Datasets:
Tasks:
Token Classification
Sub-tasks:
named-entity-recognition
Languages:
English
Size:
1K<n<10K
License:
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, | |
} |