TigreGotico/jevasio-decisions

Jevásio decisions

Content warning. The safety sources contain harmful requests (violence, self-harm, hate, illegal activity) and toxic messages. They are here so that a model can learn to detect them.

This dataset holds typed decisions in European Portuguese (pt-PT) for training Jevásio, a decision model for European Portuguese. A record is a state (text or JSON) and one or more questions. Each question is a choice (pick one option), a score (an ordered level) or a noul (yes or no), with its label. The format is the training format of Kev, an open model that answers this request shape with calibrated probabilities in one forward pass.

The labels of 27 sources come from the source data: an answer key, a safety annotation, a preference pair, a variety tag, or a wrong option taken from another row. The labels of the tierc rows come from a teacher model, AMALIA-9B (see Teacher-labelled rows). The source data is the amalia-llm collection of the AMALIA project.

A larger version with more tasks is TigreGotico/jevasio-decisions-v2. It holds every record of this dataset.

Files

File	Requests	Content
train.jsonl	147,254	training requests
dev.jsonl	15,393	model selection
calib.jsonl	12,473	temperature fitting
test.jsonl	17,332	held-out evaluation, including the evaluation-only sources
manifest.json		per source: dataset revision, licence, rows read, requests per split, and each drop reason with its count
tierc/unlabelled.jsonl	11,197 states	the teacher-labelled questions before labelling, with every question type
tierc/teacher-amalia.jsonl	33,591 questions	the teacher's probability over the options of every question

The state is a string in some records and a JSON object or list in others, as in the Jev request format. One table schema cannot hold that column, so the Hub viewer is off. Read the files as JSON lines:

import json
from huggingface_hub import hf_hub_download

path = hf_hub_download("TigreGotico/jevasio-decisions", "train.jsonl", repo_type="dataset")
records = [json.loads(line) for line in open(path, encoding="utf-8")]

To train Kev on it, run kev.train --data train.jsonl --init_from jaredpalmer/kev-0.8b --p_none 0 --p_none_distract 0 --p_distract 0. The three zeros turn off Kev's English "none of the above" options and English distractors.

Record format

{"state": {"question": "..."},
 "questions": {"answer": {"type": "choice", "instructions": "Escolhe a resposta correta.",
                            "criteria": {"opt_1": "...", "opt_2": "..."}, "label": "opt_2", "src": "csqa_pt"}},
 "_meta": {"source": "csqa_pt", "repo": "amalia-llm/commonsense_qa-mt-pt", "revision": "...", "split": "train",
           "variety": "mt-pt", "id": "...", "group_id": "...", "text_sha256": "..."}}

Keys are code identifiers, so they are in English and only the values are in Portuguese, as in a request a developer writes: the state's field names, the instruction keys (question, focus), the names of choice options (health, finance) and the question ids.

The label is the option key for choice, true or false for noul, and the level index from 0 for score. Option order is shuffled per record, so the key never carries the answer. _meta.revision is the commit of the source dataset that the build read. A tierc record also carries _meta.teacher_probs, the teacher's probabilities over the options.

Splits

A record goes to train, dev, calib or test by a hash of its group key, weighted 80/10/5/5. Records that must stay together share a group key, for example the two sentences of a WinoGrande twin pair. Sources with their own splits keep them. When the upstream test labels are hidden (CommonsenseQA, PIQA, WinoGrande), the validation split is divided into dev and test. A training record is removed when its state appears in any held-out split of any source.

Each source gives at most 10,000 training requests and 1,000 requests to each held-out split. Evaluation-only sources give up to 5,000 test requests and nothing to train.

Sources

The variety column is pt-PT for native European Portuguese data and mt-pt for machine translations into Portuguese. The licence column gives the licence and where it is stated: the amalia-llm card, or the card or repository of the original dataset.

Source	Dataset	Variety	Train	Dev	Calib	Test	Licence
bbh_pt	amalia-llm/bigbenchhard-mt-pt	mt-pt	2,189	285	123	144	MIT (upstream suzgunmirac/BIG-Bench-Hard GitHub)
corege_fos	amalia-llm/CorEGe-PT	pt-PT	10,000	1,000	1,000	1,000	CC-BY-NC-SA-4.0 (amalia-llm card)
csqa_pt	amalia-llm/commonsense_qa-mt-pt	mt-pt	9,004	777	528	421	MIT (upstream tau/commonsense_qa Hub card)
dpo_pt	amalia-llm/DPO-Dataset	pt-PT	10,000	1,000	1,000	1,000	not stated; mixed upstream sources, including CC-BY-NC-SA-4.0 persona data
dsl_tl	amalia-llm/dsl_tl_fewshot	pt-PT	2,274	298	124	147	not stated (no upstream licence found for DSL-TL)
egsciqa	amalia-llm/EGSciQA-ptPT-V1	pt-PT	10,000	1,000	772	1,000	not stated; generated from CorEGe-PT (CC-BY-NC-SA-4.0)
frmt	amalia-llm/frmt_fewshot	pt-PT	196	24	10	4	CC-BY-SA-3.0 (upstream google-research FRMT README)
openbookqa_pt	amalia-llm/openbookqa-mt-pt	mt-pt	4,530	488	309	478	Apache-2.0 (upstream allenai/OpenBookQA GitHub)
piqa_pt	amalia-llm/piqa-mt-pt	mt-pt	10,000	1,000	865	542	not stated (upstream ybisk/piqa Hub card says unknown)
pt_culture	amalia-llm/PT-Culture_Data	pt-PT	10,000	1,000	1,000	1,000	Apache-2.0 (amalia-llm card)
ptradutor	amalia-llm/amalia-PTradutor	pt-PT	10,000	1,000	995	987	not stated
rewrite_faithful	amalia-llm/smol-rewrite-PT	mt-pt	10,000	1,000	981	1,000	Apache-2.0 (upstream HuggingFaceTB/smoltalk card, smol-rewrite)
saudade	amalia-llm/saudade-pt	pt-PT	6,804	854	457	458	not stated
siqa_pt	amalia-llm/siqa-mt-pt	mt-pt	10,000	1,000	1,000	685	not stated (no upstream licence found)
summary_faithful	amalia-llm/smol_summarize_pt	mt-pt	10,000	1,000	1,000	994	Apache-2.0 (upstream HuggingFaceTB/smoltalk card, smol-summarize)
toxichat	amalia-llm/toxichat-ptpt	pt-PT	3,934	509	228	239	CC-BY-NC-4.0 (upstream lmsys/toxic-chat Hub card)
wildguardmix	amalia-llm/wildguardmix-ptpt	pt-PT	1,363	174	86	102	ODC-BY (upstream allenai/wildguardmix Hub card)
winogrande_pt	amalia-llm/winogrande-mt-pt	mt-pt	10,000	826	1,000	396	Apache-2.0 (upstream allenai/winogrande GitHub)
wmt24pp	amalia-llm/wmt24pp_xx_to_pt_10k	pt-PT	8,008	1,000	463	497	Apache-2.0 (upstream google/wmt24pp Hub card)
tierc	four amalia-llm conversation sets	pt-PT	8,953	1,158	532	554	not stated (persona_general, persona_nemotron, wikipedia_conversations); Apache-2.0 (smoltalk2_everyday_conv_pt)
alba_mcq	amalia-llm/alba_mcq	pt-PT	0	0	0	237	not stated
alba_rating	amalia-llm/alba	pt-PT	0	0	0	719	not stated
cultura_viva	amalia-llm/cultura-viva-pt-mcq	pt-PT	0	0	0	2,463	not stated
harmbench	amalia-llm/harmbench-ptpt	pt-PT	0	0	0	300	MIT (upstream walledai/HarmBench Hub card)
pt_exams	amalia-llm/pt_exams	pt-PT	0	0	0	1,346	not stated
pt_text_completion	amalia-llm/pt_text_completion	pt-PT	0	0	0	69	not stated
sst	amalia-llm/sst_pt-pt	pt-PT	0	0	0	100	CC-BY-4.0 (amalia-llm card)
xstest	amalia-llm/xstest_ptpt	pt-PT	0	0	0	450	CC-BY-4.0 (amalia-llm card)

Teacher-labelled rows

The tierc rows ask Jev-style questions about real pt-PT conversations from persona_general, persona_nemotron, wikipedia_conversations and smoltalk2_everyday_conv_pt. The state is the user's request, and in half of the records also the assistant's answer.

AMALIA-9B-1225-DPO labels each question. It reads the question with its options as letters, and the label is the letter with the highest next-token probability. No text is generated. These labels are the teacher's judgement, not a gold standard.

Question	Type	Questions kept
portugal	noul	5,822
registo	score	5,676
tema	choice	5,847
tipo_pedido	choice	5,761
variedade_resposta	choice	2,369

Two question types were dropped because the teacher gave nearly every record the same label: whether the text holds personal data (dados_pessoais, 5,843 questions) and whether the answer is complete (resposta_completa, 2,273 questions). A label that is the same for over 98% of records teaches a bias, not a decision.

Licence

This dataset is released under CC BY-NC-SA 4.0, for non-commercial research. Some sources are non-commercial (toxichat, CorEGe-PT and the data generated from it). CorEGe-PT is share-alike, and FRMT is CC BY-SA 3.0.

The source cards for 12 sources state no licence: alba_mcq, alba_rating, cultura_viva, dpo_pt, dsl_tl, egsciqa, piqa_pt, pt_exams, pt_text_completion, ptradutor, saudade, siqa_pt. These records follow the terms under which the AMALIA project publishes its data. If you hold rights to one of these sources and want it removed, open a discussion on this repository.

Limits

• Six knowledge sets are machine translations. They teach the task format but carry translation artefacts. The native pt-PT sources (saudade, pt_culture, corege_fos, egsciqa, pt_exams, alba, cultura_viva) are the better test of pt-PT ability.
• The wrong options in pt_culture and the wrong outputs in the faithfulness sources come from other rows. They are easier than real errors.
• DPO rows are kept when Portuguese function words outnumber English ones three to one. This is a heuristic, not a language identifier.
• harmbench and sst contain harmful requests only, so they measure recall and nothing else.
• dsl_tl labels a text PT-PT or PT-BR. The source has no "both" label, so a text that fits both varieties still gets one label.
• One teacher labels the tierc rows, and nobody checked its answers. Its errors are in the labels.

Citation

The source data comes from the AMALIA project. Cite it, and the original dataset of each source you use:

@inproceedings{simplicio-etal-2026-amalia,
    title = "{AMALIA}: A Fully Open Large Language Model for {E}uropean {P}ortuguese",
    author = "Simpl{\'i}cio, Afonso and Vinagre, Gon{\c{c}}alo and Ramos, Miguel Moura and Tavares, Diogo and Ferreira, Rafael and Attanasio, Giuseppe and Alves, Duarte M. and Calvo, In{\^e}s and others",
    booktitle = "Proceedings of the 17th International Conference on Computational Processing of {P}ortuguese ({PROPOR} 2026) - Vol. 1",
    year = "2026",
    address = "Salvador, Brazil",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.propor-1.38/",
    pages = "380--391"
}