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Datasetting for AI Modules

Dataset Cleaning Guidelines

Preparation of a clean dataset is the most important factor in the creation of a proper AI Module. Simply scraping a wiki or throwing together some PDF conversions is vastly insufficient and will at best result in suboptimal outputs riddled with leaked symbols, odd spacing, and circular repetition.

Luckily cleaning your dataset is not difficult, only time-consuming. Assuming you have the patience and follow these guidelines, you too can create high quality AI modules.

General Overview

Dataset files should be... Plain text in UTF-8 encoded .txt format with no tags/markdown/html, instead focusing on standard-formatted English prose.

One paragraph per newline. That is, no paragraphs should be split onto multiple lines. To visualize this, it helps to enable Word Wrapping on whatever text editor you are using (in Notepad++ this done by checking View > Word Wrap).

No empty newlines. There should be no empty rows left in the data. For chapter and scene breaks, it is recommend that you instead use *** on its own row.

Check the newline style. NovelAI is trained on the standard n, but Windows apps tend to default to rn. The latter will function worse than n, so it is recommended to fix this if you use Windows for editing. As the difference is not visually apparent, you can check the usage by searching for regex /rn/.

Do not leave any leading/trailing space, tabs or other whitespace. This includes checking for any spaces after the end of a sentence before a newline.

Use regular single and double quotation characters (" and ') not the fancy ones (❝ and ❜). The AI is trained using the former and thus will not use the latter properly.

Try to focus in on only one specific subject matter and ensure all included material is focused on what your module should achieve in terms of what you expect from its output. This process necessitates nuance, not stuffing as much as you can into your dataset. Keep in mind this can be tricky, as for example some Steampunk novels don't actually talk all that much about the type of content one would relate to the Steampunk genre and thus in practice it is not very effective using them to train a "Steampunk" module.

A little data goes a long way. 1MB to 5MB still provides excellent results for an authorial or thematic style assuming you provide it enough training steps. On this note, feel free to experiment with short data in general. There is nothing stopping you from turning a short prompt into a module, and this would also require much fewer training steps (perhaps within the range of 50-100 for a typical scenario prompt).

If you want to avoid the same characters appearing constantly or other forms of overfitting, try to keep the data balanced with a variety of names, phrases, and terms by including stories featuring different characters or locations.

Do not expect a module to memorize relational/factual data. For instance if you feed it data containing Pokémon descriptions, it'll recognize the species but will randomly mix up their types, moves, appearance, and other data.

The format of the data matters. If you want the module to generate prose, train it on prose. Avoid training on wikis or other encyclopedic data, unless you've set out to create an utility module such as a random generator. (Example: Training a module on tabletop RPG style monster data blocks will result in a module that generates random creatures in that style.)

Cleaning Headings & Auxiliary Sections

Before anything else, it is important to note that discretion is key here, as you typically want to be as least destructive to your data as possible, considering how easy it is to completely ruin your data with a single misguided Find and Replace. Unless you want to see them leaked in the AI's output, make sure to remove Fore/Afterwords, Acknowledgements, Author's Notes, About the Authors, and any other sections that have nothing to do with the story or data. This also includes any author commentary or excerpts unless it is diagetic to (takes place within the narrative of) the story.

Chapter titles can be replaced with *** alone on a newline to signify breaks between chapters or ⁂ for breaks between individual short stories as these are conventions of the base training data and the AI is used to working with them. If the chapter titles seem useful (instead of just Chapter #) you can instead opt to keep them by enclosing them in square brackets ([ Title ]).

---- is used instead of *** if the text directly after it consists of data such as glossaries, timelines, Attributes style, or other non-prose.

In general it is a good idea to remove, replace, or trim down anything too repetitive (such as numbered chapters, titles that use the same prefixes, or stylistic phrases like <ERROR> repeated twenty times in a row) as this will increase the chance of these leaking into your output. That said, if you find yourself removing too much from a work it might just be a better idea to exclude it from your dataset altogether.

Cleaning Prose

Keep an eye out for odd symbols, characters, or other unusual formatting such as odd card suits or other symbols used as chapter breaks (●, ■, ✽, ᚖ, ♣, ◇, ◆, †) or Japanese quotations (『 and 』) which are often found in visual novels (and can be replaced with regular quotations). Sometimes even the replacement character () can be found; this is usually a sign that something's gone badly wrong with the file conversion.

On this note, often times scanning a book to a digital copy certain formatting will produce errors or won't scan correctly. You can see examples of this occasionally with underscores such as from OCR software reading ... as ___ or for things like telepathic dialogue communication, which is typically italicized, but since raw text has no characters for italics, they will appear as dialogue encapsulated by underscores like this which need to be replaced with quotations or angled brackets (< and >) as the AI knows to associate them with non-verbal dialogue.

Another common scanning issue is having chapters start in all uppercase capital letters (ie. TUESDAY MARKED THE beginning of the end) or with the first letter of the first word separated from the rest of the word (ie. T uesday marked the beginning of the end).

Extra spacing is yet another common issue such as with possessive indicators (ie. It was Mark 's dog) or at the end of sentences before a newline. On that note extra newlines aren't good either as the AI tends to associate them with chapter breaks or a passage of time.

Also be on the lookout for vertical bars ( | ) which can be replaced with colons ( : ). Lastly, if there any square brackets ([ and ]) in an unedited file, you might want to remove them unless they are encapsulating something such as an indication of time, location, point-of-view or some other note you intend to use to nudge the AI in your story.

Miscellaneous Tips

Notepad++ A free and highly-extendable text editor that features custom macros and (somewhat limited) Regex Find and Replace.

Zaltys' Notepad++ Macros A set of keyboard macros for use with Notepad++ courtesy of Zaltys. The main functions are keyed to Ctrl+1-4 for various problems that may need manual fixing and Ctrl+F1-F5 for other scripts. Keep in mind Ctrl+1-4 can find some issues that do not need to be fixed but may need to be manually looked over, so it is recommended against blind usage. More details here.

Regex101 A website focused on the quick creation, testing, and learning of Regular Expressions. If you make an account you can also save and share your regex with others.

Chris' Wordstat A tool for counting word frequency in files. Useful for balancing modules and avoiding overtraining of names.

Belverk's Cleaning Python Script One of the scripts used by the datasetting team. This is a "set it and forget it" Python script for automatically cleaning many common issues in data files. Note that while there is no graphical user interface or indication of progress while running, it is incredibly thorough while being minimally destructive. It is recommend users manually check through each file afterwards as it does not catch everything.

Gnurro's ReFormatter A powerful set of tools for data cleaning with an accessible graphical user interface. Requires Python to run. Detailed operation information can be found on its own Wiki page along with a handy guide on how to structure your data for a text adventure type module.

Zermelane's Dumb Reformatter A stupidly simple but quite convenient tool for reformatting text for module training in your browser. No downloads, Python, or script running necessary. This tool has some functionality overlap with some of the above scripts, but is more destructive than Belverk's. Use with caution, and note that your data will still require manual tweaking (such as removing Afterwords and Author's Notes) afterwards.

ScrapeFandom A Python script which scrapes English Fandom sites for updated Wiki dumps. Using Wiki data is not recommended unless heavily cleaning is done and/or the resulting module is intended for utility use such as with content generators.

What Unicode? A website for identifying unicode characters. Handy when you need to differentiate between various types of spaces, dashes, or just need to identify some symbol you don't recognize.

Useful Regular Expressions

A regular expression, or regex, is a sequence of characters that can be used to quickly find and replace more complex patterns of text. The following are some useful regular expressions for cleaning datasets. Keep in mind these are potentially highly destructive and one should exercise much care when using these. Batch replacement (ie. "Replace All") with these is ''not'' recommended.

/^([^.!?'":]|[0-9].?)+r?n/ Selects headers, titles, and anything else that does not end in punctuation before a new line.

/^(["'. ]*b[^a-z])([^a-z]{3,}b)+/ Selects sequences of text in all uppercase capital letters. You can optionally replace the selected text with normal lower case (preserving the first uppercase letter) using this in the Replace field /$1L$2E/ though keep in mind character names and other proper nouns may lose their first uppercase letter this way.

/( '?[dis?!] |[“”"][“”"]|[A-z][“”"][“”"]?[A-z]|^"?.")/ Detects various quotation and spacing problems.

/^[". ]*b[^IA] w/ Selects cases where the first letter of a word is separated by a space (common OCR error).

/[«»©^■◇◆●•★†‡�✽☙《》【】※|─_™ ]/ Locates common OCR artifacts and other characters that should normally (with rare exceptions) not be present.

/(opyright|ISBN|http|html|o be continued|^.?THE END.?$|endoftext|bout the author|able of Contents|nterlude|^[Gg]lossary|(ro|pi)logue|hapter|xcerpt|[Rr]eader|^Afterword)/ Catches many of the auxiliary sections of a work.

Common Text Containers and Affixes

Final Thoughts {#final_thoughts}

Dataset cleaning may seem intimidating at first, but once you familiarize yourself with the tools and resources it's quite easy to get into the groove of things and prep a dataset in an afternoon. Take your time finding good source material, too, as no matter how well you clean something, if its inherent quality is already subpar, then it won't make much difference.

Lastly, if you have any follow up questions feel free to reach out to NAI's datasetting team including Zaltys (via the main NAI Discord), Belverk, and Lion (who wrote this guide). Special thanks to all of them!

Setting a Module Image {#setting_a_module_image}

To add a preview image to your module modules, use a site such as [https://onlinejpgtools.com/convert-jpg-to-base64%5Dto convert a (small!) jpg to base64, then, open your module file in a text editor, find:

"mode":0

Near the bottom, and add the following after it:

,"image":"data:image/jpeg;base64,codehere"

Replace "codehere" in the string with the base64generated by the site.