Delete loading script

nkjp-ner.py

@@ -1,107 +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.
-"""NKJP-NER"""
-
-
-import csv
-import os
-
-import datasets
-from datasets.tasks import TextClassification
-
-
-_CITATION = """\
-@book{przepiorkowski2012narodowy,
-title={Narodowy korpus jezyka polskiego},
-author={Przepi{\'o}rkowski, Adam},
-year={2012},
-publisher={Naukowe PWN}
-}
-"""
-
-_DESCRIPTION = """\
-The NKJP-NER is based on a human-annotated part of National Corpus of Polish (NKJP). We extracted sentences with named entities of exactly one type. The task is to predict the type of the named entity.
-"""
-
-_HOMEPAGE = "https://klejbenchmark.com/tasks/"
-
-_LICENSE = "GNU GPL v.3"
-
-_URLs = "https://klejbenchmark.com/static/data/klej_nkjp-ner.zip"
-
-
-class NkjpNer(datasets.GeneratorBasedBuilder):
-    """NKJP-NER"""
-
-    VERSION = datasets.Version("1.1.0")
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "sentence": datasets.Value("string"),
-                    "target": datasets.ClassLabel(
-                        names=[
-                            "geogName",
-                            "noEntity",
-                            "orgName",
-                            "persName",
-                            "placeName",
-                            "time",
-                        ]
-                    ),
-                }
-            ),
-            supervised_keys=None,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-            task_templates=[TextClassification(text_column="sentence", label_column="target")],
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        data_dir = dl_manager.download_and_extract(_URLs)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "filepath": os.path.join(data_dir, "train.tsv"),
-                    "split": "train",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={"filepath": os.path.join(data_dir, "test_features.tsv"), "split": "test"},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "filepath": os.path.join(data_dir, "dev.tsv"),
-                    "split": "dev",
-                },
-            ),
-        ]
-
-    def _generate_examples(self, filepath, split):
-        """Yields examples."""
-        with open(filepath, encoding="utf-8") as f:
-            reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
-            for id_, row in enumerate(reader):
-                yield id_, {
-                    "sentence": row["sentence"],
-                    "target": -1 if split == "test" else row["target"],
-                }