File size: 4,643 Bytes
4ec75e3 |
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 |
import datasets
# coding=utf-8
# Copyright 2024 HuggingFace Datasets Authors.
# Lint as: python3
"""The Shipping label Dataset."""
logger = datasets.logging.get_logger(__name__)
_CITATION = """
"""
_DESCRIPTION = """
The goal of this task is to provide a dataset for name entity recognition."""
_URL = "https://raw.githubusercontent.com/SanghaviHarshPankajkumar/shipping_label_project/main/NER/data/"
_TRAINING_FILE = "train.txt"
_VAL_FILE = "val.txt"
_TEST_FILE = "test.txt"
class shipping_labels_Config(datasets.BuilderConfig):
"""Shipping Label Dataset for ner"""
def __init__(self, **kwargs):
"""BuilderConfig for Shipping Label data.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(shipping_labels_Config, self).__init__(**kwargs)
class shiping_label_ner(datasets.GeneratorBasedBuilder):
"""Shipping Label Dataset for ner"""
BUILDER_CONFIGS = [
shipping_labels_Config(
name="shipping_label_ner", version=datasets.Version("1.0.0"), description="Shipping Label Dataset for ner"
),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"id": datasets.Value("string"),
"tokens": datasets.Sequence(datasets.Value("string")),
"ner_tags": datasets.Sequence(
datasets.features.ClassLabel(
names=[
"O",
"B-GCNUM",
"I-GCNUM",
"B-BGNUM",
"I-BGNUM",
"B-DATE",
"I-DATE",
"B-ORG",
"I-ORG",
"B-LOCATION",
"I-LOCATION",
"B-NAME",
"I-NAME",
"B-BARCODE",
"I-BARCODE",
]
)
),
}
),
supervised_keys=None,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
urls_to_download = {
"train": f"{_URL}{_TRAINING_FILE}",
"test": f"{_URL}{_TEST_FILE}",
"val": f"{_URL}{_VAL_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.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["val"]}),
]
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(" ")
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,
} |