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""" NER dataset compiled by T-NER library https://github.com/asahi417/tner/tree/master/tner """
import json
from itertools import chain
import datasets
logger = datasets.logging.get_logger(__name__)
_DESCRIPTION = """[BioNLP2004 NER dataset](https://aclanthology.org/W04-1213.pdf)"""
_NAME = "bionlp2"
_VERSION = "1.0.0"
_CITATION = """
@inproceedings{collier-kim-2004-introduction,
title = "Introduction to the Bio-entity Recognition Task at {JNLPBA}",
author = "Collier, Nigel and
Kim, Jin-Dong",
booktitle = "Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications ({NLPBA}/{B}io{NLP})",
month = aug # " 28th and 29th",
year = "2004",
address = "Geneva, Switzerland",
publisher = "COLING",
url = "https://aclanthology.org/W04-1213",
pages = "73--78",
}
https://huggingface.co/datasets/chintagunta85/bionlp/raw/main/test_bionlp.json
"""
_HOME_PAGE = "https://huggingface.co/datasets/chintagunta85"
# https://huggingface.co/datasets/chintagunta85/bionlp/raw/main/train_bionlp.json
_URL = f'https://huggingface.co/datasets/chintagunta85/{_NAME}/raw/main'
_URLS = {
str(datasets.Split.TEST): [f'{_URL}/test_bionlp.json'],
str(datasets.Split.TRAIN): [f'{_URL}/train_bionlp.json'],
str(datasets.Split.VALIDATION): [f'{_URL}/valid_bionlp.json'],
}
def map_ner_tags(tlist):
nlist=[]
for indx in tlist:
#if(inv_map[indx]):
# print(inv_map[indx], custom_names.index(inv_map[indx]), indx)
nlist.append(custom_names.index(inv_map[indx]))
return nlist
class BioNLP2004Config(datasets.BuilderConfig):
"""BuilderConfig"""
def __init__(self, **kwargs):
"""BuilderConfig.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(BioNLP2004Config, self).__init__(**kwargs)
class BioNLP2004(datasets.GeneratorBasedBuilder):
"""Dataset."""
BUILDER_CONFIGS = [
BioNLP2004Config(name=_NAME, version=datasets.Version(_VERSION), description=_DESCRIPTION),
]
def _split_generators(self, dl_manager):
downloaded_file = dl_manager.download_and_extract(_URLS)
return [datasets.SplitGenerator(name=i, gen_kwargs={"filepaths": downloaded_file[str(i)]})
for i in [datasets.Split.TRAIN, datasets.Split.VALIDATION, datasets.Split.TEST]]
def _generate_examples(self, filepaths):
custom_names = ['O','B-GENE','I-GENE','B-CHEMICAL','I-CHEMICAL','B-DISEASE','I-DISEASE',
'B-DNA', 'I-DNA', 'B-RNA', 'I-RNA', 'B-CELL_LINE', 'I-CELL_LINE', 'B-CELL_TYPE', 'I-CELL_TYPE',
'B-PROTEIN', 'I-PROTEIN', 'B-SPECIES', 'I-SPECIES']
pre_def = {"O": 0, "B-DNA": 1, "I-DNA": 2, "B-PROTEIN": 3, "I-PROTEIN": 4,
"B-CELL_TYPE": 5, "I-CELL_TYPE": 6, "B-CELL_LINE": 7, "I-CELL_LINE": 8,
"B-RNA": 9, "I-RNA": 10}
inv_map = {0: 'O', 1: 'B-DNA', 2: 'I-DNA', 3: 'B-PROTEIN', 4: 'I-PROTEIN',
5: 'B-CELL_TYPE', 6: 'I-CELL_TYPE', 7: 'B-CELL_LINE', 8: 'I-CELL_LINE', 9: 'B-RNA', 10: 'I-RNA'}
_key = 0
for filepath in filepaths:
logger.info(f"generating examples from = {filepath}")
with open(filepath, encoding="utf-8") as f:
_list = [i for i in f.read().split('\n') if len(i) > 0]
for i in _list:
data = json.loads(i)
#print(data)
nlist = []
for indx in data['ner_tags']:
nlist.append(custom_names.index(inv_map[indx]))
#data['ner_tags'] = map_ner_tags(data['ner_tags'])
data['ner_tags']=nlist
#del data['tags']
xstr = str(_key)
yield xstr,{"id":xstr,"tokens":data['tokens'], "ner_tags":data['ner_tags']}
#yield _key,data
_key += 1
def _info(self):
custom_names = ['O','B-GENE','I-GENE','B-CHEMICAL','I-CHEMICAL','B-DISEASE','I-DISEASE',
'B-DNA', 'I-DNA', 'B-RNA', 'I-RNA', 'B-CELL_LINE', 'I-CELL_LINE', 'B-CELL_TYPE', 'I-CELL_TYPE',
'B-PROTEIN', 'I-PROTEIN', 'B-SPECIES', 'I-SPECIES']
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"id": datasets.Value("string"),
"tokens": datasets.Sequence(datasets.Value("string")),
"ner_tags": datasets.Sequence(
datasets.features.ClassLabel(
names=custom_names
)
),
}
),
supervised_keys=None,
homepage=_HOME_PAGE,
citation=_CITATION,
)
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