updated the script

Controlled-Text-Reduction-dataset.py

@@ -1,3 +1,242 @@
+# # coding=utf-8
+# # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+# #
+# # Licensed under the Apache License, Version 2.0 (the "License");
+# # you may not use this file except in compliance with the License.
+# # You may obtain a copy of the License at
+# #
+# #     http://www.apache.org/licenses/LICENSE-2.0
+# #
+# # Unless required by applicable law or agreed to in writing, software
+# # distributed under the License is distributed on an "AS IS" BASIS,
+# # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# # See the License for the specific language governing permissions and
+# # limitations under the License.
+# """A Dataset loading script for the Controlled Text Reduction dataset."""
+
+
+# import datasets
+# from dataclasses import dataclass
+# from pathlib import Path
+# from typing import List, Tuple
+# import pandas as pd
+# import json
+# import gzip
+# import itertools
+
+
+# _CITATION = """"""
+# # _CITATION = """\
+# # @inproceedings{roit2020controlled,
+# #   title={Controlled Crowdsourcing for High-Quality QA-SRL Annotation},
+# #   author={Roit, Paul and Klein, Ayal and Stepanov, Daniela and Mamou, Jonathan and Michael, Julian and Stanovsky, Gabriel and Zettlemoyer, Luke and Dagan, Ido},
+# #   booktitle={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
+# #   pages={7008--7013},
+# #   year={2020}
+# # }
+# # """
+
+
+# _DESCRIPTION = """\
+# The dataset contains document-summary pairs with document spans (referred to as "highlights"), indicating the "pre-selected" spans that lead to the creation of the summary.
+# The evaluation and test datasets were constructed via controlled crowdsourcing.
+# The train datasets were automatically generated using the summary-source proposition-level alignment model SuperPAL (Ernst et al., 2021).
+# """
+
+# _HOMEPAGE = "https://github.com/lovodkin93/Controlled_Text_Reduction/tree/main"
+
+# _LICENSE = """MIT License
+# Copyright (c) 2022 lovodkin93
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE."""
+
+
+# # _URLs = {
+# #     "csv": {
+# #         "sentences": {
+# #             "wikinews.dev": "https://github.com/plroit/qasrl-gs/raw/master/data/sentences/wikinews.dev.full.csv",
+# #             "wikinews.test": "https://github.com/plroit/qasrl-gs/raw/master/data/sentences/wikinews.test.full.csv",
+# #             "wikipedia.dev": "https://github.com/plroit/qasrl-gs/raw/master/data/sentences/wikipedia.dev.full.csv",
+# #             "wikipedia.test": "https://github.com/plroit/qasrl-gs/raw/master/data/sentences/wikipedia.test.full.csv",
+# #         },
+# #         "qasrl-annotations": {
+# #             "wikinews.dev": "https://github.com/plroit/qasrl-gs/raw/master/data/gold/wikinews.dev.gold.csv",
+# #             "wikinews.test": "https://github.com/plroit/qasrl-gs/raw/master/data/gold/wikinews.test.gold.csv",
+# #             "wikipedia.dev": "https://github.com/plroit/qasrl-gs/raw/master/data/gold/wikipedia.dev.gold.csv",
+# #             "wikipedia.test": "https://github.com/plroit/qasrl-gs/raw/master/data/gold/wikipedia.test.gold.csv",
+# #         }, 
+# #     },
+# #     "jsonl": "https://qasrl.org/data/qasrl-gs.tar"       
+# # }
+
+# _URLs = {
+#     "DUC-2001-2002": {
+#         "dev": "https://github.com/lovodkin93/Controlled_Text_Reduction/tree/main/data/dev_DUC-2001-2002.csv",
+#         "test": "https://github.com/lovodkin93/Controlled_Text_Reduction/tree/main/data/test_DUC-2001-2002.csv",
+#         "train": "https://github.com/lovodkin93/Controlled_Text_Reduction/tree/main/data/train_DUC-2001-2002.csv"
+#     },  
+#     "CNN-DM": {
+#         "train": "https://github.com/lovodkin93/Controlled_Text_Reduction/tree/main/data/train_CNNDM.csv",
+#         "dev": "https://github.com/lovodkin93/Controlled_Text_Reduction/tree/main/data/dev_DUC-2001-2002.csv",
+#         "test": "https://github.com/lovodkin93/Controlled_Text_Reduction/tree/main/data/test_DUC-2001-2002.csv",
+#     },    
+# }
+
+
+# @dataclass
+# class ControlledTextReductionConfig(datasets.BuilderConfig):
+#     """ Allow the loader to re-distribute the original dev and test splits between train, dev and test. """
+#     data_source: str = "DUC-2001-2002" # "DUC-2001-2002" or "CNN-DM"
+
+
+
+# class ControlledTextReduction(datasets.GeneratorBasedBuilder):
+#     """Controlled Text Reduction: dataset for the Controlled Text Reduction task ().
+#     Each data point consists of a document, a summary, and a list of spans of the document that are the pre-selected content whose summary is the summary"""
+
+
+#     VERSION = datasets.Version("1.0.0")
+
+#     BUILDER_CONFIG_CLASS = ControlledTextReductionConfig
+
+#     BUILDER_CONFIGS = [
+#         ControlledTextReductionConfig(
+#             name="DUC-2001-2002", 
+#             version=VERSION, 
+#             description="This provides the Controlled Text Reduction dataset extracted from the DUC 2001-2002 Single Document Summarization benchmark",
+#             data_source="DUC-2001-2002"
+#         ),
+#         ControlledTextReductionConfig(
+#             name="CNN-DM", 
+#             version=VERSION, 
+#             description="This provides the Controlled Text Reduction dataset extracted from the CNN-DM dataset (the train split)",
+#             data_source="CNN-DM"
+#         )
+#     ]
+
+#     DEFAULT_CONFIG_NAME = (
+#         "default"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+#     )
+
+#     def _info(self):
+#         features = datasets.Features(
+#             {
+#                 "doc_text": datasets.Value("string"),
+#                 "summary_text": datasets.Value("string"),
+#                 "highlight_spans": datasets.Value("string")
+#             }
+#         )
+#         return datasets.DatasetInfo(
+#             # This is the description that will appear on the datasets page.
+#             description=_DESCRIPTION,
+#             # This defines the different columns of the dataset and their types
+#             features=features,  # Here we define them above because they are different between the two configurations
+#             # If there's a common (input, target) tuple from the features,
+#             # specify them here. They'll be used if as_supervised=True in
+#             # builder.as_dataset.
+#             supervised_keys=None,
+#             # Homepage of the dataset for documentation
+#             homepage=_HOMEPAGE,
+#             # License for the dataset if available
+#             license=_LICENSE,
+#             # Citation for the dataset
+#             citation=_CITATION,
+#         )
+            
+#     def _split_generators(self, dl_manager: datasets.utils.download_manager.DownloadManager):
+#         """Returns SplitGenerators."""            
+        
+#         URLs = _URLs[self.config.data_source]
+#         # Download and prepare all files - keep same structure as URLs 
+#         corpora = {section:  Path(dl_manager.download_and_extract(URLs[section])) 
+#                    for section in URLs} 
+        
+#         if self.config.data_source=="CNN-DM":
+#             return [
+#                 datasets.SplitGenerator(
+#                     name=datasets.Split.TRAIN,
+#                     # These kwargs will be passed to _generate_examples
+#                     gen_kwargs={
+#                         "filepath": corpora["train"]
+#                     },
+#                 ),
+#                 datasets.SplitGenerator(
+#                     name=datasets.Split.VALIDATION,
+#                     # These kwargs will be passed to _generate_examples
+#                     gen_kwargs={
+#                         "filepath": corpora["dev"]
+#                     },
+#                 ),
+#                 datasets.SplitGenerator(
+#                     name=datasets.Split.TEST,
+#                     # These kwargs will be passed to _generate_examples
+#                     gen_kwargs={
+#                         "filepath": corpora["test"]
+#                     },
+#                 ),
+#             ]
+
+#         else:
+#             return [
+#                 datasets.SplitGenerator(
+#                     name=datasets.Split.TRAIN,
+#                     # These kwargs will be passed to _generate_examples
+#                     gen_kwargs={
+#                         "filepath": corpora["train"]
+#                     },
+#                 ),
+#                 datasets.SplitGenerator(
+#                     name=datasets.Split.VALIDATION,
+#                     # These kwargs will be passed to _generate_examples
+#                     gen_kwargs={
+#                         "filepath": corpora["dev"]
+#                     },
+#                 ),
+#                 datasets.SplitGenerator(
+#                     name=datasets.Split.TEST,
+#                     # These kwargs will be passed to _generate_examples
+#                     gen_kwargs={
+#                         "filepath": corpora["test"]
+#                     },
+#                 ),
+#             ]
+    
+#     def _generate_examples(self, filepath: List[str]):
+
+#         """ 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.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
+
+
+
+
+
+#################################################################################################################################################
+
+
+
+
+
+
 # coding=utf-8
 # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
 #
@@ -12,131 +251,74 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-"""A Dataset loading script for the Controlled Text Reduction dataset."""
+"""A Dataset loading script for the QA-Discourse dataset (Pyatkin et. al., ACL 2020)."""
 
 
 import datasets
-from dataclasses import dataclass
 from pathlib import Path
-from typing import List, Tuple
+from typing import List
 import pandas as pd
-import json
-import gzip
-import itertools
-
-
-_CITATION = """"""
-# _CITATION = """\
-# @inproceedings{roit2020controlled,
-#   title={Controlled Crowdsourcing for High-Quality QA-SRL Annotation},
-#   author={Roit, Paul and Klein, Ayal and Stepanov, Daniela and Mamou, Jonathan and Michael, Julian and Stanovsky, Gabriel and Zettlemoyer, Luke and Dagan, Ido},
-#   booktitle={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
-#   pages={7008--7013},
-#   year={2020}
-# }
-# """
+
+
+_CITATION = """\
+@inproceedings{pyatkin2020qadiscourse,
+  title={QADiscourse-Discourse Relations as QA Pairs: Representation, Crowdsourcing and Baselines},
+  author={Pyatkin, Valentina and Klein, Ayal and Tsarfaty, Reut and Dagan, Ido},
+  booktitle={Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
+  pages={2804--2819},
+  year={2020}
+}"""
 
 
 _DESCRIPTION = """\
-The dataset contains document-summary pairs with document spans (referred to as "highlights"), indicating the "pre-selected" spans that lead to the creation of the summary.
-The evaluation and test datasets were constructed via controlled crowdsourcing.
-The train datasets were automatically generated using the summary-source proposition-level alignment model SuperPAL (Ernst et al., 2021).
+The dataset contains question-answer pairs to model discourse relations. 
+While answers roughly correspond to spans of the sentence, these spans could have been freely adjusted by annotators to grammaticaly fit the question;
+Therefore, answers are given just as text and not as identified spans of the original sentence.    
+See the paper for details: QADiscourse - Discourse Relations as QA Pairs: Representation, Crowdsourcing and Baselines, Pyatkin et. al., 2020
 """
 
-_HOMEPAGE = "https://github.com/lovodkin93/Controlled_Text_Reduction/tree/main"
-
-_LICENSE = """MIT License
-Copyright (c) 2022 lovodkin93
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE."""
+_HOMEPAGE = "https://github.com/ValentinaPy/QADiscourse"
 
+_LICENSE = """Resources on this page are licensed CC-BY 4.0, a Creative Commons license requiring Attribution (https://creativecommons.org/licenses/by/4.0/)."""
 
-# _URLs = {
-#     "csv": {
-#         "sentences": {
-#             "wikinews.dev": "https://github.com/plroit/qasrl-gs/raw/master/data/sentences/wikinews.dev.full.csv",
-#             "wikinews.test": "https://github.com/plroit/qasrl-gs/raw/master/data/sentences/wikinews.test.full.csv",
-#             "wikipedia.dev": "https://github.com/plroit/qasrl-gs/raw/master/data/sentences/wikipedia.dev.full.csv",
-#             "wikipedia.test": "https://github.com/plroit/qasrl-gs/raw/master/data/sentences/wikipedia.test.full.csv",
-#         },
-#         "qasrl-annotations": {
-#             "wikinews.dev": "https://github.com/plroit/qasrl-gs/raw/master/data/gold/wikinews.dev.gold.csv",
-#             "wikinews.test": "https://github.com/plroit/qasrl-gs/raw/master/data/gold/wikinews.test.gold.csv",
-#             "wikipedia.dev": "https://github.com/plroit/qasrl-gs/raw/master/data/gold/wikipedia.dev.gold.csv",
-#             "wikipedia.test": "https://github.com/plroit/qasrl-gs/raw/master/data/gold/wikipedia.test.gold.csv",
-#         }, 
-#     },
-#     "jsonl": "https://qasrl.org/data/qasrl-gs.tar"       
-# }
 
 _URLs = {
-    "DUC-2001-2002": {
-        "dev": "https://github.com/lovodkin93/Controlled_Text_Reduction/tree/main/data/dev_DUC-2001-2002.csv",
-        "test": "https://github.com/lovodkin93/Controlled_Text_Reduction/tree/main/data/test_DUC-2001-2002.csv",
-        "train": "https://github.com/lovodkin93/Controlled_Text_Reduction/tree/main/data/train_DUC-2001-2002.csv"
-    },  
-    "CNN-DM": {
-        "train": "https://github.com/lovodkin93/Controlled_Text_Reduction/tree/main/data/train_CNNDM.csv",
-        "dev": "https://github.com/lovodkin93/Controlled_Text_Reduction/tree/main/data/dev_DUC-2001-2002.csv",
-        "test": "https://github.com/lovodkin93/Controlled_Text_Reduction/tree/main/data/test_DUC-2001-2002.csv",
-    },    
+    "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",      
 }
 
-
-@dataclass
-class ControlledTextReductionConfig(datasets.BuilderConfig):
-    """ Allow the loader to re-distribute the original dev and test splits between train, dev and test. """
-    data_source: str = "DUC-2001-2002" # "DUC-2001-2002" or "CNN-DM"
-
+COLUMNS = ['qasrl_id', 'sentence', 'worker_id', 'full_question', 'full_answer',
+       'question_start', 'question_aux', 'question_body', 'answer',
+       'untokenized sentence', 'target indices for untok sent']
 
 
-class ControlledTextReduction(datasets.GeneratorBasedBuilder):
-    """Controlled Text Reduction: dataset for the Controlled Text Reduction task ().
-    Each data point consists of a document, a summary, and a list of spans of the document that are the pre-selected content whose summary is the summary"""
+# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
+class QaDiscourse(datasets.GeneratorBasedBuilder):
+    """QA-Discourse: Discourse Relations as Question-Answer Pairs.  """
 
-
-    VERSION = datasets.Version("1.0.0")
-
-    BUILDER_CONFIG_CLASS = ControlledTextReductionConfig
+    VERSION = datasets.Version("1.0.2")
 
     BUILDER_CONFIGS = [
-        ControlledTextReductionConfig(
-            name="DUC-2001-2002", 
-            version=VERSION, 
-            description="This provides the Controlled Text Reduction dataset extracted from the DUC 2001-2002 Single Document Summarization benchmark",
-            data_source="DUC-2001-2002"
+        datasets.BuilderConfig(
+            name="plain_text", version=VERSION, description="This provides the QA-Discourse dataset"
         ),
-        ControlledTextReductionConfig(
-            name="CNN-DM", 
-            version=VERSION, 
-            description="This provides the Controlled Text Reduction dataset extracted from the CNN-DM dataset (the train split)",
-            data_source="CNN-DM"
-        )
     ]
 
     DEFAULT_CONFIG_NAME = (
-        "default"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+        "plain_text"  # It's not mandatory to have a default configuration. Just use one if it make sense.
     )
 
     def _info(self):
         features = datasets.Features(
             {
-                "doc_text": datasets.Value("string"),
-                "summary_text": datasets.Value("string"),
-                "highlight_spans": datasets.Value("string")
+                "sentence": datasets.Value("string"),
+                "sent_id": datasets.Value("string"),
+                "question": datasets.Sequence(datasets.Value("string")),
+                "answers": datasets.Sequence(datasets.Value("string")),
             }
         )
         return datasets.DatasetInfo(
@@ -159,69 +341,58 @@ class ControlledTextReduction(datasets.GeneratorBasedBuilder):
     def _split_generators(self, dl_manager: datasets.utils.download_manager.DownloadManager):
         """Returns SplitGenerators."""            
         
-        URLs = _URLs[self.config.data_source]
-        # Download and prepare all files - keep same structure as URLs 
-        corpora = {section:  Path(dl_manager.download_and_extract(URLs[section])) 
-                   for section in URLs} 
-        
-        if self.config.data_source=="CNN-DM":
-            return [
-                datasets.SplitGenerator(
-                    name=datasets.Split.TRAIN,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={
-                        "filepath": corpora["train"]
-                    },
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.VALIDATION,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={
-                        "filepath": corpora["dev"]
-                    },
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.TEST,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={
-                        "filepath": corpora["test"]
-                    },
-                ),
-            ]
-
-        else:
-            return [
-                datasets.SplitGenerator(
-                    name=datasets.Split.TRAIN,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={
-                        "filepath": corpora["train"]
-                    },
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.VALIDATION,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={
-                        "filepath": corpora["dev"]
-                    },
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.TEST,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={
-                        "filepath": corpora["test"]
-                    },
-                ),
-            ]
+        # Download and prepare all files - keep same structure as _URLs 
+        corpora = {section:  Path(dl_manager.download_and_extract(_URLs[section])) 
+                   for section in _URLs} 
+            
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepaths": [corpora["wikinews.train"], 
+                                  corpora["wikipedia.train"]],
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepaths": [corpora["wikinews.dev"], 
+                                  corpora["wikipedia.dev"]],
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepaths": [corpora["wikinews.test"], 
+                                  corpora["wikipedia.test"]],
+                },
+            ),
+        ]
     
-    def _generate_examples(self, filepath: List[str]):
+    def _generate_examples(self, filepaths: List[str]):
 
-        """ Yields Controlled Text Reduction examples from a csv file. Each instance contains the document, the summary and the pre-selected spans."""
+        """ 
+        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.
+        """
 
         # merge annotations from sections 
-        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
+        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,
+            }