Datasets:
Tasks:
Text Classification
Sub-tasks:
intent-classification
Languages:
English
Multilinguality:
monolingual
Size Categories:
1K<n<10K
Source Datasets:
extended|other-nus-sms-corpus
Tags:
License:
File size: 3,208 Bytes
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# coding=utf-8
# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Lint as: python3
"""SMS Spam Collection Data Set"""
import os
import datasets
from datasets.tasks import TextClassification
_CITATION = """\
@inproceedings{Almeida2011SpamFiltering,
title={Contributions to the Study of SMS Spam Filtering: New Collection and Results},
author={Tiago A. Almeida and Jose Maria Gomez Hidalgo and Akebo Yamakami},
year={2011},
booktitle = "Proceedings of the 2011 ACM Symposium on Document Engineering (DOCENG'11)",
}
"""
_DESCRIPTION = """\
The SMS Spam Collection v.1 is a public set of SMS labeled messages that have been collected for mobile phone spam research.
It has one collection composed by 5,574 English, real and non-enconded messages, tagged according being legitimate (ham) or spam.
"""
_DATA_URL = "http://archive.ics.uci.edu/ml/machine-learning-databases/00228/smsspamcollection.zip"
class SmsSpam(datasets.GeneratorBasedBuilder):
"""SMS Spam Collection Data Set"""
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="plain_text",
version=datasets.Version("1.0.0", ""),
description="Plain text import of SMS Spam Collection Data Set",
)
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"sms": datasets.Value("string"),
"label": datasets.features.ClassLabel(names=["ham", "spam"]),
}
),
supervised_keys=("sms", "label"),
homepage="http://archive.ics.uci.edu/ml/datasets/SMS+Spam+Collection",
citation=_CITATION,
task_templates=[TextClassification(text_column="sms", label_column="label")],
)
def _split_generators(self, dl_manager):
dl_dir = dl_manager.download_and_extract(_DATA_URL)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN, gen_kwargs={"filepath": os.path.join(dl_dir, "SMSSpamCollection")}
),
]
def _generate_examples(self, filepath):
"""This function returns the examples in the raw (text) form."""
with open(filepath, encoding="utf-8") as sms_file:
for idx, line in enumerate(sms_file):
fields = line.split("\t")
if fields[0] == "ham":
label = 0
else:
label = 1
yield idx, {
"sms": fields[1],
"label": label,
}
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