Delete loading script

assin2.py

@@ -1,125 +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.
-"""ASSIN 2 dataset."""
-
-
-import xml.etree.ElementTree as ET
-
-import datasets
-
-
-_CITATION = """
-@inproceedings{real2020assin,
-  title={The assin 2 shared task: a quick overview},
-  author={Real, Livy and Fonseca, Erick and Oliveira, Hugo Goncalo},
-  booktitle={International Conference on Computational Processing of the Portuguese Language},
-  pages={406--412},
-  year={2020},
-  organization={Springer}
-}
-"""
-
-_DESCRIPTION = """
-The ASSIN 2 corpus is composed of rather simple sentences. Following the procedures of SemEval 2014 Task 1.
-The training and validation data are composed, respectively, of 6,500 and 500 sentence pairs in Brazilian Portuguese,
-annotated for entailment and semantic similarity. Semantic similarity values range from 1 to 5, and text entailment
-classes are either entailment or none. The test data are composed of approximately 3,000 sentence pairs with the same
-annotation. All data were manually annotated.
-"""
-
-_HOMEPAGE = "https://sites.google.com/view/assin2"
-
-_LICENSE = ""
-
-_URLS = {
-    "train": "https://github.com/ruanchaves/assin/raw/master/sources/assin2-train-only.xml",
-    "dev": "https://github.com/ruanchaves/assin/raw/master/sources/assin2-dev.xml",
-    "test": "https://github.com/ruanchaves/assin/raw/master/sources/assin2-test.xml",
-}
-
-
-class Assin2(datasets.GeneratorBasedBuilder):
-    """ASSIN 2 dataset."""
-
-    VERSION = datasets.Version("1.0.0")
-
-    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"]),
-            }
-        )
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            supervised_keys=None,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        data_dir = dl_manager.download(_URLS)
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "filepath": data_dir["train"],
-                    "split": "train",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "filepath": data_dir["test"],
-                    "split": "test",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "filepath": data_dir["dev"],
-                    "split": "dev",
-                },
-            ),
-        ]
-
-    def _generate_examples(self, filepath, split):
-        """Yields examples."""
-
-        id_ = 0
-
-        with open(filepath, "rb") as f:
-
-            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