Adding dataset generation files

squad_modified_for_t5_qg.py

@@ -0,0 +1,120 @@
+# 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
+"""SQUAD: The Stanford Question Answering Dataset."""
+"""Modified version for fine tuning T5 on Question Generation """
+
+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 = """\
+Stanford Question Answering Dataset (SQuAD) is a reading comprehension \
+dataset, consisting of questions posed by crowdworkers on a set of Wikipedia \
+articles, where the answer to every question is a segment of text, or span, \
+from the corresponding reading passage, or the question might be unanswerable.
+"""
+
+_URL = "https://rajpurkar.github.io/SQuAD-explorer/dataset/"
+_URLS = {
+    "train": _URL + "train-v1.1.json",
+    "dev": _URL + "dev-v1.1.json",
+    }
+
+
+class SquadConfig(datasets.BuilderConfig):
+    """BuilderConfig for SQUAD."""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for SQUAD.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(SquadConfig, self).__init__(**kwargs)
+
+
+class Squad(datasets.GeneratorBasedBuilder):
+    """SQUAD: The Stanford Question Answering Dataset. Version 1.1."""
+
+    BUILDER_CONFIGS = [
+        SquadConfig(
+                name="plain_text",
+                version=datasets.Version("2.9.0", ""),
+                description="Plain text",
+                ),
+        ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+                description=_DESCRIPTION,
+                features=datasets.Features(
+                        {
+                            "context": datasets.Value("string"),
+                            "questions": datasets.Value("string"),
+                            }
+                        ),
+                # No default supervised_keys (as we have to pass both question
+                # and context as input).
+                supervised_keys=None,
+                homepage="https://rajpurkar.github.io/SQuAD-explorer/",
+                citation=_CITATION,
+                task_templates=[
+
+                    ],
+                )
+
+    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["dev"]}),
+            ]
+
+    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:
+            squad = json.load(f)
+            for article in squad["data"]:
+                for paragraph in article["paragraphs"]:
+                    source_text = f"generate questions: {paragraph['context'].strip()}"
+                    questions = [qas['question'].strip() for qas in paragraph['qas']]
+                    target_text = " {sep_token} ".join(questions)
+                    target_text = f"{target_text} {{sep_token}}"
+                    yield key, {
+                        "context": source_text,
+                        "questions": target_text}
+                    key += 1