From 30638514df959bec283ea490914bb01b46536d66 Mon Sep 17 00:00:00 2001 From: Niroj33 <86530493+professor3333@users.noreply.github.com> Date: Sun, 15 Nov 2026 09:05:59 +0545 Subject: [PATCH] Run a prospective behaviour check on 259 advisories published after the snapshot The advisory database at 9b0169eb (2026-11-15) is built into separate paths; 259 PyPI advisories absent from the pinned corpus and published after its commit date form the frozen prospective-1 batch. There are no labels, so only behaviour is reported, against the 272 dev documents of batch-03, batch-06 and batch-07. New advisories are much longer (median 515 words against 79; 71% need more than one 512-token window against 13%), continuing a rise through 2026. The CRF suggests 4.4 conditions per advisory (dev 1.1) and abstains on 7.7% (dev 43.8%); DeBERTa suggests 15.3 (dev 4.5). NOT_REQUIRED, EXCEPTION and negation-flagged items are more common. --- README.md | 10 +- docs/dataset_card.md | 4 + reports/prospective/README.md | 75 + .../predictions/crf-batches-01-02.jsonl | 259 +++ .../predictions/crf-batches-01-02_dev.jsonl | 272 +++ ...ightingsqrt-learning_rate5e-05-seed2.jsonl | 259 +++ reports/prospective/prospective-1.json | 1829 +++++++++++++++++ reports/prospective/snapshot-9b0169eb.json | 24 + reports/prospective/summary.md | 85 + scripts/experiments/prospective.sh | 190 ++ 10 files changed, 3006 insertions(+), 1 deletion(-) create mode 100644 reports/prospective/README.md create mode 100644 reports/prospective/predictions/crf-batches-01-02.jsonl create mode 100644 reports/prospective/predictions/crf-batches-01-02_dev.jsonl create mode 100644 reports/prospective/predictions/deberta-3-base-bio_weightingsqrt-learning_rate5e-05-seed2.jsonl create mode 100644 reports/prospective/prospective-1.json create mode 100644 reports/prospective/snapshot-9b0169eb.json create mode 100644 reports/prospective/summary.md create mode 100644 scripts/experiments/prospective.sh diff --git a/README.md b/README.md index bc22c32..7103887 100644 --- a/README.md +++ b/README.md @@ -166,7 +166,7 @@ and none was used for training. | 4 · Reliability | Calibration and review policy, formatting robustness (rules: 100%), challenge-set and error-catalogue tooling | 🟨 tooling ready · thresholds await labels | | 5 · Application | FastAPI API, review UI, SQLite (WAL) with append-only history, exports, Docker Compose stack, release manifests, tested rollback, CI image smoke test | ✅ built | | 6 · Production | Benchmark harness, ONNX/INT8 export (FP32 parity exact; INT8 fails the accuracy gate on the trained DeBERTa), local proxy benchmarks and load tests, monitoring; CRF v0.1.0 measured and deployed (`app verify` passed) on the 2 vCPU / 4 GB target host | 🟨 CRF meets every serving target on the target host · transformers not yet measured there | -| 7 · Validation | Declared held-out test evaluation, fresh-clone reproduction (macOS and Linux), [public demo](https://professor3333.github.io/TriggerMap/), downloadable models | 🟨 test evaluated once (agreement; gates not judgeable with 4 hand-labelled test records) · no prospective batch or usability study | +| 7 · Validation | Declared held-out test evaluation, fresh-clone reproduction (macOS and Linux), [public demo](https://professor3333.github.io/TriggerMap/), downloadable models | 🟨 test evaluated once (agreement; gates not judgeable with 4 hand-labelled test records) · prospective check on 259 new advisories (behaviour only, no labels) · no usability study | ### Held-out test results (declared release evaluation, 2026-11-15) @@ -190,6 +190,14 @@ prerequisites. DeBERTa has clearly higher recall and clearly fewer reversed mean are judged on hand-labelled test records only, so they **cannot be judged** with 4. Full report, with per-class support: [`reports/test-evaluation/`](reports/test-evaluation/README.md). +**Prospective check (259 advisories published after the snapshot, behaviour only).** These +have no labels, so there is no accuracy or agreement. The input has shifted: new advisories +have a median of 515 words (dev: 79). 71% need more than one 512-token transformer window +(dev: 13%), and median length in the corpus has risen steadily through 2026. On them the CRF +suggests 4.4 conditions per advisory (dev: 1.1) and reports "no explicit conditions found" +for 7.7% (dev: 43.8%). NOT_REQUIRED, EXCEPTION and negation-flagged items are more common, which +is where both models are weakest. See [`reports/prospective/`](reports/prospective/README.md). + ### Preliminary pilot results (dev split only) Measured on the **10 hand-labelled pilot documents in the dev split** (17 gold conditions, diff --git a/docs/dataset_card.md b/docs/dataset_card.md index bec96f2..88838f6 100644 --- a/docs/dataset_card.md +++ b/docs/dataset_card.md @@ -159,6 +159,10 @@ logged in [`test-access-log.md`](test-access-log.md)). pilot gate marks `UNCERTAIN` and `UNRESOLVED` unsupported ([`pilot_gate.md`](pilot_gate.md)). - **There is a single annotator.** Repeat annotation will be reported as self-consistency, never as inter-annotator agreement. +- **Newer advisories are much longer.** Median length was 43–133 words from 2018 to 2025, then + 215 (2026 first half) and 394 (second half, to the snapshot). The 259 advisories published + after the snapshot have a median of 515 words, and most need several transformer windows. The + annotated data is mostly shorter and older ([`reports/prospective/`](../reports/prospective/README.md)). ## License and attribution diff --git a/reports/prospective/README.md b/reports/prospective/README.md new file mode 100644 index 0000000..cc34987 --- /dev/null +++ b/reports/prospective/README.md @@ -0,0 +1,75 @@ +# Prospective check: newly published advisories (behaviour only) + +This check runs the frozen models on advisories that **did not exist** when the corpus was +pinned. It asks how the models behave on genuinely new text. There are no labels for these +advisories, and none will be made, so this report has **no accuracy or agreement numbers**. It +describes the input and the output, and compares both with the 272 dev documents of +`batch-03`, `batch-06` and `batch-07`. + +## The prospective batch + +- **Source:** `github/advisory-database` at commit `9b0169eb7fb17b26d940fbcc7cdd6f2e88bebd37` + (2026-11-15). The corpus was built with the same pipeline into its own paths; its manifest is + [`snapshot-9b0169eb.json`](snapshot-9b0169eb.json). The pinned snapshot `1912cbd6` was not + changed. +- **Selection** ([`prospective-1.json`](prospective-1.json), frozen): PyPI advisories whose id + is absent from the pinned corpus and that were published after the pinned snapshot's commit + date (2026-10-20T03:38:30Z). That gives **259 advisories**, published 2026-10-20 → 2026-11-14. + Seven advisories that newly joined the reviewed PyPI set but were published earlier are left + out. Four ids from the pinned corpus no longer appear in the newer snapshot. +- **Overlap:** 141 of the 259 share a PyPI package with an annotated `pilot-2`, `batch-01` or + `batch-02` record, so this is new text, but often from known projects. +- **Systems:** the CRF `crf-batches-01-02` (v0.1.0, with the live review policy + `review-policy-0`) and the run-3 DeBERTa (`model-deberta-3-run3`), both unchanged. + +Reproduce with `bash scripts/experiments/prospective.sh [DEBERTA_BUNDLE_DIR]`. All tables: +[`summary.md`](summary.md). + +## The input has shifted: new advisories are much longer + +| | Prospective (259) | Dev (272) | +|---|---:|---:| +| Median words | 515 | 79 | +| Median DeBERTa tokens | 931 | 115 | +| Longer than one 512-token window | 71% | 13% | +| Longer than 2,000 tokens | 29% | 3% | +| With a Markdown list | 64% | 25% | +| With a fenced code block | 65% | 18% | + +This is a trend, not an outlier. Median length in the pinned corpus was 43–133 words from 2018 +to 2025, then 215 words in the first half of 2026 and 394 in the second half (to the snapshot). +The prospective batch is at 515. The annotated data is mostly older and shorter. + +## The output changes with it + +| | CRF, prospective | CRF, dev | DeBERTa, prospective | DeBERTa, dev | +|---|---:|---:|---:|---:| +| No explicit conditions found | 7.7% | 43.8% | 0.8% | 9.9% | +| Unsupported structure | 0% | 0% | 0% | 0% | +| Conditions per document | 4.37 | 1.11 | 15.29 | 4.50 | +| Groups per document | 0.15 | 0.03 | 2.63 | 0.75 | +| Share NOT_REQUIRED | 14.0% | 5.3% | 10.3% | 5.1% | +| Share EXCEPTION | 1.9% | 0.3% | 5.0% | 2.7% | +| Median confidence | 0.475 | 0.510 | 0.644 | 0.674 | +| CRF conditions with a negation review flag | 21.0% | 8.3% | n/a | n/a | + +## Reading + +- **Reviewers would see far more items per advisory.** The CRF suggests 4.4 conditions per new + advisory, against 1.1 on dev. DeBERTa suggests 15.3. Under `review-policy-0` every suggested + condition is marked *review required*, so reviewing load grows with advisory length. +- **The CRF almost never abstains on new advisories** (7.7%, against 43.8% on dev). Longer + texts state more conditions and more cue words, so this is expected. It also means that the + "no explicit conditions found" state, which this project keeps distinct from "not affected", + will appear rarely on new advisories. +- **The hard roles appear more often.** NOT_REQUIRED and EXCEPTION make up a larger share of + predictions, and the CRF's negation flag fires on 21% of conditions (8% on dev). These are + the roles where both models were weakest on dev and test, so new advisories lean towards the + known failure modes. +- **Long inputs are now the norm, not the exception.** 71% of new advisories need more than one + 512-token transformer window, and 29% exceed 2,000 tokens, which is the size of the + benchmark's long-input latency target. Structure that crosses windows is a documented failure + mode, and it will matter more on newer advisories. +- **What this does not show:** whether the predictions on new advisories are right. That would + need labels. The behaviour shift suggests that the measured dev and test scores, taken mostly + on shorter advisories, may not carry over unchanged to current advisories. diff --git a/reports/prospective/predictions/crf-batches-01-02.jsonl b/reports/prospective/predictions/crf-batches-01-02.jsonl new file mode 100644 index 0000000..7de1e40 --- /dev/null +++ b/reports/prospective/predictions/crf-batches-01-02.jsonl @@ -0,0 +1,259 @@ 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"advisory_id": "GHSA-xvh5-g6gv-559v", + "content_hash": "f8876ff1dbe477942c0d7bb1a432743ed1edfa8e39a2214f90d15adc7c8b9404", + "published": "2026-10-21T21:29:16Z", + "words": 5469, + "intended_use": "prospective" + }, + { + "advisory_id": "GHSA-xw6w-gcfp-cf8m", + "content_hash": "48905c67f3579c5552075b7cc1081f0cbe849cd87fc989dfe68480ca38c3195c", + "published": "2026-10-22T16:18:05Z", + "words": 961, + "intended_use": "prospective" + } + ] +} diff --git a/reports/prospective/snapshot-9b0169eb.json b/reports/prospective/snapshot-9b0169eb.json new file mode 100644 index 0000000..058f5fc --- /dev/null +++ b/reports/prospective/snapshot-9b0169eb.json @@ -0,0 +1,24 @@ +{ + "counts": { + "excluded:not_pypi": 30939, + "excluded:withdrawn": 259, + "kept": 6612, + "scanned": 37810 + }, + "filter": { + "ecosystem": "PyPI", + "exclude_withdrawn": true + }, + "outputs": { + "advisories.jsonl": "d81bf85c39818897f05f40208fd3ae8ec413a6f2a918dd1c78e21eea7a47f6f9" + }, + "preprocessing_version": "1", + "quarantined": [], + "source": { + "commit": "9b0169eb7fb17b26d940fbcc7cdd6f2e88bebd37", + "commit_date": "2026-11-15T00:35:35Z", + "license": "CC-BY-4.0", + "path": "advisories/github-reviewed", + "repository": "https://github.com/github/advisory-database.git" + } +} diff --git a/reports/prospective/summary.md b/reports/prospective/summary.md new file mode 100644 index 0000000..d4f3aa6 --- /dev/null +++ b/reports/prospective/summary.md @@ -0,0 +1,85 @@ +# Prospective check: summary + +Generated by `scripts/experiments/prospective.sh`. No labels: behaviour only. + +- New advisories sharing a PyPI package with an annotated pilot/batch-01/batch-02 record: 141 of 259 + +## Input text + +| | prospective | dev (272) | +|---|---:|---:| +| documents | 259 | 272 | +| median words | 515 | 79 | +| mean words | 891 | 167 | +| with a Markdown list | 0.641 | 0.254 | +| with a fenced code block | 0.653 | 0.180 | + +## Advisory length by publication date (pinned corpus, then the prospective batch) + +| Published | Advisories | Median words | +|---|---:|---:| +| 2018 | 113 | 43 | +| 2019 | 106 | 48 | +| 2020 | 141 | 122 | +| 2021 | 516 | 121.5 | +| 2022 | 1337 | 48 | +| 2023 | 502 | 77 | +| 2024 | 674 | 82 | +| 2025-H1 | 337 | 76 | +| 2025-H2 | 367 | 133 | +| 2026-H1 | 1441 | 215 | +| 2026-H2 | 816 | 393.5 | +| prospective (2026-10-20 to 2026-11-14) | 259 | 515 | + +## Length in DeBERTa tokens (canonical text, no special tokens) + +| | prospective | dev (272) | +|---|---:|---:| +| median tokens | 931 | 115 | +| more than one 512-token window | 0.714 | 0.129 | +| more than 2,000 tokens | 0.290 | 0.029 | + +## CRF crf-batches-01-02 (review-policy-0) + +| | prospective | dev (272) | +|---|---:|---:| +| documents | 259 | 272 | +| no explicit conditions found | 0.077 | 0.438 | +| unsupported structure | 0 | 0 | +| conditions per document | 4.37 | 1.11 | +| groups per document | 0.151 | 0.026 | +| role REQUIRED | 0.770 | 0.884 | +| role NOT_REQUIRED | 0.140 | 0.053 | +| role UNCERTAIN | 0.072 | 0.060 | +| role EXCEPTION | 0.019 | 0.003 | +| category USAGE | 0.447 | 0.510 | +| category ACCESS | 0.347 | 0.341 | +| category CONFIGURATION | 0.135 | 0.113 | +| category PLATFORM | 0.071 | 0.036 | +| median confidence | 0.475 | 0.510 | +| review reason uncalibrated (per condition) | 1 | 1 | +| review reason negation (per condition) | 0.210 | 0.083 | +| review reason role_not_required (per condition) | 0.140 | 0.053 | +| review reason role_uncertain (per condition) | 0.072 | 0.060 | +| review reason role_exception (per condition) | 0.019 | 0.003 | +| review reason unresolved (per condition) | 0.007 | 0.007 | +| review reason workaround_section (per condition) | 0.004 | 0 | + +## DeBERTa run 3 (no review policy) + +| | prospective | dev (272) | +|---|---:|---:| +| documents | 259 | 272 | +| no explicit conditions found | 0.008 | 0.099 | +| unsupported structure | 0 | 0 | +| conditions per document | 15.29 | 4.50 | +| groups per document | 2.63 | 0.746 | +| role REQUIRED | 0.728 | 0.838 | +| role UNCERTAIN | 0.120 | 0.083 | +| role NOT_REQUIRED | 0.103 | 0.051 | +| role EXCEPTION | 0.050 | 0.027 | +| category USAGE | 0.470 | 0.551 | +| category ACCESS | 0.255 | 0.274 | +| category PLATFORM | 0.149 | 0.082 | +| category CONFIGURATION | 0.126 | 0.093 | +| median confidence | 0.644 | 0.674 | diff --git a/scripts/experiments/prospective.sh b/scripts/experiments/prospective.sh new file mode 100644 index 0000000..643f687 --- /dev/null +++ b/scripts/experiments/prospective.sh @@ -0,0 +1,190 @@ +#!/usr/bin/env bash +# Prospective check: how the frozen models behave on advisories published after the snapshot. +# +# bash scripts/experiments/prospective.sh [DEBERTA_BUNDLE_DIR] +# +# 1. Fetches github/advisory-database at PROSPECTIVE_COMMIT into its own directory (the pinned +# snapshot in data/raw/advisory-database is not touched) and builds that corpus into +# data/processed/prospective (manifest: reports/prospective/snapshot-.json). +# 2. Selects advisories whose id is absent from the pinned corpus and that were published after +# the pinned snapshot's commit date -> reports/prospective/prospective-1.json. +# 3. Predicts them with the released CRF (live review policy) and, if a bundle is given, the +# run-3 DeBERTa. As a reference it also predicts the 272 dev documents of batch-03, batch-06 +# and batch-07 with the same CRF and policy. +# 4. Writes reports/prospective/summary.md: input and output statistics, prospective against +# dev. There are no labels, so there is no accuracy or agreement here, only behaviour. +# Finished prediction files are reused. The test split is not used. +set -euo pipefail +[ -f pyproject.toml ] && [ -d annotations ] || { echo "run from the repository root" >&2; exit 1; } +export OMP_NUM_THREADS=${OMP_NUM_THREADS:-2} MKL_NUM_THREADS=${MKL_NUM_THREADS:-2} +export TOKENIZERS_PARALLELISM=false + +PROSPECTIVE_COMMIT=9b0169eb7fb17b26d940fbcc7cdd6f2e88bebd37 +RAW=data/raw/advisory-database-prospective +PROCESSED=data/processed/prospective +OUT=reports/prospective +SNAP_MANIFEST=$OUT/snapshot-${PROSPECTIVE_COMMIT:0:8}.json +DEB=deberta-3-base-bio_weightingsqrt-learning_rate5e-05-seed2 +DEV_SAMPLES=(annotations/batches/batch-03.json annotations/batches/batch-06.json annotations/batches/batch-07.json) +TM=(uv run --no-sync triggermap) +mkdir -p "$OUT/predictions" + +"${TM[@]}" ingest fetch --dest "$RAW" --commit "$PROSPECTIVE_COMMIT" +[ -s "$PROCESSED/advisories.jsonl" ] || "${TM[@]}" ingest build --snapshot "$RAW" --out "$PROCESSED" --manifest "$SNAP_MANIFEST" + +uv run --no-sync python -I - "$PROCESSED" "$SNAP_MANIFEST" "$OUT/prospective-1.json" <<'EOF' +import json, sys +from pathlib import Path +from triggermap.ingest.build import load_corpus +processed, snap_manifest, out = Path(sys.argv[1]), Path(sys.argv[2]), Path(sys.argv[3]) +pinned = {d.advisory_id for d in load_corpus(Path("data/processed/advisories.jsonl"))} +newer = {d.advisory_id: d for d in load_corpus(processed / "advisories.jsonl")} +snap = json.loads(Path("data/manifests/snapshot.json").read_text()) +newsnap = json.loads(snap_manifest.read_text()) +cutoff = snap["source"]["commit_date"] +ids = sorted(a for a, d in newer.items() if a not in pinned and d.published > cutoff) +items = [ + {"advisory_id": a, "content_hash": newer[a].content_hash, "published": newer[a].published, + "words": len((newer[a].summary + " " + newer[a].details).split()), "intended_use": "prospective"} + for a in ids +] +manifest = { + "sample_version": "prospective-1", + "params": {"rule": "advisory id absent from the pinned corpus and published after the pinned " + "snapshot's commit date", "pinned_commit": snap["source"]["commit"], "cutoff": cutoff}, + "source_commit": newsnap["source"]["commit"], + "source_commit_date": newsnap["source"]["commit_date"], + "corpus_sha256": newsnap["outputs"]["advisories.jsonl"], + "counts": {"prospective": len(items)}, + "items": items, +} +out.write_text(json.dumps(manifest, indent=1) + "\n", encoding="utf-8") +print(f"wrote {out} ({len(items)} advisories)") +EOF + +predict() { # + local out=$1 + shift + [ -s "$out" ] && { echo "reusing $out"; return; } + "${TM[@]}" predict "$@" --out "$out" +} +predict "$OUT/predictions/crf-batches-01-02.jsonl" --model crf --model-dir artifacts/crf-batches-01-02 \ + --processed "$PROCESSED" --sample "$OUT/prospective-1.json" --review-policy configs/review_policy.yaml +predict "$OUT/predictions/crf-batches-01-02_dev.jsonl" --model crf --model-dir artifacts/crf-batches-01-02 \ + --sample "${DEV_SAMPLES[@]}" --review-policy configs/review_policy.yaml +if [ -n "${1:-}" ]; then + predict "$OUT/predictions/$DEB.jsonl" --model transformer --model-dir "$1" \ + --processed "$PROCESSED" --sample "$OUT/prospective-1.json" +fi + +uv run --no-sync python -I - "$OUT" "$PROCESSED" "$DEB" "${1:-}" "${DEV_SAMPLES[@]}" <<'EOF' +import json, re, statistics, sys +from collections import Counter +from pathlib import Path +from triggermap.ingest.build import load_corpus +out, processed, deb, bundle, dev_samples = Path(sys.argv[1]), Path(sys.argv[2]), sys.argv[3], sys.argv[4], sys.argv[5:] + +def load(path): + rows = (json.loads(line) for line in Path(path).read_text(encoding="utf-8").splitlines()) + return {r["advisory_id"]: r for r in rows} + +prosp_ids = [i["advisory_id"] for i in json.loads((out / "prospective-1.json").read_text())["items"]] +dev_ids = [i["advisory_id"] for s in dev_samples for i in json.loads(Path(s).read_text())["items"]] +docs = {d.advisory_id: d for d in load_corpus(processed / "advisories.jsonl") if d.advisory_id in set(prosp_ids)} +docs |= {d.advisory_id: d for d in load_corpus(Path("data/processed/advisories.jsonl")) if d.advisory_id in set(dev_ids)} + +def text_stats(ids): + words = [len((docs[a].summary + " " + docs[a].details).split()) for a in ids] + lists = sum(bool(re.search(r"(?m)^\s*(?:[-*+]|\d+[.)])\s+\S", docs[a].details or "")) for a in ids) + fences = sum("```" in (docs[a].details or "") for a in ids) + return {"documents": len(ids), "median words": statistics.median(words), + "mean words": round(statistics.mean(words)), "with a Markdown list": lists / len(ids), + "with a fenced code block": fences / len(ids)} + +def pred_stats(pred, ids): + rows = [pred[a] for a in ids if a in pred] + conds = [c for r in rows for c in r["conditions"]] + status = Counter(r["document_status"] for r in rows) + n = len(rows) + out = {"documents": n, + "no explicit conditions found": status["NO_EXPLICIT_CONDITION"] / n, + "unsupported structure": status["UNSUPPORTED_STRUCTURE"] / n, + "conditions per document": len(conds) / n, + "groups per document": sum(len(r["groups"]) for r in rows) / n} + for role, k in Counter(c["role"] for c in conds).most_common(): + out[f"role {role}"] = k / len(conds) + for cat, k in Counter(c["category"] for c in conds).most_common(): + out[f"category {cat}"] = k / len(conds) + confs = [c["confidence"] for c in conds if c.get("confidence") is not None] + if confs: + out["median confidence"] = statistics.median(confs) + reasons = Counter(f.split(":")[2] for r in rows for f in r["review_flags"] if f.count(":") >= 2) + for reason, k in reasons.most_common(): + out[f"review reason {reason} (per condition)"] = k / max(len(conds), 1) + return out + +def fmt(v): + if isinstance(v, float) and v.is_integer(): + return str(int(v)) + if isinstance(v, float): + return f"{v:.3f}" if v < 1 else f"{v:.2f}" + return str(v) + +def table(title, left, right, names): + keys = list(dict.fromkeys([*left, *right])) + lines = [f"## {title}", "", f"| | {names[0]} | {names[1]} |", "|---|---:|---:|"] + lines += [f"| {k} | {fmt(left.get(k, 0.0))} | {fmt(right.get(k, 0.0))} |" for k in keys] + return lines + [""] + +train_ids = set() +for f in ("annotations/pilot/pilot-2.jsonl", "annotations/batches/batch-01.jsonl", "annotations/batches/batch-02.jsonl"): + for line in Path(f).read_text().splitlines(): + train_ids.add(json.loads(line)["advisory_id"]) +pinned = {d.advisory_id: d for d in load_corpus(Path("data/processed/advisories.jsonl")) if d.advisory_id in train_ids} +train_packages = {p for d in pinned.values() for p in d.pypi_packages} +shared = sum(bool(set(docs[a].pypi_packages) & train_packages) for a in prosp_ids) + +lines = ["# Prospective check: summary", "", + "Generated by `scripts/experiments/prospective.sh`. No labels: behaviour only.", "", + f"- New advisories sharing a PyPI package with an annotated pilot/batch-01/batch-02 record: " + f"{shared} of {len(prosp_ids)}", ""] +lines += table("Input text", text_stats(prosp_ids), text_stats(dev_ids), ("prospective", "dev (272)")) +periods: dict[str, list[int]] = {} +for d in load_corpus(Path("data/processed/advisories.jsonl")): + year = d.published[:4] + key = year if year < "2025" else f"{year}-{'H1' if d.published[5:7] < '07' else 'H2'}" + periods.setdefault(key, []).append(len((d.summary + " " + d.details).split())) +periods = dict(sorted(periods.items())) +periods["prospective (2026-10-20 to 2026-11-14)"] = [ + len((docs[a].summary + " " + docs[a].details).split()) for a in prosp_ids +] +lines += ["## Advisory length by publication date (pinned corpus, then the prospective batch)", "", + "| Published | Advisories | Median words |", "|---|---:|---:|"] +lines += [f"| {k} | {len(v)} | {statistics.median(v):g} |" for k, v in periods.items()] +lines.append("") +if bundle: + from transformers import AutoTokenizer + from triggermap.text.preprocessing import compose_raw, preprocess + tok = AutoTokenizer.from_pretrained(Path(bundle) / "tokenizer") + lines += ["## Length in DeBERTa tokens (canonical text, no special tokens)", "", + "| | prospective | dev (272) |", "|---|---:|---:|"] + stats = {} + for name, ids in (("prospective", prosp_ids), ("dev", dev_ids)): + n = [len(tok(preprocess(compose_raw(docs[a].summary, docs[a].details)).text, + add_special_tokens=False)["input_ids"]) for a in ids] + stats[name] = (statistics.median(n), sum(x > 510 for x in n) / len(n), sum(x > 2000 for x in n) / len(n)) + for i, label in enumerate(("median tokens", "more than one 512-token window", "more than 2,000 tokens")): + lines.append(f"| {label} | {fmt(float(stats['prospective'][i]))} | {fmt(float(stats['dev'][i]))} |") + lines.append("") +crf_p, crf_d = load(out / "predictions/crf-batches-01-02.jsonl"), load(out / "predictions/crf-batches-01-02_dev.jsonl") +lines += table("CRF crf-batches-01-02 (review-policy-0)", pred_stats(crf_p, prosp_ids), pred_stats(crf_d, dev_ids), + ("prospective", "dev (272)")) +deb_file = out / f"predictions/{deb}.jsonl" +if deb_file.exists(): + deb_dev = {**load(f"reports/neural/run-3/predictions/{deb}.jsonl"), + **load(f"reports/batch-07-comparison/predictions/{deb}.jsonl")} + lines += table("DeBERTa run 3 (no review policy)", pred_stats(load(deb_file), prosp_ids), + pred_stats(deb_dev, dev_ids), ("prospective", "dev (272)")) +(out / "summary.md").write_text("\n".join(lines), encoding="utf-8") +print(f"wrote {out / 'summary.md'}") +EOF