Datasets:
File size: 6,833 Bytes
62692d2 72bc61d 62692d2 b97c07d 62692d2 9fd3e2f 62692d2 d1a8022 62692d2 9fd3e2f 62692d2 d8797b9 62692d2 b97c07d 9fd3e2f b97c07d 9fd3e2f b97c07d 62692d2 9fd3e2f 62692d2 9fd3e2f 62692d2 b97c07d 62692d2 43ccb0d 62692d2 1320a6a 62692d2 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 |
"""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
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=("False", "True"), **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'}"
}
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']),
]
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."""
print(self.config.base_url)
print(self.config.data_url)
for i in self.config.urls:
print(self.config.urls[i])
if self.config.task == 'ner':
downloaded_files = dl_manager.download_and_extract(self.config.urls)
return self._ner_split_generator(downloaded_files)
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)
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,
}
|