First commit SourceData.py

SourceData.py

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+# coding=utf-8
+# Copyright 2020 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.
+
+
+# template from : https://github.com/huggingface/datasets/blob/master/templates/new_dataset_script.py
+
+from __future__ import absolute_import, division, print_function
+
+import json
+import datasets
+
+_BASE_URL = "https://huggingface.co/datasets/EMBO/SourceData/resolve/main/"
+
+class SourceData(datasets.GeneratorBasedBuilder):
+ """SourceDataNLP provides datasets to train NLP tasks in cell and molecular biology."""
+
+ _NER_LABEL_NAMES = [
+ "O",
+ "B-SMALL_MOLECULE",
+ "I-SMALL_MOLECULE",
+ "B-GENEPROD",
+ "I-GENEPROD",
+ "B-SUBCELLULAR",
+ "I-SUBCELLULAR",
+ "B-CELL_TYPE",
+ "I-CELL_TYPE",
+ "B-TISSUE",
+ "I-TISSUE",
+ "B-ORGANISM",
+ "I-ORGANISM",
+ "B-EXP_ASSAY",
+ "I-EXP_ASSAY",
+ "B-DISEASE",
+ "I-DISEASE",
+ "B-CELL_LINE",
+ "I-CELL_LINE"
+ ]
+ _SEMANTIC_ROLES = ["O", "B-CONTROLLED_VAR", "I-CONTROLLED_VAR", "B-MEASURED_VAR", "I-MEASURED_VAR"]
+ _PANEL_START_NAMES = ["O", "B-PANEL_START", "I-PANEL_START"]
+ _ROLES_MULTI = ["O", "GENEPROD", "SMALL_MOLECULE"]
+
+ _CITATION = """\
+ @Unpublished{
+ huggingface: dataset,
+ title = {SourceData NLP},
+ authors={Thomas Lemberger & Jorge Abreu-Vicente, EMBO},
+ year={2023}
+ }
+ """
+
+ _DESCRIPTION = """\
+ This dataset is based on the SourceData database and is intented to facilitate training of NLP tasks in the cell and molecualr biology domain.
+ """
+
+ _HOMEPAGE = "https://huggingface.co/datasets/EMBO/SourceData"
+
+ _LICENSE = "CC-BY 4.0"
+
+ VERSION = datasets.Version(self.config.version)
+
+ _URLS = {
+ "NER": f"{_BASE_URL}token_classification_v{self.config.version}/ner/",
+ "PANELIZATION": f"{_BASE_URL}token_classification_v{self.config.version}/panelization/",
+ "ROLES_GP": f"{_BASE_URL}token_classification_v{self.config.version}/roles_gene/",
+ "ROLES_SM": f"{_BASE_URL}token_classification_v{self.config.version}/roles_small_mol/",
+ "ROLES_MULTI": f"{_BASE_URL}token_classification_v{self.config.version}/roles_multi/",
+ }
+ BUILDER_CONFIGS = [
+ datasets.BuilderConfig(name="NER", version=VERSION, description="Dataset for named-entity recognition."),
+ datasets.BuilderConfig(name="PANELIZATION", version=VERSION, description="Dataset to separate figure captions into panels."),
+ datasets.BuilderConfig(name="ROLES_GP", version=VERSION, description="Dataset for semantic roles of gene products."),
+ datasets.BuilderConfig(name="ROLES_SM", version=VERSION, description="Dataset for semantic roles of small molecules."),
+ datasets.BuilderConfig(name="ROLES_MULTI", version=VERSION, description="Dataset to train roles. ROLES_GP and ROLES_SM at once."),
+ ]
+ DEFAULT_CONFIG_NAME = "NER"
+
+ def _info(self):
+ if self.config.name == "NER":
+ features = datasets.Features(
+ {
+ "words": datasets.Sequence(feature=datasets.Value("string")),
+ "labels": datasets.Sequence(
+ feature=datasets.ClassLabel(num_classes=len(self._NER_LABEL_NAMES),
+ names=self._NER_LABEL_NAMES)
+ ),
+ "is_category": datasets.Sequence(feature=datasets.Value("int8")),
+ "text": datasets.Value("string"),
+ }
+ )
+ elif self.config.name == "ROLES_GP":
+ features = datasets.Features(
+ {
+ "words": datasets.Sequence(feature=datasets.Value("string")),
+ "labels": datasets.Sequence(
+ feature=datasets.ClassLabel(
+ num_classes=len(self._SEMANTIC_ROLES),
+ names=self._SEMANTIC_ROLES
+ )
+ ),
+ "is_category": datasets.Sequence(feature=datasets.Value("int8")),
+ "text": datasets.Value("string"),
+ }
+ )
+ elif self.config.name == "ROLES_SM":
+ features = datasets.Features(
+ {
+ "words": datasets.Sequence(feature=datasets.Value("string")),
+ "labels": datasets.Sequence(
+ feature=datasets.ClassLabel(
+ num_classes=len(self._SEMANTIC_ROLES),
+ names=self._SEMANTIC_ROLES
+ )
+ ),
+ "is_category": datasets.Sequence(feature=datasets.Value("int8")),
+ "text": datasets.Value("string"),
+ }
+ )
+ elif self.config.name == "ROLES_MULTI":
+ features = datasets.Features(
+ {
+ "words": datasets.Sequence(feature=datasets.Value("string")),
+ "labels": datasets.Sequence(
+ feature=datasets.ClassLabel(
+ num_classes=len(self._SEMANTIC_ROLES),
+ names=self._SEMANTIC_ROLES
+ )
+ ),
+ "is_category": datasets.Sequence(
+ feature=datasets.ClassLabel(
+ num_classes=len(self._ROLES_MULTI),
+ names=self._ROLES_MULTI
+ ),
+ "text": datasets.Value("string"),
+ }
+ )
+ elif self.config.name == "PANELIZATION":
+ features = datasets.Features(
+ {
+ "words": datasets.Sequence(feature=datasets.Value("string")),
+ "labels": datasets.Sequence(
+ feature=datasets.ClassLabel(num_classes=len(self._PANEL_START_NAMES),
+ names=self._PANEL_START_NAMES)
+ ),
+ }
+ )
+
+ return datasets.DatasetInfo(
+ description=self._DESCRIPTION,
+ features=features,
+ supervised_keys=("words", "label_ids"),
+ homepage=self._HOMEPAGE,
+ license=self._LICENSE,
+ citation=self._CITATION,
+ )
+ 
+ def _split_generators(self, dl_manager: datasets.DownloadManager):
+ """Returns SplitGenerators.
+ Uses local files if a data_dir is specified. Otherwise downloads the files from their official url."""
+
+ if self.config.name == "NER":
+ url = self._URLS["NER"]
+ data_dir = dl_manager.download_and_extract(url)
+ data_dir += "/"
+ elif self.config.name == "PANELIZATION":
+ url = self._URLS["PANELIZATION"]
+ data_dir = dl_manager.download_and_extract(url)
+ data_dir += "/"
+ elif self.config.name == "ROLES_GP":
+ url = self._URLS["ROLES_GP"]
+ data_dir = dl_manager.download_and_extract(url)
+ data_dir += "/"
+ elif self.config.name == "ROLES_SM":
+ url = self._URLS["ROLES_SM"]
+ data_dir = dl_manager.download_and_extract(url)
+ data_dir += "/"
+ elif self.config.name == "ROLES_MULTI":
+ url = self._URLS["ROLES_MULTI"]
+ data_dir = dl_manager.download_and_extract(url)
+ data_dir += "/"
+ else:
+ raise ValueError(f"unkonwn config name: {self.config.name}")
+ 
+ return [
+ datasets.SplitGenerator(
+ name=datasets.Split.TRAIN,
+ # These kwargs will be passed to _generate_examples
+ gen_kwargs={
+ "filepath": data_dir + "/train.jsonl"},
+ ),
+ datasets.SplitGenerator(
+ name=datasets.Split.TEST,
+ gen_kwargs={
+ "filepath": data_dir + "/test.jsonl"},
+ ),
+ datasets.SplitGenerator(
+ name=datasets.Split.VALIDATION,
+ gen_kwargs={
+ "filepath": data_dir + "/eval.jsonl"},
+ ),
+ ]
+
+
+ BUILDER_CONFIGS = [
+ datasets.BuilderConfig(name="NER", version=VERSION, description="Dataset for named-entity recognition."),
+ datasets.BuilderConfig(name="PANELIZATION", version=VERSION, description="Dataset to separate figure captions into panels."),
+ datasets.BuilderConfig(name="ROLES_GP", version=VERSION, description="Dataset for semantic roles of gene products."),
+ datasets.BuilderConfig(name="ROLES_SM", version=VERSION, description="Dataset for semantic roles of small molecules."),
+ datasets.BuilderConfig(name="ROLES_MULTI", version=VERSION, description="Dataset to train roles. ROLES_GP and ROLES_SM at once."),
+ ]
+
+ def _generate_examples(self, filepath):
+ """Yields examples. This method will receive as arguments the `gen_kwargs` defined in the previous `_split_generators` method.
+ It is in charge of opening the given file and yielding (key, example) tuples from the dataset
+ The key is not important, it's more here for legacy reason (legacy from tfds)"""
+
+ with open(filepath, encoding="utf-8") as f:
+ # logger.info("⏳ Generating examples from = %s", filepath)
+ for id_, row in enumerate(f):
+ data = json.loads(row)
+ if self.config.name == "NER":
+ yield id_, {
+ "words": data["words"],
+ "labels": data["labels"],
+ "tag_mask": data["is_category"],
+ "text": data["text"]
+ }
+ elif self.config.name == "ROLES_GP":
+ yield id_, {
+ "words": data["words"],
+ "labels": data["labels"],
+ "tag_mask": data["is_category"],
+ "text": data["text"]
+ }
+ elif self.config.name == "ROLES_MULTI":
+ labels = data["labels"]
+ tag_mask = [1 if t!=0 else 0 for t in labels]
+ yield id_, {
+ "words": data["words"],
+ "labels": data["labels"],
+ "tag_mask": tag_mask,
+ "category": data["is_category"],
+ "text": data["text"]
+ }
+ elif self.config.name == "ROLES_SM":
+ yield id_, {
+ "words": data["words"],
+ "labels": data["labels"],
+ "tag_mask": data["is_category"],
+ "text": data["text"]
+ }
+ elif self.config.name == "PANELIZATION":
+ labels = data["labels"]
+ tag_mask = [1 if t == "B-PANEL_START" else 0 for t in labels]
+ yield id_, {
+ "words": data["words"],
+ "labels": data["labels"],
+ "tag_mask": tag_mask,
+ "text": data["text"]
+ }
+
+