Delete loading script

assin.py

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-# 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.
-"""ASSIN dataset."""
-
-
-import xml.etree.ElementTree as ET
-
-import datasets
-
-
-_CITATION = """
-@inproceedings{fonseca2016assin,
-  title={ASSIN: Avaliacao de similaridade semantica e inferencia textual},
-  author={Fonseca, E and Santos, L and Criscuolo, Marcelo and Aluisio, S},
-  booktitle={Computational Processing of the Portuguese Language-12th International Conference, Tomar, Portugal},
-  pages={13--15},
-  year={2016}
-}
-"""
-
-_DESCRIPTION = """
-The ASSIN (Avaliação de Similaridade Semântica e INferência textual) corpus is a corpus annotated with pairs of sentences written in
-Portuguese that is suitable for the  exploration of textual entailment and paraphrasing classifiers. The corpus contains pairs of sentences
-extracted from news articles written in European Portuguese (EP) and Brazilian Portuguese (BP), obtained from Google News Portugal
-and Brazil, respectively. To create the corpus, the authors started by collecting a set of news articles describing the
-same event (one news article from Google News Portugal and another from Google News Brazil) from Google News.
-Then, they employed Latent Dirichlet Allocation (LDA) models to retrieve pairs of similar sentences between sets of news
-articles that were grouped together around the same topic. For that, two LDA models were trained (for EP and for BP)
-on external and large-scale collections of unannotated news articles from Portuguese and Brazilian news providers, respectively.
-Then, the authors defined a lower and upper threshold for the sentence similarity score of the retrieved pairs of sentences,
-taking into account that high similarity scores correspond to sentences that contain almost the same content (paraphrase candidates),
-and low similarity scores correspond to sentences that are very different in content from each other (no-relation candidates).
-From the collection of pairs of sentences obtained at this stage, the authors performed some manual grammatical corrections
-and discarded some of the pairs wrongly retrieved. Furthermore, from a preliminary analysis made to the retrieved sentence pairs
-the authors noticed that the number of contradictions retrieved during the previous stage was very low. Additionally, they also
-noticed that event though paraphrases are not very frequent, they occur with some frequency in news articles. Consequently,
-in contrast with the majority of the currently available corpora for other languages, which consider as labels “neutral”, “entailment”
-and “contradiction” for the task of RTE, the authors of the ASSIN corpus decided to use as labels “none”, “entailment” and “paraphrase”.
-Finally, the manual annotation of pairs of sentences was performed by human annotators. At least four annotators were randomly
-selected to annotate each pair of sentences, which is done in two steps: (i) assigning a semantic similarity label (a score between 1 and 5,
-from unrelated to very similar); and (ii) providing an entailment label (one sentence entails the other, sentences are paraphrases,
-or no relation). Sentence pairs where at least three annotators do not agree on the entailment label were considered controversial
-and thus discarded from the gold standard annotations. The full dataset has 10,000 sentence pairs, half of which in Brazilian Portuguese
-and half in European Portuguese. Either language variant has 2,500 pairs for training, 500 for validation and 2,000 for testing.
-"""
-
-_HOMEPAGE = "http://nilc.icmc.usp.br/assin/"
-
-_LICENSE = ""
-
-_URL = "http://nilc.icmc.usp.br/assin/assin.tar.gz"
-
-
-class Assin(datasets.GeneratorBasedBuilder):
-    """ASSIN dataset."""
-
-    VERSION = datasets.Version("1.0.0")
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(
-            name="full",
-            version=VERSION,
-            description="If you want to use all the ASSIN data (Brazilian Portuguese and European Portuguese)",
-        ),
-        datasets.BuilderConfig(
-            name="ptpt",
-            version=VERSION,
-            description="If you want to use only the ASSIN European Portuguese subset",
-        ),
-        datasets.BuilderConfig(
-            name="ptbr",
-            version=VERSION,
-            description="If you want to use only the ASSIN Brazilian Portuguese subset",
-        ),
-    ]
-
-    DEFAULT_CONFIG_NAME = "full"
-
-    def _info(self):
-        features = datasets.Features(
-            {
-                "sentence_pair_id": datasets.Value("int64"),
-                "premise": datasets.Value("string"),
-                "hypothesis": datasets.Value("string"),
-                "relatedness_score": datasets.Value("float32"),
-                "entailment_judgment": datasets.features.ClassLabel(names=["NONE", "ENTAILMENT", "PARAPHRASE"]),
-            }
-        )
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            supervised_keys=None,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        archive = dl_manager.download(_URL)
-
-        train_paths = []
-        dev_paths = []
-        test_paths = []
-
-        if self.config.name == "full" or self.config.name == "ptpt":
-            train_paths.append("assin-ptpt-train.xml")
-            dev_paths.append("assin-ptpt-dev.xml")
-            test_paths.append("assin-ptpt-test.xml")
-
-        if self.config.name == "full" or self.config.name == "ptbr":
-            train_paths.append("assin-ptbr-train.xml")
-            dev_paths.append("assin-ptbr-dev.xml")
-            test_paths.append("assin-ptbr-test.xml")
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "filepaths": train_paths,
-                    "files": dl_manager.iter_archive(archive),
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "filepaths": test_paths,
-                    "files": dl_manager.iter_archive(archive),
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "filepaths": dev_paths,
-                    "files": dl_manager.iter_archive(archive),
-                },
-            ),
-        ]
-
-    def _generate_examples(self, filepaths, files):
-        """Yields examples."""
-
-        id_ = 0
-
-        for path, f in files:
-            if path in filepaths:
-
-                tree = ET.parse(f)
-                root = tree.getroot()
-
-                for pair in root:
-
-                    yield id_, {
-                        "sentence_pair_id": int(pair.attrib.get("id")),
-                        "premise": pair.find(".//t").text,
-                        "hypothesis": pair.find(".//h").text,
-                        "relatedness_score": float(pair.attrib.get("similarity")),
-                        "entailment_judgment": pair.attrib.get("entailment").upper(),
-                    }
-
-                    id_ += 1