n4ze3m/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 checked it. This is a fun experiment and it is not affiliated with TypeSafe in any way.

What is inside

Split	Cases	Questions
train	6,682	23,319
validation	732	2,540

The split is by case, so a state is never in both. Every case has its own questions, so the validation split also has questions the model has not seen.

There are three question types:

• noul: a yes/no question (10,192)
• choice: pick one option from a list (10,179)
• score: rate something on ordered levels (5,488)

Columns

Column	Description
state_id	Id of the case
generator	v1-general (mixed text tasks, 52 domains) or v2-workflow (decisions from 100 workflows)
domain	The domain or workflow the case was written for
n_questions	Number of questions in the case
state	The input. Plain text, or JSON as a string when state_is_json is true
state_is_json	True when state is a JSON document
questions	JSON string. {question_id: {type, instructions, criteria}}
gold	JSON string. The answer: option key for choice, level index for score, true/false for noul
teacher	JSON string. The soft label: {"probabilities": {...}} for choice and score, {"noul": 0.93} for noul
teacher_model	How the soft label was made

Usage

import json
from datasets import load_dataset

ds = load_dataset("n4ze3m/typed-decisions-synth")
row = ds["train"][0]

state = json.loads(row["state"]) if row["state_is_json"] else row["state"]
questions = json.loads(row["questions"])
gold = json.loads(row["gold"])
teacher = json.loads(row["teacher"])

state and questions are already in the System One request format, so you can send them to the Hmm server or any API with the same format without changing anything.

How it was made

I used DeepSeek V4.1 Flash (via Atlas Cloud) for everything. It used about 25M tokens, around $17.

1. Write the cases. The model wrote 5 cases per call for a domain, with a twist to make them harder (ambiguity, distractors, a misleading keyword, a detail that comes late, negation). For the workflow part it wrote the questions real software would ask: what to do next, which category, how severe, and yes/no checks.
2. Answer them again. The same model got only the state and the questions, without the answers, and answered every case 3 times with a probability for every option. The soft label is the average of the 3.
3. Remove the bad ones. A question is kept only when the soft label agrees with the answer from step 1. About 15% were removed this way.

The script is training/generate.py, it works with any OpenAI compatible API.

Things to know

• The answer means "the model agreed with itself twice", it does not mean it is true.
• Step 3 removes the most ambiguous questions, so this dataset is easier than real data and the soft labels are more confident than they should be.
• The correct option is listed first about half of the time. Shuffle the options when you train and when you test.
• The yes/no answers are not balanced: 54% no in v2-workflow, 68% yes in v1-general. Score answers lean to the higher levels.
• I left out the four workflows of the typed-decisions benchmark (customer service, invoice processing, security incidents, agent traces), so you can still use it as a test.
• It is only in English.
• All people, companies and records are made up. The medical, legal and financial cases are invented and are not advice.

Citation

@misc{nazeem2026hmm,
  author = {Muhammed Nazeem},
  title  = {Hmm: a small open model for typed decisions},
  year   = {2026},
  url    = {https://github.com/n4ze3m/hmm}
}

Support

If you like the project, you can support me on Ko-fi.

License

MIT