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add dataloader

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  1. ans-stance.py +101 -0
ans-stance.py ADDED
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+ # Copyright 2022 Mads Kongsbak and Leon Derczynski
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+ #
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+ # Licensed under the Apache License, Version 2.0 (the "License");
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+ # you may not use this file except in compliance with the License.
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+ # You may obtain a copy of the License at
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+ #
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+ # http://www.apache.org/licenses/LICENSE-2.0
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+ #
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+ # Unless required by applicable law or agreed to in writing, software
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+ # distributed under the License is distributed on an "AS IS" BASIS,
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+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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+ # See the License for the specific language governing permissions and
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+ # limitations under the License.
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+ """DataLoader for ANS, an Arabic News Stance corpus"""
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+
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+
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+ import csv
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+ import json
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+ import os
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+
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+ import datasets
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+
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+ _CITATION = """\
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+ @inproceedings{,
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+ title = "Stance Prediction and Claim Verification: An {A}rabic Perspective",
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+ author = "Khouja, Jude",
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+ booktitle = "Proceedings of the Third Workshop on Fact Extraction and {VER}ification ({FEVER})",
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+ year = "2020",
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+ address = "Seattle, USA",
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+ publisher = "Association for Computational Linguistics",
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+ }
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+ """
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+
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+ _DESCRIPTION = """\
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+ The dataset is a collection of news titles in arabic along with paraphrased and corrupted titles. The stance prediction version is a 3-class classification task. Data contains three columns: s1, s2, stance.
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+ """
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+
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+ _HOMEPAGE = ""
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+
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+ _LICENSE = "apache-2.0"
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+
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+ class ANSStanceConfig(datasets.BuilderConfig):
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+
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+ def __init__(self, **kwargs):
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+ super(ANSStanceConfig, self).__init__(**kwargs)
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+
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+ class ANSStance(datasets.GeneratorBasedBuilder):
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+ """ANS dataset made in triples of (s1, s2, stance)"""
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+
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+ VERSION = datasets.Version("1.0.0")
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+
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+ BUILDER_CONFIGS = [
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+ ANSStanceConfig(name="stance", version=VERSION, description=""),
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+ ]
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+
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+ def _info(self):
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+ features = datasets.Features(
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+ {
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+ "id": datasets.Value("string"),
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+ "s1": datasets.Value("string"),
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+ "s2": datasets.Value("string"),
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+ "stance": datasets.features.ClassLabel(
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+ names=[
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+ "disagree",
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+ "agree",
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+ "other"
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+ ]
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+ )
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+ }
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+ )
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+
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+ return datasets.DatasetInfo(
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+ description=_DESCRIPTION,
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+ features=features,
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+ homepage=_HOMEPAGE,
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+ license=_LICENSE,
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+ citation=_CITATION,
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+ )
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+
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+ def _split_generators(self, dl_manager):
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+ train_text = dl_manager.download_and_extract("ans_train.csv")
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+ valid_text = dl_manager.download_and_extract("ans_dev.csv")
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+ test_text = dl_manager.download_and_extract("ans_test.csv")
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+
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+ return [
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+ datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_text, "split": "train"}),
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+ datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": valid_text, "split": "validation"}),
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+ datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_text, "split": "test"}),
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+ ]
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+
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+ def _generate_examples(self, filepath, split):
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+ with open(filepath, encoding="utf-8") as f:
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+ reader = csv.DictReader(f, delimiter=",")
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+ guid = 0
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+ for instance in reader:
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+ instance["s1"] = instance.pop("s1")
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+ instance["s2"] = instance.pop("s2")
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+ instance["stance"] = instance.pop("stance")
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+ instance['id'] = str(guid)
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+ yield guid, instance
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+ guid += 1