A small neural network that decides whether a number is Numberwang.
The whole model is a 1.8 MB JSON file and the inference code is about 100 lines of pure Python standard library — no PyTorch, no NumPy, nothing to install. Clone it and run it.
$ python3 numberwang.py 22 22... THAT'S NUMBERWANG! (confidence: 99.3%) $ python3 numberwang.py "45 - 44" 45 - 44... That's Wangernumb! Rotate the board! (confidence: 100.0%) $ python3 numberwang.py "hello how are you" hello how are you... That's not even a number. It can never be Numberwang. (confidence: 100.0%)
git clone https://github.com/GraafHenk/numberwang
cd numberwang
python3 numberwang.py 22Run it with no arguments for an interactive session:
$ python3 numberwang.py Welcome to Numberwang! (ctrl-c to stop playing Numberwang) > zweiundzwanzig zweiundzwanzig... THAT'S NUMBERWANG! (confidence: 100.0%) > shinty-six shinty-six... That's not Numberwang. (confidence: 100.0%)
Requires Python 3.8 or newer. That's the only requirement.
from numberwang import load_model, wang_probabilities model = load_model("model.json") probs = wang_probabilities(model, "forty-seven") # [p_not_numberwang, p_numberwang, p_not_a_number, p_wangernumb] verdict = max(range(4), key=probs.__getitem__)
| id | verdict |
|---|---|
| 0 | That's not Numberwang. |
| 1 | THAT'S NUMBERWANG! |
| 2 | That's not even a number. It can never be Numberwang. |
| 3 | That's Wangernumb! |
| input | behaviour |
|---|---|
42, sixty-six, 12345 |
digits or words |
zweiundzwanzig, veintidós, tweeëntwintig |
eleven languages, accents optional |
5*2, 96 divided by 2, twelve plus four |
arithmetic, judged on the result |
45 - 44, double four, eins |
anything worth 1 or 44 rotates the board |
-7, 4.5, £5, 50%, 9:30 |
negatives, decimals, currency, units, times |
XLIV, twenty-third, 22nd |
Roman numerals and ordinals |
fortnight, vierendelen, september |
words built on a number, judged as that number |
achtneming, often, money |
words that merely contain one are not numbers |
shinty-six, twentington |
fictional numbers are numbers too |
bonjour, hello how are you |
no numeric content — can never be Numberwang |
A number's wangness is a property of the number, not the language it
is said in: four, vier, quatre and cuatro all get the same verdict.
chars → Embedding(32) → Conv1d(128, k3) → ReLU
→ Conv1d(128, k3) → ReLU → global max pool
→ Linear(128) → ReLU → Linear(4) → softmax
80,804 parameters. The network reads characters directly — there is no
tokenizer, no normalizer and no rules engine at inference. Digits,
operators, canon verdicts and the eleven languages are all held in the
weights, and model.json contains the lot.
A hosted version runs on Hugging Face Spaces. To run the same demo locally:
pip install -r requirements.txt python3 app.py
gradio is needed only for the demo. The model itself never needs it.
88.9% over 486 held-out adjudications (macro-F1 0.896), against a ceiling of roughly 98% — about 2% of training labels are inverted, in accordance with long-standing adjudication practice.
| class | precision | recall | F1 |
|---|---|---|---|
| not Numberwang | 0.820 | 0.885 | 0.851 |
| Numberwang | 0.919 | 0.900 | 0.910 |
| not a number | 0.951 | 0.830 | 0.886 |
| Wangernumb | 0.968 | 0.909 | 0.937 |
Arithmetic on unseen operands is the weak spot, at 44–72%. The
network memorises rather than computes, so small common expressions like
5*2 are reliable while 904 * 3 is an educated guess. If arithmetic
correctness matters, evaluate the expression and hand it the result.
MIT — see LICENSE.
No warranty is expressed or implied as to whether any particular number is, or is not, Numberwang.