arkamath2026/sms_spam

---
annotations_creators:
- crowdsourced
- found
language_creators:
- crowdsourced
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- extended|other-nus-sms-corpus
task_categories:
- text-classification
task_ids:
- intent-classification
paperswithcode_id: sms-spam-collection-data-set
pretty_name: SMS Spam Collection Data Set
dataset_info:
config_name: plain_text
features:
- name: sms
dtype: string
- name: label
dtype:
class_label:
names:
'0': ham
'1': spam
splits:
- name: train
num_bytes: 521752
num_examples: 5574
download_size: 358869
dataset_size: 521752
configs:
- config_name: plain_text
data_files:
- split: train
path: plain_text/train-*
default: true
train-eval-index:
- config: plain_text
task: text-classification
task_id: binary_classification
splits:
train_split: train
col_mapping:
sms: text
label: target
metrics:
- type: accuracy
name: Accuracy
- type: f1
name: F1 macro
args:
average: macro
- type: f1
name: F1 micro
args:
average: micro
- type: f1
name: F1 weighted
args:
average: weighted
- type: precision
name: Precision macro
args:
average: macro
- type: precision
name: Precision micro
args:
average: micro
- type: precision
name: Precision weighted
args:
average: weighted
- type: recall
name: Recall macro
args:
average: macro
- type: recall
name: Recall micro
args:
average: micro
- type: recall
name: Recall weighted
args:
average: weighted
---

# Dataset Card for [Dataset Name]

## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)

## Dataset Description

- **Homepage:** http://archive.ics.uci.edu/ml/datasets/SMS+Spam+Collection
- **Repository:**
- **Paper:** Almeida, T.A., Gomez Hidalgo, J.M., Yamakami, A. Contributions to the study of SMS Spam Filtering: New Collection and Results. Proceedings of the 2011 ACM Symposium on Document Engineering (ACM DOCENG'11), Mountain View, CA, USA, 2011.
- **Leaderboard:**
- **Point of Contact:**

### Dataset Summary

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.

### Supported Tasks and Leaderboards

[More Information Needed]

### Languages

English

## Dataset Structure

### Data Instances

[More Information Needed]

### Data Fields

- sms: the sms message
- label: indicating if the sms message is ham or spam, ham means it is not spam

### Data Splits

[More Information Needed]

## Dataset Creation

### Curation Rationale

[More Information Needed]

### Source Data

#### Initial Data Collection and Normalization

[More Information Needed]

#### Who are the source language producers?

[More Information Needed]

### Annotations

#### Annotation process

[More Information Needed]

#### Who are the annotators?

[More Information Needed]

### Personal and Sensitive Information

[More Information Needed]

## Considerations for Using the Data

### Social Impact of Dataset

[More Information Needed]

### Discussion of Biases

[More Information Needed]

### Other Known Limitations

[More Information Needed]

## Additional Information

### Dataset Curators

[More Information Needed]

### Licensing Information

[More Information Needed]

### Citation Information

@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)",
}

### Contributions

Thanks to [@czabo](https://github.com/czabo) for adding this dataset.

annotations_creators:
- 众包（crowdsourced）
- 公开采集（found）
language_creators:
- 众包（crowdsourced）
- 公开采集（found）
language:
- 英语（en）
license:
- 未知
multilinguality:
- 单语言（monolingual）
size_categories:
- 1000 < n < 10000
source_datasets:
- 扩展|其他-nus-sms语料库（nus-sms-corpus）
task_categories:
- 文本分类（text-classification）
task_ids:
- 意图分类（intent-classification）
paperswithcode_id: sms-spam-collection-data-set
pretty_name: SMS垃圾短信收集数据集（SMS Spam Collection Data Set）
dataset_info:
config_name: 纯文本（plain_text）
features:
- name: sms
dtype: 字符串（string）
- name: label
dtype:
class_label:
names:
'0': 正常短信（ham）
'1': 垃圾短信（spam）
splits:
- name: 训练集（train）
num_bytes: 521752
num_examples: 5574
download_size: 358869
dataset_size: 521752
configs:
- config_name: 纯文本（plain_text）
data_files:
- split: 训练集（train）
path: plain_text/train-*
default: true
train-eval-index:
- config: 纯文本（plain_text）
task: 文本分类（text-classification）
task_id: 二元分类（binary_classification）
splits:
train_split: 训练集（train）
col_mapping:
sms: 文本特征（text）
label: 目标标签（target）
metrics:
- type: 准确率（accuracy）
name: 准确率
- type: F1值（f1）
name: 宏平均F1值
args:
average: macro
- type: F1值（f1）
name: 微平均F1值
args:
average: micro
- type: F1值（f1）
name: 加权平均F1值
args:
average: weighted
- type: 精确率（precision）
name: 宏平均精确率
args:
average: macro
- type: 精确率（precision）
name: 微平均精确率
args:
average: micro
- type: 精确率（precision）
name: 加权平均精确率
args:
average: weighted
- type: 召回率（recall）
name: 宏平均召回率
args:
average: macro
- type: 召回率（recall）
name: 微平均召回率
args:
average: micro
- type: 召回率（recall）
name: 加权平均召回率
args:
average: weighted
---
# [数据集名称]数据集卡片
## 目录
- [数据集概述](#dataset-description)
- [数据集摘要](#dataset-summary)
- [支持任务与排行榜](#supported-tasks-and-leaderboards)
- [语言](#languages)
- [数据集结构](#dataset-structure)
- [数据实例](#data-instances)
- [数据字段](#data-fields)
- [数据划分](#data-splits)
- [数据集构建](#dataset-creation)
- [构建初衷](#curation-rationale)
- [源数据](#source-data)
- [注释](#annotations)
- [个人与敏感信息](#personal-and-sensitive-information)
- [数据使用注意事项](#considerations-for-using-the-data)
- [数据集的社会影响](#social-impact-of-dataset)
- [偏差讨论](#discussion-of-biases)
- [其他已知局限性](#other-known-limitations)
- [附加信息](#additional-information)
- [数据集维护者](#dataset-curators)
- [许可信息](#licensing-information)
- [引用信息](#citation-information)
- [贡献](#contributions)

## 数据集概述

- **主页**：http://archive.ics.uci.edu/ml/datasets/SMS+Spam+Collection
- **代码仓库**：
- **相关论文**：Almeida, T.A., Gomez Hidalgo, J.M., Yamakami, A. 短信垃圾过滤研究的新数据集与成果贡献 // 2011年ACM文档工程研讨会论文集（ACM DOCENG'11），美国加利福尼亚州山景城，2011年。
- **排行榜**：
- **联系人**：

### 数据集摘要

SMS垃圾短信收集数据集v1是面向手机垃圾短信研究公开采集的带标注短信消息集合。该集合包含5574条真实的、未经过编码的英语短信，根据是否为垃圾短信被标记为正常（ham）或垃圾（spam）两类。

### 支持任务与排行榜

[需补充更多信息]

### 语言

英语

## 数据集结构

### 数据实例

[需补充更多信息]

### 数据字段

- sms：短信消息内容
- label：用于标识短信是否为垃圾短信的标签，ham代表非垃圾短信

### 数据划分

[需补充更多信息]

## 数据集构建

### 构建初衷

[需补充更多信息]

### 源数据

#### 初始数据采集与标准化

[需补充更多信息]
#### 源语言生产者是谁？

[需补充更多信息]

### 注释

#### 注释流程

[需补充更多信息]
#### 注释者是谁？

[需补充更多信息]

### 个人与敏感信息

[需补充更多信息]

## 数据使用注意事项

### 数据集的社会影响

[需补充更多信息]

### 偏差讨论

[需补充更多信息]

### 其他已知局限性

[需补充更多信息]

## 附加信息

### 数据集维护者

[需补充更多信息]

### 许可信息

[需补充更多信息]

### 引用信息

bibtex
@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)",
}

### 贡献

感谢[@czabo](https://github.com/czabo) 为本数据集的添加工作。