Datasets:
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---
license: apache-2.0
language:
- ar
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.
<br>
### Some preprcoessing algorithms
- [spam_assassin.js](./spam_assassin.js), followed by [spam_assassin.py](./spam_assassin.py)
- [enron_spam.py](./enron_spam.py)
<br>
### Data composition
<br>
### Description
To make the text format between sms messages and emails consistent, email subjects and content are separated by two newlines:
```python
text = email.subject + "\n\n" + email.content
```
<br>
### Suggestions
- If you plan to train a model based on this dataset alone, I recommend adding **some** rows with `is_toxic=0` from `FredZhang7/toxi-text-3M`. Make sure the rows aren't spam.
<br>
### Other Sources
- https://huggingface.co/datasets/sms_spam
- https://github.com/MWiechmann/enron_spam_data
- https://github.com/stdlib-js/datasets-spam-assassin
- https://repository.ortolang.fr/api/content/comere/v3.3/cmr-simuligne.html |