Delete loading script

ambig_qa.py

@@ -1,150 +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.
-
-"""AmbigQA: Answering Ambiguous Open-domain Questions"""
-
-
-import json
-import os
-
-import datasets
-
-
-_CITATION = """\
-@inproceedings{ min2020ambigqa,
-    title={ {A}mbig{QA}: Answering Ambiguous Open-domain Questions },
-    author={ Min, Sewon and Michael, Julian and Hajishirzi, Hannaneh and Zettlemoyer, Luke },
-    booktitle={ EMNLP },
-    year={2020}
-}
-"""
-
-_DESCRIPTION = """\
-AmbigNQ, a dataset covering 14,042 questions from NQ-open, an existing open-domain QA benchmark. We find that over half of the questions in NQ-open are ambiguous. The types of ambiguity are diverse and sometimes subtle, many of which are only apparent after examining evidence provided by a very large text corpus.  AMBIGNQ, a dataset with
-14,042 annotations on NQ-OPEN questions containing diverse types of ambiguity.
-We provide two distributions of our new dataset AmbigNQ: a full version with all annotation metadata and a light version with only inputs and outputs.
-"""
-_HOMEPAGE = "https://nlp.cs.washington.edu/ambigqa/"
-_LICENSE = "CC BY-SA 3.0"
-
-_URL = "https://nlp.cs.washington.edu/ambigqa/data/"
-_URLS = {
-    "light": _URL + "ambignq_light.zip",
-    "full": _URL + "ambignq.zip",
-}
-
-
-class AmbigQa(datasets.GeneratorBasedBuilder):
-    """AmbigQA dataset"""
-
-    VERSION = datasets.Version("1.0.0")
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(
-            name="light",
-            version=VERSION,
-            description="AmbigNQ light version with only inputs and outputs",
-        ),
-        datasets.BuilderConfig(
-            name="full",
-            version=VERSION,
-            description="AmbigNQ full version with all annotation metadata",
-        ),
-    ]
-    DEFAULT_CONFIG_NAME = "full"
-
-    def _info(self):
-        features_dict = {
-            "id": datasets.Value("string"),
-            "question": datasets.Value("string"),
-            "annotations": datasets.features.Sequence(
-                {
-                    "type": datasets.Value("string"),  # datasets.ClassLabel(names = ["singleAnswer","multipleQAs"])
-                    "answer": datasets.features.Sequence(datasets.Value("string")),
-                    "qaPairs": datasets.features.Sequence(
-                        {
-                            "question": datasets.Value("string"),
-                            "answer": datasets.features.Sequence(datasets.Value("string")),
-                        }
-                    ),
-                }
-            ),
-        }
-        if self.config.name == "full":
-
-            detail_features = {
-                "viewed_doc_titles": datasets.features.Sequence(datasets.Value("string")),
-                "used_queries": datasets.features.Sequence(
-                    {
-                        "query": datasets.Value("string"),
-                        "results": datasets.features.Sequence(
-                            {
-                                "title": datasets.Value("string"),
-                                "snippet": datasets.Value("string"),
-                            }
-                        ),
-                    }
-                ),
-                "nq_answer": datasets.features.Sequence(datasets.Value("string")),
-                "nq_doc_title": datasets.Value("string"),
-            }
-            features_dict.update(detail_features)
-
-        features = datasets.Features(features_dict)
-
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            supervised_keys=None,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        # download and extract URLs
-        urls_to_download = _URLS
-        downloaded_files = dl_manager.download_and_extract(urls_to_download)
-
-        train_file_name = "train.json" if self.config.name == "full" else "train_light.json"
-        dev_file_name = "dev.json" if self.config.name == "full" else "dev_light.json"
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={"filepath": os.path.join(downloaded_files[self.config.name], train_file_name)},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={"filepath": os.path.join(downloaded_files[self.config.name], dev_file_name)},
-            ),
-        ]
-
-    def _generate_examples(self, filepath):
-        """Yields examples."""
-
-        with open(filepath, encoding="utf-8") as f:
-            data = json.load(f)
-            for example in data:
-                id_ = example["id"]
-                annotations = example["annotations"]
-                # Add this because we cannot have None values (all keys in the schema should be present)
-                for an in annotations:
-                    if "qaPairs" not in an:
-                        an["qaPairs"] = []
-                    if "answer" not in an:
-                        an["answer"] = []
-
-                yield id_, example