Decision-Jef-0.1

Answer several runtime-defined questions about one state, in a single forward pass, with a probability over exactly the options you supply.

307M parameters. 12.0 ms for three decisions in one forward pass.

The answer space is built from the request, so a value you did not offer is not representable — not merely unlikely. There is no classification head over a fixed label set.

Results

Typed-decisions test set, 2,000 decisions: 600 choice, 600 noul, 800 score. Same set and same split as the published models below. Figures are as shipped, with no temperature applied.

Ahead of both published figures on choice. Behind Laya on noul by 1.17 and behind Lex on score by 1.18. score is where the global deficit of 0.25 comes from: matching Lex there, and leaving the other two types alone, would put this model ahead overall, which is not true of closing the noul gap.

The figures each model publishes do not line up. Lex publishes a per-type breakdown and no aggregate metrics; Jev publishes aggregates and no breakdown. The tables here say which is which rather than filling the gaps.

Soft accuracy is the label mass on the answer we pick, which matters on a benchmark whose labels are annotator averages rather than single verdicts.

† Published Brier figures use two conventions. Laya reports 0.061 averaged per class and attributes 0.148 to Jev summed over classes, which reads as a 2.4x gap and is a unit mismatch. The 0.083 above is summed, the same convention as the 0.148, so it is comparable to Jev's figure and not to Laya's.

Option order

Every choice question in this benchmark presents its options in one fixed order, and that order is not uniform. The questions carry four or five options, so a uniform draw would put the gold answer in any one slot 23.3% of the time. Measured over the 600 choice questions:

A model can therefore score on this benchmark by learning to answer third. This one does not:

Over 1,800 permutations of the benchmark's choice questions, the answer changes on 5.6% of them. Jev is measured at 0.13 and Laya at 0.15 on the same kind of check.

This table comes from the permutation harness and the one above from the like-for-like harness, which is why the global figures read 77.95 and 77.90. The two differ by less than the +/-0.15 of bf16 arithmetic noise reported under Limitations; neither is rounded to flatter the other.

Calibration

ECE is 0.026 as shipped, which is already inside a 0.10 criterion without any post-hoc correction. Temperature scaling per (type, cardinality) bucket ships with the model and is off by default. Applying it trades one metric for the other:

The two disagree because they ask different questions. ECE asks whether stated confidence matches hit rate; Brier asks whether the reported distribution matches the annotator average. The fitted temperatures sharpen, at 0.878 globally and 1.000 for noul, which helps the first and hurts the second. Accuracy does not move either way, because a per-bucket temperature never changes an argmax.

The reliability curve sits slightly above the diagonal, which is underconfidence: this model's stated confidence runs a little below its hit rate. The bin counts are plotted underneath because the two leftmost points carry 10 and 124 decisions out of 2,000 and should not be read as a trend.

Leave them off unless you specifically need the confidence figure to track the hit rate, and fit your own on your own data if you do. Laya's published pair — Brier 0.061 with ECE 0.213 — sits at the opposite end of the same trade-off rather than at a better point on it.

Where the probabilities are honest

Measured against a generator whose true posterior is known by construction: a ticket that cues one department has a true answer, one that cues two is a genuine coin flip between them.

The model recovers the true posterior when the state settles the question, puts all of its mass on the departments the state allows, and reports close to an even split where the answer is genuinely undetermined. For scale on the third row, a 0.90 truth is the case where a stated rate rather than a verdict is the right answer.

Question isolation

Adding a question does not change another question's answer. This holds exactly, not approximately, and it holds against the length of the other questions as well as their content — each question's block is packed with the geometry it would have if it were alone in the request.

Checked across all 2,000 benchmark decisions with one, two, three and four questions sharing each sequence: in fp32 the four runs agree to every digit reported, globally and per type. In bf16 they spread by about 0.15, which is padded-shape arithmetic rather than information crossing between questions.

Files

The encoder tensors carry their canonical ModernBERT names, so the fine-tuned encoder can be loaded on its own:

from transformers import AutoModel
encoder = AutoModel.from_pretrained("BarraHome/Decision-Jef-0.1")

That returns a ModernBertModel with every one of its 134 weights loaded and the nine decision-head tensors skipped. Verified tensor by tensor: both paths give bit-identical encoder weights.

Latency

NVIDIA H100 NVL, fp32, median of 30 calls after warm-up, measured end to end through this package: packing, encoder and readout. fp32 is what from_pretrained gives you; bf16 roughly halves these numbers.

Three extra questions cost 0.44 ms in total, under 4% over a single question. The state is encoded once and the question branches are masked apart, so a request carrying four questions is not four requests — but it is not free either, and the trend across these four rows is monotonic rather than noise.

Throughput at batch 64 and 1,024 tokens is 5.36 ms per decision in fp32.

Usage

pip install decision-jef

The weights are published separately from the package.

from decision_jef import Decider, Question, email

d = Decider.from_pretrained("BarraHome/Decision-Jef-0.1")

state = email.as_state(
    "user@acme.com", "Duplicate charge on invoice #4411",
    "We were billed twice for March. Please refund the duplicate today "
    "or we will cancel our plan.")

answers = d.decide(state, {
    "department": Question("choice", "Which department should handle this?", {
        "billing": "invoices, payments, refunds",
        "technical": "bugs, outages, system errors",
        "sales": "pricing, new contracts",
        "other": "everything else",
    }),
    "urgency": Question("score", "How urgent is this request?",
                        ["not urgent", "soon", "critical or blocking"]),
    "churn_risk": Question("noul", "Does the user threaten to leave?", {
        "false": "The user makes no threat to stop using the service.",
        "true": "The user threatens to cancel, churn or leave.",
    }),
})

for qid, a in answers.items():
    print(qid, a.choice or a.p("true"), a.confidence, a.probabilities)

department  billing  1.00  {'billing': 0.9997, 'technical': 0.0001, 'sales': 0.0001, 'other': 0.0001}
urgency     1        0.93  {'0': 0.0407, '1': 0.9289, '2': 0.0304}    score=0.99
churn_risk  0.9733   0.97  {'false': 0.0267, 'true': 0.9733}

decide returns raw probabilities. Pass calibrated=True to apply the shipped temperatures, and read the calibration section first.

d.to_wire(answers) returns the same content as a JSON-ready response body.

A question must be a Question, not a plain dict. Passing a dict raises a TypeError that says so.

Give every yes/no outcome a description that carries meaning

This is a requirement, not a style note, and a template does not satisfy it. The model scores the option text, so what that text says is what it has to work with. Measured over all 600 noul questions in the benchmark, replacing their own descriptions:

A templated prefix is worth 0.33 points over supplying nothing. It can also be worse than nothing on an individual question: on the refund e-mail above, asking whether the user threatens to leave gives p(true) 0.602 with no descriptions, 0.181 with the template -- the wrong side -- and 0.973 with

{"false": "The user makes no threat to stop using the service.",
 "true":  "The user threatens to cancel, churn or leave."}

Write what each outcome means. Do not generate the two strings from the question.

Large option sets

Accuracy falls as the option list grows, and the window is not the reason: eighty options with one-line descriptions pack into 772 tokens of the 1,024 with none truncated, so the list fits and the readout is what degrades. The token figure scales with how long your descriptions are.

Laya reports 0.425 on a 77-label set and attributes it to an option-text budget; Jev is reported at 0.870 and is ahead of this model here. Narrow the set first if you are above about twenty options:

from decision_jef import shortlist_decide

answers, meta = shortlist_decide(decider, state, questions, k=16)
meta["intent"]["kept"]     # the options that survived, best first

Ranking is lexical overlap between the state and each option's text, weighted so a term every option shares counts for little. It needs no second model. Pass embed_fn to rank with your own embeddings instead. A question at or below k options passes through and costs nothing.

This model's own logits are also a usable ranker, and a better one than the lexical default. On 200 banking-intent cases cut to fifty options, recall of the gold answer in the top sixteen:

Ranking is an easier task than answering, so the logits stay informative at a list length where the argmax is already unreliable. Two passes of this model cost about 24 ms and need no second model or embedding service.

Email

A reply carries the thread below it, a signature, and often a legal disclaimer. None of that is the message, and it is often the longest part.

from decision_jef import email

state = email.as_state(sender, subject, body)

email.clean(body) cuts the quoted thread, the signature block and the disclaimer, and as_state formats the fields the way the rest of this package feeds the model.

The three question types

Option order is part of the request: the same options in a different order are a different request. The answer changes on 5.7% of choice questions under permutation, so average over permutations if you need a stable answer.

How it works

<bos> state ... <eos>                                    shared, encoded once
[Q] instructions ... [OPT] opt0 [OPT] opt1 ... [DEC]     question 1
[Q] instructions ... [OPT] opt0 [OPT] opt1 ... [DEC]     question 2

The query is read at [DEC], after the whole option list, so the decision sees every option. Keys come from each [OPT] in the same pass, so the options are read together rather than scored in isolation. Each question attends to the state and to itself only; the state attends to neither. Position ids restart at the end of the state for every question, and the local-attention band is measured in those restarted positions, which is what makes isolation hold against the other questions' length and not only their content.

No escalation head in this release

Earlier releases shipped a head that predicted whether the model's own answer was wrong, so a caller could hand the flagged cases to a person. It does not work in these weights and has been removed rather than shipped with a caveat.

Measured on the benchmark's 2,000 decisions before removal: mean predicted P(wrong) was 0.045 where the answer was right and 0.083 where it was wrong. Escalating the worst half by predicted P(wrong) raised accuracy on the rest from 0.777 to 0.795 — not enough separation to set a threshold on, and not something a different threshold recovers.

decide(..., with_escalation=True) therefore returns escalation=None. The field is documented and will carry a value again when a head earns it.

Limitations

• score is the weakest type at 75.20, 1.18 behind the best published figure, on the 800 decisions that carry 40% of the benchmark. It is where the global deficit of 0.25 comes from.
• noul is 1.17 behind the best published figure, 84.50 against 85.67.
• Large option sets degrade steadily. 0.780 at twenty options and 0.635 at sixty-five, against a random baseline of 0.050 and 0.015. Jev is reported at 0.870 at sixty-five and is ahead here. Narrow the list above twenty; the model's own logits rank well enough to do it, at 0.960 recall@16 out of fifty options, even where its argmax is already unreliable.
• A yes/no without meaningful outcome descriptions loses 19.8 points, 84.50 against 64.67, and a description templated from the question recovers 0.33 of them. Write what each outcome means.
• The shipped temperatures sharpen and are off by default. They take ECE from 0.026 to 0.006 and take Brier from 0.092 to 0.108.
• No escalation head, see above.
• Option order still changes the answer on 5.7% of choice questions. That is ahead of both published figures but it is not zero.
• Trained and measured on English typed decisions. The backbone is multilingual and the tokenizer covers 256k tokens, but no non-English benchmark has been run — treat multilingual use as untested.
• The guardrails and moderation tags reflect coverage of toxicity and hate-speech decisions. Neither capability has been benchmarked.
• Reported global figures carry about ±0.15 of bf16 arithmetic noise. The 77.90 above is a single measurement, not a mean over seeds.
• Long states are truncated to the window with the questions reserved first.

License and provenance

The code and these weights are MIT, following the jhu-clsp/mmBERT-base backbone. No weights, gradients or private data from any third-party decision service are used or claimed; the design follows publicly documented API behaviour and public benchmark splits.

Training data, stated plainly. Part of this checkpoint's training corpus comes from tasksource/tasksource-jev-typed-decisions, which carries license: other. Its own card says no single upstream licence covers every row and that each source's licence should be checked before reuse; it draws on 571 of them. That review has not been done for this release. If your use is one where upstream data licensing matters, treat this checkpoint as unreviewed on that point and say so downstream. The rest of the corpus is either generated for this project or comes from tasksource/procedural-jev, which is Apache-2.0 and procedurally generated with no upstream data.

Neither dataset is affiliated with TypeSafe or OpenJev, and neither is this model.