Datasets:
Tasks:
Token Classification
Sub-tasks:
named-entity-recognition
Languages:
English
Size:
1K<n<10K
License:
File size: 5,124 Bytes
4477da2 65b51a7 df81834 65b51a7 58dd593 8bd2e0e 2b933c2 06e4a0f 3ddb755 4477da2 06e4a0f db585e5 2b933c2 3ddb755 2b933c2 06e4a0f 4477da2 784523e 06e4a0f 4477da2 1de13a4 4477da2 e9b5e26 6013237 e9b5e26 4477da2 3ddb755 4477da2 86cd38e ed7f4e7 86cd38e ed7f4e7 8a48f8c ba86d56 8a48f8c ba86d56 8a48f8c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 |
import datasets
logger = datasets.logging.get_logger(__name__)
_URL = "https://raw.githubusercontent.com/Kriyansparsana/demorepo/main/wnut17train%20(1).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",
]
)
),
}
),
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": downloaded_files["train"]}),
]
# 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:
# # Check if the delimiter ("\t") is present in the row
# if "\t" in row:
# token, label = row.split("\t")
# current_tokens.append(token)
# current_labels.append(label)
# 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,
# }
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:
token, label = row.split("\t")
current_tokens.append(token)
current_labels.append(label)
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 last sentence in dataset 🧐
if current_tokens:
yield sentence_counter, {
"id": str(sentence_counter),
"tokens": current_tokens,
"ner_tags": current_labels,
}
|