typed-decision
typed-decisions
Typed Decisions
A benchmark for typed probabilistic decisions over shared state. You give a
model one piece of unstructured state. It answers several typed questions about
that state at once, and every answer is a probability distribution rather than a
single label.
The schema follows the System One primitives used by
TypeSafe AI: noul, choice and score. A row replays against any API that implements that shape. This benchmark is
independent. It is not affiliated with TypeSafe… See the full description on the dataset page: https://huggingface.co/datasets/LocalLLaMA/typed-decisions.typed-decisions-causal-experimenttyped-decisions-v2
typed-decisions-v2
Corrected companion corpus to pngwn/typed-decisions
(the "v1" corpus) for the typed-decision baselines. v2 repairs the
synthetic-domain label/oracle inversion that was disclosed but not fixed in v1
(nanodiff REPORT.md, finding 6) and adds a raw-text dump so that any tokenizer
(GPT-2 and Qwen) can consume byte-identical examples.
The fix
Both defects live in the synthetic ticket-triage generator (code/build_dataset_v2.py,
applied to the v1… See the full description on the dataset page: https://huggingface.co/datasets/pngwn/typed-decisions-v2.tasksource-jev-typed-decisions
tasksource-jev-typed-decisions
One million decisions from 500+ Tasksource tasks across 300+ dataset families,
in a single format for models that receive their answer criteria at runtime.
The value is breadth with traceable supervision: most rows inherit labels,
ratings, or annotator votes from existing datasets, not labels invented by a
teacher model. The source field identifies the originating task; existing
train/dev/test boundaries are retained where the source provides them.… See the full description on the dataset page: https://huggingface.co/datasets/tasksource/tasksource-jev-typed-decisions.typed-decisions-synth
Typed Decisions Synth
This is the synthetic dataset I made for Hmm, a small open model that answers questions about your data with probabilities instead of text.
It has 7,414 cases with 25,859 questions across 149 domains and workflows. Every question has an answer and a soft label (a probability for every option), so you can train a model to be unsure when it should be.
Code and the model: github.com/n4ze3m/hmm
Note: Everything here is written and labelled by an LLM. Nobody… See the full description on the dataset page: https://huggingface.co/datasets/n4ze3m/typed-decisions-synth.typed-decisions
typed-decisions
A typed-decision corpus for training a masked-diffusion LM to emit calibrated
discrete decisions instead of text. Built for fine-tuning
Sebasdi/nanodiff-350m-base
(the LLaDA recipe).
The interface
Every example is a prompt plus a response, and every decision is a single
masked token. The answer is always one option letter A-J:
### State:
<unstructured state text>
### Question:
<the decision to make>
### Options:
A) yes
B) no
### Answer:
A
The… See the full description on the dataset page: https://huggingface.co/datasets/pngwn/typed-decisions.