Datasets:
Submission: yotambr/safedecide-140m (general, then fine-tuned on train): accuracy 0.754
Hi @codelion , I'd like to add SafeDecide-140M to the fitted-on-train table.
| Field | Value |
|---|---|
| Model | yotambr/safedecide-140m, 140M, mmBERT-small cross-encoder, Apache-2.0 |
| Weights revision | fcd96ad68b2e05cbc59ad2697919dfc6bda616f1 |
| Kind | general, then fine-tuned on train |
| Test split | all/test: 400 cases, 2,000 decisions, all answered |
| Inference | one encoder pass per option, no generated tokens. Each question is scored on its own, not all five in one request |
| Latency | 80 ms p50 for a 3-option decision, fp32 ONNX on one CPU thread (Colab VM) |
| Model | Kind | Accuracy β | KL from gold β | Brier β | ECE β | Reported latency |
|---|---|---|---|---|---|---|
| SafeDecide-140M (mmBERT-small, 140M) | general, then fine-tuned on train |
0.754 | 0.126 | 0.069 | 0.147 | 80 ms, 1 CPU thread |
On the definitions: KL and Brier are per decision against the soft gold, and my scorer reproduces your Uniform row exactly (0.444 / 0.238). Accuracy is agreement with the gold label. If a tie in the gold counts as correct, as in score_model.py in SaylorTwift's evals bucket, it's 0.760. ECE is 15 equal-width bins on top-option confidence, pooled over the 2,000 decisions. With 10 bins it's 0.154, and per question (10 bins) averaged over the 20 questions it's 0.169. Tell me which convention you want in the row and I'll match it.
By question type
| n | Accuracy | KL | Brier | ECE | |
|---|---|---|---|---|---|
| noul | 600 | 0.840 | 0.065 | 0.049 | 0.111 |
| choice | 600 | 0.717 | 0.167 | 0.084 | 0.158 |
| score | 800 | 0.718 | 0.141 | 0.072 | 0.176 |
By workflow
| n | Accuracy | KL | Brier | ECE | |
|---|---|---|---|---|---|
| agent_trace_observability | 500 | 0.738 | 0.083 | 0.049 | 0.187 |
| customer_service | 500 | 0.730 | 0.190 | 0.099 | 0.137 |
| invoice_processing | 500 | 0.792 | 0.154 | 0.083 | 0.085 |
| security_incidents | 500 | 0.756 | 0.078 | 0.044 | 0.189 |
Training. I split train by case into train / validation / calibration (80 / 10 / 10). Stage 1 trains on the openjev_v2 stream of jev-distill-corpus-v3 plus the train part. Stage 2 fine-tunes on the train part alone. The checkpoint was picked by validation NLL, the per-type temperatures were fitted on validation, and the automate/escalate thresholds on calibration. Nothing from test was used: no rows, no labels, nothing derived from predictions on it. I also checked the outside data: none of the 400 test states, and none of the long text fields inside them, appear anywhere in the jev-distill train, validation or calibration splits I used (19,389 distinct states).
Predictions. Every test decision with its full distribution is in yotambr/safedecide-results, viewer tab typed_decisions_test, model SafeDecide (no ConfTr), so you can run your own scorer on it. The same numbers are in the model repo's .eval_results/typed-decisions.yaml. Code and notebooks: github.com/yotambraun/safedecide.
Something I measured along the way. The model ships with an automate/escalate threshold certified on held-out data (Learn-then-Test with exact binomial bounds). I ran the same calibration on every model in SaylorTwift's per-decision results, to see what share of the test decisions each one could take alone with the error among them certified at most 5% (100 random case splits, Ξ΄ = 0.1). The order is quite different from accuracy: JEV-27B-VL gets 20.4% with an accuracy of 0.688, d3 gets 0.6% with 0.749. Table: automation_board.md, script: scripts/automation_board.py. If a column like that would help the card, I can open a PR.
Thanks for running the benchmark.