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professor3333 merged 1 commit into from
Nov 15, 2026
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Link the downloadable model bundles from the README and model card #60

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7 changes: 7 additions & 0 deletions README.md
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Expand Up @@ -408,6 +408,13 @@ acceptance gate has been evaluated, because labels after the pilot are model dra
[release notes](docs/release_notes/v0.1.0.md). Its rollback drill (rules `r1` → CRF `v0.1.0`
→ `r1`) passed on local Docker.

**Model downloads.** The CRF bundle is attached to
[v0.1.0](https://github.com/professor3333/TriggerMap/releases/tag/v0.1.0). The fine-tuned run-3
DeBERTa-v3-small (weights, tokenizer, labels, calibration, provenance, MIT notice) is a separate
model-only pre-release,
[`model-deberta-3-run3`](https://github.com/professor3333/TriggerMap/releases/tag/model-deberta-3-run3).
It is not the default model. Checksums are in [`docs/model_card.md`](docs/model_card.md#downloads).

## Serving cost

**On the target host (2 vCPU / 4 GB x86-64, AWS EC2, 2026-11-09)**, the released CRF
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15 changes: 14 additions & 1 deletion docs/model_card.md
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Expand Up @@ -28,7 +28,7 @@ reviewer confirms or corrects every item before export.
| System | What it is | Status |
|---|---|---|
| `rules-2` | Deterministic cue rules over the shared preprocessing (no learning) | Runs; 100% formatting-consistent on 300 documents |
| CRF | `python-crfsuite` BIO span tagger with logistic-regression category, role and group classifiers. Features `crf-features-1`, optional spaCy part-of-speech tags | Trained on pilot data only |
| CRF | `python-crfsuite` BIO span tagger with logistic-regression category, role and group classifiers. Features `crf-features-1`, optional spaCy part-of-speech tags | Default model: `crf-batches-01-02` (train split of `pilot-2`, `batch-01`, `batch-02`; 239 records), released in v0.1.0 |
| DistilBERT | `distilbert/distilbert-base-cased` (Apache-2.0), revision `6ea81172465e8b0ad3fddeed32b986cdcdcffcf0` | Run 3: held-out agreement typed F1 0.099 (CRF 0.127), [`reports/neural/run-3/`](../reports/neural/run-3/README.md) |
| DeBERTa-v3-small | `microsoft/deberta-v3-small` (MIT), revision `a36c739020e01763fe789b4b85e2df55d6180012` | Run 3: held-out agreement typed F1 0.185, false-prerequisite rate 0.483 (CRF 0.127 / 0.412; differences not clear), [`reports/neural/run-3/`](../reports/neural/run-3/README.md) |

Expand All @@ -43,6 +43,19 @@ The transformer models share one design:
The configs are in `configs/experiments/`. All systems end in the same deterministic validator,
which rejects invalid offsets, dangling group members and contradictory groups.

## Downloads

| Bundle | Release | File | SHA-256 |
|---|---|---|---|
| CRF `crf-batches-01-02` (default) | [v0.1.0](https://github.com/professor3333/TriggerMap/releases/tag/v0.1.0) | `crf-batches-01-02.tgz` (861,275 bytes) | `65677a7413e02a8da4ba3b920ac7eb8e02a8cba874876d93810d4ae5fc76d29a` |
| DeBERTa-v3-small `deberta-3-base-bio_weightingsqrt-learning_rate5e-05-seed2` (not default) | [model-deberta-3-run3](https://github.com/professor3333/TriggerMap/releases/tag/model-deberta-3-run3) | `deberta-3-base-bio_weightingsqrt-learning_rate5e-05-seed2.tgz` (422,269,499 bytes) | `45b34cabfa51bc58e2eed6e4d0c6803693421b9ec93c8c572993cd2217caf4a9` |

The DeBERTa archive holds the bundle (`model.safetensors` SHA-256 `1d09c400a41da2c2e92b2fb21758c41c6f84c6fa1a148f3c3b2c5f1879b7baf6`),
its Platt calibration (`calibration/deberta-platt.yaml`), a `NOTICE` with the base model's MIT
licence and the data attribution, and a README. Loaded on CPU it reproduces its committed
predictions on the 60 `batch-06` documents. Run it as PyTorch or FP32 ONNX: dynamic INT8 fails
the accuracy gate ([`reports/onnx-int8/`](../reports/onnx-int8/README.md)).

## Provenance

Every bundle's `metadata.json` records:
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