
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
![Spam vs Non-spam (Ham)](https://i.imgur.com/p5ytV4q.png)

<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
task_categories:text-classification task_categories:zero-shot-classification size_categories:10K<n<100K language:no language:es language:so language:ca language:af language:it language:nl language:hi language:cy language:ar language:sv language:cs language:pl language:de language:lt language:sq language:uk language:tl language:sl language:hr language:en language:fi language:vi language:id language:da language:ko language:bg language:mr language:ja language:bn language:ro language:pt language:fr language:hu language:tr language:zh language:mk language:ur language:sk language:ne language:et language:sw language:ru language:multilingual license:apache-2.0 nlp moderation region:us
{"language": [false, "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"], "license": "apache-2.0", "size_categories": ["10K<n<100K"], "task_categories": ["text-classification", "zero-shot-classification"], "tags": ["nlp", "moderation"]}
TAGS
#task_categories-text-classification #task_categories-zero-shot-classification #size_categories-10K<n<100K #language-Norwegian #language-Spanish #language-Somali #language-Catalan #language-Afrikaans #language-Italian #language-Dutch #language-Hindi #language-Welsh #language-Arabic #language-Swedish #language-Czech #language-Polish #language-German #language-Lithuanian #language-Albanian #language-Ukrainian #language-Tagalog #language-Slovenian #language-Croatian #language-English #language-Finnish #language-Vietnamese #language-Indonesian #language-Danish #language-Korean #language-Bulgarian #language-Marathi #language-Japanese #language-Bengali #language-Romanian #language-Portuguese #language-French #language-Hungarian #language-Turkish #language-Chinese #language-Macedonian #language-Urdu #language-Slovak #language-Nepali (macrolanguage) #language-Estonian #language-Swahili (macrolanguage) #language-Russian #language-multilingual #license-apache-2.0 #nlp #moderation #region-us 


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, followed by spam_assassin.py
- enron_spam.py

<br>

### Data composition
!Spam vs Non-spam (Ham)

<br>

### Description
To make the text format between sms messages and emails consistent, email subjects and content are separated by two newlines:



<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
- URL
- URL
- URL
- URL
### Some preprcoessing algorithms
- spam_assassin.js, followed by spam_assassin.py
- enron_spam.py

<br> ### Data composition
!Spam vs Non-spam (Ham)

<br> ### Description
To make the text format between sms messages and emails consistent, email subjects and content are separated by two newlines:



<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
- URL
- URL
- URL
- URL
TAGS
#task_categories-text-classification #task_categories-zero-shot-classification #size_categories-10K<n<100K #language-Norwegian #language-Spanish #language-Somali #language-Catalan #language-Afrikaans #language-Italian #language-Dutch #language-Hindi #language-Welsh #language-Arabic #language-Swedish #language-Czech #language-Polish #language-German #language-Lithuanian #language-Albanian #language-Ukrainian #language-Tagalog #language-Slovenian #language-Croatian #language-English #language-Finnish #language-Vietnamese #language-Indonesian #language-Danish #language-Korean #language-Bulgarian #language-Marathi #language-Japanese #language-Bengali #language-Romanian #language-Portuguese #language-French #language-Hungarian #language-Turkish #language-Chinese #language-Macedonian #language-Urdu #language-Slovak #language-Nepali (macrolanguage) #language-Estonian #language-Swahili (macrolanguage) #language-Russian #language-multilingual #license-apache-2.0 #nlp #moderation #region-us 
 ### Some preprcoessing algorithms
- spam_assassin.js, followed by spam_assassin.py
- enron_spam.py

<br> ### Data composition
!Spam vs Non-spam (Ham)

<br> ### Description
To make the text format between sms messages and emails consistent, email subjects and content are separated by two newlines:



<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
- URL
- URL
- URL
- URL
passage: TAGS
#task_categories-text-classification #task_categories-zero-shot-classification #size_categories-10K<n<100K #language-Norwegian #language-Spanish #language-Somali #language-Catalan #language-Afrikaans #language-Italian #language-Dutch #language-Hindi #language-Welsh #language-Arabic #language-Swedish #language-Czech #language-Polish #language-German #language-Lithuanian #language-Albanian #language-Ukrainian #language-Tagalog #language-Slovenian #language-Croatian #language-English #language-Finnish #language-Vietnamese #language-Indonesian #language-Danish #language-Korean #language-Bulgarian #language-Marathi #language-Japanese #language-Bengali #language-Romanian #language-Portuguese #language-French #language-Hungarian #language-Turkish #language-Chinese #language-Macedonian #language-Urdu #language-Slovak #language-Nepali (macrolanguage) #language-Estonian #language-Swahili (macrolanguage) #language-Russian #language-multilingual #license-apache-2.0 #nlp #moderation #region-us 
### Some preprcoessing algorithms
- spam_assassin.js, followed by spam_assassin.py
- enron_spam.py

<br>### Data composition
!Spam vs Non-spam (Ham)

<br>### Description
To make the text format between sms messages and emails consistent, email subjects and content are separated by two newlines:



<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
- URL
- URL
- URL
- URL