region:us
MNIST dataset adapted to a text-based representation.

This allows testing interpolation quality for Transformer-VAEs.

System is heavily inspired by Matthew Rayfield's work https://youtu.be/Z9K3cwSL6uM

Works by quantising each MNIST pixel into one of 64 characters.
Every sample has an up & down version to encourage the model to learn rotation invarient features.

Use `.array_to_text(` and `.text_to_array(` methods to test your generated data.

Removed spaces to get better BPE compression on sequences.
**Should only be used with a trained tokenizer.**

Data format:
- text: (30 x 28 tokens, 840 tokens total): Textual representation of MNIST digit, for example:
```
00down!!!!!!!!!!!!!!!!!!!!!!!!!!!!
01down!!!!!!!!!!!!!!!!!!!!!!!!!!!!
02down!!!!!!!!!!!!!!!!!!!!!!!!!!!!
03down!!!!!!!!!!!!!!!!!!!!!!!!!!!!
04down!!!!!!!!!!!!!!!!!!!!!!!!!!!!
05down!!!!!!!!!!!!!%%%@CL'Ja^@!!!!
06down!!!!!!!!(*8GK`````YL`]Q1!!!!
07down!!!!!!!-\\````````_855/*!!!!!
08down!!!!!!!%W`````RN^]!!!!!!!!!!
09down!!!!!!!!5H;``T#!+G!!!!!!!!!!
10down!!!!!!!!!$!G`7!!!!!!!!!!!!!!
11down!!!!!!!!!!!C`P!!!!!!!!!!!!!!
12down!!!!!!!!!!!#P`2!!!!!!!!!!!!!
13down!!!!!!!!!!!!)]YI<!!!!!!!!!!!
14down!!!!!!!!!!!!!5]``>'!!!!!!!!!
15down!!!!!!!!!!!!!!,O``F'!!!!!!!!
16down!!!!!!!!!!!!!!!%8``O!!!!!!!!
17down!!!!!!!!!!!!!!!!!_`_1!!!!!!!
18down!!!!!!!!!!!!!!,AN``T!!!!!!!!
19down!!!!!!!!!!!!*FZ```_N!!!!!!!!
20down!!!!!!!!!!'=X````S4!!!!!!!!!
21down!!!!!!!!&1V````R5!!!!!!!!!!!
22down!!!!!!%KW````Q5#!!!!!!!!!!!!
23down!!!!.LY````^B#!!!!!!!!!!!!!!
24down!!!!C```VBB%!!!!!!!!!!!!!!!!
25down!!!!!!!!!!!!!!!!!!!!!!!!!!!!
26down!!!!!!!!!!!!!!!!!!!!!!!!!!!!
27down!!!!!!!!!!!!!!!!!!!!!!!!!!!!
```
- label: Just a number with the texts matching label.
@dataset{dataset,
    author = {Fraser Greenlee},
    year = {2021},
    month = {2},
    pages = {},
    title = {MNIST text dataset (no spaces).},
    doi = {}
}
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