claude-opus-5

packed 12000000 tokens from 80000 ids -> /tmp/R.npy
pool: 182016 docs

[stdout]
packed 12000000 tokens from 80000 ids -> /tmp/R.npy
pool: 182016 docs
[metadata] {"noOutputExpected": false}

packed 12000000 tokens from 80000 ids -> /tmp/R.npy
pool: 182016 docs

[stdout]
packed 12000000 tokens from 80000 ids -> /tmp/R.npy
pool: 182016 docs
[metadata] {"noOutputExpected": false}

HELDOUT_PPL 488.479  loss 6.1913  train_s 54  /tmp/R.npy
pool: 182016 docs
4669

[stdout]
HELDOUT_PPL 488.479  loss 6.1913  train_s 54  /tmp/R.npy
pool: 182016 docs
4669
[metadata] {"noOutputExpected": false}

wrote 45034 ids -> /workspace/submission/selection.json (~48M tokens of text)
-rw-r--r-- 1 root root 4551864 Aug  4 01:23 /tmp/curate_cache.npz

[stdout]
wrote 45034 ids -> /workspace/submission/selection.json (~48M tokens of text)
-rw-r--r-- 1 root root 4551864 Aug  4 01:23 /tmp/curate_cache.npz
[metadata] {"noOutputExpected": false}

dev docs: 2265
cluster sizes: [322, 526, 169, 895, 127, 226]
  c0: " On 1 January 1908 , Nimrod sailed for the Antarctic from Lyttelton Harbour , New Zealand . Shackleton 's original plans had envisaged using"
  c1: ' There remained the men of the Ross Sea Party , who were stranded at Cape Evans in McMurdo Sound , after Aurora had been blown from its anch'
  c2: ' In addition to Barker and the regular cast , actor Jeff Bergman , voice actor Chris Cox , actor Michael Clarke Duncan , actor Keir Gilchris'
  c3: ' Beardmore was sufficiently impressed with Shackleton to offer financial support , but other donations proved hard to come by . Nevertheless'
  c4: '<p>Can I access static member variables of a class using dot notation or should I stick in access operator which is double colon?</p>\n\n<p>Yo'
  c5: 'Continue reading the main story Foto\n\nRead in English\n\nLas condiciones en 10 South son inhóspitas. Las luces no se apagan en la media decena'
  clf c0: n_pos=322 acc=0.995
  clf c1: n_pos=526 acc=0.987
  clf c2: n_pos=169 acc=0.998
  clf c3: n_pos=895 acc=0.981

[stdout]
dev docs: 2265
cluster sizes: [322, 526, 169, 895, 127, 226]
  c0: " On 1 January 1908 , Nimrod sailed for the Antarctic from Lyttelton Harbour , New Zealand . Shackleton 's original plans had envisaged using"
  c1: ' There remained the men of the Ross Sea Party , who were stranded at Cape Evans in McMurdo Sound , after Aurora had been blown from its anch'
  c2: ' In addition to Barker and the regular cast , actor Jeff Bergman , voice actor Chris Cox , actor Michael Clarke Duncan , actor Keir Gilchris'
  c3: ' Beardmore was sufficiently impressed with Shackleton to offer financial support , but other donations proved hard to come by . Nevertheless'
  c4: '<p>Can I access static member variables of a class using dot notation or should I stick in access operator which is double colon?</p>\n\n<p>Yo'
  c5: 'Continue reading the main story Foto\n\nRead in English\n\nLas condiciones en 10 South son inhóspitas. Las luces no se apagan en la media decena'
  clf c0: n_pos=322 acc=0.995
  clf c1: n_pos=526 acc=0.987
  clf c2: n_pos=169 acc=0.998
  clf c3: n_pos=895 acc=0.981
[metadata] {"noOutputExpected": false}

scored 60000/133278
scored 80000/133278
scored 100000/133278
scored 120000/133278

[stdout]
scored 60000/133278
scored 80000/133278
scored 100000/133278
scored 120000/133278
[metadata] {"noOutputExpected": false}

scored 133278/133278
wrote 88890 ids -> /tmp/selB.json
-rw-r--r-- 1 root root 636626 Aug  4 01:24 /tmp/selB.json

[stdout]
scored 133278/133278
wrote 88890 ids -> /tmp/selB.json
-rw-r--r-- 1 root root 636626 Aug  4 01:24 /tmp/selB.json
[metadata] {"noOutputExpected": false}

Token indices sequence length is longer than the specified maximum sequence length for this model (4604 > 1024). Running this sequence through the model will result in indexing errors

[stdout]
Token indices sequence length is longer than the specified maximum sequence length for this model (4604 > 1024). Running this sequence through the model will result in indexing errors
[metadata] {"noOutputExpected": false}

packed 12000000 tokens from 88890 ids -> /tmp/B.npy

[stdout]
packed 12000000 tokens from 88890 ids -> /tmp/B.npy
[metadata] {"noOutputExpected": false}

packed 12000000 tokens from 88890 ids -> /tmp/B.npy
4429

[stdout]
packed 12000000 tokens from 88890 ids -> /tmp/B.npy
4429
[metadata] {"noOutputExpected": false}

HELDOUT_PPL 341.972  loss 5.8347  train_s 54  /tmp/B.npy

[stdout]
HELDOUT_PPL 341.972  loss 5.8347  train_s 54  /tmp/B.npy
[metadata] {"noOutputExpected": false}

c8: '<p>Say I would like to host my open source software by myself, i.e. not in sourceforge or google code but host my own svn and issue tracker '
  c9: 'Continue reading the main story Foto\n\nRead in English\n\nLas condiciones en 10 South son inhóspitas. Las luces no se apagan en la media decena'
  clf c0: n_pos=353 acc=0.994
  clf c1: n_pos=526 acc=0.981
  clf c3: n_pos=746 acc=0.983
  clf c4: n_pos=122 acc=1.000

[stdout]
  c8: '<p>Say I would like to host my open source software by myself, i.e. not in sourceforge or google code but host my own svn and issue tracker '
  c9: 'Continue reading the main story Foto\n\nRead in English\n\nLas condiciones en 10 South son inhóspitas. Las luces no se apagan en la media decena'
  clf c0: n_pos=353 acc=0.994
  clf c1: n_pos=526 acc=0.981
  clf c3: n_pos=746 acc=0.983
  clf c4: n_pos=122 acc=1.000
[metadata] {"noOutputExpected": false}

# ---- round-robin merge with equal per-cluster token quotas
ptr = [0] * K
seen, order, chars = set(), [], 0
LIM = 4 * 12_000_000 * 4
# cluster weights = share of dev-target characters in that cluster (distribution match)
wgt = np.array([sum(len(POS[i]) for i in range(len(POS)) if lab[i] == k) for k in range(K)], np.float64)
wgt = wgt / wgt.sum()
print("cluster weights:", np.round(wgt, 3).tolist(), flush=True)
credit = np.zeros(K)
active = [k for k in range(K) if ranks[k]]
while active and chars < LIM:
    # serve the active cluster that is furthest behind its quota
    for k in sorted(active, key=lambda k: credit[k] / max(1e-9, wgt[k])):
        adv = False
        while ptr[k] < len(ranks[k]):
            i = ranks[k][ptr[k]]; ptr[k] += 1
            if i in seen: continue
            seen.add(i); order.append(int(ids[i]))
            c = len(texts[i]) or 1
            chars += c; credit[k] += c
            adv = True; break
        if not adv: active.remove(k)
json.dump(order, open("/tmp/selC.json", "w"))
print(f"wrote {len(order)} ids -> /tmp/selC.json", flush=True)

[stdout]
# ---- round-robin merge with equal per-cluster token quotas
ptr = [0] * K
seen, order, chars = set(), [], 0
LIM = 4 * 12_000_000 * 4
# cluster weights = share of dev-target characters in that cluster (distribution match)
wgt = np.array([sum(len(POS[i]) for i in range(len(POS)) if lab[i] == k) for k in range(K)], np.float64)
wgt = wgt / wgt.sum()
print("cluster weights:", np.round(wgt, 3).tolist(), flush=True)
credit = np.zeros(K)
active = [k for k in range(K) if ranks[k]]
while active and chars < LIM:
    # serve the active cluster that is furthest behind its quota
    for k in sorted(active, key=lambda k: credit[k] / max(1e-9, wgt[k])):
        adv = False
        while ptr[k] < len(ranks[k]):
            i = ranks[k][ptr[k]]; ptr[k] += 1
            if i in seen: continue
            seen.add(i); order.append(int(ids[i]))
            c = len(texts[i]) or 1
            chars += c; credit[k] += c
            adv = True; break
        if not adv: active.remove(k)
json.dump(order, open("/tmp/selC.json", "w"))
print(f"wrote {len(order)} ids -> /tmp/selC.json", flush=True)
[metadata] {"noOutputExpected": false}

The file /workspace/curate_quota.py has been updated successfully. (file state is current in your context — no need to Read it back)

[metadata] {"filePath": "/workspace/curate_quota.py", "oldString": "    # serve the active cluster that is furthest behind its quota\n    for k in sorted(active, key=lambda k: credit[k] / max(1e-9, wgt[k])):\n        adv = False\n        while ptr[k] < len(ranks[k]):\n            i = ranks[k][ptr[k]]; ptr[k] += 1\n            if i in seen: continue\n            seen.add(i); order.append(int(ids[i]))\n            c = len(texts[i]) or 1\n            chars += c; credit[k] += c\n            adv = True; break\n        if not adv: active.remove(k)", "newString": "    # serve the SINGLE active cluster that is furthest behind its token quota\n    k = min(active, key=lambda j: credit[j] / max(1e-9, wgt[j]))\n    adv = False\n    while ptr[k] < len(ranks[k]):\n        i = ranks[k][ptr[k]]; ptr[k] += 1\n        if i in seen: continue\n        seen.add(i); order.append(int(ids[i]))\n        c = len(texts[i]) or 1\n        chars += c; credit[k] += c\n        adv = True; break\n    if not adv: active.remove(k)", "originalFile": "\"\"\"Variant B: register-balanced selection.\n\nThe target is stated to be EQUAL PARTS encyclopedic / HQ web prose / news /\ntechnical Q&A. A single target-vs-pool classifier over-selects whichever register\nis most abundant in the pool (news). So instead:\n\n  1. cluster the decoded dev-target documents into K registers (k-means on hashed\n     word features, cosine),\n  2. train one logistic regression per cluster: cluster docs vs random pool,\n  3. rank the filtered pool separately under each cluster's model,\n  4. fill the 12M budget ROUND-ROBIN across clusters with equal token quotas.\n\nReuses the hard filters and features from submission/curate.py by importing its\ncached artifacts (written by --cache), so it costs one extra pass at most.\n\"\"\"\nimport json, re, math, random, zlib, numpy as np, torch\nfrom collections import Counter\nfrom transformers import AutoTokenizer\n\nK = int(__import__(\"os\").environ.get(\"K\", \"10\"))\nNF = 1 << 20\nWORD = re.compile(r\"[A-Za-z']+\")\ndev_t = \"cuda\" if torch.cuda.is_available() else \"cpu\"\nrandom.seed(0); np.random.seed(0)\n\ncache = np.load(\"/tmp/curate_cache.npz\", allow_pickle=True)\nmask = cache[\"mask\"]; nw_full = cache[\"nw_full\"]; ppw = cache[\"ppw\"]\ndupl = cache[\"dupl\"]; boil = cache[\"boil\"]\nids = cache[\"ids\"]\ntexts = [None] * len(ids)\nfor line in open(\"/workspace/data/pool.jsonl\"):\n    r = json.loads(line); texts[r[\"id\"]] = r[\"text\"][:4000]\n\ndef hash_row(t):\n    w = [x.lower() for x in WORD.findall(t)]\n    h = [zlib.crc32(x.encode()) % NF for x in w]\n    h += [zlib.crc32((w[i] + \" \" + w[i+1]).encode()) % NF for i in range(len(w)-1)]\n    if not h: return np.zeros(0, np.int64), np.zeros(0, np.float32)\n    idx, cnt = np.unique(np.array(h, np.int64), return_counts=True)\n    v = cnt.astype(np.float32); v /= max(1e-8, float(np.linalg.norm(v)))\n    return idx, v\n\ndef sp(docs):\n    rows, cols, vals = [], [], []\n    for r, d in enumerate(docs):\n        i, v = hash_row(d)\n        rows.append(np.full(len(i), r, np.int64)); cols.append(i); vals.append(v)\n    return torch.sparse_coo_tensor(np.stack([np.concatenate(rows), np.concatenate(cols)]),\n                                   np.concatenate(vals), (len(docs), NF), device=dev_t).coalesce()\n\n# ---- decode dev target into documents\ntok = AutoTokenizer.from_pretrained(\"gpt2\")\ndv = np.load(\"/workspace/data/multi_dev.npy\").astype(np.int64)\nEOS = tok.eos_token_id\ndocs, cur = [], []\nfor t in dv:\n    if t == EOS:\n        if len(cur) > 40: docs.append(cur)\n        cur = []\n    else: cur.append(int(t))\nif len(cur) > 40: docs.append(cur)\nPOS = [tok.decode(d)[:4000] for d in docs]\nPOS = [p for p in POS if len(p) > 400]\nprint(\"dev docs:\", len(POS), flush=True)\n\nXp = sp(POS).to_dense()          # 2.5k x 1M dense is 10GB -> too big; use sparse ops\ndel Xp\n\nXp = sp(POS)\n# ---- spherical k-means in hashed space (sparse matmul against dense centroids)\nC = torch.zeros(K, NF, device=dev_t)\ninit = random.sample(range(len(POS)), K)\nXpd_rows = Xp.coalesce()\nidx = Xpd_rows.indices(); val = Xpd_rows.values()\nfor k, r in enumerate(init):\n    m = idx[0] == r\n    C[k, idx[1][m]] = val[m]\nfor it in range(15):\n    simm = torch.sparse.mm(Xp, C.t())            # n x K cosine (rows & cents l2-normed)\n    lab = simm.argmax(1)\n    Cn = torch.zeros_like(C)\n    for k in range(K):\n        sel = (lab == k).nonzero().squeeze(1)\n        if len(sel) == 0: continue\n        m = torch.isin(idx[0], sel)\n        Cn[k].index_add_(0, idx[1][m], val[m])\n        Cn[k] /= max(1e-8, float(Cn[k].norm()))\n    C = Cn\nlab = torch.sparse.mm(Xp, C.t()).argmax(1).cpu().numpy()\nprint(\"cluster sizes:\", np.bincount(lab, minlength=K).tolist(), flush=True)\nfor k in range(K):\n    ex = [POS[i] for i in range(len(POS)) if lab[i] == k][:1]\n    if ex: print(f\"  c{k}: {ex[0][:140]!r}\", flush=True)\n\nkept = np.where(mask)[0]\nneg_idx = random.sample(list(kept), 8000)\nNEG = [texts[i] for i in neg_idx]\nXn = sp(NEG)\n\n# ---- one classifier per cluster\nWk = []\nfor k in range(K):\n    pk = [POS[i] for i in range(len(POS)) if lab[i] == k]\n    if len(pk) < 30:\n        Wk.append(None); continue\n    Xk = sp(pk + NEG)\n    y = torch.tensor(np.r_[np.ones(len(pk)), np.zeros(len(NEG))], dtype=torch.float32, device=dev_t)\n    cw = torch.where(y > 0, len(y) / (2.0 * len(pk)), len(y) / (2.0 * len(NEG)))\n    w = torch.zeros(NF, device=dev_t, requires_grad=True)\n    b = torch.zeros(1, device=dev_t, requires_grad=True)\n    opt = torch.optim.Adam([w, b], lr=0.05)\n    for s in range(300):\n        lg = torch.sparse.mm(Xk, w.unsqueeze(1)).squeeze(1) + b\n        loss = (torch.nn.functional.binary_cross_entropy_with_logits(lg, y, reduction=\"none\") * cw).mean() \\\n               + 1e-5 * (w * w).sum()\n        opt.zero_grad(); loss.backward(); opt.step()\n    acc = (((torch.sparse.mm(Xk, w.unsqueeze(1)).squeeze(1) + b) > 0).float() == y).float().mean().item()\n    print(f\"  clf c{k}: n_pos={len(pk)} acc={acc:.3f}\", flush=True)\n    Wk.append((w.detach(), b.detach()))\n\n# ---- score the filtered pool under every cluster model\nS = np.full((K, len(ids)), -1e9, np.float32)\nB = 20000\nfor s in range(0, len(kept), B):\n    sub = kept[s:s+B]\n    Xs = sp([texts[i] for i in sub])\n    for k in range(K):\n        if Wk[k] is None: continue\n        with torch.no_grad():\n            S[k, sub] = (torch.sparse.mm(Xs, Wk[k][0].unsqueeze(1)).squeeze(1) + Wk[k][1]).cpu().numpy()\n    print(f\"scored {min(s+B, len(kept))}/{len(kept)}\", flush=True)\n\n# shared priors (same as variant A)\nprior = np.zeros(len(ids), np.float32)\nprior[kept] = (0.35 * np.minimum(1.0, np.log1p(nw_full[kept] / 150.0) / math.log(6.0))\n               + 0.25 * (1.0 - np.minimum(1.0, np.abs(ppw[kept] - 0.055) / 0.10))\n               - 0.30 * dupl[kept] - 0.10 * np.minimum(1.0, boil[kept]))\n\nranks = []\nfor k in range(K):\n    if Wk[k] is None: ranks.append([]); continue\n    sc = S[k] + prior\n    o = np.argsort(-sc)\n    o = [int(i) for i in o if mask[i]]\n    ranks.append(o)\n\n# ---- round-robin merge with equal per-cluster token quotas\nptr = [0] * K\nseen, order, chars = set(), [], 0\nLIM = 4 * 12_000_000 * 4\n# cluster weights = share of dev-target characters in that cluster (distribution match)\nwgt = np.array([sum(len(POS[i]) for i in range(len(POS)) if lab[i] == k) for k in range(K)], np.float64)\nwgt = wgt / wgt.sum()\nprint(\"cluster weights:\", np.round(wgt, 3).tolist(), flush=True)\ncredit = np.zeros(K)\nactive = [k for k in range(K) if ranks[k]]\nwhile active and chars < LIM:\n    # serve the active cluster that is furthest behind its quota\n    for k in sorted(active, key=lambda k: credit[k] / max(1e-9, wgt[k])):\n        adv = False\n        while ptr[k] < len(ranks[k]):\n            i = ranks[k][ptr[k]]; ptr[k] += 1\n            if i in seen: continue\n            seen.add(i); order.append(int(ids[i]))\n            c = len(texts[i]) or 1\n            chars += c; credit[k] += c\n            adv = True; break\n        if not adv: active.remove(k)\njson.dump(order, open(\"/tmp/selC.json\", \"w\"))\nprint(f\"wrote {len(order)} ids -> /tmp/selC.json\", flush=True)\n", "structuredPatch": [{"oldStart": 155, "oldLines": 16, "newStart": 155, "newLines": 16, "lines": [" credit = np.zeros(K)", " active = [k for k in range(K) if ranks[k]]", " while active and chars < LIM:", "-    # serve the active cluster that is furthest behind its quota", "-    for k in sorted(active, key=lambda k: credit[k] / max(1e-9, wgt[k])):", "-        adv = False", "-        while ptr[k] < len(ranks[k]):", "-            i = ranks[k][ptr[k]]; ptr[k] += 1", "-            if i in seen: continue", "-            seen.add(i); order.append(int(ids[i]))", "-            c = len(texts[i]) or 1", "-            chars += c; credit[k] += c", "-            adv = True; break", "-        if not adv: active.remove(k)", "+    # serve the SINGLE active cluster that is furthest behind its token quota", "+    k = min(active, key=lambda j: credit[j] / max(1e-9, wgt[j]))", "+    adv = False", "+    while ptr[k] < len(ranks[k]):", "+        i = ranks[k][ptr[k]]; ptr[k] += 1", "+        if i in seen: continue", "+        seen.add(i); order.append(int(ids[i]))", "+        c = len(texts[i]) or 1", "+        chars += c; credit[k] += c", "+        adv = True; break", "+    if not adv: active.remove(k)", " json.dump(order, open(\"/tmp/selC.json\", \"w\"))", " print(f\"wrote {len(order)} ids -> /tmp/selC.json\", flush=True)"]}], "userModified": false, "replaceAll": false}

0

[stdout]
0
[metadata] {"returnCodeInterpretation": "No matches found", "noOutputExpected": false}

scored 40000/133278
scored 60000/133278
scored 80000/133278

[stdout]
scored 40000/133278
scored 60000/133278
scored 80000/133278
[metadata] {"noOutputExpected": false}

scored 133278/133278
cluster weights: [0.103, 0.339, 0.005, 0.26, 0.082, 0.103, 0.068, 0.007, 0.033, 0.001]
wrote 87309 ids -> /tmp/selC.json

[stdout]
scored 133278/133278
cluster weights: [0.103, 0.339, 0.005, 0.26, 0.082, 0.103, 0.068, 0.007, 0.033, 0.001]
wrote 87309 ids -> /tmp/selC.json
[metadata] {"noOutputExpected": false}

Token indices sequence length is longer than the specified maximum sequence length for this model (5414 > 1024). Running this sequence through the model will result in indexing errors

[stdout]
Token indices sequence length is longer than the specified maximum sequence length for this model (5414 > 1024). Running this sequence through the model will result in indexing errors
[metadata] {"noOutputExpected": false}

packed 12000000 tokens from 87309 ids -> /tmp/C.npy
4169

[stdout]
packed 12000000 tokens from 87309 ids -> /tmp/C.npy
4169
[metadata] {"noOutputExpected": false}

HELDOUT_PPL 335.455  loss 5.8155  train_s 54  /tmp/C.npy

[stdout]
HELDOUT_PPL 335.455  loss 5.8155  train_s 54  /tmp/C.npy
[metadata] {"noOutputExpected": false}

clf c3: n_pos=746 acc=0.983
  clf c4: n_pos=122 acc=1.000

[stdout]
  clf c3: n_pos=746 acc=0.983
  clf c4: n_pos=122 acc=1.000
[metadata] {"noOutputExpected": false}

scored 60000/133278
scored 80000/133278

[stdout]
scored 60000/133278
scored 80000/133278
[metadata] {"noOutputExpected": false}

scored 133278/133278
cluster registers: ['wiki', 'news', 'wiki', 'wiki', 'qa', 'qa', 'wiki', 'wiki', 'qa', 'web']
cluster weights: [0.08, 0.333, 0.0, 0.201, 0.125, 0.157, 0.053, 0.0, 0.051, 0.0]
wrote 88141 ids -> /tmp/selD.json

[stdout]
scored 133278/133278
cluster registers: ['wiki', 'news', 'wiki', 'wiki', 'qa', 'qa', 'wiki', 'wiki', 'qa', 'web']
cluster weights: [0.08, 0.333, 0.0, 0.201, 0.125, 0.157, 0.053, 0.0, 0.051, 0.0]
wrote 88141 ids -> /tmp/selD.json
[metadata] {"noOutputExpected": false}