upload hubscripts/genia_ptm_event_corpus_hub.py to hub from bigbio repo

genia_ptm_event_corpus.py

@@ -0,0 +1,209 @@
+# 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.
+
+"""
+Post-translational-modifications (PTM), amino acid modifications of proteins after translation, are one of the posterior
+processes of protein biosynthesis for many proteins, and they are critical for determining protein function such as its
+activity state, localization, turnover and interactions with other biomolecules. While there have been many studies of
+information extraction targeting individual PTM types, there was until recently little effort to address extraction of
+multiple PTM types at once in a unified framework.
+"""
+
+import os
+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 = """\
+@inproceedings{ohta-etal-2010-event,
+    title = "Event Extraction for Post-Translational Modifications",
+    author = "Ohta, Tomoko  and
+      Pyysalo, Sampo  and
+      Miwa, Makoto  and
+      Kim, Jin-Dong  and
+      Tsujii, Jun{'}ichi",
+    booktitle = "Proceedings of the 2010 Workshop on Biomedical Natural Language Processing",
+    month = jul,
+    year = "2010",
+    address = "Uppsala, Sweden",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/W10-1903",
+    pages = "19--27",
+}
+"""
+
+_DATASETNAME = "genia_ptm_event_corpus"
+_DISPLAYNAME = "PTM Events"
+
+_DESCRIPTION = """\
+Post-translational-modifications (PTM), amino acid modifications of proteins \
+after translation, are one of the posterior processes of protein biosynthesis \
+for many proteins, and they are critical for determining protein function such \
+as its activity state, localization, turnover and interactions with other \
+biomolecules. While there have been many studies of information extraction \
+targeting individual PTM types, there was until recently little effort to \
+address extraction of multiple PTM types at once in a unified framework.
+"""
+
+_HOMEPAGE = "http://www.geniaproject.org/other-corpora/ptm-event-corpus"
+
+_LICENSE = 'GENIA Project License for Annotated Corpora'
+
+
+_URLS = {
+    _DATASETNAME: "http://www.geniaproject.org/other-corpora/ptm-event-corpus/post-translational_modifications_training_data.tar.gz?attredirects=0&d=1",
+}
+
+_SUPPORTED_TASKS = [
+    Tasks.NAMED_ENTITY_RECOGNITION,
+    Tasks.COREFERENCE_RESOLUTION,
+    Tasks.EVENT_EXTRACTION,
+]
+
+_SOURCE_VERSION = "1.0.0"
+
+_BIGBIO_VERSION = "1.0.0"
+
+
+class GeniaPtmEventCorpusDataset(datasets.GeneratorBasedBuilder):
+    """GENIA PTM event corpus."""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
+
+    BUILDER_CONFIGS = [
+        BigBioConfig(
+            name="genia_ptm_event_corpus_source",
+            version=SOURCE_VERSION,
+            description="genia_ptm_event_corpus source schema",
+            schema="source",
+            subset_id="genia_ptm_event_corpus",
+        ),
+        BigBioConfig(
+            name="genia_ptm_event_corpus_bigbio_kb",
+            version=BIGBIO_VERSION,
+            description="genia_ptm_event_corpus BigBio schema",
+            schema="bigbio_kb",
+            subset_id="genia_ptm_event_corpus",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "genia_ptm_event_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
+                        {
+                            "id": datasets.Value("string"),
+                            "type": datasets.Value(
+                                "string"
+                            ),  # refers to the text_bound_annotation of the trigger
+                            "trigger": datasets.Value("string"),
+                            "arguments": [
+                                {
+                                    "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")),
+                        }
+                    ],
+                },
+            )
+
+        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[_DATASETNAME]
+        data_dir = dl_manager.download_and_extract(urls)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "data_dir": data_dir,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, data_dir) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+        for dirpath, _, filenames in os.walk(data_dir):
+            for guid, filename in enumerate(filenames):
+                if filename.endswith(".txt"):
+                    txt_file_path = Path(dirpath, filename)
+                    if self.config.schema == "source":
+                        example = parsing.parse_brat_file(
+                            txt_file_path, annotation_file_suffixes=[".a1", ".a2"]
+                        )
+                        example["id"] = str(guid)
+                        for key in ["attributes", "normalizations"]:
+                            del example[key]
+                        yield guid, example
+                    elif self.config.schema == "bigbio_kb":
+                        example = parsing.brat_parse_to_bigbio_kb(
+                            parsing.parse_brat_file(txt_file_path)
+                        )
+                        example["id"] = str(guid)
+                        yield guid, example