Dataset cards (lab)
Task: W6.T04 (MNIST / CIFAR / synth audio) + W6.T05 (seq reverse). Datasets are not committed under data/ (gitignored).
MNIST
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|
| Use |
STE MLP pedagogy (bnn train) |
| Loader |
bnn MNIST helper (no torchvision required) |
| Split |
Standard train/test |
| License |
Yann LeCun MNIST terms (public research use) |
| Card honesty |
Accuracy floors in tests/golden_floors.json — not SOTA chase |
CIFAR-10
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|
| Use |
Bi-Real / image lane (bnn train-image, train-cifar) |
| Loader |
HF datasets or local cache under data/ (untracked) |
| Card honesty |
Proxy schedule; ImageNet SOTA is a non-goal |
Synthetic audio tones
|
|
| Use |
Audio lane pedagogy (bnn train-audio) |
| Generator |
bnn.audio synthetic tones |
| Non-goal |
Production ASR / Whisper replacement |
Seq reverse task
W6.T05. Not a downloaded corpus — generated in-process. Do not commit sequences under data/.
|
|
| Use |
Encoder–decoder STE pedagogy (bnn train-seq2seq) |
| Generator |
On-the-fly reverse sequences (bnn.seq.make_reverse_batch) |
| Split |
Synthetic batches each run; no train/test files on disk |
| License |
N/A (generated; not a third-party dataset) |
| Card honesty |
Smoke: eval token acc = 1.0 at 80 STE steps (results/seq2seq_encoder_decoder.json). Not a real NLP / translation benchmark. Attention and LayerNorm stay FP; FFN is binary or ternary. |
| Docs |
36_ENCODER_DECODER_AND_NEXT.md, tutorial 06, api/seq.md |