|
--- |
|
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 |
