SN21 training bundle — 10,791 records, refreshed 2026-08-17
About SN21 AdTAO
Training bundle for the SN21 daily stream — 10791 settled episodes, one JSON object per line. Refreshed 2026-08-17. Episodes span 2026-03-23T14:12:39.033340+00:00 – 2026-07-17T23:51:40.167487+00:00 (the fully-settled set). Each record is {episode_id, input, labels}: the input a miner sees at prediction time, and the settled outcomes at 7, 14 and 28 days (cost, conversions, CPA and conversion-value deltas). What this is drawn from. This training set is drawn from the WEEKLY ERA and predates the 72-hour episode cap now live on the daily stream. Episode clustering therefore differs slightly from the baskets you will be scored on: it is the right task on a slightly different population — good for training, NOT a like-for-like preview of the daily stream. A held-out evaluation set from the same era is retained and never published. Not scored. goal_miss_probability and instability_risk are accepted by the prediction contract for forward compatibility but are NOT SCORED and have no ground truth in this bundle: no outcome table carries them. Do not spend model capacity on them. A held-out evaluation set is retained and never published, so a submitted model can be assessed on data it cannot have trained on. How to use it: docs/SN21_TRAINING.md
Read the release notes on GitHub ↗
SN21 AdTAO at a glance
SN21, AdTAO (α token φ), is one of the independent markets on the Bittensor network, each with its own α token and its own price. Its α currently trades at 0.004705 τ (≈ $1.09), against 8,908 τ of pool liquidity. On-chain, the best-delegated validator on it yields about 27.90% APR (simple) and 32.17% APY (compounded), emissions add 0.000550 τ to the pool each block, epochs run every 360 blocks. It was registered at block #3,156,578.
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More from SN21 AdTAO
Taosis indexes announcements, articles, posts and releases about Bittensor subnets from their own channels and keeps a record of each. The text above is the source’s; the figures are read from the chain by Taosis.