Delete loading script

bc2gm_corpus.py

@@ -1,143 +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
-"""BioCreative II gene mention recognition Corpus"""
-
-import datasets
-
-
-logger = datasets.logging.get_logger(__name__)
-
-
-_CITATION = """\
-@article{smith2008overview,
-        title={Overview of BioCreative II gene mention recognition},
-        author={Smith, Larry and Tanabe, Lorraine K and nee Ando, Rie Johnson and Kuo, Cheng-Ju and Chung, I-Fang and Hsu, Chun-Nan and Lin, Yu-Shi and Klinger, Roman and Friedrich, Christoph M and Ganchev, Kuzman and others},
-        journal={Genome biology},
-        volume={9},
-        number={S2},
-        pages={S2},
-        year={2008},
-        publisher={Springer}
-}
-"""
-
-_DESCRIPTION = """\
-Nineteen teams presented results for the Gene Mention Task at the BioCreative II Workshop.
-In this task participants designed systems to identify substrings in sentences corresponding to gene name mentions.
-A variety of different methods were used and the results varied with a highest achieved F1 score of 0.8721.
-Here we present brief descriptions of all the methods used and a statistical analysis of the results.
-We also demonstrate that, by combining the results from all submissions, an F score of 0.9066 is feasible,
-and furthermore that the best result makes use of the lowest scoring submissions.
-
-For more details, see: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2559986/
-
-The original dataset can be downloaded from: https://biocreative.bioinformatics.udel.edu/resources/corpora/biocreative-ii-corpus/
-This dataset has been converted to CoNLL format for NER using the following tool: https://github.com/spyysalo/standoff2conll
-"""
-
-_HOMEPAGE = "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2559986/"
-_URL = "https://github.com/spyysalo/bc2gm-corpus/raw/master/conll/"
-_TRAINING_FILE = "train.tsv"
-_DEV_FILE = "devel.tsv"
-_TEST_FILE = "test.tsv"
-
-
-class Bc2gmCorpusConfig(datasets.BuilderConfig):
-    """BuilderConfig for Bc2gmCorpus"""
-
-    def __init__(self, **kwargs):
-        """BuilderConfig for Bc2gmCorpus.
-        Args:
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(Bc2gmCorpusConfig, self).__init__(**kwargs)
-
-
-class Bc2gmCorpus(datasets.GeneratorBasedBuilder):
-    """Bc2gmCorpus dataset."""
-
-    BUILDER_CONFIGS = [
-        Bc2gmCorpusConfig(name="bc2gm_corpus", version=datasets.Version("1.0.0"), description="bc2gm corpus"),
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "id": datasets.Value("string"),
-                    "tokens": datasets.Sequence(datasets.Value("string")),
-                    "ner_tags": datasets.Sequence(
-                        datasets.features.ClassLabel(
-                            names=[
-                                "O",
-                                "B-GENE",
-                                "I-GENE",
-                            ]
-                        )
-                    ),
-                }
-            ),
-            supervised_keys=None,
-            homepage=_HOMEPAGE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        urls_to_download = {
-            "train": f"{_URL}{_TRAINING_FILE}",
-            "dev": f"{_URL}{_DEV_FILE}",
-            "test": f"{_URL}{_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 = []
-            ner_tags = []
-            for line in f:
-                if line == "" or line == "\n":
-                    if tokens:
-                        yield guid, {
-                            "id": str(guid),
-                            "tokens": tokens,
-                            "ner_tags": ner_tags,
-                        }
-                        guid += 1
-                        tokens = []
-                        ner_tags = []
-                else:
-                    # tokens are tab separated
-                    splits = line.split("\t")
-                    tokens.append(splits[0])
-                    ner_tags.append(splits[1].rstrip())
-            # last example
-            if tokens:
-                yield guid, {
-                    "id": str(guid),
-                    "tokens": tokens,
-                    "ner_tags": ner_tags,
-                }