Delete headqa.py

headqa.py

@@ -1,165 +0,0 @@
-# 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.
-#
-# NOTE: This is an exact copy of
-# https://github.com/huggingface/datasets/blob/3804442bb7cfcb9d52044d92688115cfdc69c2da/datasets/head_qa/head_qa.py
-# with the exception of the `image` feature. This is to avoid adding `Pillow`
-# as a dependency.
-"""HEAD-QA: A Healthcare Dataset for Complex Reasoning."""
-
-
-import json
-import os
-
-import datasets
-
-
-_CITATION = """\
-@inproceedings{vilares-gomez-rodriguez-2019-head,
-    title = "{HEAD}-{QA}: A Healthcare Dataset for Complex Reasoning",
-    author = "Vilares, David  and
-      G{\'o}mez-Rodr{\'i}guez, Carlos",
-    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
-    month = jul,
-    year = "2019",
-    address = "Florence, Italy",
-    publisher = "Association for Computational Linguistics",
-    url = "https://www.aclweb.org/anthology/P19-1092",
-    doi = "10.18653/v1/P19-1092",
-    pages = "960--966",
-    abstract = "We present HEAD-QA, a multi-choice question answering testbed to encourage research on complex reasoning. The questions come from exams to access a specialized position in the Spanish healthcare system, and are challenging even for highly specialized humans. We then consider monolingual (Spanish) and cross-lingual (to English) experiments with information retrieval and neural techniques. We show that: (i) HEAD-QA challenges current methods, and (ii) the results lag well behind human performance, demonstrating its usefulness as a benchmark for future work.",
-}
-"""
-
-_DESCRIPTION = """\
-HEAD-QA is a multi-choice HEAlthcare Dataset. The questions come from exams to access a specialized position in the
-Spanish healthcare system, and are challenging even for highly specialized humans. They are designed by the Ministerio
-de Sanidad, Consumo y Bienestar Social.
-The dataset contains questions about the following topics: medicine, nursing, psychology, chemistry, pharmacology and biology.
-"""
-
-_HOMEPAGE = "https://aghie.github.io/head-qa/"
-
-# The Spanish data comes from the "Ministerio de Sanidad, Consumo y Bienestar Social", as indicated here : https://github.com/aghie/head-qa
-# This Spanish data seems to follow the intellectual property rights stated here : https://www.sanidad.gob.es/avisoLegal/home.htm
-# The English data was translated by the authors of head-qa (https://arxiv.org/pdf/1906.04701.pdf).
-_LICENSE = "Custom license"
-
-_URL = "https://drive.google.com/uc?export=download&confirm=t&id=1a_95N5zQQoUCq8IBNVZgziHbeM-QxG2t"
-
-_DIRS = {"es": "HEAD", "en": "HEAD_EN"}
-
-
-class HeadQA(datasets.GeneratorBasedBuilder):
-    """HEAD-QA: A Healthcare Dataset for Complex Reasoning"""
-
-    VERSION = datasets.Version("1.1.0")
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(
-            name="es", version=VERSION, description="Spanish HEAD dataset"
-        ),
-        datasets.BuilderConfig(
-            name="en", version=VERSION, description="English HEAD dataset"
-        ),
-    ]
-
-    DEFAULT_CONFIG_NAME = "es"
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "name": datasets.Value("string"),
-                    "year": datasets.Value("string"),
-                    "category": datasets.Value("string"),
-                    "qid": datasets.Value("int32"),
-                    "qtext": datasets.Value("string"),
-                    "ra": datasets.Value("int32"),
-                    "answers": [
-                        {
-                            "aid": datasets.Value("int32"),
-                            "atext": datasets.Value("string"),
-                        }
-                    ],
-                }
-            ),
-            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)
-
-        dir = _DIRS[self.config.name]
-        data_lang_dir = os.path.join(data_dir, dir)
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "data_dir": data_dir,
-                    "filepath": os.path.join(data_lang_dir, f"train_{dir}.json"),
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "data_dir": data_dir,
-                    "filepath": os.path.join(data_lang_dir, f"test_{dir}.json"),
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "data_dir": data_dir,
-                    "filepath": os.path.join(data_lang_dir, f"dev_{dir}.json"),
-                },
-            ),
-        ]
-
-    def _generate_examples(self, data_dir, filepath):
-        """Yields examples."""
-        with open(filepath, encoding="utf-8") as f:
-            head_qa = json.load(f)
-            for exam_id, exam in enumerate(head_qa["exams"]):
-                content = head_qa["exams"][exam]
-                name = content["name"].strip()
-                year = content["year"].strip()
-                category = content["category"].strip()
-                for question in content["data"]:
-                    qid = int(question["qid"].strip())
-                    qtext = question["qtext"].strip()
-                    ra = int(question["ra"].strip())
-
-                    aids = [answer["aid"] for answer in question["answers"]]
-                    atexts = [answer["atext"].strip() for answer in question["answers"]]
-                    answers = [
-                        {"aid": aid, "atext": atext} for aid, atext in zip(aids, atexts)
-                    ]
-
-                    id_ = f"{exam_id}_{qid}"
-                    yield id_, {
-                        "name": name,
-                        "year": year,
-                        "category": category,
-                        "qid": qid,
-                        "qtext": qtext,
-                        "ra": ra,
-                        "answers": answers,
-                    }