Datasets:
Tasks:
Token Classification
Sub-tasks:
named-entity-recognition
Languages:
English
Size:
1K<n<10K
License:
File size: 6,772 Bytes
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import datasets
logger = datasets.logging.get_logger(__name__)
_URL = "https://raw.githubusercontent.com/Kriyansparsana/demorepo/main/train.txt"
class indian_namesConfig(datasets.BuilderConfig):
"""The WNUT 17 Emerging Entities Dataset."""
def __init__(self, **kwargs):
"""BuilderConfig for WNUT 17.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(indian_namesConfig, self).__init__(**kwargs)
class indian_names(datasets.GeneratorBasedBuilder):
"""The WNUT 17 Emerging Entities Dataset."""
BUILDER_CONFIGS = [
indian_namesConfig(
name="indian_names", version=datasets.Version("1.0.0"), description="The WNUT 17 Emerging Entities Dataset"
),
]
def _info(self):
return datasets.DatasetInfo(
features=datasets.Features(
{
"id": datasets.Value("string"),
"tokens": datasets.Sequence(datasets.Value("string")),
"pos_tags": datasets.Sequence(
datasets.features.ClassLabel(
names=[
'"',
"''",
"#",
"$",
"(",
")",
",",
".",
":",
"``",
"CC",
"CD",
"DT",
"EX",
"FW",
"IN",
"JJ",
"JJR",
"JJS",
"LS",
"MD",
"NN",
"NNP",
"NNPS",
"NNS",
"NN|SYM",
"PDT",
"POS",
"PRP",
"PRP$",
"RB",
"RBR",
"RBS",
"RP",
"SYM",
"TO",
"UH",
"VB",
"VBD",
"VBG",
"VBN",
"VBP",
"VBZ",
"WDT",
"WP",
"WP$",
"WRB",
]
)
),
"chunk_tags": datasets.Sequence(
datasets.features.ClassLabel(
names=[
"O",
"B-ADJP",
"I-ADJP",
"B-ADVP",
"I-ADVP",
"B-CONJP",
"I-CONJP",
"B-INTJ",
"I-INTJ",
"B-LST",
"I-LST",
"B-NP",
"I-NP",
"B-PP",
"I-PP",
"B-PRT",
"I-PRT",
"B-SBAR",
"I-SBAR",
"B-UCP",
"I-UCP",
"B-VP",
"I-VP",
]
)
),
"ner_tags": datasets.Sequence(
datasets.features.ClassLabel(
names=[
"O",
"B-PER",
"I-PER",
"B-ORG",
"I-ORG",
"B-LOC",
"I-LOC",
"B-MISC",
"I-MISC",
]
)
),
}
),
supervised_keys=None,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
urls_to_download = {
"train": f"{_URL}",
}
downloaded_files = dl_manager.download_and_extract(urls_to_download)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_files["train"]}),
]
def _generate_examples(self, filepath):
logger.info("⏳ Generating examples from = %s", filepath)
with open(filepath, encoding="utf-8") as f:
guid = 0
tokens = []
pos_tags = []
chunk_tags = []
ner_tags = []
for line in f:
if line.startswith("-DOCSTART-") or line == "" or line == "\n":
if tokens:
yield guid, {
"id": str(guid),
"tokens": tokens,
"pos_tags": pos_tags,
"chunk_tags": chunk_tags,
"ner_tags": ner_tags,
}
guid += 1
tokens = []
pos_tags = []
chunk_tags = []
ner_tags = []
else:
# conll2003 tokens are space separated
splits = line.split(" ")
tokens.append(splits[0])
pos_tags.append(splits[1])
chunk_tags.append(splits[2])
ner_tags.append(splits[3].rstrip())
# last example
if tokens:
yield guid, {
"id": str(guid),
"tokens": tokens,
"pos_tags": pos_tags,
"chunk_tags": chunk_tags,
"ner_tags": ner_tags,
} |