Create drop.py

drop.py

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+# 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.
+#
+# Custom DROP dataset that, unlike HF, keeps all question-answer pairs
+# even if there are multiple types of answers for the same question.
+"""DROP dataset."""
+
+
+import json
+import os
+
+import datasets
+
+
+_CITATION = """\
+@misc{dua2019drop,
+    title={DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs},
+    author={Dheeru Dua and Yizhong Wang and Pradeep Dasigi and Gabriel Stanovsky and Sameer Singh and Matt Gardner},
+    year={2019},
+    eprint={1903.00161},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+"""
+
+_DESCRIPTION = """\
+DROP is a QA dataset which tests comprehensive understanding of paragraphs. In
+this crowdsourced, adversarially-created, 96k question-answering benchmark, a
+system must resolve multiple references in a question, map them onto a paragraph,
+and perform discrete operations over them (such as addition, counting, or sorting).
+"""
+
+_HOMEPAGE = "https://allenai.org/data/drop"
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = ""
+
+_URLS = {
+    "drop": "https://s3-us-west-2.amazonaws.com/allennlp/datasets/drop/drop_dataset.zip",
+}
+
+_EMPTY_VALIDATED_ANSWER = [
+    {
+        "number": "",
+        "date": {
+            "day": "",
+            "month": "",
+            "year": "",
+        },
+        "spans": [],
+        "worker_id": "",
+        "hit_id": "",
+    }
+]
+
+
+class Drop(datasets.GeneratorBasedBuilder):
+    """DROP is a QA dataset which tests comprehensive understanding of paragraphs."""
+
+    VERSION = datasets.Version("0.0.1")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="drop", version=VERSION, description="The DROP dataset."
+        ),
+    ]
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "section_id": datasets.Value("string"),
+                "passage": datasets.Value("string"),
+                "question": datasets.Value("string"),
+                "query_id": datasets.Value("string"),
+                "answer": {
+                    "number": datasets.Value("string"),
+                    "date": {
+                        "day": datasets.Value("string"),
+                        "month": datasets.Value("string"),
+                        "year": datasets.Value("string"),
+                    },
+                    "spans": datasets.features.Sequence(datasets.Value("string")),
+                    "worker_id": datasets.Value("string"),
+                    "hit_id": datasets.Value("string"),
+                },
+                "validated_answers": datasets.features.Sequence(
+                    {
+                        "number": datasets.Value("string"),
+                        "date": {
+                            "day": datasets.Value("string"),
+                            "month": datasets.Value("string"),
+                            "year": datasets.Value("string"),
+                        },
+                        "spans": datasets.features.Sequence(datasets.Value("string")),
+                        "worker_id": datasets.Value("string"),
+                        "hit_id": datasets.Value("string"),
+                    }
+                ),
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        urls = _URLS[self.config.name]
+        data_dir = dl_manager.download_and_extract(urls)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(
+                        data_dir, "drop_dataset", "drop_dataset_train.json"
+                    ),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(
+                        data_dir, "drop_dataset", "drop_dataset_dev.json"
+                    ),
+                    "split": "validation",
+                },
+            ),
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, filepath, split):
+        with open(filepath, encoding="utf-8") as f:
+            data = json.load(f)
+            key = 0
+            for section_id, example in data.items():
+                # Each example (passage) has multiple sub-question-answer pairs.
+                for qa in example["qa_pairs"]:
+                    # Build answer.
+                    answer = qa["answer"]
+                    answer = {
+                        "number": answer["number"],
+                        "date": {
+                            "day": answer["date"].get("day", ""),
+                            "month": answer["date"].get("month", ""),
+                            "year": answer["date"].get("year", ""),
+                        },
+                        "spans": answer["spans"],
+                        "worker_id": answer.get("worker_id", ""),
+                        "hit_id": answer.get("hit_id", ""),
+                    }
+                    validated_answers = []
+                    if "validated_answers" in qa:
+                        for validated_answer in qa["validated_answers"]:
+                            va = {
+                                "number": validated_answer.get("number", ""),
+                                "date": {
+                                    "day": validated_answer["date"].get("day", ""),
+                                    "month": validated_answer["date"].get("month", ""),
+                                    "year": validated_answer["date"].get("year", ""),
+                                },
+                                "spans": validated_answer.get("spans", ""),
+                                "worker_id": validated_answer.get("worker_id", ""),
+                                "hit_id": validated_answer.get("hit_id", ""),
+                            }
+                            validated_answers.append(va)
+                    else:
+                        validated_answers = _EMPTY_VALIDATED_ANSWER
+                    yield key, {
+                        "section_id": section_id,
+                        "passage": example["passage"],
+                        "question": qa["question"],
+                        "query_id": qa["query_id"],
+                        "answer": answer,
+                        "validated_answers": validated_answers,
+                    }
+                    key += 1