upload hubscripts/jnlpba_hub.py to hub from bigbio repo

jnlpba.py

@@ -0,0 +1,181 @@
+# 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.
+
+"""
+The data came from the GENIA version 3.02 corpus (Kim et al., 2003).
+This was formed from a controlled search on MEDLINE using the MeSH terms human, blood cells and transcription factors.
+From this search 2,000 abstracts were selected and hand annotated according to a small taxonomy of 48 classes based on
+a chemical classification. Among the classes, 36 terminal classes were used to annotate the GENIA corpus.
+"""
+
+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
+
+# TODO: Add BibTeX citation
+_CITATION = """\
+@inproceedings{collier-kim-2004-introduction,
+title = "Introduction to the Bio-entity Recognition Task at {JNLPBA}",
+author = "Collier, Nigel and Kim, Jin-Dong",
+booktitle = "Proceedings of the International Joint Workshop
+on Natural Language Processing in Biomedicine and its Applications
+({NLPBA}/{B}io{NLP})",
+month = aug # " 28th and 29th", year = "2004",
+address = "Geneva, Switzerland",
+publisher = "COLING",
+url = "https://aclanthology.org/W04-1213",
+pages = "73--78",
+}
+"""
+
+_DATASETNAME = "jnlpba"
+_DISPLAYNAME = "JNLPBA"
+
+_DESCRIPTION = """\
+NER For Bio-Entities
+"""
+
+_HOMEPAGE = "http://www.geniaproject.org/shared-tasks/bionlp-jnlpba-shared-task-2004"
+
+_LICENSE = 'Creative Commons Attribution 3.0 Unported'
+
+_URLS = {
+    _DATASETNAME: "http://www.nactem.ac.uk/GENIA/current/Shared-tasks/JNLPBA/Train/Genia4ERtraining.tar.gz",
+}
+
+# TODO: add supported task by dataset. One dataset may support multiple tasks
+_SUPPORTED_TASKS = [
+    Tasks.NAMED_ENTITY_RECOGNITION
+]  # example: [Tasks.TRANSLATION, Tasks.NAMED_ENTITY_RECOGNITION, Tasks.RELATION_EXTRACTION]
+
+# TODO: set this to a version that is associated with the dataset. if none exists use "1.0.0"
+#  This version doesn't have to be consistent with semantic versioning. Anything that is
+#  provided by the original dataset as a version goes.
+_SOURCE_VERSION = "3.2.0"
+
+_BIGBIO_VERSION = "1.0.0"
+
+
+class JNLPBADataset(datasets.GeneratorBasedBuilder):
+    """
+    The data came from the GENIA version 3.02 corpus
+    (Kim et al., 2003).
+    This was formed from a controlled search on MEDLINE
+    using the MeSH terms human, blood cells and transcription factors.
+    From this search 2,000 abstracts were selected and hand annotated
+    according to a small taxonomy of 48 classes based on
+    a chemical classification.
+    Among the classes, 36 terminal classes were used to annotate the GENIA corpus.
+    """
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
+
+    BUILDER_CONFIGS = [
+        BigBioConfig(
+            name="jnlpba_source",
+            version=SOURCE_VERSION,
+            description="jnlpba source schema",
+            schema="source",
+            subset_id="jnlpba",
+        ),
+        BigBioConfig(
+            name="jnlpba_bigbio_kb",
+            version=BIGBIO_VERSION,
+            description="jnlpba BigBio schema",
+            schema="bigbio_kb",
+            subset_id="jnlpba",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "jnlpba_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+            features = datasets.load_dataset("jnlpba", split="train").features
+
+        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."""
+        data = datasets.load_dataset("jnlpba")
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # Whatever you put in gen_kwargs will be passed to _generate_examples
+                gen_kwargs={"data": data["train"]},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"data": data["validation"]},
+            ),
+        ]
+
+    def _generate_examples(self, data: datasets.Dataset) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+        uid = 0
+
+        if self.config.schema == "source":
+            for key, sample in enumerate(data):
+                yield key, sample
+
+        elif self.config.schema == "bigbio_kb":
+            for i, sample in enumerate(data):
+                feature_dict = {
+                    "id": uid,
+                    "document_id": "NULL",
+                    "passages": [],
+                    "entities": [],
+                    "relations": [],
+                    "events": [],
+                    "coreferences": [],
+                }
+
+                uid += 1
+                offset_start = 0
+                for token, tag in zip(sample["tokens"], sample["ner_tags"]):
+                    offset_start += len(token) + 1
+                    feature_dict["entities"].append(
+                        {
+                            "id": uid,
+                            "offsets": [[offset_start, offset_start + len(token)]],
+                            "text": [token],
+                            "type": tag,
+                            "normalized": [],
+                        }
+                    )
+                    uid += 1
+
+                # entities
+                yield i, feature_dict