Delete EpiSet4NER.py

EpiSet4NER.py

@@ -1,135 +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
-"""INSERT TITLE"""
-
-import logging
-
-import datasets
-
-
-_CITATION = """\
-*REDO*
-@inproceedings{wang2019crossweigh,
-  title={CrossWeigh: Training Named Entity Tagger from Imperfect Annotations},
-  author={Wang, Zihan and Shang, Jingbo and Liu, Liyuan and Lu, Lihao and Liu, Jiacheng and Han, Jiawei},
-  booktitle={Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)},
-  pages={5157--5166},
-  year={2019}
-}
-"""
-
-_DESCRIPTION = """\
-**REWRITE*
-EpiSet4NER is a dataset generated from 620 rare disease abstracts labeled using statistical and rule-base methods. The test set was then manually corrected by a rare disease expert.
-For more details see *INSERT PAPER* and https://github.com/ncats/epi4GARD/tree/master/EpiExtract4GARD#epiextract4gard
-"""
-
-_URL = "https://github.com/NCATS/epi4GARD/raw/master/EpiExtract4GARD/datasets/EpiCustomV3/"
-_TRAINING_FILE = "train.txt"
-_VAL_FILE = "val.txt"
-_TEST_FILE = "test.txt"
-
-
-class EpiSetConfig(datasets.BuilderConfig):
-    """BuilderConfig for Conll2003"""
-
-    def __init__(self, **kwargs):
-        """BuilderConfig forConll2003.
-        Args:
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(EpiSetConfig, self).__init__(**kwargs)
-
-
-class EpiSet(datasets.GeneratorBasedBuilder):
-    """EpiSet4NER by GARD."""
-
-    BUILDER_CONFIGS = [
-        EpiSetConfig(name="EpiSet4NER", version=datasets.Version("1.0.0"), description="EpiSet4NER by NIH NCATS GARD"),
-    ]
-
-    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-EPI",
-                                "B-LOC",
-                                "B-STAT",
-                                "I-EPI",
-                                "I-LOC",
-                                "I-STAT",
-                            ]
-                        )
-                    ),
-                }
-            ),
-            supervised_keys=None,
-            homepage="https://github.com/ncats/epi4GARD/tree/master/EpiExtract4GARD#epiextract4gard",
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        urls_to_download = {
-            "train": f"{_URL}{_TRAINING_FILE}",
-            "val": f"{_URL}{_VAL_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["val"]}),
-            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
-        ]
-
-    def _generate_examples(self, filepath):
-        logging.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.startswith("-DOCSTART-") or line == "" or line == "\n":
-                    if tokens:
-                        yield guid, {
-                            "id": str(guid),
-                            "tokens": tokens,
-                            "ner_tags": ner_tags,
-                        }
-                        guid += 1
-                        tokens = []
-                        ner_tags = []
-                else:
-                    # EpiSet tokens are space separated
-                    splits = line.split(" ")
-                    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,
-                }