Fine-tune of answerdotai/ModernBERT-base (149 M params, 1 regression output) for the grouped multi-positive softmax ranker task in the episodic-ingestion-compiler training pipeline: given a requested extraction field and a set of candidate source events in a trace, rank each event by how strongly it supports the field.

Training details

Train set: 1201 rows across 4 tagged modes:
- 109 conversational (original hand-authored)
- 313 autonomous agent loops (generated via local GLM harness)
- 892 prompt→action (mined from local codex/claude session jsonl)
- 99 customer-support (from jonathansuru/customer_service_information_extraction)
Eval set: 220 rows of held-out mixed-mode data (session-hash disjoint from train)
Steps: 160 optimizer steps (--accum-steps 8 → 1280 forwards)
Optimizer: AdamW, lr 5.7e-5 (= 2e-5 × sqrt(8) for effective batch size 8), warmup 30
Precision: bf16 autocast (no GradScaler)
Memory optimizations: gradient checkpointing (use_reentrant=False)
Final train loss: 0.0000
Peak VRAM: 3.858 GiB (was 12.57 without perf stack)
Hardware: single RTX 5090 Laptop (Blackwell SM 12.0, 24 GiB), CUDA 13.2, PyTorch 2.11

Eval metrics (220-row mixed eval, 1792 source pairs)

metric	value
top-1 recall	0.257
top-2 recall	0.491
top-3 recall	0.677
top-5 recall	0.901
MRR	0.504
mean expected rank	2.92

Per-field MRR (top-n fields in the eval):

field	n	MRR	top-1
`attempted_actions`	846	0.491	0.24
`observed_outcomes`	845	0.491	0.24
`failed_attempts`	36	0.866	0.75
`next_actions`	32	0.701	0.47
`customer_identity`	11	0.689	0.55
`transaction_reference`	5	0.567	0.20
`product_name`	4	0.625	0.25
`discarded_options`	2	0.292	0.00
`invalidation_hints`	2	0.625	0.50
`non_promotable_context`	2	0.625	0.50
`payment_or_warranty_detail`	2	0.312	0.00
`assistant_claims_to_verify`	1	0.200	0.00
`explicit_decisions`	1	0.500	0.00
`resolved_context`	1	0.333	0.00
`touched_files`	1	1.000	1.00
`unsupported_hypotheses`	1	0.250	0.00

Lineage vs earlier checkpoints

checkpoint	train rows	MRR	top-1	notes
V2 (commit 87cb089)	109 conversational	0.678	0.440	Evaluated on 8-row conversational adversarial eval — different and smaller test set
Stage-4 mixed-mode	1201 mixed	0.323	0.130	Same 220-row eval as this checkpoint
perf-H4 (this)	1201 mixed	0.506	0.260	bf16 + gradient checkpointing + grad accumulation + sqrt LR

Intended use

This is the ranker head of a multi-stage episodic-ingestion compiler — NOT a general-purpose classifier. Input is a JSON-serialized (requested field, candidate event) record from the compiler's training pipeline. 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

Current eval shows attempted_actions and observed_outcomes (the two largest field slices, 846 + 845 of 1792 eval pairs) stuck at MRR ≈ 0.49. This is the tool_call-vs-tool_result confusion inherent to per-candidate context-free scoring. Addressing it is planned future work (marker-pool architecture at mixed-mode scale).
Training loss of 0.0001 indicates the model has converged on train. Longer training at this config is unlikely to improve eval.
Not intended for deployment as a standalone extractor; it's one head in a larger ingestion pipeline.

Avifenesh/episodic-ingestion-modernbert-field-event-ranker-mixed-mode-perf-h4

episodic-ingestion-compiler / ModernBERT field-event ranker (perf-H4, mixed-mode)

Training details

Eval metrics (220-row mixed eval, 1792 source pairs)

Lineage vs earlier checkpoints

Intended use

Limitations

Avifenesh/
episodic-ingestion-modernbert-field-event-ranker-mixed-mode-perf-h4