Delete loading script

tweet_qa.py

@@ -1,110 +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.
-"""TWEETQA: A Social Media Focused Question Answering Dataset"""
-
-
-import json
-import os
-
-import datasets
-
-
-_CITATION = """\
-@inproceedings{xiong2019tweetqa,
-  title={TweetQA: A Social Media Focused Question Answering Dataset},
-  author={Xiong, Wenhan and Wu, Jiawei and Wang, Hong and Kulkarni, Vivek and Yu, Mo and Guo, Xiaoxiao and Chang, Shiyu and Wang, William Yang},
-  booktitle={Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics},
-  year={2019}
-}
-"""
-
-_DESCRIPTION = """\
-TweetQA is the first dataset for QA on social media data by leveraging news media and crowdsourcing.
-"""
-
-_HOMEPAGE = "https://tweetqa.github.io/"
-
-_LICENSE = "CC BY-SA 4.0"
-
-_URL = "https://sites.cs.ucsb.edu/~xwhan/datasets/tweetqa.zip"
-
-
-class TweetQA(datasets.GeneratorBasedBuilder):
-    """TweetQA: first large-scale dataset for QA over social media data"""
-
-    VERSION = datasets.Version("1.0.0")
-
-    def _info(self):
-        features = datasets.Features(
-            {
-                "Question": datasets.Value("string"),
-                "Answer": datasets.Sequence(datasets.Value("string")),
-                "Tweet": datasets.Value("string"),
-                "qid": 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):
-        """Returns SplitGenerators."""
-        data_dir = dl_manager.download_and_extract(_URL)
-        train_path = os.path.join(data_dir, "TweetQA_data", "train.json")
-        test_path = os.path.join(data_dir, "TweetQA_data", "test.json")
-        dev_path = os.path.join(data_dir, "TweetQA_data", "dev.json")
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "filepath": train_path,
-                    "split": "train",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "filepath": test_path,
-                    "split": "test",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "filepath": dev_path,
-                    "split": "dev",
-                },
-            ),
-        ]
-
-    def _generate_examples(self, filepath, split):
-        """Yields examples."""
-
-        with open(filepath, encoding="utf-8") as f:
-            tweet_qa = json.load(f)
-            idx = 0
-            for data in tweet_qa:
-                yield idx, {
-                    "Question": data["Question"],
-                    "Answer": [] if split == "test" else data["Answer"],
-                    "Tweet": data["Tweet"],
-                    "qid": data["qid"],
-                }
-                idx += 1