Datasets:
metadata
license: apache-2.0
language:
- 'no'
- es
- so
- ca
- af
- it
- nl
- hi
- cy
- ar
- sv
- cs
- pl
- de
- lt
- sq
- uk
- tl
- sl
- hr
- en
- fi
- vi
- id
- da
- ko
- bg
- mr
- ja
- bn
- ro
- pt
- fr
- hu
- tr
- zh
- mk
- ur
- sk
- ne
- et
- sw
- ru
- multilingual
task_categories:
- text-classification
- zero-shot-classification
tags:
- nlp
- moderation
size_categories:
- 10K<n<100K
This is a large corpus of 42,619 preprocessed text messages and emails sent by humans in 43 languages. is_spam=1 means spam and is_spam=0 means ham.
1040 rows of balanced data, consisting of casual conversations and scam emails in ≈10 languages, were manually collected and annotated by me, with some help from ChatGPT.
Some preprcoessing algorithms
- spam_assassin.js, followed by spam_assassin.py
- enron_spam.py
Data composition
Description
To make the text format between sms messages and emails consistent, email subjects and content are separated by two newlines:
text = email.subject + "\n\n" + email.content
Suggestions
- If you plan to train a model based on this dataset alone, I recommend adding some rows with
is_toxic=0fromFredZhang7/toxi-text-3M. Make sure the rows aren't spam.