upload hubscripts/seth_corpus_hub.py to hub from bigbio repo

seth_corpus.py

@@ -0,0 +1,233 @@
+# coding=utf-8
+# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# 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.
+
+"""Descriptions of genetic variations and their effect are widely spread across the biomedical literature. However,
+finding all mentions of a specific variation, or all mentions of variations in a specific gene, is difficult to
+achieve due to the many ways such variations are described. Here, we describe SETH, a tool for the recognition of
+variations from text and their subsequent normalization to dbSNP or UniProt. SETH achieves high precision and recall
+on several evaluation corpora of PubMed abstracts. It is freely available and encompasses stand-alone scripts for
+isolated application and evaluation as well as a thorough documentation for integration into other applications.
+The script loads dataset in bigbio schema (using knowledgebase schema: schemas/kb) AND/OR source (default) schema """
+
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+
+from .bigbiohub import kb_features
+from .bigbiohub import BigBioConfig
+from .bigbiohub import Tasks
+
+_LANGUAGES = ['English']
+_PUBMED = True
+_LOCAL = False
+_CITATION = """\
+@Article{SETH2016,
+    Title       = {SETH detects and normalizes genetic variants in text.},
+    Author      = {Thomas, Philippe and Rockt{"{a}}schel, Tim and Hakenberg, J{"{o}}rg and Lichtblau, Yvonne and Leser, Ulf},
+    Journal     = {Bioinformatics},
+    Year        = {2016},
+    Month       = {Jun},
+    Doi         = {10.1093/bioinformatics/btw234},
+    Language    = {eng},
+    Medline-pst = {aheadofprint},
+    Pmid        = {27256315},
+    Url         = {http://dx.doi.org/10.1093/bioinformatics/btw234
+}
+"""
+
+_DATASETNAME = "seth_corpus"
+_DISPLAYNAME = "SETH Corpus"
+
+_DESCRIPTION = (
+    """SNP named entity recognition corpus consisting of 630 PubMed citations."""
+)
+
+_HOMEPAGE = "https://github.com/rockt/SETH"
+
+_LICENSE = 'Apache License 2.0'
+_URLS = {
+    "source": "https://github.com/rockt/SETH/archive/refs/heads/master.zip",
+    "bigbio_kb": "https://github.com/rockt/SETH/archive/refs/heads/master.zip",
+}
+
+_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION, Tasks.RELATION_EXTRACTION]
+
+_SOURCE_VERSION = "1.0.0"
+_BIGBIO_VERSION = "1.0.0"
+
+
+class SethCorpusDataset(datasets.GeneratorBasedBuilder):
+    """SNP named entity recognition corpus consisting of 630 PubMed citations."""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
+
+    BUILDER_CONFIGS = [
+        BigBioConfig(
+            name="seth_corpus_source",
+            version=SOURCE_VERSION,
+            description="SETH corpus source schema",
+            schema="source",
+            subset_id="seth_corpus",
+        ),
+        BigBioConfig(
+            name="seth_corpus_bigbio_kb",
+            version=BIGBIO_VERSION,
+            description="SETH corpus BigBio schema",
+            schema="bigbio_kb",
+            subset_id="seth_corpus",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "seth_corpus_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "document_id": datasets.Value("string"),
+                    "text": datasets.Value("string"),
+                    "text_bound_annotations": [  # T line in brat, e.g. type or event trigger
+                        {
+                            "offsets": datasets.Sequence([datasets.Value("int32")]),
+                            "text": datasets.Sequence(datasets.Value("string")),
+                            "type": datasets.Value("string"),
+                            "id": datasets.Value("string"),
+                        }
+                    ],
+                    "events": [  # E line in brat
+                        {
+                            "trigger": datasets.Value("string"),
+                            "id": datasets.Value("string"),
+                            "type": datasets.Value("string"),
+                            "arguments": datasets.Sequence(
+                                {
+                                    "role": datasets.Value("string"),
+                                    "ref_id": datasets.Value("string"),
+                                }
+                            ),
+                        }
+                    ],
+                    "relations": [  # R line in brat
+                        {
+                            "id": datasets.Value("string"),
+                            "head": {
+                                "ref_id": datasets.Value("string"),
+                                "role": datasets.Value("string"),
+                            },
+                            "tail": {
+                                "ref_id": datasets.Value("string"),
+                                "role": datasets.Value("string"),
+                            },
+                            "type": datasets.Value("string"),
+                        }
+                    ],
+                    "equivalences": [  # Equiv line in brat
+                        {
+                            "id": datasets.Value("string"),
+                            "ref_ids": datasets.Sequence(datasets.Value("string")),
+                        }
+                    ],
+                    "attributes": [  # M or A lines in brat
+                        {
+                            "id": datasets.Value("string"),
+                            "type": datasets.Value("string"),
+                            "ref_id": datasets.Value("string"),
+                            "value": datasets.Value("string"),
+                        }
+                    ],
+                    "normalizations": [  # N lines in brat
+                        {
+                            "id": datasets.Value("string"),
+                            "type": datasets.Value("string"),
+                            "ref_id": datasets.Value("string"),
+                            "resource_name": datasets.Value("string"),
+                            "cuid": datasets.Value("string"),
+                            "text": 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]:
+        """Returns SplitGenerators."""
+
+        urls = _URLS[self.config.schema]
+        data_dir = Path(dl_manager.download_and_extract(urls))
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": data_dir / "SETH-master" / "resources" / "SETH-corpus",
+                    "corpus_file": "corpus.txt",
+                    "split": "train",
+                },
+            ),
+        ]
+
+    def _generate_examples(
+        self, filepath: Path, corpus_file: str, split: str
+    ) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+
+        if self.config.schema == "source":
+            with open(filepath / corpus_file, encoding="utf-8") as f:
+                contents = f.readlines()
+            for guid, content in enumerate(contents):
+                file_name, text = content.split("\t")
+                example = parsing.parse_brat_file(
+                    filepath / "annotations" / f"{file_name}.ann"
+                )
+                example["id"] = str(guid)
+                example["text"] = text
+                yield guid, example
+
+        elif self.config.schema == "bigbio_kb":
+            with open(filepath / corpus_file, encoding="utf-8") as f:
+                contents = f.readlines()
+            for guid, content in enumerate(contents):
+                file_name, text = content.split("\t")
+                example = parsing.parse_brat_file(
+                    filepath / "annotations" / f"{file_name}.ann"
+                )
+
+                # this example contains event lines
+                # but events have not arguments
+                # this is most likely an error on the annotation side
+                if example["document_id"] == "11058905":
+                    example["events"] = []
+
+                example["text"] = text
+                example = parsing.brat_parse_to_bigbio_kb(example)
+                example["id"] = str(guid)
+                yield guid, example
+        else:
+            raise ValueError(f"Invalid config: {self.config.name}")