Datasets:
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import datasets
logger = datasets.logging.get_logger(__name__)
_URL = "https://raw.githubusercontent.com/Kriyansparsana/demorepo/main/"
_TRAINING_FILE = "wnut17train%20(1).conll"
_DEV_FILE = "indian_ner_dev.conll"
_TEST_FILE = "indian_ner_test.conll"
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")),
"ner_tags": datasets.Sequence(
datasets.features.ClassLabel(
names=[
"O",
"B-corporation",
"I-corporation",
"B-person",
"I-person",
]
)
),
}
),
supervised_keys=None,
)
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):
logger.info("⏳ Generating examples from = %s", filepath)
with open(filepath, encoding="utf-8") as f:
current_tokens = []
current_labels = []
sentence_counter = 0
for row in f:
row = row.rstrip()
if row:
if "\t" in row:
token, label = row.split("\t")
current_tokens.append(token)
current_labels.append(label)
else:
# Handle cases where the delimiter is missing
# You can choose to skip these rows or handle them differently
logger.warning(f"Delimiter missing in row: {row}")
else:
# New sentence
if not current_tokens:
# Consecutive empty lines will cause empty sentences
continue
assert len(current_tokens) == len(current_labels), "💔 between len of tokens & labels"
sentence = (
sentence_counter,
{
"id": str(sentence_counter),
"tokens": current_tokens,
"ner_tags": current_labels,
},
)
sentence_counter += 1
current_tokens = []
current_labels = []
yield sentence
# Don't forget the last sentence in the dataset 🧐
if current_tokens:
yield sentence_counter, {
"id": str(sentence_counter),
"tokens": current_tokens,
"ner_tags": current_labels,
} |