Datasets:
Tasks:
Text Classification
Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
expert-generated
Annotations Creators:
expert-generated
License:
# 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 | |
"""Loading script for the biolang dataset for language modeling in biology.""" | |
from __future__ import absolute_import, division, print_function | |
import json | |
import pdb | |
import datasets | |
class SourceDataNLP(datasets.GeneratorBasedBuilder): | |
"""SourceDataNLP provides datasets to train NLP tasks in cell and molecular biology.""" | |
_NER_LABEL_NAMES = [ | |
"O", | |
"I-SMALL_MOLECULE", | |
"B-SMALL_MOLECULE", | |
"I-GENEPROD", | |
"B-GENEPROD", | |
"I-SUBCELLULAR", | |
"B-SUBCELLULAR", | |
"I-CELL", | |
"B-CELL", | |
"I-TISSUE", | |
"B-TISSUE", | |
"I-ORGANISM", | |
"B-ORGANISM", | |
"I-EXP_ASSAY", | |
"B-EXP_ASSAY", | |
] | |
_SEMANTIC_GENEPROD_ROLES_LABEL_NAMES = ["O", "I-CONTROLLED_VAR", "B-CONTROLLED_VAR", "I-MEASURED_VAR", "B-MEASURED_VAR"] | |
_SEMANTIC_SMALL_MOL_ROLES_LABEL_NAMES = ["O", "I-CONTROLLED_VAR", "B-CONTROLLED_VAR", "I-MEASURED_VAR", "B-MEASURED_VAR"] | |
_BORING_LABEL_NAMES = ["O", "I-BORING", "B-BORING"] | |
_PANEL_START_NAMES = ["O", "B-PANEL_START"] | |
_CITATION = """\ | |
@Unpublished{ | |
huggingface: dataset, | |
title = {SourceData NLP}, | |
authors={Thomas Lemberger, EMBO}, | |
year={2021} | |
} | |
""" | |
_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/sd-nlp" | |
_LICENSE = "CC-BY 4.0" | |
_URLS = { | |
"NER": "https://huggingface.co/datasets/EMBO/sd-nlp/resolve/main/sd_panels.zip", | |
"ROLES": "https://huggingface.co/datasets/EMBO/sd-nlp/resolve/main/sd_panels.zip", | |
"BORING": "https://huggingface.co/datasets/EMBO/sd-nlp/resolve/main/sd_panels.zip", | |
"PANELIZATION": "https://huggingface.co/datasets/EMBO/sd-nlp/resolve/main/sd_figs.zip", | |
} | |
VERSION = datasets.Version("0.0.1") | |
BUILDER_CONFIGS = [ | |
datasets.BuilderConfig(name="NER", version="0.0.1", description="Dataset for entity recognition"), | |
datasets.BuilderConfig(name="GENEPROD_ROLES", version="0.0.1", description="Dataset for semantic roles."), | |
datasets.BuilderConfig(name="SMALL_MOL_ROLES", version="0.0.1", description="Dataset for semantic roles."), | |
datasets.BuilderConfig(name="BORING", version="0.0.1", description="Dataset for semantic roles."), | |
datasets.BuilderConfig( | |
name="PANELIZATION", | |
version="0.0.1", | |
description="Dataset for figure legend segmentation into panel-specific legends.", | |
), | |
] | |
DEFAULT_CONFIG_NAME = "NER" | |
def _info(self): | |
if self.config.name == "NER": | |
features = datasets.Features( | |
{ | |
"input_ids": datasets.Sequence(feature=datasets.Value("int32")), | |
"labels": datasets.Sequence( | |
feature=datasets.ClassLabel(num_classes=len(self._NER_LABEL_NAMES), names=self._NER_LABEL_NAMES) | |
), | |
"tag_mask": datasets.Sequence(feature=datasets.Value("int8")), | |
} | |
) | |
elif self.config.name == "GENEPROD_ROLES": | |
features = datasets.Features( | |
{ | |
"input_ids": datasets.Sequence(feature=datasets.Value("int32")), | |
"labels": datasets.Sequence( | |
feature=datasets.ClassLabel( | |
num_classes=len(self._SEMANTIC_GENEPROD_ROLES_LABEL_NAMES), names=self._SEMANTIC_GENEPROD_ROLES_LABEL_NAMES | |
) | |
), | |
"tag_mask": datasets.Sequence(feature=datasets.Value("int8")), | |
} | |
) | |
elif self.config.name == "SMALL_MOL_ROLES": | |
features = datasets.Features( | |
{ | |
"input_ids": datasets.Sequence(feature=datasets.Value("int32")), | |
"labels": datasets.Sequence( | |
feature=datasets.ClassLabel( | |
num_classes=len(self._SEMANTIC_SMALL_MOL_ROLES_LABEL_NAMES), names=self._SEMANTIC_SMALL_MOL_ROLES_LABEL_NAMES | |
) | |
), | |
"tag_mask": datasets.Sequence(feature=datasets.Value("int8")), | |
} | |
) | |
elif self.config.name == "BORING": | |
features = datasets.Features( | |
{ | |
"input_ids": datasets.Sequence(feature=datasets.Value("int32")), | |
"labels": datasets.Sequence( | |
feature=datasets.ClassLabel(num_classes=len(self._BORING_LABEL_NAMES), names=self._BORING_LABEL_NAMES) | |
), | |
} | |
) | |
elif self.config.name == "PANELIZATION": | |
features = datasets.Features( | |
{ | |
"input_ids": datasets.Sequence(feature=datasets.Value("int32")), | |
"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")), | |
} | |
) | |
return datasets.DatasetInfo( | |
description=self._DESCRIPTION, | |
features=features, | |
supervised_keys=("input_ids", "labels"), | |
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.data_dir: | |
data_dir = self.config.data_dir | |
else: | |
url = self._URLS[self.config.name] | |
data_dir = dl_manager.download_and_extract(url) | |
if self.config.name in ["NER", "GENEPROD_ROLES", "SMALL_MOL_ROLES", "BORING"]: | |
data_dir += "/sd_panels" | |
elif self.config.name == "PANELIZATION": | |
data_dir += "/sd_figs" | |
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", | |
"split": "train", | |
}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.TEST, | |
gen_kwargs={ | |
"filepath": data_dir + "/test.jsonl", | |
"split": "test"}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.VALIDATION, | |
gen_kwargs={ | |
"filepath": data_dir + "/eval.jsonl", | |
"split": "eval", | |
}, | |
), | |
] | |
def _generate_examples(self, filepath, split): | |
"""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: | |
for id_, row in enumerate(f): | |
data = json.loads(row) | |
if self.config.name == "NER": | |
labels = data["label_ids"]["entity_types"] | |
tag_mask = [0 if tag == "O" else 1 for tag in labels] | |
yield id_, { | |
"input_ids": data["input_ids"], | |
"labels": labels, | |
"tag_mask": tag_mask, | |
} | |
elif self.config.name == "GENEPROD_ROLES": | |
labels = data["label_ids"]["entity_types"] | |
geneprod = ["B-GENEPROD", "I-GENEPROD", "B-PROTEIN", "I-PROTEIN", "B-GENE", "I-GENE"] | |
tag_mask = [1 if t in geneprod else 0 for t in labels] | |
yield id_, { | |
"input_ids": data["input_ids"], | |
"labels": data["label_ids"]["geneprod_roles"], | |
"tag_mask": tag_mask, | |
} | |
elif self.config.name == "SMALL_MOL_ROLES": | |
labels = data["label_ids"]["entity_types"] | |
small_mol = ["B-SMALL_MOLECULE", "I-SMALL_MOLECULE"] | |
tag_mask = [1 if t in small_mol else 0 for t in labels] | |
yield id_, { | |
"input_ids": data["input_ids"], | |
"labels": data["label_ids"]["small_mol_roles"], | |
"tag_mask": tag_mask, | |
} | |
elif self.config.name == "BORING": | |
yield id_, {"input_ids": data["input_ids"], "labels": data["label_ids"]["boring"]} | |
elif self.config.name == "PANELIZATION": | |
labels = data["label_ids"]["panel_start"] | |
tag_mask = [1 if t == "B-PANEL_START" else 0 for t in labels] | |
yield id_, { | |
"input_ids": data["input_ids"], | |
"labels": data["label_ids"]["panel_start"], | |
"tag_mask": tag_mask, | |
} | |