Delete CSMD.py

CSMD.py

@@ -1,179 +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.
-"""CSMD: a dataset for assessing meaning preservation between sentences"""
-
-import csv
-
-import datasets
-from datasets import load_dataset
-
-_CITATION = """\
-@ARTICLE{10.3389/frai.2023.1223924,
-AUTHOR={Beauchemin, David and Saggion, Horacio and Khoury, Richard},   
-TITLE={{MeaningBERT: Assessing Meaning Preservation Between Sentences}},      
-JOURNAL={Frontiers in Artificial Intelligence},      
-VOLUME={6},           
-YEAR={2023},      
-URL={https://www.frontiersin.org/articles/10.3389/frai.2023.1223924},       
-DOI={10.3389/frai.2023.1223924},      	
-ISSN={2624-8212},   
-}
-"""
-
-_DESCRIPTION = """\
-Continuous Scale Meaning Dataset (CSMD) is a dataset for assessing meaning preservation between sentences.
-"""
-
-_HOMEPAGE = "https://github.com/GRAAL-Research/csmd"
-
-_LICENSE = "Attribution 4.0 International (CC BY 4.0)"
-
-_URL_LIST = [
-    (
-        "meaning.train",
-        "https://github.com/GRAAL-Research/csmd/blob/main/dataset/meaning/train.tsv",
-    ),
-    (
-        "meaning.dev",
-        "https://github.com/GRAAL-Research/csmd/blob/main/dataset/meaning/dev.tsv",
-    ),
-    (
-        "meaning.test",
-        "https://github.com/GRAAL-Research/csmd/blob/main/dataset/meaning/test.tsv",
-    ),
-    (
-        "meaning_with_data_augmentation.train",
-        "https://github.com/GRAAL-Research/csmd/blob/main/dataset/meaning_with_data_augmentation/train.tsv",
-    ),
-    (
-        "meaning_with_data_augmentation.dev",
-        "https://github.com/GRAAL-Research/csmd/blob/main/dataset/meaning_with_data_augmentation/dev.tsv",
-    ),
-    (
-        "meaning_with_data_augmentation.test",
-        "https://github.com/GRAAL-Research/csmd/blob/main/dataset/meaning_with_data_augmentation/test.tsv",
-    ),
-    (
-        "identical",
-        "https://github.com/GRAAL-Research/csmd/blob/main/dataset/holdout/identical.tsv",
-    ),
-    (
-        "unrelated",
-        "https://github.com/GRAAL-Research/csmd/blob/main/dataset/holdout/unrelated.tsv",
-    ),
-]
-
-_URLs = dict(_URL_LIST)
-
-
-class CSMD(datasets.GeneratorBasedBuilder):
-    VERSION = datasets.Version("2.0.0")
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(
-            name="meaning",
-            version=VERSION,
-            description="An instance consists of 1,355 meaning preservation triplets (Document, simplification, "
-            "label).",
-        ),
-        datasets.BuilderConfig(
-            name="meaning_with_data_augmentation",
-            version=VERSION,
-            description="An instance consists of 1,355 meaning preservation triplets (Document, simplification, label) "
-            "along with 1,355 data augmentation triplets (Document, Document, 1) and 1,355 data "
-            "augmentation triplets (Document, Unrelated Document, 0) (See the sanity checks in our "
-            "article).",
-        ),
-        datasets.BuilderConfig(
-            name="meaning_holdout_identical",
-            version=VERSION,
-            description="An  instance consists of 359 meaning holdout preservation identical triplets (Document, "
-            "Document, 1) based on the ASSET Simplification dataset.",
-        ),
-        datasets.BuilderConfig(
-            name="meaning_holdout_unrelated",
-            version=VERSION,
-            description="An instance consists of 359 meaning holdout preservation unrelated triplets (Document, "
-            "Unrelated Document, 0) based on the ASSET Simplification dataset.",
-        ),
-    ]
-
-    DEFAULT_CONFIG_NAME = "meaning"
-
-    def _info(self):
-        features = datasets.Features(
-            {
-                "document": datasets.Value(dtype="string"),
-                "simplification": datasets.Value(dtype="string"),
-                "labels": datasets.Value(dtype="string"),
-            }
-        )
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            supervised_keys=None,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        data_dir = dl_manager.download_and_extract(_URLs)
-        if self.config.name in ("meaning", "meaning_with_data_augmentation"):
-            return [
-                datasets.SplitGenerator(
-                    name=datasets.Split.TRAIN,
-                    gen_kwargs={
-                        "filepaths": data_dir,
-                        "split": "train",
-                    },
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.VALIDATION,
-                    gen_kwargs={
-                        "filepaths": data_dir,
-                        "split": "valid",
-                    },
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.TEST,
-                    gen_kwargs={"filepaths": data_dir, "split": "test"},
-                ),
-            ]
-        elif self.config.name in ("identical", "unrelated"):
-            return [
-                datasets.SplitGenerator(
-                    name=f"{self.config.name}_{datasets.Split.TEST}",
-                    gen_kwargs={
-                        "filepaths": data_dir,
-                        "split": "test",
-                    },
-                ),
-            ]
-
-    def _generate_examples(self, filepaths, split):
-        with open(filepaths[split], encoding="utf-8") as f:
-            reader = csv.reader(f, delimiter="\t")
-            for id_, row in enumerate(reader):
-                if id_ == 0:
-                    # Columns header
-                    keys = row[:]
-                else:
-                    res = dict([(k, v) for k, v in zip(keys, row)])
-                    for k in ["document", "simplification", "labels"]:
-                        res[k] = int(res[k])
-                    yield (
-                        id_ - 1
-                    ), res  # Minus 1, since first idx is the columns header