Delete loading script

conll2002.py

@@ -1,228 +0,0 @@
-# coding=utf-8
-# Copyright 2020 HuggingFace Datasets Authors.
-#
-# 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.
-
-# Lint as: python3
-"""Introduction to the CoNLL-2002 Shared Task: Language-Independent Named Entity Recognition"""
-
-import datasets
-
-
-logger = datasets.logging.get_logger(__name__)
-
-
-_CITATION = """\
-@inproceedings{tjong-kim-sang-2002-introduction,
-    title = "Introduction to the {C}o{NLL}-2002 Shared Task: Language-Independent Named Entity Recognition",
-    author = "Tjong Kim Sang, Erik F.",
-    booktitle = "{COLING}-02: The 6th Conference on Natural Language Learning 2002 ({C}o{NLL}-2002)",
-    year = "2002",
-    url = "https://www.aclweb.org/anthology/W02-2024",
-}
-"""
-
-_DESCRIPTION = """\
-Named entities are phrases that contain the names of persons, organizations, locations, times and quantities.
-
-Example:
-[PER Wolff] , currently a journalist in [LOC Argentina] , played with [PER Del Bosque] in the final years of the seventies in [ORG Real Madrid] .
-
-The shared task of CoNLL-2002 concerns language-independent named entity recognition.
-We will concentrate on four types of named entities: persons, locations, organizations and names of miscellaneous entities that do not belong to the previous three groups.
-The participants of the shared task will be offered training and test data for at least two languages.
-They will use the data for developing a named-entity recognition system that includes a machine learning component.
-Information sources other than the training data may be used in this shared task.
-We are especially interested in methods that can use additional unannotated data for improving their performance (for example co-training).
-
-The train/validation/test sets are available in Spanish and Dutch.
-
-For more details see https://www.clips.uantwerpen.be/conll2002/ner/ and https://www.aclweb.org/anthology/W02-2024/
-"""
-
-_URL = "https://raw.githubusercontent.com/teropa/nlp/master/resources/corpora/conll2002/"
-_ES_TRAINING_FILE = "esp.train"
-_ES_DEV_FILE = "esp.testa"
-_ES_TEST_FILE = "esp.testb"
-_NL_TRAINING_FILE = "ned.train"
-_NL_DEV_FILE = "ned.testa"
-_NL_TEST_FILE = "ned.testb"
-
-
-class Conll2002Config(datasets.BuilderConfig):
-    """BuilderConfig for Conll2002"""
-
-    def __init__(self, **kwargs):
-        """BuilderConfig forConll2002.
-
-        Args:
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(Conll2002Config, self).__init__(**kwargs)
-
-
-class Conll2002(datasets.GeneratorBasedBuilder):
-    """Conll2002 dataset."""
-
-    BUILDER_CONFIGS = [
-        Conll2002Config(name="es", version=datasets.Version("1.0.0"), description="Conll2002 Spanish dataset"),
-        Conll2002Config(name="nl", version=datasets.Version("1.0.0"), description="Conll2002 Dutch dataset"),
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "id": datasets.Value("string"),
-                    "tokens": datasets.Sequence(datasets.Value("string")),
-                    "pos_tags": datasets.Sequence(
-                        datasets.features.ClassLabel(
-                            names=[
-                                "AO",
-                                "AQ",
-                                "CC",
-                                "CS",
-                                "DA",
-                                "DE",
-                                "DD",
-                                "DI",
-                                "DN",
-                                "DP",
-                                "DT",
-                                "Faa",
-                                "Fat",
-                                "Fc",
-                                "Fd",
-                                "Fe",
-                                "Fg",
-                                "Fh",
-                                "Fia",
-                                "Fit",
-                                "Fp",
-                                "Fpa",
-                                "Fpt",
-                                "Fs",
-                                "Ft",
-                                "Fx",
-                                "Fz",
-                                "I",
-                                "NC",
-                                "NP",
-                                "P0",
-                                "PD",
-                                "PI",
-                                "PN",
-                                "PP",
-                                "PR",
-                                "PT",
-                                "PX",
-                                "RG",
-                                "RN",
-                                "SP",
-                                "VAI",
-                                "VAM",
-                                "VAN",
-                                "VAP",
-                                "VAS",
-                                "VMG",
-                                "VMI",
-                                "VMM",
-                                "VMN",
-                                "VMP",
-                                "VMS",
-                                "VSG",
-                                "VSI",
-                                "VSM",
-                                "VSN",
-                                "VSP",
-                                "VSS",
-                                "Y",
-                                "Z",
-                            ]
-                        )
-                        if self.config.name == "es"
-                        else datasets.features.ClassLabel(
-                            names=["Adj", "Adv", "Art", "Conj", "Int", "Misc", "N", "Num", "Prep", "Pron", "Punc", "V"]
-                        )
-                    ),
-                    "ner_tags": datasets.Sequence(
-                        datasets.features.ClassLabel(
-                            names=[
-                                "O",
-                                "B-PER",
-                                "I-PER",
-                                "B-ORG",
-                                "I-ORG",
-                                "B-LOC",
-                                "I-LOC",
-                                "B-MISC",
-                                "I-MISC",
-                            ]
-                        )
-                    ),
-                }
-            ),
-            supervised_keys=None,
-            homepage="https://www.aclweb.org/anthology/W02-2024/",
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        urls_to_download = {
-            "train": f"{_URL}{_ES_TRAINING_FILE if self.config.name == 'es' else _NL_TRAINING_FILE}",
-            "dev": f"{_URL}{_ES_DEV_FILE if self.config.name == 'es' else _NL_DEV_FILE}",
-            "test": f"{_URL}{_ES_TEST_FILE if self.config.name == 'es' else _NL_TEST_FILE}",
-        }
-        downloaded_files = dl_manager.download_and_extract(urls_to_download)
-
-        return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
-            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
-            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
-        ]
-
-    def _generate_examples(self, filepath):
-        logger.info("⏳ Generating examples from = %s", filepath)
-        with open(filepath, encoding="utf-8") as f:
-            guid = 0
-            tokens = []
-            pos_tags = []
-            ner_tags = []
-            for line in f:
-                if line.startswith("-DOCSTART-") or line == "" or line == "\n":
-                    if tokens:
-                        yield guid, {
-                            "id": str(guid),
-                            "tokens": tokens,
-                            "pos_tags": pos_tags,
-                            "ner_tags": ner_tags,
-                        }
-                        guid += 1
-                        tokens = []
-                        pos_tags = []
-                        ner_tags = []
-                else:
-                    # conll2002 tokens are space separated
-                    splits = line.split(" ")
-                    tokens.append(splits[0])
-                    pos_tags.append(splits[1])
-                    ner_tags.append(splits[2].rstrip())
-            # last example
-            yield guid, {
-                "id": str(guid),
-                "tokens": tokens,
-                "pos_tags": pos_tags,
-                "ner_tags": ner_tags,
-            }