Delete aka_generic_ner.py

aka_generic_ner.py

@@ -1,247 +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-2003 Shared Task: Language-Independent Named Entity Recognition"""
-
-import os
-
-import datasets
-
-
-logger = datasets.logging.get_logger(__name__)
-
-
-_CITATION = """\
-@inproceedings{tjong-kim-sang-de-meulder-2003-introduction,
-    title = "Introduction to the {C}o{NLL}-2003 Shared Task: Language-Independent Named Entity Recognition",
-    author = "Tjong Kim Sang, Erik F.  and
-      De Meulder, Fien",
-    booktitle = "Proceedings of the Seventh Conference on Natural Language Learning at {HLT}-{NAACL} 2003",
-    year = "2003",
-    url = "https://www.aclweb.org/anthology/W03-0419",
-    pages = "142--147",
-}
-"""
-
-_DESCRIPTION = """\
-The shared task of CoNLL-2003 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 CoNLL-2003 shared task data files contain four columns separated by a single space. Each word has been put on
-a separate line and there is an empty line after each sentence. The first item on each line is a word, the second
-a part-of-speech (POS) tag, the third a syntactic chunk tag and the fourth the named entity tag. The chunk tags
-and the named entity tags have the format I-TYPE which means that the word is inside a phrase of type TYPE. Only
-if two phrases of the same type immediately follow each other, the first word of the second phrase will have tag
-B-TYPE to show that it starts a new phrase. A word with tag O is not part of a phrase. Note the dataset uses IOB2
-tagging scheme, whereas the original dataset uses IOB1.
-For more details see https://www.clips.uantwerpen.be/conll2003/ner/ and https://www.aclweb.org/anthology/W03-0419
-"""
-
-_URL = "https://embraceletsupply.com/wp-content/uploads/2024/04/aka2024.zip" #"https://data.deepai.org/conll2003.zip"
-_TRAINING_FILE = "train.txt"
-_DEV_FILE = "valid.txt"
-_TEST_FILE = "test.txt"
-
-
-class AkaGenericNerConfig(datasets.BuilderConfig):
-    """BuilderConfig for AkaGenericNer"""
-
-    def __init__(self, **kwargs):
-        """BuilderConfig forAkaGenericNer.
-        Args:
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(AkaGenericNerConfig, self).__init__(**kwargs)
-
-
-class AkaGenericNer(datasets.GeneratorBasedBuilder):
-    """AkaGenericNer dataset."""
-
-    BUILDER_CONFIGS = [
-        AkaGenericNerConfig(name="aka2024", version=datasets.Version("1.0.0"), description="AKA Generic NER 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=[
-                                '"',
-                                "''",
-                                "#",
-                                "$",
-                                "(",
-                                ")",
-                                ",",
-                                ".",
-                                ":",
-                                "``",
-                                "CC",
-                                "CD",
-                                "DT",
-                                "EX",
-                                "FW",
-                                "IN",
-                                "JJ",
-                                "JJR",
-                                "JJS",
-                                "LS",
-                                "MD",
-                                "NN",
-                                "NNP",
-                                "NNPS",
-                                "NNS",
-                                "NN|SYM",
-                                "PDT",
-                                "POS",
-                                "PRP",
-                                "PRP$",
-                                "RB",
-                                "RBR",
-                                "RBS",
-                                "RP",
-                                "SYM",
-                                "TO",
-                                "UH",
-                                "VB",
-                                "VBD",
-                                "VBG",
-                                "VBN",
-                                "VBP",
-                                "VBZ",
-                                "WDT",
-                                "WP",
-                                "WP$",
-                                "WRB",
-                            ]
-                        )
-                    ),
-                    "chunk_tags": datasets.Sequence(
-                        datasets.features.ClassLabel(
-                            names=[
-                                "O",
-                                "B-ADJP",
-                                "I-ADJP",
-                                "B-ADVP",
-                                "I-ADVP",
-                                "B-CONJP",
-                                "I-CONJP",
-                                "B-INTJ",
-                                "I-INTJ",
-                                "B-LST",
-                                "I-LST",
-                                "B-NP",
-                                "I-NP",
-                                "B-PP",
-                                "I-PP",
-                                "B-PRT",
-                                "I-PRT",
-                                "B-SBAR",
-                                "I-SBAR",
-                                "B-UCP",
-                                "I-UCP",
-                                "B-VP",
-                                "I-VP",
-                            ]
-                        )
-                    ),
-                    "ner_tags": datasets.Sequence(
-                        datasets.features.ClassLabel(
-                            names=[
-                                "O",
-                                "B-KPI",
-                                "I-KPI",
-                                "B-DIM",
-                                "I-DIM",
-                                "B-FSC",
-                                "I-FSC",
-                                "B-FWEL",
-                                "I-FWEL",
-                                "B-FRIG",
-                                "I-FRIG",
-                                "B-FDEP",
-                                "I-FDEP",
-                                "B-MISC",
-                                "I-MISC",
-                            ]
-                        )
-                    ),
-                }
-            ),
-            supervised_keys=None,
-            homepage="https://www.aclweb.org/anthology/W03-0419/",
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        downloaded_file = dl_manager.download_and_extract(_URL)
-        data_files = {
-            "train": os.path.join(downloaded_file, _TRAINING_FILE),
-            "dev": os.path.join(downloaded_file, _DEV_FILE),
-            "test": os.path.join(downloaded_file, _TEST_FILE),
-        }
-
-        return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_files["train"]}),
-            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": data_files["dev"]}),
-            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": data_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 = []
-            chunk_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,
-                            "chunk_tags": chunk_tags,
-                            "ner_tags": ner_tags,
-                        }
-                        guid += 1
-                        tokens = []
-                        pos_tags = []
-                        chunk_tags = []
-                        ner_tags = []
-                else:
-                    # conll2003 tokens are space separated
-                    splits = line.split(" ")
-                    tokens.append(splits[0])
-                    pos_tags.append(splits[1])
-                    chunk_tags.append(splits[2])
-                    ner_tags.append(splits[3].rstrip())
-            # last example
-            if tokens:
-                yield guid, {
-                    "id": str(guid),
-                    "tokens": tokens,
-                    "pos_tags": pos_tags,
-                    "chunk_tags": chunk_tags,
-                    "ner_tags": ner_tags,
-                }