Delete loading script

com_qa.py

@@ -1,122 +0,0 @@
-"""TODO(com_qa): Add a description here."""
-
-
-import json
-
-import datasets
-
-
-# TODO(com_qa): BibTeX citation
-_CITATION = """\
-@inproceedings{abujabal-etal-2019-comqa,
-    title = "{ComQA: A Community-sourced Dataset for Complex Factoid Question Answering with Paraphrase Clusters",
-    author = {Abujabal, Abdalghani  and
-      Saha Roy, Rishiraj  and
-      Yahya, Mohamed  and
-      Weikum, Gerhard},
-    booktitle = {Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)},
-    month = {jun},
-    year = {2019},
-    address = {Minneapolis, Minnesota},
-    publisher = {Association for Computational Linguistics},
-    url = {https://www.aclweb.org/anthology/N19-1027},
-    doi = {10.18653/v1/N19-1027{,
-    pages = {307--317},
-    }
-"""
-
-# TODO(com_qa):
-_DESCRIPTION = """\
-ComQA is a dataset of 11,214 questions, which were collected from WikiAnswers, a community question answering website.
-By collecting questions from such a site we ensure that the information needs are ones of interest to actual users.
-Moreover, questions posed there are often cannot be answered by commercial search engines or QA technology, making them
-more interesting for driving future research compared to those collected from an engine's query log. The dataset contains
-questions with various challenging phenomena such as the need for temporal reasoning, comparison (e.g., comparatives,
-superlatives, ordinals), compositionality (multiple, possibly nested, subquestions with multiple entities), and
-unanswerable questions (e.g., Who was the first human being on Mars?). Through a large crowdsourcing effort, questions
-in ComQA are grouped into 4,834 paraphrase clusters that express the same information need. Each cluster is annotated
-with its answer(s). ComQA answers come in the form of Wikipedia entities wherever possible. Wherever the answers are
-temporal or measurable quantities, TIMEX3 and the International System of Units (SI) are used for normalization.
-"""
-
-_URL = "https://qa.mpi-inf.mpg.de/comqa/"
-_URLS = {
-    "train": _URL + "comqa_train.json",
-    "dev": _URL + "comqa_dev.json",
-    "test": _URL + "comqa_test.json",
-}
-
-
-class ComQa(datasets.GeneratorBasedBuilder):
-    """TODO(com_qa): Short description of my dataset."""
-
-    # TODO(com_qa): Set up version.
-    VERSION = datasets.Version("0.1.0")
-
-    def _info(self):
-        # TODO(com_qa): Specifies the datasets.DatasetInfo object
-        return datasets.DatasetInfo(
-            # This is the description that will appear on the datasets page.
-            description=_DESCRIPTION,
-            # datasets.features.FeatureConnectors
-            features=datasets.Features(
-                {
-                    "cluster_id": datasets.Value("string"),
-                    "questions": datasets.features.Sequence(datasets.Value("string")),
-                    "answers": datasets.features.Sequence(datasets.Value("string")),
-                    # These are the features of your dataset like images, labels ...
-                }
-            ),
-            # 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="http://qa.mpi-inf.mpg.de/comqa/",
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        # TODO(com_qa): Downloads the data and defines the splits
-        # dl_manager is a datasets.download.DownloadManager that can be used to
-        # download and extract URLs
-        urls_to_download = _URLS
-        dl_dir = dl_manager.download_and_extract(urls_to_download)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={"filepath": dl_dir["train"], "split": "train"},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={"filepath": dl_dir["test"], "split": "test"},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={"filepath": dl_dir["dev"], "split": "dev"},
-            ),
-        ]
-
-    def _generate_examples(self, filepath, split):
-        """Yields examples."""
-        # TODO(com_qa): Yields (key, example) tuples from the dataset
-        with open(filepath, encoding="utf-8") as f:
-            data = json.load(f)
-            for id_, example in enumerate(data):
-                questions = []
-                if split == "test":
-                    cluster_id = str(example["id"])
-                    questions.append(example["question"])
-                else:
-                    cluster_id = example["cluster_id"]
-                    questions = example["questions"]
-                answers = example["answers"]
-                yield id_, {
-                    "cluster_id": cluster_id,
-                    "questions": questions,
-                    "answers": answers,
-                }