Delete loading script

asset.py

@@ -1,174 +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.
-"""ASSET: a dataset for sentence simplification evaluation"""
-
-
-import csv
-
-import datasets
-
-
-_CITATION = """\
-@inproceedings{alva-manchego-etal-2020-asset,
-    title = "{ASSET}: {A} Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations",
-    author = "Alva-Manchego, Fernando  and
-      Martin, Louis  and
-      Bordes, Antoine  and
-      Scarton, Carolina  and
-      Sagot, Benoit  and
-      Specia, Lucia",
-    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
-    month = jul,
-    year = "2020",
-    address = "Online",
-    publisher = "Association for Computational Linguistics",
-    url = "https://www.aclweb.org/anthology/2020.acl-main.424",
-    pages = "4668--4679",
-}
-"""
-
-_DESCRIPTION = """\
-ASSET is a dataset for evaluating Sentence Simplification systems with multiple rewriting transformations,
-as described in "ASSET: A Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations".
-The corpus is composed of 2000 validation and 359 test original sentences that were each simplified 10 times by different annotators.
-The corpus also contains human judgments of meaning preservation, fluency and simplicity for the outputs of several automatic text simplification systems.
-"""
-
-_HOMEPAGE = "https://github.com/facebookresearch/asset"
-
-_LICENSE = "Creative Common Attribution-NonCommercial 4.0 International"
-
-_URL_LIST = [
-    (
-        "human_ratings.csv",
-        "https://raw.githubusercontent.com/facebookresearch/asset/main/human_ratings/human_ratings.csv",
-    ),
-    (
-        "asset.valid.orig",
-        "https://raw.githubusercontent.com/facebookresearch/asset/main/dataset/asset.valid.orig",
-    ),
-    (
-        "asset.test.orig",
-        "https://raw.githubusercontent.com/facebookresearch/asset/main/dataset/asset.test.orig",
-    ),
-]
-_URL_LIST += [
-    (
-        f"asset.{spl}.simp.{i}",
-        f"https://raw.githubusercontent.com/facebookresearch/asset/main/dataset/asset.{spl}.simp.{i}",
-    )
-    for spl in ["valid", "test"]
-    for i in range(10)
-]
-
-_URLs = dict(_URL_LIST)
-
-
-class Asset(datasets.GeneratorBasedBuilder):
-
-    VERSION = datasets.Version("1.0.0")
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(
-            name="simplification",
-            version=VERSION,
-            description="A set of original sentences aligned with 10 possible simplifications for each.",
-        ),
-        datasets.BuilderConfig(
-            name="ratings",
-            version=VERSION,
-            description="Human ratings of automatically produced text implification.",
-        ),
-    ]
-
-    DEFAULT_CONFIG_NAME = "simplification"
-
-    def _info(self):
-        if self.config.name == "simplification":
-            features = datasets.Features(
-                {
-                    "original": datasets.Value("string"),
-                    "simplifications": datasets.Sequence(datasets.Value("string")),
-                }
-            )
-        else:
-            features = datasets.Features(
-                {
-                    "original": datasets.Value("string"),
-                    "simplification": datasets.Value("string"),
-                    "original_sentence_id": datasets.Value("int32"),
-                    "aspect": datasets.ClassLabel(names=["meaning", "fluency", "simplicity"]),
-                    "worker_id": datasets.Value("int32"),
-                    "rating": 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 == "simplification":
-            return [
-                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"},
-                ),
-            ]
-        else:
-            return [
-                datasets.SplitGenerator(
-                    name="full",
-                    gen_kwargs={
-                        "filepaths": data_dir,
-                        "split": "full",
-                    },
-                ),
-            ]
-
-    def _generate_examples(self, filepaths, split):
-        """Yields examples."""
-        if self.config.name == "simplification":
-            files = [open(filepaths[f"asset.{split}.orig"], encoding="utf-8")] + [
-                open(filepaths[f"asset.{split}.simp.{i}"], encoding="utf-8") for i in range(10)
-            ]
-            for id_, lines in enumerate(zip(*files)):
-                yield id_, {
-                    "original": lines[0].strip(),
-                    "simplifications": [line.strip() for line in lines[1:]],
-                }
-        else:
-            with open(filepaths["human_ratings.csv"], encoding="utf-8") as f:
-                reader = csv.reader(f, delimiter=",")
-                for id_, row in enumerate(reader):
-                    if id_ == 0:
-                        keys = row[:]
-                    else:
-                        res = dict([(k, v) for k, v in zip(keys, row)])
-                        for k in ["original_sentence_id", "worker_id", "rating"]:
-                            res[k] = int(res[k])
-                        yield (id_ - 1), res