
# Dataset Card for the SpamAssassin public mail corpus

## Dataset Description

- **Homepage:** https://spamassassin.apache.org/old/publiccorpus/readme.html

### Dataset Summary

This is a selection of mail messages, suitable for use in testing spam filtering systems assembled by members of the SpamAssassin project.

### Supported Tasks and Leaderboards

[More Information Needed]

### Languages

[More Information Needed]

## Dataset Structure

### Data Instances

 - The `text` config normalizes all character sets to utf8 and dumps the
   MIME tree as a JSON list of lists.
 - The `unprocessed` config does not parse messages at all, leaving the
   full headers and content as binary.

### Data Fields

 - `label`: `spam` or `ham`
 - `group`: SpamAssassin has grouped these samples into categories
   {'hard_ham', 'spam_2', 'spam', 'easy_ham', 'easy_ham_2'}
 - `text`: normalized text of the message bodies
 - `raw`: full binary headers and contents of messages

### Data Splits

Only a _train_ split has been provided.

## Dataset Creation

### Curation Rationale

It is hoped this dataset can help verify that modern NLP tools can solve
old NLP problems.

### Source Data

#### Initial Data Collection and Normalization

[The upstream corpus description](https://spamassassin.apache.org/old/publiccorpus/readme.html)
goes into detail on collection methods.  The work here to recover text bodies
is largely done with [email.parser](https://docs.python.org/3/library/email.parser.html)
and [ftfy](https://pypi.org/project/ftfy/).

#### 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

[More Information Needed]

### Contributions

[More Information Needed]
license:unknown region:us
{"license": "unknown"}
TAGS
#license-unknown #region-us 


# Dataset Card for the SpamAssassin public mail corpus

## Dataset Description

- Homepage: URL

### Dataset Summary

This is a selection of mail messages, suitable for use in testing spam filtering systems assembled by members of the SpamAssassin project.

### Supported Tasks and Leaderboards



### Languages



## Dataset Structure

### Data Instances

 - The 'text' config normalizes all character sets to utf8 and dumps the
   MIME tree as a JSON list of lists.
 - The 'unprocessed' config does not parse messages at all, leaving the
   full headers and content as binary.

### Data Fields

 - 'label': 'spam' or 'ham'
 - 'group': SpamAssassin has grouped these samples into categories
   {'hard_ham', 'spam_2', 'spam', 'easy_ham', 'easy_ham_2'}
 - 'text': normalized text of the message bodies
 - 'raw': full binary headers and contents of messages

### Data Splits

Only a _train_ split has been provided.

## Dataset Creation

### Curation Rationale

It is hoped this dataset can help verify that modern NLP tools can solve
old NLP problems.

### Source Data

#### Initial Data Collection and Normalization

The upstream corpus description
goes into detail on collection methods.  The work here to recover text bodies
is largely done with URL
and ftfy.

#### Who are the source language producers?



### Annotations

#### Annotation process



#### Who are the annotators?



### Personal and Sensitive Information



## Considerations for Using the Data

### Social Impact of Dataset



### Discussion of Biases



### Other Known Limitations



## Additional Information

### Dataset Curators



### Licensing Information







### Contributions


# Dataset Card for the SpamAssassin public mail corpus ## Dataset Description

- Homepage: URL ### Dataset Summary

This is a selection of mail messages, suitable for use in testing spam filtering systems assembled by members of the SpamAssassin project. ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances

 - The 'text' config normalizes all character sets to utf8 and dumps the
   MIME tree as a JSON list of lists.
 - The 'unprocessed' config does not parse messages at all, leaving the
   full headers and content as binary. ### Data Fields

 - 'label': 'spam' or 'ham'
 - 'group': SpamAssassin has grouped these samples into categories
   {'hard_ham', 'spam_2', 'spam', 'easy_ham', 'easy_ham_2'}
 - 'text': normalized text of the message bodies
 - 'raw': full binary headers and contents of messages ### Data Splits

Only a _train_ split has been provided. ## Dataset Creation ### Curation Rationale

It is hoped this dataset can help verify that modern NLP tools can solve
old NLP problems. ### Source Data #### Initial Data Collection and Normalization

The upstream corpus description
goes into detail on collection methods.  The work here to recover text bodies
is largely done with URL
and ftfy. #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions
TAGS
#license-unknown #region-us 
 # Dataset Card for the SpamAssassin public mail corpus ## Dataset Description

- Homepage: URL ### Dataset Summary

This is a selection of mail messages, suitable for use in testing spam filtering systems assembled by members of the SpamAssassin project. ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances

 - The 'text' config normalizes all character sets to utf8 and dumps the
   MIME tree as a JSON list of lists.
 - The 'unprocessed' config does not parse messages at all, leaving the
   full headers and content as binary. ### Data Fields

 - 'label': 'spam' or 'ham'
 - 'group': SpamAssassin has grouped these samples into categories
   {'hard_ham', 'spam_2', 'spam', 'easy_ham', 'easy_ham_2'}
 - 'text': normalized text of the message bodies
 - 'raw': full binary headers and contents of messages ### Data Splits

Only a _train_ split has been provided. ## Dataset Creation ### Curation Rationale

It is hoped this dataset can help verify that modern NLP tools can solve
old NLP problems. ### Source Data #### Initial Data Collection and Normalization

The upstream corpus description
goes into detail on collection methods.  The work here to recover text bodies
is largely done with URL
and ftfy. #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions
passage: TAGS
#license-unknown #region-us 
# Dataset Card for the SpamAssassin public mail corpus## Dataset Description

- Homepage: URL### Dataset Summary

This is a selection of mail messages, suitable for use in testing spam filtering systems assembled by members of the SpamAssassin project.### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances

 - The 'text' config normalizes all character sets to utf8 and dumps the
   MIME tree as a JSON list of lists.
 - The 'unprocessed' config does not parse messages at all, leaving the
   full headers and content as binary.### Data Fields

 - 'label': 'spam' or 'ham'
 - 'group': SpamAssassin has grouped these samples into categories
   {'hard_ham', 'spam_2', 'spam', 'easy_ham', 'easy_ham_2'}
 - 'text': normalized text of the message bodies
 - 'raw': full binary headers and contents of messages### Data Splits

Only a _train_ split has been provided.## Dataset Creation### Curation Rationale

It is hoped this dataset can help verify that modern NLP tools can solve
old NLP problems.### Source Data#### Initial Data Collection and Normalization

The upstream corpus description
goes into detail on collection methods.  The work here to recover text bodies
is largely done with URL
and ftfy.#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions