Datasets:

EMBO
/

BLURB

	"""Loading script for the BLURB (Biomedical Language Understanding and Reasoning Benchmark)
	benchmark for biomedical NLP."""

	import json
	from pathlib import Path
	import datasets
	import shutil
	import pandas as pd
	from constants import CITATIONS, DESCRIPTIONS, HOMEPAGES, DATA_URL

	_LICENSE = "TBD"
	_VERSION = "1.0.0"
	DATA_DIR = "blurb/"


	logger = datasets.logging.get_logger(__name__)

	class BlurbConfig(datasets.BuilderConfig):
	"""BuilderConfig for BLURB."""

	def __init__(self, task, data_url, citation, homepage, label_classes=None, **kwargs):
	"""BuilderConfig for BLURB.
	Args:
	task: `string` task the dataset is used for: 'ner', 'pico', 'rel-ext', 'sent-sim', 'doc-clas', 'qa'
	features: `list[string]`, list of the features that will appear in the
	feature dict. Should not include "label".
	data_url: `string`, url to download the data files from.
	citation: `string`, citation for the data set.
	url: `string`, url for information about the data set.
	label_classes: `list[string]`, the list of classes for the label if the
	label is present as a string. Non-string labels will be cast to either
	'False' or 'True'.
	**kwargs: keyword arguments forwarded to super.
	"""
	# Version history:
	super(BlurbConfig, self).__init__(version=datasets.Version(_VERSION), **kwargs)
	self.task = task
	self.label_classes = label_classes
	self.data_url = data_url
	self.citation = citation
	self.homepage = homepage
	if self.task == 'ner':
	self.features = datasets.Features(
	{"id": datasets.Value("string"),
	"tokens": datasets.Sequence(datasets.Value("string")),
	"ner_tags": datasets.Sequence(
	datasets.features.ClassLabel(names=self.label_classes)
	)}
	)
	self.base_url = f"{self.data_url}{self.name}/"
	self.urls = {
	"train": f"{self.base_url}{'train.tsv'}",
	"validation": f"{self.base_url}{'devel.tsv'}",
	"test": f"{self.base_url}{'test.tsv'}"
	}
	if self.task == 'sent-sim':
	self.features = datasets.Features(
	{
	"sentence1": datasets.Value("string"),
	"sentence2": datasets.Value("string"),
	"score": datasets.Value("float32"),
	}
	)




	class Blurb(datasets.GeneratorBasedBuilder):
	"""BLURB benchmark dataset for Biomedical Language Understanding and Reasoning Benchmark."""

	BUILDER_CONFIGS = [
	BlurbConfig(name='BC5CDR-chem-IOB', task='ner', label_classes=['O', 'B-Chemical', 'I-Chemical'],
	data_url = DATA_URL['BC5CDR-chem-IOB'],
	description=DESCRIPTIONS['BC5CDR-chem-IOB'],
	citation=CITATIONS['BC5CDR-chem-IOB'],
	homepage=HOMEPAGES['BC5CDR-chem-IOB']),

	BlurbConfig(name='BC5CDR-disease-IOB', task='ner', label_classes=['O', 'B-Disease', 'I-Disease'],
	data_url = DATA_URL['BC5CDR-disease-IOB'],
	description=DESCRIPTIONS['BC5CDR-disease-IOB'],
	citation=CITATIONS['BC5CDR-disease-IOB'],
	homepage=HOMEPAGES['BC5CDR-disease-IOB']),

	BlurbConfig(name='BC2GM-IOB', task='ner', label_classes=['O', 'B-GENE', 'I-GENE'],
	data_url = DATA_URL['BC2GM-IOB'],
	description=DESCRIPTIONS['BC2GM-IOB'],
	citation=CITATIONS['BC2GM-IOB'],
	homepage=HOMEPAGES['BC2GM-IOB']),

	BlurbConfig(name='NCBI-disease-IOB', task='ner', label_classes=['O', 'B-Disease', 'I-Disease'],
	data_url = DATA_URL['NCBI-disease-IOB'],
	description=DESCRIPTIONS['NCBI-disease-IOB'],
	citation=CITATIONS['NCBI-disease-IOB'],
	homepage=HOMEPAGES['NCBI-disease-IOB']),

	BlurbConfig(name='JNLPBA', task='ner', label_classes=['O', 'B-protein', 'I-protein',
	'B-cell_type', 'I-cell_type',
	'B-cell_line', 'I-cell_line',
	'B-DNA','I-DNA', 'B-RNA', 'I-RNA'],
	data_url = DATA_URL['JNLPBA'],
	description=DESCRIPTIONS['JNLPBA'],
	citation=CITATIONS['JNLPBA'],
	homepage=HOMEPAGES['JNLPBA']),
	BlurbConfig(name='BIOSSES', task='sent-sim', label_classes=None,
	data_url = DATA_URL['BIOSSES'],
	description=DESCRIPTIONS['BIOSSES'],
	citation=CITATIONS['BIOSSES'],
	homepage=HOMEPAGES['BIOSSES']),

	]


	def _info(self):
	return datasets.DatasetInfo(
	description=self.config.description,
	features=self.config.features,
	supervised_keys=None,
	homepage=self.config.homepage,
	citation=self.config.citation,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	if self.config.task == 'ner':
	downloaded_files = dl_manager.download_and_extract(self.config.urls)
	return self._ner_split_generator(downloaded_files)
	if self.config.task == 'sent-sim':
	downloaded_file = dl_manager.download_and_extract(self.config.data_url)
	return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_file})]


	def _generate_examples(self, filepath):
	print("Before the download")
	logger.info("⏳ Generating examples from = %s", filepath)

	if self.config.task == 'ner':
	return self._ner_example_generator(filepath)
	if self.config.task == 'sent-sim':
	return self._sentsim_example_generator(filepath)

	def _ner_split_generator(self, downloaded_files):
	return [
	datasets.SplitGenerator(name=datasets.Split.TRAIN,
	gen_kwargs={"filepath": downloaded_files["train"]}),
	datasets.SplitGenerator(name=datasets.Split.VALIDATION,
	gen_kwargs={"filepath": downloaded_files["validation"]}),
	datasets.SplitGenerator(name=datasets.Split.TEST,
	gen_kwargs={"filepath": downloaded_files["test"]}),
	]

	def _ner_example_generator(self, filepath):
	with open(filepath, encoding="utf-8") as f:
	guid = 0
	tokens = []
	ner_tags = []
	for line in f:
	if line == "" or line == "\n":
	if tokens:
	yield guid, {
	"id": str(guid),
	"tokens": tokens,
	"ner_tags": ner_tags,
	}
	guid += 1
	tokens = []
	ner_tags = []
	else:
	# tokens are tab separated
	splits = line.split("\t")
	tokens.append(splits[0])
	ner_tags.append(splits[1].rstrip())
	# last example
	yield guid, {
	"id": str(guid),
	"tokens": tokens,
	"ner_tags": ner_tags,
	}


	def _sentsim_example_generator(self, filepath):
	"""Yields examples as (key, example) tuples."""

	df = pd.read_csv(filepath, sep="\t", encoding="utf-8")
	for idx, row in df.iterrows():
	yield idx, {
	"sentence1": row["sentence1"],
	"sentence2": row["sentence2"],
	"score": row["score"],
	}