datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
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.brazilian_court_decisions
Dataset Card for predicting-brazilian-court-decisions
Dataset Summary
The dataset is a collection of 4043 Ementa (summary) court decisions and their metadata from
the Tribunal de Justiça de Alagoas (TJAL, the State Supreme Court of Alagoas (Brazil). The court decisions are labeled
according to 7 categories and whether the decisions were unanimous on the part of the judges or not. The dataset
supports the task of Legal Judgment Prediction.
Supported Tasks and… See the full description on the dataset page: https://huggingface.co/datasets/joelniklaus/brazilian_court_decisions.typed-decisions-code-holes
typed-decisions: code-holes
Single-token substitutions mined from the git history of 64 public repositories in the
kotoba-lang organisation, each turned into one choice question
with a gold answer: the token the commit actually put there. Part of
kotoba-lang/typed-decisions (README 第8反復),
whose trained model is com-kotobalabs/open-jev-deberta-v3-large.
Sibling dataset (ungoaled): com-kotobalabs/typed-decisions-repo-governance.
What a record is
state = the commit… See the full description on the dataset page: https://huggingface.co/datasets/com-kotobalabs/typed-decisions-code-holes.granite-decisions-synthetic
Granite Decisions synthetic datasets
Original, deterministic English fixtures for Adam Pippert's personal
Granite Decisions project.
The original default config has 162 examples: 54 train, 54 calibration, and 54 test.
These exercise the pipeline; they are not a representative quality benchmark.
Source and license
The source is the project's original template generator, published here as
make_smoke_data.py, from
release v0.1.0,
commit… See the full description on the dataset page: https://huggingface.co/datasets/adampippert/granite-decisions-synthetic.this-that-complex-decisions
this-that-complex-decisions
1,710 decisions where the answer follows from a stated policy applied to a state, and where no
single field of that state gives it away.
1,710 questions 19 decision types 40 domains chance rate 0.258
Each row is a state, a question, a closed set of options, and the index of the one option the
policy selects. The answer is determinate: given the state and the policy there is exactly one
correct choice, and it does not depend on anyone's… See the full description on the dataset page: https://huggingface.co/datasets/limberc/this-that-complex-decisions.a-s-flc-decisions
A-S-FLC Decision Dataset
Training data for fine-tuning LLMs on Asymmetric Signed Force-Loop-Chain reasoning.
What is A-S-FLC?
A decision-making framework where:
Positives are trusted exactly (known benefits)
Negatives are estimated with a conservative buffer proportional to uncertainty
Multiple event chains are scored and the highest stable-net path is chosen
This catches "trap" decisions where uncertain downsides are underestimated.
Dataset Details… See the full description on the dataset page: https://huggingface.co/datasets/denialkhmbot/a-s-flc-decisions.next-jev-tetris-decisions-10k
Tetris placement decisions with known answers (10k)
10,000 Tetris decisions for collecting reasoning from an LLM teacher: the teacher writes its reasoning and a final \box{X}, and verify.py keeps only answers that match the known best option.
Each item is exactly what the community Jev Tetris game (commit a0b411c) sends to a local model in its "Laya" slot: a one-paragraph board description, the instruction, and the game heuristic's top 6 placements. Option order is shuffled and… See the full description on the dataset page: https://huggingface.co/datasets/JonesLin/next-jev-tetris-decisions-10k.typed-decisions-repo-governance
typed-decisions: repo-governance family
A seed, not a corpus (n=1 as of first publish, 2026-09-19): real governance findings from a
private software monorepo, each run through TypeSafe's Jev (a "System One"
model — it returns typed choice / score / noul decisions, never free text or code) as part of
kotoba-lang/typed-decisions, a reproduction and
measurement of the Jev shape whose trained model is published at
com-kotobalabs/open-jev-deberta-v3-large.
What this is… See the full description on the dataset page: https://huggingface.co/datasets/com-kotobalabs/typed-decisions-repo-governance.eikos-decisions
Eikos Decisions
The training data of Eikos-4B and Eikos-27B, open single-pass typed-decision models.
Each row is one typed decision. It has:
a state (the evidence: a ticket, an email thread, a policy, a table, a log…);
a question of type noul (yes/no), choice (one of N options) or score (ordinal levels);
the options, in the canonical order the model sees them;
a probability distribution over the options (target_probs), which is the soft target the models were trained on.
The… See the full description on the dataset page: https://huggingface.co/datasets/caiovicentino1/eikos-decisions.swiss_leading_decisions
Dataset Card for Swiss Leading Decisions
Dataset Summary
Swiss Leading Decisions is a multilingual, diachronic dataset of 21K Swiss Federal Supreme Court (FSCS) cases. This dataset is part of a challenging text classification task. We also provide additional metadata as the publication year, the law area and the canton of origin per case, to promote robustness and fairness studies on the critical area of legal NLP.
Supported Tasks and Leaderboards
Swiss Leading… See the full description on the dataset page: https://huggingface.co/datasets/rcds/swiss_leading_decisions.morocco-cassation-court-decisions
Morocco Cassation Court Decisions
29,000+ full-text decisions from the Moroccan Court of Cassation (محكمة النقض)Source: juriscassation.cspj.ma — Official portal of the Supreme Council of the Judiciary (CSPJ)License: CC BY 4.0
Why this dataset exists
In 2026, accessing the jurisprudence of the Court of Cassation in Morocco requires being physically located in Morocco and armed with patience. The official website does not allow searching by date range, imposes a… See the full description on the dataset page: https://huggingface.co/datasets/OpenDataMoroccanLaw/morocco-cassation-court-decisions.typed-decisions-pt-es
Typed Decisions PT/ES
Unofficial Brazilian Portuguese and Spanish translations of
LocalLLaMA/typed-decisions.
The dataset covers the choice, score, and noul typed-decision tasks. It preserves the
original case IDs, workflows, question IDs, task types, option keys, splits, gold labels, and
soft target distributions.
Contents
Config
Train
Test
pt
1,200
400
es
1,200
400
Each row contains:
id and workflow from the source case;
language, either pt or… See the full description on the dataset page: https://huggingface.co/datasets/telepatia-ai/typed-decisions-pt-es.certo-synthetic-decisions
certo — synthetic decision dataset
Synthetic decisions with a known, exact answer distribution, for training and evaluating
calibrated decision models. Each example is generated from a conditional naive-Bayes evidence
world, so the posterior over the answer is computed in closed form — you can grade a model against
the true probabilities (posterior fidelity), not just accuracy.
Part of certo · project page:
https://altslate-labs.github.io/certo/
Schema (JSONL, one… See the full description on the dataset page: https://huggingface.co/datasets/rajpdus/certo-synthetic-decisions.scopeguard-decisions
ScopeGuard Decisions
ScopeGuard Decisions is a deterministic synthetic instruction dataset for training an LLM to classify an agent request before tools execute.
Splits
Split
Rows
Exact prompt overlap
Train
400
0
Validation
60
0
Test
100
0
Each row uses chat-style messages with a system policy, user request, and compact JSON assistant decision.
Output schema
{
"intent": "send_message",
"constraints":… See the full description on the dataset page: https://huggingface.co/datasets/praveenkumarpranjal/scopeguard-decisions.notam-typed-decisions
NOTAM typed decisions
Frozen, hashed evaluation suites for typed decisions about NOTAMs — a choice, a
yes/no, or a score, each with a confidence — plus a catalogue of how NOTAMs describe
areas in free text. Built by Airside Labs so that any model,
served any way, can be scored on the same rows and read with the same per-class table
and calibration curve.
Not for operational use. These suites and the numbers quoted here are for research
and for triage tooling. NOTAMs are… See the full description on the dataset page: https://huggingface.co/datasets/AirsideLabs/notam-typed-decisions.fast-decisions
Fast Decisions
The classification suite behind GLiNER2.5-Decide. Seventeen domains, one file each. Every row is a document plus the decisions a product has to make: the task name, the candidate labels, and the gold label. One call can carry several heads. Single-label heads have one gold string. Multi-label heads list every label that applies.
This release is the development split: 100 examples per domain, 1,700 rows. The test split… See the full description on the dataset page: https://huggingface.co/datasets/fastino/fast-decisions.adaption-preference-trace-decisions
PreferenceTrace — Source Corpus and Adaption Export
PreferenceTrace tests exact decision-making under competing preferences, evidence, approvals, abstention requirements, temporal/contextual precedence, and machine-readable citation contracts.
Two explicit lineage artifacts
File
Rows
Role
SHA-256
preferencetrace-source-96.jsonl
96
Canonical PreferenceTrace source corpus
7a447f9bf47c3ea455ed96ec36860360aa0e7b9e2dc604450e3a1c665b52363e… See the full description on the dataset page: https://huggingface.co/datasets/darthludious/adaption-preference-trace-decisions.cs_czech-court-decisions-ner
Dataset Card for Czech Court Decisions NER
Dataset Description
Czech Court Decisions NER is a dataset of 300 court decisions published by The Supreme Court of the Czech Republic and the Constitutional Court of the Czech Republic.
In the documents, 4 types of named entities are selected.
Dataset Features
Each sample contains:
filename: file name in the original dataset
text: court decision document in plain text
entities: list of selected entities. Each entity… See the full description on the dataset page: https://huggingface.co/datasets/fewshot-goes-multilingual/cs_czech-court-decisions-ner.gevva-decisions
⚡ Gevva Decisions: Complete Training Curriculum & Benchmark Suite
Official training mixtures, committee-verified synthetic datasets, and held-out benchmarks used to train and evaluate the Gevva family of large-context (128K), multimodal System 1 decision engines (#1 Global on JevBench: 77.54 Composite Score).
Associated Models:
Flagship: davidburhans/gevva-e2b (Default main branch)
Multimodal: davidburhans/gevva-e2b (branch multimodal)
Codebase & Reproduction Scripts:… See the full description on the dataset page: https://huggingface.co/datasets/davidburhans/gevva-decisions.Turkish_Constutional_Court_Decisionsadaption-lhw-imnci-case-decisions
This dataset is a remastered version prepared using Adaption's Adaptive Data platform.
adaption-lhw_imnci_case_decisions
This dataset contains clinical case scenarios involving Lady Health Workers (LHW) in Pakistan assessing children and mothers using IMNCI guidelines. Each sample presents a patient prompt with symptoms and a structured completion detailing the reasoning, classification, treatment plan, medication dosage, and referral urgency. The content covers common… See the full description on the dataset page: https://huggingface.co/datasets/abdullah693/adaption-lhw-imnci-case-decisions.german-court-decisions
Dataset Card for german-court-decisions
60k judicial decisions in Germany retrieved on January 1, 2024.
Dataset Description
Language(s) (NLP): German
License: MIT
Copyright notice: Automated retrieval of decisions from federal and state databases in Germany is permitted for non-commercial purposes only. As a result, the use of this dataset is permitted for non-commercial purposes only.
Uses
Prediction of verdicts based on statement of facts.
Direct Use… See the full description on the dataset page: https://huggingface.co/datasets/SH108/german-court-decisions.brazilian_court_decisions
Dataset Card for predicting-brazilian-court-decisions
Dataset Summary
The dataset is a collection of 4043 Ementa (summary) court decisions and their metadata from
the Tribunal de Justiça de Alagoas (TJAL, the State Supreme Court of Alagoas (Brazil). The court decisions are labeled
according to 7 categories and whether the decisions were unanimous on the part of the judges or not. The dataset
supports the task of Legal Judgment Prediction.
Supported Tasks and… See the full description on the dataset page: https://huggingface.co/datasets/charannatra/brazilian_court_decisions.ner_court_decisions
Basic Information
This dataset is converted from fewshot-goes-multilingual/cs_czech-court-decisions-ner using script convert_ner_court_decisions.py.
For longer texts (>200 ws tokens), the script samples text around the selected entity. It always follows form "<initial 20 ws tokens>, ..., <sampled window>".
Then it extracts category name for the entity, all occurences of such entity in the text, and creates simple json representation. For example:
{
"label": "Reference na rozhodnutí… See the full description on the dataset page: https://huggingface.co/datasets/CZLC/ner_court_decisions.seektraces-decisions
SeekTraces decisions v0 — the judgment record of an autonomous research agent
Context: what system is this from?
Seek is an autonomous research agent running nightly since June 2026
on a Mac mini. She follows her own curiosity across the open web and
writes findings into a linked knowledge base (a "vault") of Markdown
notes: claim notes (one falsifiable claim each, anchored to a source
quote and URL/DOI), observations, entity pages, open questions, and
essays… See the full description on the dataset page: https://huggingface.co/datasets/seekbot/seektraces-decisions.BGB_Court_Decisions_Alpaca_Thirddatabird-decisionsua-council-decisions
Ukrainian Municipal Council Decisions — Masthead Identity Extraction
Structured-extraction dataset of 1,075 Ukrainian municipal council decisions (рішення) from
43 local councils (громади / ради) — balanced to exactly 25 decisions per council, each paired with the five identity fields that appear in the
document masthead. The task: given the full text of a single decision, extract its masthead identity.
These are public government records. All personal names in the data are… See the full description on the dataset page: https://huggingface.co/datasets/oshyshatskyi/ua-council-decisions.BGB_Court_Decisions_Alpaca