data loading script

TopiOCQA.py

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+# coding=utf-8
+# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
+#
+# 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.
+
+# Lint as: python3
+"""TopiOCQA: Open-domain Conversational Question Answering with Topic Switching"""
+
+
+import json
+
+import datasets
+# from datasets.tasks import QuestionAnsweringExtractive
+
+
+logger = datasets.logging.get_logger(__name__)
+
+
+# _CITATION = """\
+# @article{2016arXiv160605250R,
+#        author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev},
+#                  Konstantin and {Liang}, Percy},
+#         title = "{SQuAD: 100,000+ Questions for Machine Comprehension of Text}",
+#       journal = {arXiv e-prints},
+#          year = 2016,
+#           eid = {arXiv:1606.05250},
+#         pages = {arXiv:1606.05250},
+# archivePrefix = {arXiv},
+#        eprint = {1606.05250},
+# }
+# """
+
+_DESCRIPTION = """\
+TopiOCQA is an information-seeking conversational dataset with challenging topic switching phenomena.
+"""
+
+# _URL = "https://rajpurkar.github.io/SQuAD-explorer/dataset/"
+_URLS = {
+    "train": "data/topiocqa_train.jsonl",
+    "valid": "data/topiocqa_valid.jsonl",
+}
+
+
+class TopiOCQAConfig(datasets.BuilderConfig):
+    """BuilderConfig for SQUAD."""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for TopiOCQA.
+
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(TopiOCQAConfig, self).__init__(**kwargs)
+
+
+class Squad(datasets.GeneratorBasedBuilder):
+    """SQUAD: The Stanford Question Answering Dataset. Version 1.1."""
+
+    BUILDER_CONFIGS = [
+        TopiOCQAConfig(
+            name="plain_text",
+            version=datasets.Version("1.0.0", ""),
+            description="Plain text",
+        ),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "Conversation_no": datasets.Value("int32"),
+                    "Turn_no": datasets.Value("int32"),
+                    "Question": datasets.Value("string"),
+                    "Answer": datasets.Value("string"),
+                    "Topic": datasets.Value("string"),
+                    "Topic_section": datasets.Value("string"),
+                    "Rationale": datasets.Value("string"),
+                    "is_nq": datasets.Value("bool"),
+                    "Context": datasets.features.Sequence(datasets.Value("string")),
+                    "Additional_answers": datasets.features.Sequence(
+                        {
+                            "Answer": datasets.Value("string"),
+                            "Topic": datasets.Value("string"),
+                            "Topic_section": datasets.Value("string"),
+                            "Rationale": datasets.Value("string"),
+                        }
+                    ),
+                }
+            ),
+            supervised_keys=None,
+            homepage="https://mcgill-nlp.github.io/topiocqa/",
+            # citation=_CITATION,
+            # task_templates=[
+            #     QuestionAnsweringExtractive(
+            #         question_column="Question", context_column="context", answers_column="answers"
+            #     )
+            # ],
+        )
+
+    def _split_generators(self, dl_manager):
+        downloaded_files = dl_manager.download_and_extract(_URLS)
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["valid"]}),
+        ]
+
+    def _generate_examples(self, filepath):
+        """This function returns the examples in the raw (text) form."""
+        logger.info("generating examples from = %s", filepath)
+        key = 0
+        with open(filepath, encoding="utf-8") as f:
+            for line in f:
+                data = json.loads(line)
+                yield key, data
+                key += 1
+