license: apache-2.0
library_name: transformers
base_model: answerdotai/ModernBERT-base
tags:
- modernbert
- episodic-ingestion-compiler
- grouped-softmax-ranker
- field-event-ranker
- mixed-mode-training
- semantic-reasoning-labels
- v2-labels
language:
- en
episodic-ingestion-compiler / ModernBERT field-event ranker (mixed-mode v2, perf-H4)
Fine-tune of answerdotai/ModernBERT-base for the grouped multi-positive
softmax ranker task with v2 semantic-reasoning labels.
What changed vs the v1 mixed-mode checkpoint
An audit of the v1 mixed-mode training data showed that 94% of eval pairs came
from two fields (attempted_actions, observed_outcomes) whose labels were
exact functions of event role on 97% of rows (attempted_actions=tool_call,
observed_outcomes=tool_result). On these degenerate labels:
- E[MRR | random scores] ≈ 0.26 (random-permutation floor for multi-positive softmax)
- v1 perf-H4 MRR = 0.506 ≈ 1.97× random
- Architecture experiments (H3, H3a, H3b, H3b-span, etc.) could not break through because the ceiling was arithmetic, not informational.
v2 adds five semantic-reasoning fields whose labels require more than role:
| field | kind | rule |
|---|---|---|
initiating_command |
single-pick | First tool_call in a multi-call chain |
outcome_of_latest_attempt |
single-pick | Tool_result whose call_id matches the LAST tool_call |
recent_error |
single-pick | Most-recent tool_result with success=false |
attempt_outcome_pairs |
array | Paired tool_results (only when window has ≥1 orphan result — otherwise empty, so it can't collapse to role==tool_result) |
action_causality |
array | Tool_results whose content tokens recur in a LATER assistant turn |
Tautology rates on these new fields: 0–10%. Legacy fields kept for back-compat.
Training details
- Train rows: 1217 (mixed-mode v2: autonomous + prompt→action + customer-support + conversational)
- Train groups: 5882 (one per field × row pair)
- Eval rows: 220 (1900 groups, 21986 candidate pairs)
- Steps: 160 optimizer steps (
--accum-steps 8) - Optimizer: AdamW, lr 5.7e-5, warmup 30
- Precision: bf16 autocast
- Memory: gradient checkpointing (use_reentrant=False)
- Final train loss: 0.0854
- Peak VRAM: 4.08 GiB
- Hardware: RTX 5090 Laptop (Blackwell SM 12.0, 24 GiB)
Eval metrics (220-row mixed v2 eval, {'1': 0.2964509394572025, '2': 0.5173277661795407, '3': 0.6931106471816284, '5': 0.8881002087682672})
| metric | value |
|---|---|
| overall MRR | 0.526 |
| top-1 recall | 0.296 |
| top-2 recall | 0.517 |
| top-3 recall | 0.693 |
| top-5 recall | 0.888 |
| mean expected rank | 3.09 |
Per-field MRR (sorted by eval-pair count):
| field | n | MRR | top-1 |
|---|---|---|---|
attempted_actions |
846 | 0.469 | 0.23 |
observed_outcomes |
845 | 0.486 | 0.24 |
initiating_command |
199 | 0.667 | 0.50 |
outcome_of_latest_attempt |
181 | 0.763 | 0.60 |
action_causality |
141 | 0.483 | 0.24 |
attempt_outcome_pairs |
53 | 0.563 | 0.30 |
failed_attempts |
36 | 0.615 | 0.42 |
next_actions |
32 | 0.565 | 0.34 |
recent_error |
29 | 0.743 | 0.59 |
customer_identity |
11 | 0.818 | 0.73 |
transaction_reference |
5 | 0.467 | 0.20 |
product_name |
4 | 1.000 | 1.00 |
discarded_options |
2 | 0.375 | 0.00 |
invalidation_hints |
2 | 0.750 | 0.50 |
non_promotable_context |
2 | 0.333 | 0.00 |
payment_or_warranty_detail |
2 | 0.350 | 0.00 |
assistant_claims_to_verify |
1 | 0.500 | 0.00 |
explicit_decisions |
1 | 1.000 | 1.00 |
resolved_context |
1 | 0.250 | 0.00 |
touched_files |
1 | 0.333 | 0.00 |
unsupported_hypotheses |
1 | 0.333 | 0.00 |
Lineage
| checkpoint | labels | overall MRR | top-1 | notes |
|---|---|---|---|---|
| V2 (commit 87cb089) | conversational only | 0.678 | 0.440 | 8-row adversarial eval, not comparable |
| mixed-mode stage-4 | v1 | 0.323 | 0.130 | pre-perf stack |
| mixed-mode perf-H4 | v1 | 0.506 | 0.260 | winning perf stack — but on role-tautological labels |
| mixed-mode v2 perf-H4 (this) | v2 | 0.526 | 0.296 | same perf stack, semantic-reasoning labels |
Intended use
Ranker head of a multi-stage episodic-ingestion compiler. Input is a JSON-serialized (requested field, candidate event) record. Output is a scalar logit that, after grouped softmax over siblings in the same trace, estimates the probability that the candidate event supports the requested field.
Limitations
- Aggregate MRR (0.526) is a blend across fields of very different difficulty.
Legacy fields (
attempted_actions,observed_outcomes) are still role-tautological on most rows; the model can hit ~0.48 on them without semantic reasoning. The signal of real learning is in the new fields:outcome_of_latest_attempt: MRR 0.763 (2.76× random)recent_error: MRR 0.743 (2.90× random)initiating_command: MRR 0.667 (2.42× random)
- Not a standalone extractor. One head in a larger ingestion pipeline.
Related work
See docs/ranker-hypothesis-log-2026-05-08.md in the
episodic-ingestion-compiler repo for the full experimental ladder.