|
import datasets |
|
logger = datasets.logging.get_logger(__name__) |
|
|
|
|
|
_HOMEPAGE = "https://www.google.com" |
|
_URL = f'https://huggingface.co./datasets/chintagunta85/pv_dataset/raw/main/' |
|
_TRAINING_FILE = "pv_train.tsv" |
|
_DEV_FILE = "pv_val.tsv" |
|
_TEST_FILE = "pv_test.tsv" |
|
|
|
|
|
class PVDatasetConfig(datasets.BuilderConfig): |
|
"""BuilderConfig for Bc2gmCorpus""" |
|
|
|
def __init__(self, **kwargs): |
|
"""BuilderConfig for Bc2gmCorpus. |
|
Args: |
|
**kwargs: keyword arguments forwarded to super. |
|
""" |
|
super(PVDatasetConfig, self).__init__(**kwargs) |
|
|
|
class PVDataset(datasets.GeneratorBasedBuilder): |
|
"""Bc2gmCorpus dataset.""" |
|
|
|
BUILDER_CONFIGS = [ |
|
PVDatasetConfig(name="PVDatasetCorpus", version=datasets.Version("1.0.0"), description="PVDataset"), |
|
] |
|
|
|
def _info(self): |
|
|
|
custom_names = ['O','B-GENE','I-GENE','B-CHEMICAL','I-CHEMICAL','B-DISEASE','I-DISEASE','B-SPECIES', 'I-SPECIES'] |
|
|
|
return datasets.DatasetInfo( |
|
description='abhi', |
|
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=_HOMEPAGE, |
|
citation='cite me', |
|
) |
|
|
|
def _split_generators(self, dl_manager): |
|
"""Returns SplitGenerators.""" |
|
urls_to_download = { |
|
"train": f"{_URL}{_TRAINING_FILE}", |
|
"dev": f"{_URL}{_DEV_FILE}", |
|
"test": f"{_URL}{_TEST_FILE}", |
|
} |
|
downloaded_files = dl_manager.download_and_extract(urls_to_download) |
|
|
|
return [ |
|
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}), |
|
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}), |
|
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}), |
|
] |
|
|
|
def _generate_examples(self, filepath): |
|
shift = 0 |
|
logger.info("⏳ Generating examples from = %s", 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: |
|
|
|
splits = line.split("\t") |
|
tokens.append(splits[0]) |
|
if(splits[1].rstrip()=="B"): |
|
ner_tags.append("B-SPECIES") |
|
elif(splits[1].rstrip()=="I"): |
|
ner_tags.append("I-SPECIES") |
|
elif(splits[1].rstrip()=="B-Chemical"): |
|
ner_tags.append("B-CHEMICAL") |
|
elif(splits[1].rstrip()=="I-Chemical"): |
|
ner_tags.append("I-CHEMICAL") |
|
elif(splits[1].rstrip()=="B-Disease"): |
|
ner_tags.append("B-DISEASE") |
|
elif(splits[1].rstrip()=="I-Disease"): |
|
ner_tags.append("I-DISEASE") |
|
else: |
|
ner_tags.append(splits[1].rstrip()) |
|
|
|
yield guid, { |
|
"id": str(guid), |
|
"tokens": tokens, |
|
"ner_tags": ner_tags, |
|
} |
|
|
|
|
|
|
|
|
|
|
