example-repro: spec-faithful Qwen3-Emb+LoRA+prototype-head training on synthetic data

Reimplements the jev-topic-head training from the public spec checklist
(no original code available): 56-anchor prototype head seeded from topic
descriptions, LoRA r16, two AdamW groups, warmup->decay, 1/sqrt(count)
sampling with hard-pair x2, early stop on val macro-F1, 3 seeds + eval.
Synthetic structural twin of the private dataset; real-data rerun pending.

.gitignore

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+runs/
+__pycache__/
+*.pyc
+.venv/

README.md

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+# example-repro — jev-topic-head training, reproduced from the public spec
+
+A self-contained reimplementation of the training described in
+`../jev-topic-head/README.md` (the scaffold), written **without the original code**:
+Qwen3-Embedding-0.6B + LoRA(r16) + prototype head (56 anchors + learned scale),
+on 279/85/105-item synthetic data matching the real split structure.
+
+**This validates the pipeline, not the numbers.** The real labels live in the private
+`Kodep/jev-topic-data` (school-email text, not pushed yet). When they land, point
+`config.yaml: data.dir` at them and rerun; everything else stays.
+
+## Files
+| file | what |
+|---|---|
+| `config.yaml` | every hyperparameter from the spec checklist |
+| `make_toy_data.py` | synthetic 56-topic dataset (same shapes/skew/14-unseen-topics/hard-pairs) |
+| `train_head.py` | the training loop: seed protos from descriptions, LoRA, 2 AdamW groups, warmup→decay, 1/√count sampler, early stop on val macro-F1 |
+| `evaluate.py` | per-item dump + top-1/macro-F1/confidence buckets (eval/ schema) |
+
+## Run (on Sunny, 4090)
+```bash
+cd ~/jev-example
+.venv/bin/python make_toy_data.py
+.venv/bin/python train_head.py --config config.yaml --seed 0   # downloads base model once
+.venv/bin/python evaluate.py --config config.yaml --run runs/seed0
+```
+
+## Spec fidelity notes
+- Implemented from the checklist: r16/α32/dropout 0.05 on q/k/v/o + gate/up/down;
+  protos seeded from description embeddings; scale init 20; CE + label smoothing 0.05;
+  lora 3e-4 wd .01 / head 2e-3 no wd; warmup 10% → linear decay to 5%; batch 16;
+  ≤12 epochs; early stop on val macro-F1 (patience 3); sampler 1/√count × hard-pair 2.
+- Inferences the spec left open, marked in code: **last-token pooling** (official
+  Qwen3-Embedding recipe), **bf16 = bf16 base weights + fp32 head**.
+- Show/hide rules (`rules_table.json`) are not modeled — they're a fixed downstream
+  table, not part of the head.
+
+## Results (synthetic data, 4090, 2026-10-04)
+- Trainable: **10,092,544 LoRA + 57,345 head params** (1.67% of 605.9M) — matches the
+  spec's shape: head = 56×1024 + 1 exactly.
+- Early stop at epoch 4 (patience 3); best val macro-F1 0.949 (seed 0).
+- Test vs labels, identical across seeds 0/1/2: **top-1 0.971, macro-F1 0.956**;
+  78/105 items ≥0.9 confidence; **zero errors above 0.9 confidence** — the same
+  "errors live in the low-confidence bucket" pattern the real report claims.
+- These numbers validate plumbing only: synthetic labels are clean, the real ones are
+  Jev's noisy silver answers (real span: macro-F1 0.49–0.59).
+
+## Status
+Code path fully exercised end-to-end (3 seeds + evals, artifacts in `results/`).
+Awaiting the real `Kodep/jev-topic-data` push → set `config.yaml: data.dir` to it and
+rerun unchanged to attempt the actual 92/105 · 0.572 · 90.5% numbers.

config.yaml

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+base_model: Qwen/Qwen3-Embedding-0.6B
+max_length: 512
+
+lora:
+  r: 16
+  alpha: 32
+  dropout: 0.05
+  target_modules: [q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj]
+
+head:
+  scale_init: 20.0
+
+loss:
+  label_smoothing: 0.05
+
+optimizer:
+  lora_lr: 3.0e-4
+  lora_wd: 0.01
+  head_lr: 2.0e-3
+  head_wd: 0.0
+  warmup_frac: 0.10
+  final_lr_frac: 0.05
+
+train:
+  batch_size: 16
+  epochs: 12
+  patience: 3
+  dtype: bf16          # spec; if metrics look odd on this stack, try fp32
+
+data:
+  dir: data
+  taxonomy: topic_taxonomy.json
+  train: train.jsonl
+  validation: validation.jsonl
+  test: test.jsonl
+  # hard topic pairs get x2 sampling weight (mirrors the spec's "hard-pair topics x2")
+  hard_pairs:
+    - [soccer, sports_general]
+    - [swim_club, sports_general]
+    - [art_club, crafts_club]
+    - [music_band, choir]
+    - [flu_report, allergies]
+    - [bake_sale, fundraiser]
+
+out_root: runs

evaluate.py

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+"""Evaluate a trained run: top-1 vs jev_topic, macro-F1, per-item dump, confidence buckets.
+
+Mirrors the eval/ schema promised in jev-topic-head (per_item jsonl + headline numbers).
+No rules_table.json exists in this example, so show/hide is skipped (it's a fixed
+lookup table downstream of the topic, not part of the head).
+"""
+import argparse, json, os
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+import yaml
+from sklearn.metrics import f1_score
+from transformers import AutoModel, AutoTokenizer
+from peft import PeftModel
+
+from train_head import PrototypeHead, encode, read_jsonl
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--config", default="config.yaml")
+    ap.add_argument("--run", required=True, help="e.g. runs/seed0")
+    args = ap.parse_args()
+    cfg = yaml.safe_load(open(args.config))
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    dtype = torch.bfloat16 if cfg["train"]["dtype"] == "bf16" else torch.float32
+
+    hs = torch.load(os.path.join(args.run, "head", "head.pt"), map_location="cpu")
+    topics = hs["topics"]
+    topic2id = {t: i for i, t in enumerate(topics)}
+
+    tok = AutoTokenizer.from_pretrained(cfg["base_model"])
+    tok.padding_side = "right"
+    base = AutoModel.from_pretrained(hs.get("base_model", cfg["base_model"]),
+                                     torch_dtype=dtype).to(device)
+    base.config.use_cache = False
+    model = PeftModel.from_pretrained(base, os.path.join(args.run, "lora"))
+
+    head = PrototypeHead(hs["protos"], float(hs["scale"]))
+    head.load_state_dict({"protos": hs["protos"], "scale": hs["scale"]})
+    head = head.to(device).eval()
+
+    rows = read_jsonl(os.path.join(cfg["data"]["dir"], cfg["data"]["test"]))
+    texts = [r["text"] for r in rows]
+    logits, BATCH = [], 32
+    with torch.no_grad():
+        for i in range(0, len(texts), BATCH):
+            v = encode(model, tok, texts[i:i + BATCH], cfg["max_length"], device)
+            logits.append(head.logits(v).float().cpu())
+    logits = torch.cat(logits)
+    probs = F.softmax(logits, dim=-1)
+    conf, pred = probs.max(-1)
+    pred = pred.numpy()
+    gold = np.array([topic2id[r["jev_topic"]] for r in rows])
+
+    per_item = []
+    for r, p, c in zip(rows, pred, conf.numpy()):
+        per_item.append({"id": r["id"], "model_topic": topics[p],
+                         "jev_topic": r["jev_topic"], "confidence": round(float(c), 4),
+                         "agree": bool(p == topic2id[r["jev_topic"]])})
+    os.makedirs(os.path.join(args.run, "eval"), exist_ok=True)
+    with open(os.path.join(args.run, "eval", "per_item_test.jsonl"), "w") as f:
+        for r in per_item:
+            f.write(json.dumps(r) + "\n")
+
+    top1 = float((pred == gold).mean())
+    mf1 = float(f1_score(gold, pred, average="macro", zero_division=0,
+                         labels=list(range(len(topics)))))
+    conf_all = conf.numpy()
+    summary = {"n": len(rows), "top1_vs_jev": round(top1, 4),
+               "macro_f1_vs_jev": round(mf1, 4),
+               "conf_lt_05": int((conf_all < 0.5).sum()),
+               "conf_gte_09": int((conf_all >= 0.9).sum()),
+               "errors_lt_05": int(((pred != gold) & (conf_all < 0.5)).sum()),
+               "errors_gte_09": int(((pred != gold) & (conf_all >= 0.9)).sum())}
+    json.dump(summary, open(os.path.join(args.run, "eval", "summary.json"), "w"), indent=2)
+    print(json.dumps(summary, indent=2))
+
+
+if __name__ == "__main__":
+    main()

make_toy_data.py

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+"""Synthetic stand-in for Kodep/jev-topic-data (which is private and not yet pushed).
+
+Same structure as the spec: 56 topics with descriptions, 279 train / 85 val / 105 test
+items, skewed class counts, 14 topics with no training items, hard pairs sharing
+vocabulary. The TOPICS are ours and the text is machine-generated — treat the metrics
+as pipeline validation, not as a benchmark.
+"""
+import json, os, random
+
+TOPICS = [
+    # (name, description, keywords) — pairs listed in config hard_pairs share keywords
+    ("soccer", "soccer practice, games and team news", ["soccer", "practice", "field", "game"]),
+    ("sports_general", "general sports and athletic program news", ["sports", "practice", "game", "athletic"]),
+    ("swim_club", "swim club sessions, meets and pool rules", ["swim", "pool", "meet", "lap"]),
+    ("basketball", "basketball schedule and gym bookings", ["basketball", "gym", "court", "tournament"]),
+    ("athletics_office", "athletics office announcements", ["athletic", "office", "booster"]),
+    ("field_trip", "field trip itineraries, buses and permission", ["field trip", "bus", "itinerary", "chaperone"]),
+    ("permission_slip", "permission slips and signature reminders", ["permission", "slip", "signature", "consent"]),
+    ("lunch_menu", "weekly lunch menu and meal options", ["lunch", "menu", "meal", "cafeteria"]),
+    ("cafeteria_rules", "cafeteria expectations and seating", ["cafeteria", "seating", "rules", "lunch"]),
+    ("bake_sale", "bake sales and baked goods fundraisers", ["bake sale", "cookies", "sale", "fundraiser"]),
+    ("fundraiser", "fundraisers, drives and donation goals", ["fundraiser", "donation", "goal", "sale"]),
+    ("art_club", "art club projects, medium and shows", ["art", "paint", "project", "exhibit"]),
+    ("crafts_club", "crafts club projects and supplies", ["crafts", "glue", "project", "supplies"]),
+    ("drama_club", "drama club rehearsals and tickets", ["drama", "rehearsal", "play", "tickets"]),
+    ("music_band", "concert band rehearsals and concerts", ["band", "concert", "performance", "instrument"]),
+    ("choir", "choir practices and winter concert", ["choir", "concert", "performance", "sing"]),
+    ("orchestra", "orchestra sectionals and repertoire", ["orchestra", "sectional", "violin", "concert"]),
+    ("reading_week", "reading week challenges and logs", ["reading", "challenge", "log", "books"]),
+    ("library", "library hours, returns and fines", ["library", "return", "books", "due"]),
+    ("spelling_bee", "spelling bee rounds and registration", ["spelling", "bee", "round", "word"]),
+    ("math_club", "math club contests and puzzles", ["math", "contest", "puzzle", "problem"]),
+    ("math_olympiad", "math olympiad training and teams", ["math", "olympiad", "team", "contest"]),
+    ("science_fair", "science fair projects and judging", ["science", "fair", "project", "judging"]),
+    ("robotics", "robotics builds and competition", ["robotics", "robot", "build", "competition"]),
+    ("coding_club", "coding club sessions and projects", ["coding", "app", "scratch", "project"]),
+    ("history_day", "history day topics and papers", ["history", "paper", "topic", "exhibit"]),
+    ("debate_club", "debate rounds and motions", ["debate", "round", "motion", "argument"]),
+    ("chess_club", "chess club ladder and matches", ["chess", "match", "ladder", "tournament"]),
+    ("homework", "homework policies and support", ["homework", "assignment", "due", "tutoring"]),
+    ("grades", "grade reporting and conferences", ["grades", "report card", "conference", "gpa"]),
+    ("attendance", "attendance windows and absences", ["attendance", "absence", "tardy", "excused"]),
+    ("late_pickup", "late pickup procedures and fees", ["pickup", "late", "fee", "after school"]),
+    ("early_dismissal", "early dismissal and snow schedules", ["dismissal", "early", "schedule", "snow"]),
+    ("weather_closing", "weather closings and delays", ["closing", "delay", "weather", "snow"]),
+    ("bus_route", "bus route changes and stops", ["bus", "route", "stop", "ride"]),
+    ("carpool", "carpool coordination boards", ["carpool", "drivers", "roster", "pickup"]),
+    ("flu_report", "illness, flu and nurse notes", ["flu", "nurse", "illness", "outbreak"]),
+    ("allergies", "allergy alerts and food policies", ["allergy", "food", "nurse", "epipen"]),
+    ("photo_day", "school photos and portrait packages", ["photo", "portrait", "package", "picture"]),
+    ("dentist_note", "dental screening and vision checks", ["dental", "vision", "screening", "note"]),
+    ("volunteer", "volunteer hours and signups", ["volunteer", "hours", "signup", "helper"]),
+    ("pta_meeting", "PTA meetings and officers", ["pta", "meeting", "officer", "dues"]),
+    ("textbook", "textbook and supply lists", ["textbook", "supply", "list", "checkout"]),
+    ("locker", "locker assignments and rules", ["locker", "combination", "assignment", "hall"]),
+    ("dress_code", "dress code and spirit days", ["dress code", "spirit", "attire", "jeans"]),
+    ("concert_tickets", "event ticket sales and seating", ["tickets", "seating", "presale", "entry"]),
+    ("yearbook", "yearbook ads, photos and delivery", ["yearbook", "ad", "delivery", "super senior"]),
+    ("litter_pickup", "litter and grounds volunteer days", ["litter", "grounds", "cleanup", "volunteer"]),
+    ("garden", "school garden planting and care", ["garden", "plant", "soil", "harvest"]),
+    ("astronomy_night", "stargazing and planetarium trips", ["astronomy", "stars", "planetarium", "telescope"]),
+    ("museums_late", "museum nights and docent tours", ["museum", "docent", "tour", "gallery"]),
+    ("wellness", "wellness checks and health screenings", ["wellness", "health", "screening", "check"]),
+    ("tech_help", "device help and wifi support", ["device", "wifi", "chromebook", "support"]),
+    ("hall_pass", "hall pass and study hall policies", ["hall pass", "study hall", "pass", "policy"]),
+    ("lunch_duty", "duty rosters and coverage", ["duty", "lunch duty", "coverage", "roster"]),
+    ("staff_appreciation", "staff thank-you and Appreciation Week", ["staff", "appreciation", "thank you", "week"]),
+]
+
+HARD = {"soccer", "sports_general", "swim_club", "art_club", "crafts_club", "music_band",
+        "choir", "flu_report", "allergies", "bake_sale", "fundraiser"}
+
+FILLERS = [
+    "Please reply-all if anything changed since last week.",
+    "Mrs. Delgado left this note for the whole distribution list.",
+    "Reminder forwarded from the office with no further edits.",
+    "The link in the original message was left out of this copy.",
+    "This item repeats in three separate newsletters this month.",
+    "A follow-up message may or may not arrive later.",
+    "Someone on the list has asked to be removed after the fact.",
+    "The date appears twice and the two dates disagree.",
+]
+EVENTS = ["reminder", "update", "signup now", "schedule change", "final call", "quick note"]
+
+
+def make_text(rng, topic):
+    kws = TOPIC_KW[topic]
+    bits = [rng.choice(kws), rng.choice(kws)]
+    rng.shuffle(bits)
+    text = f"{topic.replace('_', ' ').title()}: {rng.choice(EVENTS)} - {bits[0]} and {bits[1]} this week."
+    if rng.random() < 0.35:
+        text += f" Also mention {rng.choice(kws)} briefly near the end."
+    if rng.random() < 0.30:
+        text += " " + rng.choice(FILLERS)
+    if rng.random() < 0.12:
+        text += " " + " ".join([rng.choice(FILLERS), rng.choice(FILLERS)])
+    return text
+
+
+def main(out_dir):
+    rng = random.Random(42)
+    os.makedirs(out_dir, exist_ok=True)
+    global TOPIC_KW
+    TOPIC_KW = {t[0]: t[2] for t in TOPICS}
+    names = [t[0] for t in TOPICS]
+    assert len(names) == 56, len(names)
+
+    taxonomy = {name: desc for name, desc, _ in TOPICS}
+    with open(os.path.join(out_dir, "topic_taxonomy.json"), "w") as f:
+        json.dump(taxonomy, f, indent=2)
+
+    # skewed train counts: a few big topics, a long tail, 14 topics with no train items
+    train_pool = [n for n in names if n not in rng.sample(names, 14)]
+    counts = {}
+    remaining = 279
+    for i, n in enumerate(train_pool):
+        w = rng.choice([1, 1, 2, 2, 3, 5]) if n in HARD else rng.choice([1, 1, 1, 2, 2, 4, 7])
+        counts[n] = max(1, w)
+    scale = remaining / sum(counts.values())
+    counts = {n: max(1, int(c * scale + 0.5)) for n, c in counts.items()}
+    # land exactly on 279, keeping every kept topic >= 1 item
+    order = [n for n in train_pool if counts[n] > 1]
+    i = 0
+    while sum(counts.values()) != 279:
+        n = order[i % len(order)]
+        if sum(counts.values()) < 279:
+            counts[n] += 1
+        else:
+            counts[n] -= 1
+        i += 1
+
+    idx = 0
+    def emit(split, topic):
+        nonlocal idx
+        idx += 1
+        return {"id": f"toy-{idx:04d}", "email_id": f"email-{idx:03d}",
+                "text": make_text(rng, topic), "jev_topic": topic, "human_topic": None}
+
+    train = [emit("train", n) for n, c in counts.items() for _ in range(c)]
+    assert len(train) == 279, len(train)
+    rng.shuffle(train)
+
+    # val + test: cover all 56 topics; unseen topics appear only here
+    val, test = [], []
+    for n in names:
+        for _ in range(1):
+            val.append(emit("validation", n))
+    while len(val) < 85:
+        val.append(emit("validation", rng.choice(names)))
+    for n in names:
+        test.append(emit("test", n))
+    while len(test) < 105:
+        test.append(emit("test", rng.choice(names)))
+
+    for split, rows in [("train", train), ("validation", val), ("test", test)]:
+        with open(os.path.join(out_dir, f"{split}.jsonl"), "w") as f:
+            for r in rows:
+                f.write(json.dumps(r) + "\n")
+        print(split, len(rows))
+    big = sorted(counts.items(), key=lambda kv: -kv[1])[:5]
+    print("busiest topics:", big, "| train topics:", len(counts), "/56")
+
+
+if __name__ == "__main__":
+    import argparse
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--out", default="data")
+    main(ap.parse_args().out)

requirements.txt

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+# versions as pinned on the box (2026-10-04, inside ~/jev-example/.venv with
+# --system-site-packages on conda base: torch 2.10.0+cu126 comes from base)
+numpy==1.26.4
+peft==0.21.2
+# from conda base (do NOT reinstall into the venv):
+#   torch 2.10.0+cu126, transformers 5.2.0, scikit-learn 1.3.2, scipy 1.11.3, huggingface-hub

results/seed0/history.json

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+{
+  "seed": 0,
+  "best_val_macro_f1": 0.949404761904762,
+  "history": [
+    {
+      "epoch": 0,
+      "loss": 0.5672127770053016,
+      "val_acc": 0.9529411764705882,
+      "val_macro_f1": 0.9289965986394558
+    },
+    {
+      "epoch": 1,
+      "loss": 0.44206444422403973,
+      "val_acc": 0.9647058823529412,
+      "val_macro_f1": 0.949404761904762
+    },
+    {
+      "epoch": 2,
+      "loss": 0.41050556467639077,
+      "val_acc": 0.9647058823529412,
+      "val_macro_f1": 0.949404761904762
+    },
+    {
+      "epoch": 3,
+      "loss": 0.4040314075019624,
+      "val_acc": 0.9647058823529412,
+      "val_macro_f1": 0.949404761904762
+    },
+    {
+      "epoch": 4,
+      "loss": 0.40162890156110126,
+      "val_acc": 0.9647058823529412,
+      "val_macro_f1": 0.949404761904762
+    }
+  ]
+}

results/seed0/summary.json

@@ -0,0 +1,9 @@
+{
+  "n": 105,
+  "top1_vs_jev": 0.9714,
+  "macro_f1_vs_jev": 0.9562,
+  "conf_lt_05": 3,
+  "conf_gte_09": 78,
+  "errors_lt_05": 1,
+  "errors_gte_09": 0
+}

results/seed1/history.json

@@ -0,0 +1,36 @@
+{
+  "seed": 1,
+  "best_val_macro_f1": 0.949404761904762,
+  "history": [
+    {
+      "epoch": 0,
+      "loss": 0.589680939912796,
+      "val_acc": 0.9529411764705882,
+      "val_macro_f1": 0.9289965986394558
+    },
+    {
+      "epoch": 1,
+      "loss": 0.43270348343584275,
+      "val_acc": 0.9647058823529412,
+      "val_macro_f1": 0.949404761904762
+    },
+    {
+      "epoch": 2,
+      "loss": 0.40894367297490436,
+      "val_acc": 0.9647058823529412,
+      "val_macro_f1": 0.949404761904762
+    },
+    {
+      "epoch": 3,
+      "loss": 0.4042018949985504,
+      "val_acc": 0.9647058823529412,
+      "val_macro_f1": 0.949404761904762
+    },
+    {
+      "epoch": 4,
+      "loss": 0.40101376672585803,
+      "val_acc": 0.9647058823529412,
+      "val_macro_f1": 0.9488095238095239
+    }
+  ]
+}

results/seed1/summary.json

@@ -0,0 +1,9 @@
+{
+  "n": 105,
+  "top1_vs_jev": 0.9714,
+  "macro_f1_vs_jev": 0.9562,
+  "conf_lt_05": 2,
+  "conf_gte_09": 78,
+  "errors_lt_05": 1,
+  "errors_gte_09": 0
+}

results/seed2/history.json

@@ -0,0 +1,36 @@
+{
+  "seed": 2,
+  "best_val_macro_f1": 0.949404761904762,
+  "history": [
+    {
+      "epoch": 0,
+      "loss": 0.5810238288508521,
+      "val_acc": 0.9529411764705882,
+      "val_macro_f1": 0.9289965986394558
+    },
+    {
+      "epoch": 1,
+      "loss": 0.4381559623612298,
+      "val_acc": 0.9647058823529412,
+      "val_macro_f1": 0.949404761904762
+    },
+    {
+      "epoch": 2,
+      "loss": 0.4109099937809838,
+      "val_acc": 0.9647058823529412,
+      "val_macro_f1": 0.949404761904762
+    },
+    {
+      "epoch": 3,
+      "loss": 0.4046083192030589,
+      "val_acc": 0.9647058823529412,
+      "val_macro_f1": 0.949404761904762
+    },
+    {
+      "epoch": 4,
+      "loss": 0.4006368964910507,
+      "val_acc": 0.9647058823529412,
+      "val_macro_f1": 0.949404761904762
+    }
+  ]
+}

results/seed2/summary.json

@@ -0,0 +1,9 @@
+{
+  "n": 105,
+  "top1_vs_jev": 0.9714,
+  "macro_f1_vs_jev": 0.9562,
+  "conf_lt_05": 3,
+  "conf_gte_09": 71,
+  "errors_lt_05": 1,
+  "errors_gte_09": 0
+}

train_head.py

@@ -0,0 +1,236 @@
+"""Qwen3-Embedding-0.6B + LoRA + prototype head — faithful example of the jev-topic-head spec.
+
+Mirrors: LoRA r16/a32/dropout .05 on attention+FFN (rest frozen); head = 56 anchors seeded
+from topic-description embeddings + one learned scale (init 20); logits = scale * cos(v, P);
+CE w/ label smoothing .05; two AdamW groups (lora 3e-4 wd .01 / head 2e-3 no wd); warmup
+10% -> linear decay to 5%; WeightedRandomSampler 1/sqrt(count), hard pairs x2; early stop on
+val macro-F1; seeds; ship nothing, just save runs/seed{S}/.
+
+Deviations from the (partly inferred) spec, on purpose and documented:
+  - last-token pooling per the official Qwen3-Embedding recipe
+  - "bf16" read as bf16 base weights, fp32 head; fp32 end to end if dtype: fp32
+  - data is synthetic (make_toy_data.py), so scores validate plumbing, not quality
+"""
+import argparse, copy, json, math, os, random
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import yaml
+from sklearn.metrics import f1_score
+from torch.utils.data import DataLoader, Dataset, WeightedRandomSampler
+from transformers import AutoModel, AutoTokenizer
+from peft import LoraConfig, get_peft_model
+
+
+def set_seed(s):
+    random.seed(s)
+    np.random.seed(s)
+    torch.manual_seed(s)
+    torch.cuda.manual_seed_all(s)
+
+
+def read_jsonl(path):
+    rows = []
+    with open(path) as f:
+        for line in f:
+            rows.append(json.loads(line))
+    return rows
+
+
+class ItemDataset(Dataset):
+    def __init__(self, path, topic2id):
+        self.rows = read_jsonl(path)
+        self.t2i = topic2id
+
+    def __len__(self):
+        return len(self.rows)
+
+    def __getitem__(self, i):
+        r = self.rows[i]
+        return r["text"], self.t2i[r["jev_topic"]]
+
+
+def collate(batch):
+    texts, ys = zip(*batch)
+    return list(texts), torch.tensor(ys, dtype=torch.long)
+
+
+def encode(model, tok, texts, max_length, device):
+    """Last-token pooling (Qwen3-Embedding recipe), fp32 output."""
+    enc = tok(texts, padding=True, truncation=True, max_length=max_length,
+              return_tensors="pt").to(device)
+    out = model(**enc).last_hidden_state
+    mask = enc["attention_mask"]
+    last = mask.sum(dim=1) - 1
+    v = out[torch.arange(out.size(0), device=device), last]
+    return v.float()
+
+
+class PrototypeHead(nn.Module):
+    def __init__(self, seed_protos, scale_init):
+        super().__init__()
+        self.protos = nn.Parameter(seed_protos.clone().float())
+        self.scale = nn.Parameter(torch.tensor(float(scale_init)))
+
+    def logits(self, v):
+        v = F.normalize(v, dim=-1)
+        p = F.normalize(self.protos, dim=-1)
+        return self.scale * (v @ p.t())
+
+
+@torch.no_grad()
+def predict_logits(model, head, tok, texts, max_length, device, batch=32):
+    model.eval()
+    all_logits = []
+    for i in range(0, len(texts), batch):
+        v = encode(model, tok, texts[i:i + batch], max_length, device)
+        all_logits.append(head.logits(v).float().cpu())
+    return torch.cat(all_logits)
+
+
+def macro_f1(y_true, y_pred, n_topics):
+    return f1_score(y_true, y_pred, average="macro", zero_division=0,
+                    labels=list(range(n_topics)))
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--config", default="config.yaml")
+    ap.add_argument("--seed", type=int, default=None)
+    args = ap.parse_args()
+    cfg = yaml.safe_load(open(args.config))
+    seed = args.seed if args.seed is not None else 0
+    set_seed(seed)
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    dtype = torch.bfloat16 if cfg["train"]["dtype"] == "bf16" else torch.float32
+
+    data_dir = cfg["data"]["dir"]
+    taxonomy = json.load(open(os.path.join(data_dir, cfg["data"]["taxonomy"])))
+    topics = sorted(taxonomy)                      # fixed, seed-independent order
+    topic2id = {t: i for i, t in enumerate(topics)}
+    K = len(topics)
+
+    tok = AutoTokenizer.from_pretrained(cfg["base_model"])
+    tok.padding_side = "right"
+    base = AutoModel.from_pretrained(cfg["base_model"], torch_dtype=dtype).to(device)
+    base.config.use_cache = False
+
+    # ---- seed prototypes from topic-description embeddings (one forward pass) ----
+    descs = [taxonomy[t] for t in topics]
+    seed_protos = encode(base, tok, descs, cfg["max_length"], device)
+    print(f"seeded protos {tuple(seed_protos.shape)}")
+
+    # ---- LoRA: the only thing that changes the encoder ----
+    lcfg = LoraConfig(r=cfg["lora"]["r"], lora_alpha=cfg["lora"]["alpha"],
+                      lora_dropout=cfg["lora"]["dropout"],
+                      target_modules=cfg["lora"]["target_modules"])
+    model = get_peft_model(base, lcfg)
+    model.print_trainable_parameters()
+
+    head = PrototypeHead(seed_protos, cfg["head"]["scale_init"]).to(device)
+
+    lora_params = [p for p in model.parameters() if p.requires_grad]
+    n_lora = sum(p.numel() for p in lora_params)
+    n_head = sum(p.numel() for p in head.parameters())
+    print(f"trainable: {n_lora:,} lora + {n_head:,} head, rest frozen")
+
+    # ---- data + class-balanced sampling (1/sqrt(count), hard pairs x2) ----
+    train_rows = read_jsonl(os.path.join(data_dir, cfg["data"]["train"]))
+    counts = {}
+    for r in train_rows:
+        counts[r["jev_topic"]] = counts.get(r["jev_topic"], 0) + 1
+    hard = set()
+    for a, b in cfg["data"].get("hard_pairs", []):
+        hard.update([a, b])
+    weights = [1.0 / math.sqrt(counts.get(r["jev_topic"], 1)) * (2.0 if r["jev_topic"] in hard else 1.0)
+               for r in train_rows]
+    sampler = WeightedRandomSampler(weights, num_samples=len(weights), replacement=True)
+
+    ds = ItemDataset(os.path.join(data_dir, cfg["data"]["train"]), topic2id)
+    dl = DataLoader(ds, batch_size=cfg["train"]["batch_size"], sampler=sampler,
+                    collate_fn=collate)
+    val_ds = ItemDataset(os.path.join(data_dir, cfg["data"]["validation"]), topic2id)
+    val_texts = [r["text"] for r in val_ds.rows]
+    val_y = [topic2id[r["jev_topic"]] for r in val_ds.rows]
+
+    # ---- two AdamW groups + warmup->linear-decay-to-5% schedule ----
+    o = cfg["optimizer"]
+    opt = torch.optim.AdamW([
+        {"params": lora_params, "lr": o["lora_lr"], "weight_decay": o["lora_wd"]},
+        {"params": list(head.parameters()), "lr": o["head_lr"], "weight_decay": o["head_wd"]},
+    ])
+    epochs = cfg["train"]["epochs"]
+    spe = math.ceil(len(ds) / cfg["train"]["batch_size"])
+    total_steps = epochs * spe
+    warmup = max(1, int(o["warmup_frac"] * total_steps))
+    floor = o["final_lr_frac"]
+
+    def lr_lambda(step):
+        if step < warmup:
+            return (step + 1) / warmup
+        prog = (step - warmup) / max(1, total_steps - warmup)
+        return floor + (1.0 - floor) * (1.0 - min(1.0, prog))
+
+    sched = torch.optim.lr_scheduler.LambdaLR(opt, lr_lambda)
+
+    out_dir = os.path.join(cfg["out_root"], f"seed{seed}")
+    os.makedirs(out_dir, exist_ok=True)
+    history, best_f1, best_state = [], -1.0, None
+    patience, bad = cfg["train"]["patience"], 0
+
+    for ep in range(epochs):
+        model.train()
+        head.train()
+        losses = []
+        for texts, y in dl:
+            y = y.to(device)
+            v = encode(model, tok, texts, cfg["max_length"], device)
+            logits = head.logits(v)
+            loss = F.cross_entropy(logits, y,
+                                   label_smoothing=cfg["loss"]["label_smoothing"])
+            opt.zero_grad(set_to_none=True)
+            loss.backward()
+            opt.step()
+            sched.step()
+            losses.append(loss.item())
+
+        lg = predict_logits(model, head, tok, val_texts, cfg["max_length"], device)
+        pred = lg.argmax(-1).numpy()
+        f1 = macro_f1(val_y, pred, K)
+        acc = float((pred == np.array(val_y)).mean())
+        print(f"epoch {ep:2d} | loss {np.mean(losses):.3f} | val acc {acc:.3f} | val macro-F1 {f1:.4f}")
+        history.append({"epoch": ep, "loss": float(np.mean(losses)),
+                        "val_acc": acc, "val_macro_f1": float(f1)})
+
+        if f1 > best_f1:
+            best_f1, bad = float(f1), 0
+            best_state = {"head": copy.deepcopy(head.state_dict()),
+                          "lora": {k: v.detach().cpu().clone()
+                                   for k, v in model.state_dict().items()
+                                   if "lora_" in k and "requires_grad" not in k}}
+        else:
+            bad += 1
+            if bad >= patience:
+                print(f"early stop at epoch {ep} (patience {patience})")
+                break
+
+    # ---- save best (the shipped artifact is seed 0, like the spec) ----
+    model.load_state_dict(best_state["lora"], strict=False)
+    model.save_pretrained(os.path.join(out_dir, "lora"))
+    head.load_state_dict(best_state["head"])
+    head_dir = os.path.join(out_dir, "head")
+    os.makedirs(head_dir, exist_ok=True)
+    torch.save({"protos": head.protos.detach().cpu(),
+                "scale": head.scale.detach().cpu(),
+                "dim": head.protos.shape[1], "topics": topics,
+                "base_model": cfg["base_model"]},
+               os.path.join(head_dir, "head.pt"))
+    json.dump({"seed": seed, "best_val_macro_f1": best_f1, "history": history},
+              open(os.path.join(out_dir, "history.json"), "w"), indent=2)
+    print(f"saved {out_dir} | best val macro-F1 {best_f1:.4f}")
+
+
+if __name__ == "__main__":
+    main()