upload hubscripts/chebi_nactem_hub.py to hub from bigbio repo

chebi_nactem.py

@@ -0,0 +1,237 @@
+# 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.
+
+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{Shardlow2018,
+  title        = {
+    A New Corpus to Support Text Mining for the Curation of Metabolites in the
+    {ChEBI} Database
+  },
+  author       = {
+    Shardlow, M J and Nguyen, N and Owen, G and O'Donovan, C and Leach, A and
+    McNaught, J and Turner, S and Ananiadou, S
+  },
+  year         = 2018,
+  month        = may,
+  booktitle    = {
+    Proceedings of the Eleventh International Conference on Language Resources
+    and Evaluation ({LREC} 2018)
+  },
+  location     = {Miyazaki, Japan},
+  pages        = {280--285},
+  conference   = {
+    Eleventh International Conference on Language Resources and Evaluation
+    (LREC 2018)
+  },
+  language     = {en}
+}
+"""
+
+_DATASETNAME = "chebi_nactem"
+_DISPLAYNAME = "CHEBI Corpus"
+
+_DESCRIPTION = """\
+The ChEBI corpus contains 199 annotated abstracts and 100 annotated full papers.
+All documents in the corpus have been annotated for named entities and relations
+between these. In total, our corpus provides over 15000 named entity annotations
+and over 6,000 relations between entities.
+"""
+
+_HOMEPAGE = "http://www.nactem.ac.uk/chebi"
+
+_LICENSE = 'Creative Commons Attribution 4.0 International'
+
+_URLS = {
+    _DATASETNAME: "http://www.nactem.ac.uk/chebi/ChEBI.zip",
+}
+
+_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION, Tasks.RELATION_EXTRACTION]
+
+_SOURCE_VERSION = "1.0.0"
+
+_BIGBIO_VERSION = "1.0.0"
+
+
+class ChebiNactemDatasset(datasets.GeneratorBasedBuilder):
+    """Chemical Entities of Biological Interest (ChEBI) corpus."""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
+
+    BUILDER_CONFIGS = []
+    for subset_id in ["abstr_ann1", "abstr_ann2", "fullpaper"]:
+        BUILDER_CONFIGS += [
+            BigBioConfig(
+                name=f"chebi_nactem_{subset_id}_source",
+                version=SOURCE_VERSION,
+                description="chebi_nactem source schema",
+                schema="source",
+                subset_id=f"chebi_nactem_{subset_id}",
+            ),
+            BigBioConfig(
+                name=f"chebi_nactem_{subset_id}_bigbio_kb",
+                version=BIGBIO_VERSION,
+                description="chebi_nactem BigBio schema",
+                schema="bigbio_kb",
+                subset_id=f"chebi_nactem_{subset_id}",
+            ),
+        ]
+
+    DEFAULT_CONFIG_NAME = "chebi_nactem_fullpaper_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "document_id": datasets.Value("string"),
+                    "text": datasets.Value("string"),
+                    "entities": [
+                        {
+                            "id": datasets.Value("string"),
+                            "type": datasets.Value("string"),
+                            "text": datasets.Value("string"),
+                            "offsets": datasets.Sequence([datasets.Value("int32")]),
+                        }
+                    ],
+                    "relations": [
+                        {
+                            "id": datasets.Value("string"),
+                            "type": datasets.Value("string"),
+                            "arg1": datasets.Value("string"),
+                            "arg2": datasets.Value("string"),
+                        }
+                    ],
+                }
+            )
+
+        elif self.config.schema == "bigbio_kb":
+            features = kb_features
+        else:
+            raise NotImplementedError(self.config.schema)
+
+        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)
+
+        subset_paths = {
+            "chebi_nactem_abstr_ann1": "ChEBI/abstracts/Annotator1",
+            "chebi_nactem_abstr_ann2": "ChEBI/abstracts/Annotator2",
+            "chebi_nactem_fullpaper": "ChEBI/fullpapers",
+        }
+
+        subset_dir = Path(data_dir) / subset_paths[self.config.subset_id]
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # Whatever you put in gen_kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "data_dir": subset_dir,
+                },
+            )
+        ]
+
+    def _generate_examples(self, data_dir: Path) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+
+        def uid_gen():
+            _uid = 0
+            while True:
+                yield str(_uid)
+                _uid += 1
+
+        uid = iter(uid_gen())
+
+        txt_files = (f for f in os.listdir(data_dir) if f.endswith(".txt"))
+        for idx, file_name in enumerate(txt_files):
+
+            brat_file = data_dir / file_name
+            contents = parse_brat_file(brat_file)
+
+            if self.config.schema == "source":
+                yield idx, {
+                    "document_id": contents["document_id"],
+                    "text": contents["text"],
+                    "entities": contents["text_bound_annotations"],
+                    "relations": [
+                        {
+                            "id": relation["id"],
+                            "type": relation["type"],
+                            "arg1": relation["head"]["ref_id"],
+                            "arg2": relation["tail"]["ref_id"],
+                        }
+                        for relation in contents["relations"]
+                    ],
+                }
+
+            elif self.config.schema == "bigbio_kb":
+                yield idx, {
+                    "id": next(uid),
+                    "document_id": contents["document_id"],
+                    "passages": [
+                        {
+                            "id": next(uid),
+                            "type": "",
+                            "text": [contents["text"]],
+                            "offsets": [(0, len(contents["text"]))],
+                        }
+                    ],
+                    "entities": [
+                        {
+                            "id": f"{idx}_{entity['id']}",
+                            "type": entity["type"],
+                            "offsets": entity["offsets"],
+                            "text": entity["text"],
+                            "normalized": [],
+                        }
+                        for entity in contents["text_bound_annotations"]
+                    ],
+                    "events": [],
+                    "coreferences": [],
+                    "relations": [
+                        {
+                            "id": f"{idx}_{relation['id']}",
+                            "type": relation["type"],
+                            "arg1_id": f"{idx}_{relation['head']['ref_id']}",
+                            "arg2_id": f"{idx}_{relation['tail']['ref_id']}",
+                            "normalized": [],
+                        }
+                        for relation in contents["relations"]
+                    ],
+                }
+            else:
+                raise NotImplementedError(self.config.schema)