updated the script

Controlled-Text-Reduction-dataset.py

@@ -313,21 +313,28 @@ SOFTWARE."""
 #     },    
 # }
 
-# COLUMNS = ["doc_text", "summary_text", "highlight_spans"]
-
-
 _URLs = {
-    "wikinews.train": "https://github.com/ValentinaPy/QADiscourse/raw/master/Dataset/wikinews_train.tsv",
-    "wikinews.dev": "https://github.com/ValentinaPy/QADiscourse/raw/master/Dataset/wikinews_dev.tsv",
-    "wikinews.test": "https://github.com/ValentinaPy/QADiscourse/raw/master/Dataset/wikinews_test.tsv",
-    "wikipedia.train": "https://github.com/ValentinaPy/QADiscourse/raw/master/Dataset/wikipedia_train.tsv",
-    "wikipedia.dev": "https://github.com/ValentinaPy/QADiscourse/raw/master/Dataset/wikipedia_dev.tsv",
-    "wikipedia.test": "https://github.com/ValentinaPy/QADiscourse/raw/master/Dataset/wikipedia_test.tsv",      
+        "dev_DUC-2001-2002": "https://github.com/lovodkin93/Controlled_Text_Reduction/tree/main/data/dev_DUC-2001-2002.csv",
+        "test_DUC-2001-2002": "https://github.com/lovodkin93/Controlled_Text_Reduction/tree/main/data/test_DUC-2001-2002.csv",
+        "train_DUC-2001-2002": "https://github.com/lovodkin93/Controlled_Text_Reduction/tree/main/data/train_DUC-2001-2002.csv"   
 }
 
-COLUMNS = ['qasrl_id', 'sentence', 'worker_id', 'full_question', 'full_answer',
-       'question_start', 'question_aux', 'question_body', 'answer',
-       'untokenized sentence', 'target indices for untok sent']
+
+COLUMNS = ["doc_text", "summary_text", "highlight_spans"]
+
+
+# _URLs = {
+#     "wikinews.train": "https://github.com/ValentinaPy/QADiscourse/raw/master/Dataset/wikinews_train.tsv",
+#     "wikinews.dev": "https://github.com/ValentinaPy/QADiscourse/raw/master/Dataset/wikinews_dev.tsv",
+#     "wikinews.test": "https://github.com/ValentinaPy/QADiscourse/raw/master/Dataset/wikinews_test.tsv",
+#     "wikipedia.train": "https://github.com/ValentinaPy/QADiscourse/raw/master/Dataset/wikipedia_train.tsv",
+#     "wikipedia.dev": "https://github.com/ValentinaPy/QADiscourse/raw/master/Dataset/wikipedia_dev.tsv",
+#     "wikipedia.test": "https://github.com/ValentinaPy/QADiscourse/raw/master/Dataset/wikipedia_test.tsv",      
+# }
+
+# COLUMNS = ['qasrl_id', 'sentence', 'worker_id', 'full_question', 'full_answer',
+#        'question_start', 'question_aux', 'question_body', 'answer',
+#        'untokenized sentence', 'target indices for untok sent']
 
 
 # TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
@@ -345,17 +352,17 @@ class ControlledTectReduction(datasets.GeneratorBasedBuilder):
     ]
 
     DEFAULT_CONFIG_NAME = (
-        "plain_text"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+        "default"  # It's not mandatory to have a default configuration. Just use one if it make sense.
     )
 
     def _info(self):
         features = datasets.Features(
             {
-                "sentence": datasets.Value("string"),
-                "sent_id": datasets.Value("string"),
-                "question": datasets.Sequence(datasets.Value("string")),
-                "answers": datasets.Sequence(datasets.Value("string")),
+                "doc_text": datasets.Value("string"),
+                "summary_text": datasets.Value("string"),
+                "question": datasets.Value("string"),
             }
+
         )
         return datasets.DatasetInfo(
             # This is the description that will appear on the datasets page.
@@ -386,49 +393,33 @@ class ControlledTectReduction(datasets.GeneratorBasedBuilder):
                 name=datasets.Split.TRAIN,
                 # These kwargs will be passed to _generate_examples
                 gen_kwargs={
-                    "filepaths": [corpora["wikinews.train"], 
-                                  corpora["wikipedia.train"]],
+                    "filepaths": corpora["train_DUC-2001-2002"],
                 },
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.VALIDATION,
                 # These kwargs will be passed to _generate_examples
                 gen_kwargs={
-                    "filepaths": [corpora["wikinews.dev"], 
-                                  corpora["wikipedia.dev"]],
+                    "filepaths": corpora["dev_DUC-2001-2002"],
                 },
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.TEST,
                 # These kwargs will be passed to _generate_examples
                 gen_kwargs={
-                    "filepaths": [corpora["wikinews.test"], 
-                                  corpora["wikipedia.test"]],
+                    "filepaths": corpora["test_DUC-2001-2002"],
                 },
             ),
         ]
     
-    def _generate_examples(self, filepaths: List[str]):
+    def _generate_examples(self, filepath: List[str]):
 
-        """ 
-        Yields QA-Discourse examples from a tsv file.
-        Sentences with no QAs will yield an ``empty QA'' record, where both 'question' and 'answers' are empty lists.
-        """
+        """ Yields Controlled Text Reduction examples from a csv file. Each instance contains the document, the summary and the pre-selected spans."""
 
         # merge annotations from sections 
-        df = pd.concat([pd.read_csv(fn, sep='\t', error_bad_lines=False) for fn in filepaths]).reset_index(drop=True) 
-        df = df.applymap(str) # must turn all values to strings explicitly to avoid type errors
-        for counter, row in df.iterrows():
-            # Prepare question (3 "slots" and question mark)
-            question = [row.question_start, row.question_aux, row.question_body.rstrip('?'), '?']
-            answer = [row.answer]
-            if row.question_start == "_":    # sentence has no QAs
-                question = []
-                answer = []
-            
-            yield counter, {
-                "sentence": row.sentence,
-                "sent_id": row.qasrl_id,
-                "question": question,
-                "answers": answer,
-            }
+        df = pd.read_csv(filepath, index_col=False)
+        for counter, dic in enumerate(df.to_dict('records')):
+            columns_to_load_into_object = ["doc_text", "summary_text", "highlight_spans"]
+            for key in columns_to_load_into_object:
+                dic[key] = eval(dic[key])
+            yield counter, dic