Test : remove the loading script

WikiNER.py

@@ -1,124 +0,0 @@
-# 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.
-
-import os
-import pandas as pd
-
-import datasets
-
-
-_CITATION = """
-@inproceedings{ghaddar-langlais-2017-winer,
-    title = "{W}i{NER}: A {W}ikipedia Annotated Corpus for Named Entity Recognition",
-    author = "Ghaddar, Abbas  and
-      Langlais, Phillippe",
-    booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
-    month = nov,
-    year = "2017",
-    address = "Taipei, Taiwan",
-    publisher = "Asian Federation of Natural Language Processing",
-    url = "https://aclanthology.org/I17-1042",
-    pages = "413--422",
-    abstract = "We revisit the idea of mining Wikipedia in order to generate named-entity annotations. We propose a new methodology that we applied to English Wikipedia to build WiNER, a large, high quality, annotated corpus. We evaluate its usefulness on 6 NER tasks, comparing 4 popular state-of-the art approaches. We show that LSTM-CRF is the approach that benefits the most from our corpus. We report impressive gains with this model when using a small portion of WiNER on top of the CONLL training material. Last, we propose a simple but efficient method for exploiting the full range of WiNER, leading to further improvements.",
-}
-"""
-
-_DESCRIPTION = """
-Created by Nothman et al. at 2013, the WikiNER Dataset
-contains 7,200 manually-labelled Wikipedia articles 
-across nine languages: English, German, French, Polish,
-Italian, Spanish,Dutch, Portuguese and Russian., in
-Multi-Lingual language. Containing 7,2 in Text file format.
-"""
-
-_HOMEPAGE = ""
-LANGUAGES = ["en", "fr", "de", "es", "it", "nl", "pt", "pl", "ru"]
-_URLS = {lang: "https://huggingface.co/datasets/meczifho/WikiNER/tree/main/data" for lang in LANGUAGES}
-
-
-class WikiNER(datasets.GeneratorBasedBuilder):
-    """
-    This is the WikiNER dataset. It is a dataset of sentences from Wikipedia with named entities tagged.
-    """
-
-    VERSION = datasets.Version("2.0.0")
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="en", version=VERSION, description="English dataset"),
-        datasets.BuilderConfig(name="fr", version=VERSION, description="French dataset"),
-        datasets.BuilderConfig(name="de", version=VERSION, description="German dataset"),
-        datasets.BuilderConfig(name="es", version=VERSION, description="Spanish dataset"),
-        datasets.BuilderConfig(name="it", version=VERSION, description="Italian dataset"),
-        datasets.BuilderConfig(name="nl", version=VERSION, description="Dutch dataset"),
-        datasets.BuilderConfig(name="pt", version=VERSION, description="Portuguese dataset"),
-        datasets.BuilderConfig(name="pl", version=VERSION, description="Polish dataset"),
-        datasets.BuilderConfig(name="ru", version=VERSION, description="Russian dataset"),
-    ]
-
-    DEFAULT_CONFIG_NAME = "en"
-
-    def _info(self):
-        features = datasets.Features(
-            {
-                "id": datasets.Value("string"),
-                "words": datasets.Sequence(datasets.Value("string")),
-                "ner_tags": datasets.Sequence(datasets.Value("int32")),
-            }
-        )
-
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            supervised_keys=("words", "ner_tags"),
-            homepage=_HOMEPAGE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        train_pq = dl_manager.download_and_extract(
-            'https://huggingface.co/datasets/meczifho/WikiNER/resolve/main/data/train.parquet')
-        test_pq = dl_manager.download_and_extract(
-            'https://huggingface.co/datasets/meczifho/WikiNER/resolve/main/data/test.parquet')
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": train_pq,
-                    "split": "train",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": test_pq,
-                    "split": "test"
-                },
-            ),
-        ]
-
-    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
-    def _generate_examples(self, filepath, split):
-        # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
-        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
-        # read the parquet file
-        df = pd.read_parquet(filepath)
-        df = df[df['id'].str.startswith(self.config.name)]
-        for key, row in df.iterrows():
-            yield key, {
-                "id": row["id"],
-                "words": row["words"],
-                "ner_tags": row["tags"],
-            }