text stringlengths 9 31 |
|---|
old new 1.58 |
smart intelligent 9.2 |
hard difficult 8.77 |
happy cheerful 9.55 |
hard easy 0.95 |
fast rapid 8.75 |
happy glad 9.17 |
short long 1.23 |
stupid dumb 9.58 |
weird strange 8.93 |
wide narrow 1.03 |
bad awful 8.42 |
easy difficult 0.58 |
bad terrible 7.78 |
hard simple 1.38 |
smart dumb 0.55 |
insane crazy 9.57 |
happy mad 0.95 |
large huge 9.47 |
hard tough 8.05 |
new fresh 6.83 |
sharp dull 0.6 |
quick rapid 9.7 |
dumb foolish 6.67 |
wonderful terrific 8.63 |
strange odd 9.02 |
happy angry 1.28 |
narrow broad 1.18 |
simple easy 9.4 |
old fresh 0.87 |
apparent obvious 8.47 |
inexpensive cheap 8.72 |
nice generous 5 |
weird normal 0.72 |
weird odd 9.2 |
bad immoral 7.62 |
sad funny 0.95 |
wonderful great 8.05 |
guilty ashamed 6.38 |
beautiful wonderful 6.5 |
confident sure 8.27 |
dumb dense 7.27 |
large big 9.55 |
nice cruel 0.67 |
impatient anxious 6.03 |
big broad 6.73 |
strong proud 3.17 |
unnecessary necessary 0.63 |
restless young 1.6 |
dumb intelligent 0.75 |
bad great 0.35 |
difficult simple 0.87 |
necessary important 7.37 |
bad terrific 0.65 |
mad glad 1.45 |
honest guilty 1.18 |
easy tough 0.52 |
easy flexible 4.1 |
certain sure 8.42 |
essential necessary 8.97 |
different normal 1.08 |
sly clever 7.25 |
crucial important 8.82 |
harsh cruel 8.18 |
childish foolish 5.5 |
scarce rare 9.17 |
friendly generous 5.9 |
fragile frigid 2.38 |
long narrow 3.57 |
big heavy 6.18 |
rough frigid 2.47 |
bizarre strange 9.37 |
illegal immoral 4.28 |
bad guilty 4.2 |
modern ancient 0.73 |
new ancient 0.23 |
dull funny 0.55 |
happy young 2 |
easy big 1.12 |
great awful 1.17 |
tiny huge 0.6 |
polite proper 7.63 |
modest ashamed 2.65 |
exotic rare 8.05 |
dumb clever 1.17 |
delightful wonderful 8.65 |
noticeable obvious 8.48 |
afraid anxious 5.07 |
formal proper 8.02 |
dreary dull 8.25 |
delightful cheerful 6.58 |
unhappy mad 5.95 |
sad terrible 5.4 |
sick crazy 3.57 |
violent angry 6.98 |
laden heavy 5.9 |
dirty cheap 1.6 |
elastic flexible 7.78 |
hard dense 5.9 |
recent new 7.05 |
End of preview. Expand in Data Studio
AI Residency — blog toy data
Small slices of the datasets used by the nine systems in
RoshBeed/ai-residency, cut down so the
toy models in the posts on roshbeed.com train in seconds on a
GitHub Actions runner.
Every post pins a commit revision of this dataset rather than tracking main, so a
figure on the site cannot change because something here did.
| path | what it is | source |
|---|---|---|
text8/text8-2m.txt |
first 2,000,000 characters of text8 |
roshbeed/ai-residency-text8 |
mnist/mnist-small.npz |
6,000 train / 1,500 test MNIST digits, uint8 28×28 |
MNIST |
hn/hn-sample.json.gz |
20,000 train / 5,000 test Hacker News posts, split by time | roshbeed/ai-residency-hn-posts |
audio/clip-16k.npz |
one 1.06 s clip, 16 kHz mono float32 | the whisper-fine-tuning demo clip |
These are for illustration. The real training runs use the full corpora; see each service's README in the residency repo for the numbers that came out of them.
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