SourceData.py

# 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 os
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 = """\
@article{abreu2023sourcedata,
 title={The SourceData-NLP dataset: integrating curation into scientific publishing
 for training large language models},
 author={Abreu-Vicente, Jorge and Sonntag, Hannah and Eidens, Thomas and Lemberger, Thomas},
 journal={arXiv preprint arXiv:2310.20440},
 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"

 DEFAULT_CONFIG_NAME = "NER"

 _LATEST_VERSION = "2.0.3" # Should this be updated to 2.0.3

 def _info(self):
 VERSION = (
 self.config.version
 if self.config.version not in ["0.0.0", "latest"]
 else self._LATEST_VERSION
 )
 self._URLS = {
 "NER": f"{_BASE_URL}token_classification/v_{VERSION}/ner/",
 "PANELIZATION": f"{_BASE_URL}token_classification/v_{VERSION}/panelization/",
 "ROLES_GP": f"{_BASE_URL}token_classification/v_{VERSION}/roles_gene/",
 "ROLES_SM": f"{_BASE_URL}token_classification/v_{VERSION}/roles_small_mol/",
 "ROLES_MULTI": f"{_BASE_URL}token_classification/v_{VERSION}/roles_multi/",
 "FULL": os.path.join(
 _BASE_URL,
 "bigbio",
 # f"v_{VERSION}",
 ),
 }
 self.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.",
 ),
 datasets.BuilderConfig(
 name="FULL",
 version=VERSION,
 description="Full dataset including all NER + entity linking annotations, links to figure images, etc.",
 ),
 # datasets.BuilderConfig(
 # name="BIGBIO_KB",
 # version=VERSION,
 # description="Full dataset formatted according to BigBio KB schema (see https://huggingface.co/bigbio). Includes all NER + entity linking annotations.",
 # ),
 ]

 if self.config.name in ["NER", "default"]:
 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")),
 "tag_mask": 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")),
 "tag_mask": 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")),
 "tag_mask": 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
 )
 ),
 "tag_mask": datasets.Sequence(feature=datasets.Value("int8")),
 "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,
 )
 ),
 "tag_mask": datasets.Sequence(feature=datasets.Value("int8")),
 }
 )

 elif self.config.name == "FULL":
 features = datasets.Features(
 {
 "doi": datasets.Value("string"),
 "abstract": datasets.Value("string"),
 # "split": datasets.Value("string"),
 "figures": [
 {
 "fig_id": datasets.Value("string"),
 "label": datasets.Value("string"),
 "fig_graphic_url": datasets.Value("string"),
 "panels": [
 {
 "panel_id": datasets.Value("string"),
 "text": datasets.Value("string"),
 "panel_graphic_url": datasets.Value("string"),
 "entities": [
 {
 "annotation_id": datasets.Value("string"),
 "source": datasets.Value("string"),
 "category": datasets.Value("string"),
 "entity_type": datasets.Value("string"),
 "role": datasets.Value("string"),
 "text": datasets.Value("string"),
 "ext_ids": datasets.Value("string"),
 "norm_text": datasets.Value("string"),
 "ext_dbs": datasets.Value("string"),
 "in_caption": datasets.Value("bool"),
 "ext_names": datasets.Value("string"),
 "ext_tax_ids": datasets.Value("string"),
 "ext_tax_names": datasets.Value("string"),
 "ext_urls": datasets.Value("string"),
 "offsets": [datasets.Value("int64")],
 }
 ],
 }
 ],
 }
 ],
 }
 )

 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.
 """

 try:
 config_name = self.config.name if self.config.name != "default" else "NER"

 if config_name == "FULL":
 url = os.path.join(
 self._URLS[config_name],
 # "source_data_full.zip"
 "source_data_json_splits_2.0.2.zip",
 )
 data_dir = dl_manager.download_and_extract(url)
 data_files = [
 os.path.join(data_dir, filename)
 for filename in ["train.jsonl", "test.jsonl", "validation.jsonl"]
 ]
 else:
 urls = [
 os.path.join(self._URLS[config_name], "train.jsonl"),
 os.path.join(self._URLS[config_name], "test.jsonl"),
 os.path.join(self._URLS[config_name], "validation.jsonl"),
 ]
 data_files = dl_manager.download(urls)
 except:
 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_files[0]},
 ),
 datasets.SplitGenerator(
 name=datasets.Split.TEST,
 gen_kwargs={"filepath": data_files[1]},
 ),
 datasets.SplitGenerator(
 name=datasets.Split.VALIDATION,
 gen_kwargs={"filepath": data_files[2]},
 ),
 ]

 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)"""

 no_panels = 0
 no_entities = 0
 has_panels = 0
 has_entities = 0

 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.strip())
 if self.config.name in ["NER", "default"]:
 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,
 "is_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,
 }

 elif self.config.name == "FULL":
 doc_figs = data["figures"]
 all_figures = []
 for fig in doc_figs:
 all_panels = []
 figure = {
 "fig_id": fig["fig_id"],
 "label": fig["label"],
 "fig_graphic_url": fig["fig_graphic_url"],
 }

 for p in fig["panels"]:
 panel = {
 "panel_id": p["panel_id"],
 "text": p["text"].strip(),
 "panel_graphic_url": p["panel_graphic_url"],
 "entities": [
 {
 "annotation_id": t["tag_id"],
 "source": t["source"],
 "category": t["category"],
 "entity_type": t["entity_type"],
 "role": t["role"],
 "text": t["text"],
 "ext_ids": t["ext_ids"],
 "norm_text": t["norm_text"],
 "ext_dbs": t["ext_dbs"],
 "in_caption": bool(t["in_caption"]),
 "ext_names": t["ext_names"],
 "ext_tax_ids": t["ext_tax_ids"],
 "ext_tax_names": t["ext_tax_names"],
 "ext_urls": t["ext_urls"],
 "offsets": t["local_offsets"],
 }
 for t in p["tags"]
 ],
 }
 for e in panel["entities"]:
 assert type(e["offsets"]) == list
 if len(panel["entities"]) == 0:
 no_entities += 1
 continue
 else:
 has_entities += 1
 all_panels.append(panel)

 figure["panels"] = all_panels

 # Pass on all figures that aren't split into panels
 if len(all_panels) == 0:
 no_panels += 1
 continue
 else:
 has_panels += 1
 all_figures.append(figure)

 output = {
 "doi": data["doi"],
 "abstract": data["abstract"],
 "figures": all_figures,
 }
 yield id_, output