Upload ncbi_disease.py

ncbi_disease.py

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+# 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
+"""NCBI disease corpus: a resource for disease name recognition and concept normalization"""
+
+import datasets
+
+
+logger = datasets.logging.get_logger(__name__)
+
+
+_CITATION = """\
+@article{dougan2014ncbi,
+         title={NCBI disease corpus: a resource for disease name recognition and concept normalization},
+         author={Dogan, Rezarta Islamaj and Leaman, Robert and Lu, Zhiyong},
+         journal={Journal of biomedical informatics},
+         volume={47},
+         pages={1--10},
+         year={2014},
+         publisher={Elsevier}
+}
+"""
+
+_DESCRIPTION = """\
+This paper presents the disease name and concept annotations of the NCBI disease corpus, a collection of 793 PubMed
+abstracts fully annotated at the mention and concept level to serve as a research resource for the biomedical natural
+language processing community. Each PubMed abstract was manually annotated by two annotators with disease mentions
+and their corresponding concepts in Medical Subject Headings (MeSH®) or Online Mendelian Inheritance in Man (OMIM®).
+Manual curation was performed using PubTator, which allowed the use of pre-annotations as a pre-step to manual annotations.
+Fourteen annotators were randomly paired and differing annotations were discussed for reaching a consensus in two
+annotation phases. In this setting, a high inter-annotator agreement was observed. Finally, all results were checked
+against annotations of the rest of the corpus to assure corpus-wide consistency.
+
+For more details, see: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3951655/
+
+The original dataset can be downloaded from: https://www.ncbi.nlm.nih.gov/CBBresearch/Dogan/DISEASE/NCBI_corpus.zip
+This dataset has been converted to CoNLL format for NER using the following tool: https://github.com/spyysalo/standoff2conll
+Note: there is a duplicate document (PMID 8528200) in the original data, and the duplicate is recreated in the converted data.
+"""
+
+_HOMEPAGE = "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3951655/"
+_URL = "https://github.com/spyysalo/ncbi-disease/raw/master/conll/"
+_TRAINING_FILE = "train.tsv"
+_DEV_FILE = "devel.tsv"
+_TEST_FILE = "test.tsv"
+
+
+class NCBIDiseaseConfig(datasets.BuilderConfig):
+    """BuilderConfig for NCBIDisease"""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for NCBIDisease.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(NCBIDiseaseConfig, self).__init__(**kwargs)
+
+
+class NCBIDisease(datasets.GeneratorBasedBuilder):
+    """NCBIDisease dataset."""
+
+    BUILDER_CONFIGS = [
+        NCBIDiseaseConfig(name="ncbi_disease", version=datasets.Version("1.0.0"), description="NCBIDisease dataset"),
+    ]
+
+    def _info(self):
+        custom_names = ['O','B-GENE','I-GENE','B-CHEMICAL','I-CHEMICAL','B-DISEASE','I-DISEASE',
+            'B-DNA', 'I-DNA', 'B-RNA', 'I-RNA', 'B-CELL_LINE', 'I-CELL_LINE', 'B-CELL_TYPE', 'I-CELL_TYPE', 
+            'B-PROTEIN', 'I-PROTEIN', 'B-SPECIES', 'I-SPECIES'] 
+        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=custom_names 
+                        )
+                    ),
+                }
+            ),
+            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])
+                    if(splits[1].rstrip()=="B-Disease"):
+                        ner_tags.append("B-DISEASE")
+                    elif(splits[1].rstrip()=="I-Disease"):
+                        ner_tags.append("I-DISEASE")
+                    else:
+                        ner_tags.append(splits[1].rstrip())
+            # last example
+            yield guid, {
+                "id": str(guid),
+                "tokens": tokens,
+                "ner_tags": ner_tags,
+            }