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. 12.2 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.

model	global	choice	noul	score
Decision-1.0-Lex	78.15	74.00	84.67	76.38
Decision-Jef-0.1	77.80	75.00	84.80	74.60
Laya Typed Decisions	76.60	73.33	85.67	72.25

Ahead of both published figures on choice. Behind Laya on noul by 0.87 and behind Lex on score by 1.78. score is where the global deficit of 0.35 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.

	this model	Laya	Jev
soft accuracy	0.607	0.471	0.580
Brier, summed over classes	0.083	0.061 †	0.148
ECE, 10 bins	0.043	0.213	0.144
score MAE	0.244	0.242	0.391
majority class on this set	0.457
random guess on this set	0.318

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:

slot	share of gold answers	against uniform
1st	20.7%	0.89x
2nd	20.2%	0.86x
3rd	39.7%	1.70x
4th	12.2%	0.52x
5th	7.3%	0.31x

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

	native order	options permuted	change
global	77.80	77.70	+0.10
choice	75.00	74.67	+0.33
noul	84.80	84.80	+0.00
score	74.60	74.60	+0.00

Over 1,800 permutations of the benchmark's choice questions, the answer changes on 5.7% of them. Jev is measured at 0.13 and Laya at 0.15 on the same kind of check.

Calibration

ECE is 0.043 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:

	as shipped	with the temperatures
ECE, 10 bins	0.043	0.008
Brier, summed	0.083	0.108
score MAE	0.244	0.263
global accuracy	77.80	77.75

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 0.789 globally, which helps the first and hurts the second. Accuracy does not move either way, because a per-bucket temperature never changes an argmax.

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.

case	true answer	this model
one department cued	1.00	1.000
two departments cued	0.50	0.552
escalation implied by priority	0.90	0.912
mass on the two cued departments	1.00	1.000

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

file	what
model.safetensors	the weights, 143 tensors. No pickle is published, so a load executes nothing.
config.json	the encoder's own config, verbatim from jhu-clsp/mmBERT-base, plus one decision_jef key for this model's geometry.
temperatures.json	the fitted per-bucket temperatures. Off by default; see above.
metrics.json	this checkpoint's benchmark figures, raw and calibrated.
tokenizer.json, tokenizer_config.json, special_tokens_map.json	vendored, so a load needs one repository and no second download.

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.

questions in one call	median	p95
1	11.67 ms	12.44 ms
2	12.01 ms	13.14 ms
3	12.19 ms	12.85 ms
4	12.30 ms	12.57 ms

Three extra questions cost 0.63 ms in total, about 5% 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 5.35 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        0.92  {'0': 0.0576, '1': 0.9151, '2': 0.0273}    score=0.97
churn_risk  0.9726   0.97  {'false': 0.0274, 'true': 0.9726}

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

This is a requirement, not a style note. The model scores the option text, so a bare yes/no gives it nothing to compare, and the answer collapses toward an uninformative half. On the example above:

question	p(true) with bare yes/no	p(true) with descriptions
threatens to leave	0.564	0.973
requests a refund	0.539	0.961

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.

options	accuracy	random
5	0.900	0.200
10	0.815	0.100
20	0.750	0.050
40	0.610	0.025
65	0.535	0.015

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:

ranker	recall@16
this model's logits	0.960
lexical overlap	0.865
random	0.320

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

type	criteria	answer
choice	ordered map of key to description, up to 255	choice, probabilities
noul	optional map of false and true to a description — supply it	noul probability
score	ordered array of 2 to 10 level descriptions	probability-weighted score, legend

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 74.60, 1.78 behind the best published figure, on the 800 decisions that carry 40% of the benchmark. It is where the global deficit of 0.35 comes from.
• noul is 0.87 behind the best published figure, 84.80 against 85.67.
• Large option sets degrade steadily. 0.750 at twenty options and 0.535 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 bare yes/no is close to uninformative. 0.564 where the described form gives 0.973. Supply a description for false and for true.
• The shipped temperatures sharpen and are off by default. They take ECE from 0.043 to 0.008 and take Brier from 0.083 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 77.80 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

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.