Datasets:
Tasks:
Text Classification
Languages:
Persian
File size: 3,095 Bytes
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import csv
import datasets
from datasets.tasks import TextClassification
logger = datasets.logging.get_logger(__name__)
_CITATION = """Citation"""
_DESCRIPTION = """Tickets of IUT university"""
_DOWNLOAD_URLS = {
"train": "https://huggingface.co/datasets/mahdiyehebrahimi/University_Ticket_Classification/raw/main/tc_train.csv",
"test": "https://huggingface.co/datasets/mahdiyehebrahimi/University_Ticket_Classification/raw/main/tc_test.csv",
}
class DatasetNameConfig(datasets.BuilderConfig):
def __init__(self, **kwargs):
super(DatasetNameConfig, self).__init__(**kwargs)
class DatasetName(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
DatasetNameConfig(
name="University's Tickets",
version=datasets.Version("1.1.1"),
description=_DESCRIPTION,
),
]
def _info(self):
text_column = "text"
label_column = "label"
# TODO PROVIDE THE LABELS HERE
label_names = ['drop_withdraw', 'centralauthentication_email', 'supervisor_seminar_proposal_defense', 'registration']
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{text_column: datasets.Value("string"), label_column: datasets.features.ClassLabel(names=label_names)}
),
homepage="https://huggingface.co/datasets/mahdiyehebrahimi/University_Ticket_Classification",
citation=_CITATION,
task_templates=[TextClassification(text_column=text_column, label_column=label_column)],
)
def _split_generators(self, dl_manager):
"""
Return SplitGenerators.
"""
train_path = dl_manager.download_and_extract(_DOWNLOAD_URLS["train"])
test_path = dl_manager.download_and_extract(_DOWNLOAD_URLS["test"])
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}),
]
# TODO
def _generate_examples(self, filepath):
"""
Per each file_path read the csv file and iterate it.
For each row yield a tuple of (id, {"text": ..., "label": ..., ...})
Each call to this method yields an output like below:
```
(123, {"text": "I liked it", "label": "positive"})
```
"""
label2id = self.info.features[self.info.task_templates[0].label_column].str2int
logger.info("⏳ Generating examples from = %s", filepath)
with open(filepath, encoding="utf-8") as csv_file:
csv_reader = csv.reader(csv_file, quotechar='"', skipinitialspace=True)
# Uncomment below line to skip the first row if your csv file has a header row
next(csv_reader, None)
for id_, row in enumerate(csv_reader):
label, text = row
label = label2id(label)
# Optional preprocessing here
yield id_, {"text": text, "label": label} |