Datasets:

kleinay
/

qa_srl

@@ -1,147 +0,0 @@
-# coding=utf-8
-# 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.
-"""Dataset loading script for loading the Large-Scale-QASRL (FitzGeralds et. al., ACL 2018) training set, along with the QASRL-GS evaluation dataset (Roit et. al., ACL 2020)."""
-import datasets
-from pathlib import Path
-import gzip
-import json
-_CITATION = """\
-@inproceedings{fitzgerald2018large,
-  title={Large-Scale QA-SRL Parsing},
-  author={FitzGerald, Nicholas and Michael, Julian and He, Luheng and Zettlemoyer, Luke},
-  booktitle={Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
-  pages={2051--2060},
-  year={2018}
-}
-@inproceedings{roit2020controlled,
-  title={Controlled Crowdsourcing for High-Quality QA-SRL Annotation},
-  author={Roit, Paul and Klein, Ayal and Stepanov, Daniela and Mamou, Jonathan and Michael, Julian and Stanovsky, Gabriel and Zettlemoyer, Luke and Dagan, Ido},
-  booktitle={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
-  pages={7008--7013},
-  year={2020}
-}
-"""
-_DESCRIPTION = """\
-The dataset contains question-answer pairs to model verbal predicate-argument structure.
-The questions start with wh-words (Who, What, Where, What, etc.) and contain a verb predicate in the sentence; the answers are phrases in the sentence.
-This dataset loads the train split from "QASRL Bank", a.k.a "QASRL-v2" or "QASRL-LS" (Large Scale),
-which was constructed via crowdsourcing and presented at (FitzGeralds et. al., ACL 2018),
-and the dev and test splits from QASRL-GS (Gold Standard), introduced in (Roit et. al., ACL 2020).
-"""
-_HOMEPAGE = "https://qasrl.org"
-# TODO: Add the licence for the dataset here if you can find it
-_LICENSE = ""
-SpanFeatureType = datasets.Sequence(datasets.Value("int32"), length=2)
-# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
-class QaSrl(datasets.GeneratorBasedBuilder):
-    """QA-SRL: Question-Answer Driven Semantic Role Labeling corpus"""
-    VERSION = datasets.Version("1.0.0")
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(
-            name="plain_text", version=VERSION, description=""
-        ),
-    ]
-    DEFAULT_CONFIG_NAME = (
-        "plain_text"  # It's not mandatory to have a default configuration. Just use one if it make sense.
-    )
-    def _info(self):
-        features = datasets.Features(
-            {
-                "sentence": datasets.Value("string"),
-                "sent_id": datasets.Value("string"),
-                "predicate_idx": datasets.Value("int32"),
-                "predicate": datasets.Value("string"),
-                "is_verbal": datasets.Value("bool"),
-                "verb_form": datasets.Value("string"),
-                "question": datasets.Sequence(datasets.Value("string")),
-                "answers": datasets.Sequence(datasets.Value("string")),
-                "answer_ranges": datasets.Sequence(SpanFeatureType)
-            }
-        )
-        return datasets.DatasetInfo(
-            # This is the description that will appear on the datasets page.
-            description=_DESCRIPTION,
-            # This defines the different columns of the dataset and their types
-            features=features,  # Here we define them above because they are different between the two configurations
-            # 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=_HOMEPAGE,
-            # License for the dataset if available
-            license=_LICENSE,
-            # Citation for the dataset
-            citation=_CITATION,
-        )
-    def _split_generators(self, dl_manager: datasets.utils.download_manager.DownloadManager):
-        """Returns SplitGenerators."""
-        # iterate the tar file of the corpus
-        # Older version of the corpus (has some format errors):
-        # corpus_base_path = Path(dl_manager.download_and_extract(_URLs["qasrl_v2.0"]))
-        # corpus_orig = corpus_base_path / "qasrl-v2" / "orig"
-        self.qasrl2018 = datasets.load_dataset("biu-nlp/qa_srl2018")
-        self.qasrl2020 = datasets.load_dataset("biu-nlp/qa_srl2020")
-        # TODO add optional kwarg for genre (wikinews)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "dataset": self.qasrl2018["train"],
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "dataset": self.qasrl2020["validation"],
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "dataset": self.qasrl2020["test"],
-                },
-            ),
-        ]
-    def _generate_examples(self, dataset):
-        """ Yields examples from a '.jsonl.gz' file ."""
-        for idx, instance in enumerate(dataset):
-            yield idx, instance