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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 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
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