Delete loading script

xcsr.py

@@ -1,192 +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.
-"""XCSR: A dataset for cross-lingual commonsense reasoning."""
-
-
-import json
-import os
-
-import datasets
-
-
-_CITATION = """\
-# X-CSR
-@inproceedings{lin-etal-2021-common,
-    title = "Common Sense Beyond {E}nglish: Evaluating and Improving Multilingual Language Models for Commonsense Reasoning",
-    author = "Lin, Bill Yuchen  and
-      Lee, Seyeon  and
-      Qiao, Xiaoyang  and
-      Ren, Xiang",
-    booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
-    month = aug,
-    year = "2021",
-    address = "Online",
-    publisher = "Association for Computational Linguistics",
-    url = "https://aclanthology.org/2021.acl-long.102",
-    doi = "10.18653/v1/2021.acl-long.102",
-    pages = "1274--1287",
-}
-
-# CSQA
-@inproceedings{Talmor2019commonsenseqaaq,
-    address = {Minneapolis, Minnesota},
-    author = {Talmor, Alon  and Herzig, Jonathan  and Lourie, Nicholas and Berant, Jonathan},
-    booktitle = {Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)},
-    doi = {10.18653/v1/N19-1421},
-    pages = {4149--4158},
-    publisher = {Association for Computational Linguistics},
-    title = {CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge},
-    url = {https://www.aclweb.org/anthology/N19-1421},
-    year = {2019}
-}
-
-# CODAH
-@inproceedings{Chen2019CODAHAA,
-    address = {Minneapolis, USA},
-    author = {Chen, Michael  and D{'}Arcy, Mike  and Liu, Alisa  and Fernandez, Jared  and Downey, Doug},
-    booktitle = {Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for {NLP}},
-    doi = {10.18653/v1/W19-2008},
-    pages = {63--69},
-    publisher = {Association for Computational Linguistics},
-    title = {CODAH: An Adversarially-Authored Question Answering Dataset for Common Sense},
-    url = {https://www.aclweb.org/anthology/W19-2008},
-    year = {2019}
-}
-"""
-
-_DESCRIPTION = """\
-To evaluate multi-lingual language models (ML-LMs) for commonsense reasoning in a cross-lingual zero-shot transfer setting (X-CSR), i.e., training in English and test in other languages, we create two benchmark datasets, namely X-CSQA and X-CODAH. Specifically, we automatically translate the original CSQA and CODAH datasets, which only have English versions, to 15 other languages, forming development and test sets for studying X-CSR. As our goal is to evaluate different ML-LMs in a unified evaluation protocol for X-CSR, we argue that such translated examples, although might contain noise, can serve as a starting benchmark for us to obtain meaningful analysis, before more human-translated datasets will be available in the future.
-"""
-
-_HOMEPAGE = "https://inklab.usc.edu//XCSR/"
-
-# TODO: Add the licence for the dataset here if you can find it
-# _LICENSE = ""
-
-# The HuggingFace dataset library don't host the datasets but only point to the original files
-# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
-
-_URL = "https://inklab.usc.edu/XCSR/xcsr_datasets.zip"
-
-_LANGUAGES = ("en", "zh", "de", "es", "fr", "it", "jap", "nl", "pl", "pt", "ru", "ar", "vi", "hi", "sw", "ur")
-
-
-class XcsrConfig(datasets.BuilderConfig):
-    """BuilderConfig for XCSR."""
-
-    def __init__(self, subset: str, language: str, **kwargs):
-        """BuilderConfig for XCSR.
-        Args:
-        language: One of {en, zh, de, es, fr, it, jap, nl, pl, pt, ru, ar, vi, hi, sw, ur}, or all_languages
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super().__init__(name=f"{subset}-{language}", **kwargs)
-        self.subset = subset
-        self.language = language
-
-
-class Xcsr(datasets.GeneratorBasedBuilder):
-    """XCSR: A dataset for evaluating multi-lingual language models (ML-LMs) for commonsense reasoning in a
-    cross-lingual zero-shot transfer setting"""
-
-    BUILDER_CONFIG_CLASS = XcsrConfig
-    BUILDER_CONFIGS = [
-        XcsrConfig(
-            subset="X-CSQA",
-            language=lang,
-            version=datasets.Version("1.1.0", ""),
-            description=f"Plain text import of X-CSQA for the {lang} language",
-        )
-        for lang in _LANGUAGES
-    ] + [
-        XcsrConfig(
-            subset="X-CODAH",
-            language=lang,
-            version=datasets.Version("1.1.0", ""),
-            description=f"Plain text import of X-CODAH for the {lang} language",
-        )
-        for lang in _LANGUAGES
-    ]
-
-    def _info(self):
-        if self.config.subset == "X-CSQA":
-            features = datasets.Features(
-                {
-                    "id": datasets.Value("string"),
-                    "lang": datasets.Value("string"),
-                    "question": {
-                        "stem": datasets.Value("string"),
-                        "choices": datasets.features.Sequence(
-                            {
-                                "label": datasets.Value("string"),
-                                "text": datasets.Value("string"),
-                            }
-                        ),
-                    },
-                    "answerKey": datasets.Value("string"),
-                }
-            )
-        elif self.config.subset == "X-CODAH":
-            features = datasets.Features(
-                {
-                    "id": datasets.Value("string"),
-                    "lang": datasets.Value("string"),
-                    "question_tag": datasets.Value("string"),
-                    "question": {
-                        "stem": datasets.Value("string"),
-                        "choices": datasets.features.Sequence(
-                            {
-                                "label": datasets.Value("string"),
-                                "text": datasets.Value("string"),
-                            }
-                        ),
-                    },
-                    "answerKey": datasets.Value("string"),
-                }
-            )
-
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            homepage=_HOMEPAGE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        data_dir = dl_manager.download_and_extract(_URL)
-        filepath = os.path.join(data_dir, "X-CSR_datasets", self.config.subset, self.config.language, "{split}.jsonl")
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "filepath": filepath.format(split="test"),
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "filepath": filepath.format(split="dev"),
-                },
-            ),
-        ]
-
-    def _generate_examples(self, filepath):
-        """Yields examples as (key, example) tuples."""
-        with open(filepath, encoding="utf-8") as f:
-            for key, row in enumerate(f):
-                data = json.loads(row)
-                _ = data.setdefault("answerKey", "")
-                yield key, data