Delete drop.py

drop.py

@@ -1,192 +0,0 @@
-# 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"
-
-# License declared at https://allenai.org/data/drop
-_LICENSE = "CC BY 4.0"
-
-_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