Delete loading script

qasper.py

@@ -1,152 +0,0 @@
-# coding=utf-8
-# Copyright 2022 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
-"""Qasper: A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers."""
-
-
-import json
-
-import datasets
-
-
-logger = datasets.logging.get_logger(__name__)
-
-
-_CITATION = """\
-@inproceedings{Dasigi2021ADO,
-  title={A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers},
-  author={Pradeep Dasigi and Kyle Lo and Iz Beltagy and Arman Cohan and Noah A. Smith and Matt Gardner},
-  year={2021}
-}
-"""
-_LICENSE = "CC BY 4.0"
-_DESCRIPTION = """\
-A dataset containing 1585 papers with 5049 information-seeking questions asked by regular readers of NLP papers, and answered by a separate set of NLP practitioners.
-"""
-
-_HOMEPAGE = "https://allenai.org/data/qasper"
-_URL_TRAIN_DEV = "https://qasper-dataset.s3.us-west-2.amazonaws.com/qasper-train-dev-v0.3.tgz"
-_URL_TEST = "https://qasper-dataset.s3.us-west-2.amazonaws.com/qasper-test-and-evaluator-v0.3.tgz"
-_DATA_FILES = {"train": "qasper-train-v0.3.json",
-               "dev": "qasper-dev-v0.3.json",
-               "test": "qasper-test-v0.3.json"}
-
-_VERSION = "0.3.0"
-
-
-class Qasper(datasets.GeneratorBasedBuilder):
-    """Qasper: A Dataset of Information-Seeking Q&A Anchored in Research Papers."""
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(
-            name="qasper",
-            version=datasets.Version(_VERSION),
-            description=_DESCRIPTION,
-        )
-    ]
-
-    def _info(self):
-
-        features = datasets.Features(
-            {
-                "id": datasets.Value("string"),
-                "title": datasets.Value("string"),
-                "abstract": datasets.Value("string"),
-                "full_text": datasets.features.Sequence(
-                    {
-                        "section_name": datasets.Value("string"),
-                        "paragraphs": [datasets.Value("string")],
-                    }
-                ),
-                "qas": datasets.features.Sequence(
-                    {
-                        "question": datasets.Value("string"),
-                        "question_id": datasets.Value("string"),
-                        "nlp_background": datasets.Value("string"),
-                        "topic_background": datasets.Value("string"),
-                        "paper_read": datasets.Value("string"),
-                        "search_query": datasets.Value("string"),
-                        "question_writer": datasets.Value("string"),
-                        "answers": datasets.features.Sequence(
-                            {
-                                "answer": {
-                                    "unanswerable": datasets.Value("bool"),
-                                    "extractive_spans": datasets.features.Sequence(datasets.Value("string")),
-                                    "yes_no": datasets.Value("bool"),
-                                    "free_form_answer": datasets.Value("string"),
-                                    "evidence": datasets.features.Sequence(datasets.Value("string")),
-                                    "highlighted_evidence": datasets.features.Sequence(datasets.Value("string")),
-                                },
-                                "annotation_id": datasets.Value("string"),
-                                "worker_id": datasets.Value("string"),
-                            }
-                        ),
-                    }
-                ),
-                "figures_and_tables": datasets.features.Sequence(
-                    {
-                        "caption": datasets.Value("string"),
-                        "file": datasets.Value("string"),
-                    }
-                ),
-            }
-        )
-
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            supervised_keys=None,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        archive_train_dev, archive_test = dl_manager.download((
-            _URL_TRAIN_DEV, _URL_TEST)
-        )
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "filepath": _DATA_FILES["train"],
-                    "files": dl_manager.iter_archive(archive_train_dev)},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "filepath": _DATA_FILES["dev"],
-                    "files": dl_manager.iter_archive(archive_train_dev)},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "filepath": _DATA_FILES["test"],
-                    "files": dl_manager.iter_archive(archive_test)},
-            ),
-        ]
-
-    def _generate_examples(self, filepath, files):
-        """This function returns the examples in the raw (text) form."""
-        logger.info("generating examples from = %s", filepath)
-        for path, f in files:
-            if path == filepath:
-                qasper = json.loads(f.read().decode("utf-8"))
-                for id_ in qasper:
-                    qasper[id_]["id"] = id_
-                    yield id_, qasper[id_]
-                break