
     """
-    logging.info("Working on Target Extraction")
+    logging.info("Working on action/target extraction")
     if not classifier_model:
         classifier_model = st.session_state['target_classifier']