Create EpiSet4NER-v1.py

EpiSet4NER-v1.py

@@ -0,0 +1,135 @@
+# 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.tsv"
+_VAL_FILE = "val.tsv"
+_TEST_FILE = "test.tsv"
+
+
+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("3.2.1"), 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", #(0)
+                                "B-LOC", #(1)
+                                "I-LOC", #(2)
+                                "B-EPI", #(3)
+                                "I-EPI", #(4)
+                                "B-STAT", #(5)
+                                "I-STAT", #(6)
+                            ]
+                        )
+                    ),
+                }
+            ),
+            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("\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,
+                }