KennethEnevoldsen/decidable-responses

decidable/responses

Per-sample predictions, one row per (respondent, item). Nothing is sampled: a respondent is scored by comparing log probabilities over the item's answer surface, so re-scoring is possible without re-querying anything.

One file per respondent, predictions/<name>.parquet. The name is the registry key in decidable, which pins the commit each file was produced from.

The single split is called runs because there is no train/test boundary here: a respondent either covered an item or it did not, and that is in the data. (all cannot be used — datasets reserves it for the union of splits.)

Row schema

Field	Type	Meaning
run_settings	struct	the respondent: model_id, revision, dtype, quantisation, interface, thinking
item_id, item_hash, prompt_hash	string	what was asked; joins to decidable-items
scoring_rule	string	single_token, first_token or full_label
scored	bool	false when the answer surface could not be separated
predicted	string	the surface with the highest score under scoring_rule
correct	bool, nullable	null exactly when scored is false
scores	list	log probability per entry of the item's surface, under scoring_rule
full_label_scores	list	whole-surface log probability, kept so a re-tokenised surface can be re-scored without a re-run
surface_tokens	list	tokens each surface occupied; this is what decides the rule
floored	bool	a log probability hit the provider's floor
response	string, nullable	raw text; null under logprob scoring, where nothing is sampled

correct is nullable on purpose: a collided or unavailable answer surface must never be recorded as a wrong answer. Coverage is reported, not silently scored.

Settings live in the row rather than in a side table because they are the thing that distinguishes respondents — fp16 and 4-bit weights of the same repo are not the same measurement, and a re-uploaded repo id is not the same weights.

Recipes

-- who has been measured
SELECT DISTINCT run_settings.model_id, run_settings.revision, run_settings.dtype
FROM 'predictions/*.parquet';

-- accuracy per respondent for one config
SELECT run_settings.model_id,
       avg(correct::INT) FILTER (WHERE scored) AS accuracy,
       count(*) FILTER (WHERE NOT scored)      AS unscored
FROM 'predictions/*.parquet'
WHERE starts_with(item_id, 'dala#')
GROUP BY 1 ORDER BY 2 DESC;

-- the item by respondent matrix a fit wants
SELECT item_id,
       run_settings.model_id || '@' || run_settings.dtype AS respondent,
       correct
FROM 'predictions/*.parquet' WHERE scored;