upload hubscripts/medmentions_hub.py to hub from bigbio repo

medmentions.py

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+# coding=utf-8
+# Copyright 2022 The HuggingFace Datasets Authors and Simon Ott, github: nomisto
+#
+# 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.
+
+"""
+MedMentions is a new manually annotated resource for the recognition of biomedical concepts.
+What distinguishes MedMentions from other annotated biomedical corpora is its size (over 4,000
+abstracts and over 350,000 linked mentions), as well as the size of the concept ontology (over
+3 million concepts from UMLS 2017) and its broad coverage of biomedical disciplines.
+
+Corpus: The MedMentions corpus consists of 4,392 papers (Titles and Abstracts) randomly selected
+from among papers released on PubMed in 2016, that were in the biomedical field, published in
+the English language, and had both a Title and an Abstract.
+
+Annotators: We recruited a team of professional annotators with rich experience in biomedical
+content curation to exhaustively annotate all UMLS® (2017AA full version) entity mentions in
+these papers.
+
+Annotation quality: We did not collect stringent IAA (Inter-annotator agreement) data. To gain
+insight on the annotation quality of MedMentions, we randomly selected eight papers from the
+annotated corpus, containing a total of 469 concepts. Two biologists ('Reviewer') who did not
+participate in the annotation task then each reviewed four papers. The agreement between
+Reviewers and Annotators, an estimate of the Precision of the annotations, was 97.3%.
+
+For more information visit: https://github.com/chanzuckerberg/MedMentions
+"""
+
+import itertools as it
+from typing import List
+
+import datasets
+
+from .bigbiohub import kb_features
+from .bigbiohub import BigBioConfig
+from .bigbiohub import Tasks
+
+_LANGUAGES = ['English']
+_PUBMED = True
+_LOCAL = False
+_CITATION = """\
+@misc{mohan2019medmentions,
+      title={MedMentions: A Large Biomedical Corpus Annotated with UMLS Concepts},
+      author={Sunil Mohan and Donghui Li},
+      year={2019},
+      eprint={1902.09476},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+"""
+
+_DATASETNAME = "medmentions"
+_DISPLAYNAME = "MedMentions"
+
+_DESCRIPTION = """\
+MedMentions is a new manually annotated resource for the recognition of biomedical concepts.
+What distinguishes MedMentions from other annotated biomedical corpora is its size (over 4,000
+abstracts and over 350,000 linked mentions), as well as the size of the concept ontology (over
+3 million concepts from UMLS 2017) and its broad coverage of biomedical disciplines.
+
+Corpus: The MedMentions corpus consists of 4,392 papers (Titles and Abstracts) randomly selected
+from among papers released on PubMed in 2016, that were in the biomedical field, published in
+the English language, and had both a Title and an Abstract.
+
+Annotators: We recruited a team of professional annotators with rich experience in biomedical
+content curation to exhaustively annotate all UMLS® (2017AA full version) entity mentions in
+these papers.
+
+Annotation quality: We did not collect stringent IAA (Inter-annotator agreement) data. To gain
+insight on the annotation quality of MedMentions, we randomly selected eight papers from the
+annotated corpus, containing a total of 469 concepts. Two biologists ('Reviewer') who did not
+participate in the annotation task then each reviewed four papers. The agreement between
+Reviewers and Annotators, an estimate of the Precision of the annotations, was 97.3%.
+"""
+
+_HOMEPAGE = "https://github.com/chanzuckerberg/MedMentions"
+
+_LICENSE = 'Creative Commons Zero v1.0 Universal'
+
+_URLS = {
+    "medmentions_full": [
+        "https://github.com/chanzuckerberg/MedMentions/raw/master/full/data/corpus_pubtator.txt.gz",
+        "https://github.com/chanzuckerberg/MedMentions/raw/master/full/data/corpus_pubtator_pmids_trng.txt",
+        "https://github.com/chanzuckerberg/MedMentions/raw/master/full/data/corpus_pubtator_pmids_dev.txt",
+        "https://github.com/chanzuckerberg/MedMentions/raw/master/full/data/corpus_pubtator_pmids_test.txt",
+    ],
+    "medmentions_st21pv": [
+        "https://github.com/chanzuckerberg/MedMentions/raw/master/st21pv/data/corpus_pubtator.txt.gz",
+        "https://github.com/chanzuckerberg/MedMentions/raw/master/full/data/corpus_pubtator_pmids_trng.txt",
+        "https://github.com/chanzuckerberg/MedMentions/raw/master/full/data/corpus_pubtator_pmids_dev.txt",
+        "https://github.com/chanzuckerberg/MedMentions/raw/master/full/data/corpus_pubtator_pmids_test.txt",
+    ],
+}
+
+_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_DISAMBIGUATION, Tasks.NAMED_ENTITY_RECOGNITION]
+
+_SOURCE_VERSION = "1.0.0"
+
+_BIGBIO_VERSION = "1.0.0"
+
+
+class MedMentionsDataset(datasets.GeneratorBasedBuilder):
+    """MedMentions dataset for named-entity disambiguation (NED)"""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
+
+    BUILDER_CONFIGS = [
+        BigBioConfig(
+            name="medmentions_full_source",
+            version=SOURCE_VERSION,
+            description="MedMentions Full source schema",
+            schema="source",
+            subset_id="medmentions_full",
+        ),
+        BigBioConfig(
+            name="medmentions_full_bigbio_kb",
+            version=BIGBIO_VERSION,
+            description="MedMentions Full BigBio schema",
+            schema="bigbio_kb",
+            subset_id="medmentions_full",
+        ),
+        BigBioConfig(
+            name="medmentions_st21pv_source",
+            version=SOURCE_VERSION,
+            description="MedMentions ST21pv source schema",
+            schema="source",
+            subset_id="medmentions_st21pv",
+        ),
+        BigBioConfig(
+            name="medmentions_st21pv_bigbio_kb",
+            version=BIGBIO_VERSION,
+            description="MedMentions ST21pv BigBio schema",
+            schema="bigbio_kb",
+            subset_id="medmentions_st21pv",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "medmentions_full_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "pmid": datasets.Value("string"),
+                    "passages": [
+                        {
+                            "type": datasets.Value("string"),
+                            "text": datasets.Sequence(datasets.Value("string")),
+                            "offsets": datasets.Sequence([datasets.Value("int32")]),
+                        }
+                    ],
+                    "entities": [
+                        {
+                            "text": datasets.Sequence(datasets.Value("string")),
+                            "offsets": datasets.Sequence([datasets.Value("int32")]),
+                            "concept_id": datasets.Value("string"),
+                            "semantic_type_id": datasets.Sequence(
+                                datasets.Value("string")
+                            ),
+                        }
+                    ],
+                }
+            )
+
+        elif self.config.schema == "bigbio_kb":
+            features = kb_features
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=str(_LICENSE),
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager) -> List[datasets.SplitGenerator]:
+
+        urls = _URLS[self.config.subset_id]
+        (
+            corpus_path,
+            pmids_train,
+            pmids_dev,
+            pmids_test,
+        ) = dl_manager.download_and_extract(urls)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"corpus_path": corpus_path, "pmids_path": pmids_train},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"corpus_path": corpus_path, "pmids_path": pmids_test},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"corpus_path": corpus_path, "pmids_path": pmids_dev},
+            ),
+        ]
+
+    def _generate_examples(self, corpus_path, pmids_path):
+        with open(pmids_path, encoding="utf8") as infile:
+            pmids = infile.readlines()
+        pmids = {int(x.strip()) for x in pmids}
+
+        if self.config.schema == "source":
+            with open(corpus_path, encoding="utf8") as corpus:
+                for document in self._generate_parsed_documents(corpus, pmids):
+                    yield document["pmid"], document
+
+        elif self.config.schema == "bigbio_kb":
+            uid = it.count(0)
+            with open(corpus_path, encoding="utf8") as corpus:
+                for document in self._generate_parsed_documents(corpus, pmids):
+                    document["id"] = next(uid)
+                    document["document_id"] = document.pop("pmid")
+
+                    entities_ = []
+                    for entity in document["entities"]:
+                        for type in entity["semantic_type_id"]:
+                            entities_.append(
+                                {
+                                    "id": next(uid),
+                                    "type": type,
+                                    "text": entity["text"],
+                                    "offsets": entity["offsets"],
+                                    "normalized": [
+                                        {
+                                            "db_name": "UMLS",
+                                            "db_id": entity["concept_id"],
+                                        }
+                                    ],
+                                }
+                            )
+                    document["entities"] = entities_
+
+                    for passage in document["passages"]:
+                        passage["id"] = next(uid)
+                    document["relations"] = []
+                    document["events"] = []
+                    document["coreferences"] = []
+                    yield document["document_id"], document
+
+    def _generate_parsed_documents(self, fstream, pmids):
+        for raw_document in self._generate_raw_documents(fstream):
+            if self._parse_pmid(raw_document) in pmids:
+                yield self._parse_document(raw_document)
+
+    def _generate_raw_documents(self, fstream):
+        raw_document = []
+        for line in fstream:
+            if line.strip():
+                raw_document.append(line.strip())
+            elif raw_document:
+                yield raw_document
+                raw_document = []
+        # needed for last document
+        if raw_document:
+            yield raw_document
+
+    def _parse_pmid(self, raw_document):
+        pmid, _ = raw_document[0].split("|", 1)
+        return int(pmid)
+
+    def _parse_document(self, raw_document):
+        pmid, type, title = raw_document[0].split("|", 2)
+        pmid_, type, abstract = raw_document[1].split("|", 2)
+        passages = [
+            {"type": "title", "text": [title], "offsets": [[0, len(title)]]},
+            {
+                "type": "abstract",
+                "text": [abstract],
+                "offsets": [[len(title) + 1, len(title) + len(abstract) + 1]],
+            },
+        ]
+
+        entities = []
+        for line in raw_document[2:]:
+            (
+                pmid_,
+                start_idx,
+                end_idx,
+                mention,
+                semantic_type_id,
+                entity_id,
+            ) = line.split("\t")
+            entity = {
+                "offsets": [[int(start_idx), int(end_idx)]],
+                "text": [mention],
+                "semantic_type_id": semantic_type_id.split(","),
+                "concept_id": entity_id,
+            }
+            entities.append(entity)
+
+        return {"pmid": int(pmid), "entities": entities, "passages": passages}