Create CSMD.py

CSMD.py

@@ -0,0 +1,179 @@
+# 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("string"),
+                "simplification": datasets.Sequence(datasets.Value("string")),
+                "labels": datasets.Value("int32"),
+            }
+        )
+        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