Datasets:
Tasks:
Text Classification
Languages:
Persian
import csv | |
import datasets | |
from datasets.tasks import TextClassification | |
_DESCRIPTION = """\ | |
IUT Ticket Classification | |
""" | |
_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 SentimentDKSF(datasets.GeneratorBasedBuilder): | |
"""IUT Tickets""" | |
def _info(self): | |
return datasets.DatasetInfo( | |
description=_DESCRIPTION, | |
features=datasets.Features( | |
{"text": datasets.Value("string"), "label": datasets.features.ClassLabel(names=['drop_withdraw', 'centralauthentication_email', 'supervisor_seminar_proposal_defense', 'registration'])} | |
), | |
supervised_keys=None, | |
homepage="https://huggingface.co/datasets/mahdiyehebrahimi/University_Ticket_Classification", | |
task_templates=[TextClassification(text_column="text", label_column="label")], | |
) | |
def _split_generators(self, dl_manager): | |
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}), | |
] | |
def _generate_examples(self, filepath): | |
"""Generate examples.""" | |
label2id = self.info.features[self.info.task_templates[0].label_column].str2int | |
with open(filepath, encoding="utf-8") as csv_file: | |
csv_reader = csv.reader( | |
csv_file, quotechar='"', skipinitialspace=True | |
) | |
# 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) | |
yield id_, {"text": text, "label": label} | |