WangNet – 1.8 MB, zero-dependency Numberwang adjudication in 11 languages
Posted by Liogra123 13 hours ago
Comments
Comment by tazjin 12 hours ago
So I think as it stands right now this model can only determine whether a number would be Numberwang as the first number of a sequence, but even for that I still wouldn't rely on this in the actual game show.
Comment by wrs 12 hours ago
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Comment by toyg 11 hours ago
"WATCH THE FOOTBALL! WATCH IT! IT'S GONNA MOVE!!"
"This tree smells like cum."
Mitchell and Webb had so many legendary lines.
Comment by yoz 7 hours ago
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Comment by codeulike 10 hours ago
... And forever to play it in!"
Comment by tclancy 11 hours ago
Comment by tialaramex 10 hours ago
I think that series came out slightly after that era ended, I remember as a child (so years earlier) realising that snooker players at the time were often tipsy if not actually drunk, and also darts players, but by the time I was an adult the professionalism really changed that, turns out that if you're very good you probably are better at snooker when sober. I think the commentators, being often a previous generation of players, lagged that slightly.
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Comment by mellosouls 2 hours ago
In the UK we watched it on the Beeb when it was aired.
Comment by JuniperMesos 21 minutes ago
Comment by shervinafshar 8 hours ago
Then I watched that show and learned about Numberwang.
Comment by karim79 9 hours ago
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Comment by karim79 9 hours ago
I'm glad that someone has stepped up to revive the legacy of numberwang.
Comment by karim79 9 hours ago
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Comment by minitech 6 hours ago
The “Usage” section doesn’t involve installing any dependencies, for example: https://github.com/GraafHenk/numberwang#usage
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Comment by Liogra123 13 hours ago
What it is: a character-level CNN with 80,804 parameters. The weights are a 1.79 MB JSON file and inference is about 100 lines of Python standard library — no PyTorch, no NumPy. It runs on a Pi Zero. It accepts digits, number words in eleven languages, arithmetic ("96 divided by 2", "deux fois trois"), Roman numerals, ordinals, clock times, currency, and fictional numbers ("shinty-six"). Anything with no numeric content is correctly ruled out as never able to be Numberwang. Whatever comes to 1 or 44 is Wangernumb and you rotate the board.
Held-out accuracy is 88.9% on 486 probes reserved from training by construction. The ceiling is ~98%, because roughly 2% of training labels are inverted at compilation time, in accordance with long-standing adjudication practice.
For comparison I ran Qwen3-1.7B on the same suite with the four verdicts as a constrained multiple choice: 51.9%, which is 2.3 points above answering "Numberwang" to everything. It answers "Numberwang" to 93% of inputs and never once identifies a Wangernumb. So the accuracy table has a verdict-distribution column, since one number can't tell a model that decides from one that agrees.
Honest weak spot: arithmetic is memorised, not computed. A conv net can't add. On operands reserved from training it gets 60% on symbolic expressions and 44% on foreign-language ones.
Dataset (185k adjudicated utterances), training script, evaluation harness and benchmark are all in the repo and reproduce from a fixed seed. Model card on HF: https://huggingface.co/graafhenk/numberwang