Delete loading script

faquad.py

@@ -1,149 +0,0 @@
-# coding=utf-8
-# Copyright 2020 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.
-#
-# Adapted from the SQuAD script.
-#
-
-# Lint as: python3
-"""FaQuAD: Reading Comprehension Dataset in the Domain of Brazilian Higher Education."""
-
-
-import json
-
-import datasets
-
-
-logger = datasets.logging.get_logger(__name__)
-
-
-_CITATION = """\
-@INPROCEEDINGS{
-    8923668,
-    author={Sayama, Hélio Fonseca and Araujo, Anderson Viçoso and Fernandes, Eraldo Rezende},
-    booktitle={2019 8th Brazilian Conference on Intelligent Systems (BRACIS)},
-    title={FaQuAD: Reading Comprehension Dataset in the Domain of Brazilian Higher Education},
-    year={2019},
-    volume={},
-    number={},
-    pages={443-448},
-    doi={10.1109/BRACIS.2019.00084}
-}
-"""
-
-_DESCRIPTION = """\
-Academic secretaries and faculty members of higher education institutions face a common problem: 
-  the abundance of questions sent by academics 
-  whose answers are found in available institutional documents. 
-The official documents produced by Brazilian public universities are vast and disperse, 
-  which discourage students to further search for answers in such sources.
-In order to lessen this problem, we present FaQuAD: 
-  a novel machine reading comprehension dataset 
-  in the domain of Brazilian higher education institutions. 
-FaQuAD follows the format of SQuAD (Stanford Question Answering Dataset) [Rajpurkar et al. 2016]. 
-It comprises 900 questions about 249 reading passages (paragraphs), 
-  which were taken from 18 official documents of a computer science college 
-  from a Brazilian federal university 
-  and 21 Wikipedia articles related to Brazilian higher education system. 
-As far as we know, this is the first Portuguese reading comprehension dataset in this format.
-"""
-
-_URL = "https://raw.githubusercontent.com/liafacom/faquad/master/data/"
-_URLS = {
-    "train": _URL + "train.json",
-    "dev": _URL + "dev.json",
-}
-
-
-class FaquadConfig(datasets.BuilderConfig):
-    """BuilderConfig for FaQuAD."""
-
-    def __init__(self, **kwargs):
-        """BuilderConfig for FaQuAD.
-
-        Args:
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(FaquadConfig, self).__init__(**kwargs)
-
-
-class Faquad(datasets.GeneratorBasedBuilder):
-    """FaQuAD: Reading Comprehension Dataset in the Domain of Brazilian Higher Education. Version 1.0."""
-
-    BUILDER_CONFIGS = [
-        FaquadConfig(
-            name="plain_text",
-            version=datasets.Version("1.0.0", ""),
-            description="Plain text",
-        ),
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "id": datasets.Value("string"),
-                    "title": datasets.Value("string"),
-                    "context": datasets.Value("string"),
-                    "question": datasets.Value("string"),
-                    "answers": datasets.features.Sequence(
-                        {
-                            "text": datasets.Value("string"),
-                            "answer_start": datasets.Value("int32"),
-                        }
-                    ),
-                }
-            ),
-            # No default supervised_keys (as we have to pass both question
-            # and context as input).
-            supervised_keys=None,
-            homepage="https://github.com/liafacom/faquad",
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        downloaded_files = dl_manager.download_and_extract(_URLS)
-
-        return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
-            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
-        ]
-
-    def _generate_examples(self, filepath):
-        """This function returns the examples in the raw (text) form."""
-        logger.info("generating examples from = %s", filepath)
-        key = 0
-        with open(filepath, encoding="utf-8") as f:
-            faquad = json.load(f)
-            for article in faquad["data"]:
-                title = article.get("title", "")
-                for paragraph in article["paragraphs"]:
-                    context = paragraph["context"]  # do not strip leading blank spaces GH-2585
-                    for qa in paragraph["qas"]:
-                        answer_starts = [answer["answer_start"] for answer in qa["answers"]]
-                        answers = [answer["text"] for answer in qa["answers"]]
-                        # Features currently used are "context", "question", and "answers".
-                        # Others are extracted here for the ease of future expansions.
-                        yield key, {
-                            "title": title,
-                            "context": context,
-                            "question": qa["question"],
-                            "id": qa["id"],
-                            "answers": {
-                                "answer_start": answer_starts,
-                                "text": answers,
-                            },
-                        }
-                        key += 1