claude-opus-5

Command running in background with ID: bjmgl31s1. Output is being written to: /tmp/claude-0/-workspace/efeaea96-281e-4798-9cb0-f58a908f0340/tasks/bjmgl31s1.output. You will be notified when it completes. To check interim output, use Read on that file path.

[metadata] {"noOutputExpected": false, "backgroundTaskId": "bjmgl31s1"}

<system-reminder>Warning: the file exists but the contents are empty.</system-reminder>

[metadata] {"type": "text", "file": {"filePath": "/tmp/claude-0/-workspace/efeaea96-281e-4798-9cb0-f58a908f0340/tasks/bjmgl31s1.output", "content": "", "numLines": 1, "startLine": 1, "totalLines": 1}}

total 10
drwxr-xr-x 1 root root  120 Aug  4 01:12 .
drwxr-xr-x 1 root root  200 Aug  4 01:13 ..
-rw-r--r-- 1 root root 9367 Aug  4 01:12 curate.py
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drwxr-xr-x 1 root root  120 Aug  4 01:12 .
drwxr-xr-x 1 root root  200 Aug  4 01:13 ..
-rw-r--r-- 1 root root 9367 Aug  4 01:12 curate.py
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Command running in background with ID: b7h6abrm3. Output is being written to: /tmp/claude-0/-workspace/efeaea96-281e-4798-9cb0-f58a908f0340/tasks/b7h6abrm3.output. You will be notified when it completes. To check interim output, use Read on that file path.

[metadata] {"noOutputExpected": false, "backgroundTaskId": "b7h6abrm3"}

<system-reminder>Warning: the file exists but the contents are empty.</system-reminder>

[metadata] {"type": "text", "file": {"filePath": "/tmp/claude-0/-workspace/efeaea96-281e-4798-9cb0-f58a908f0340/tasks/b7h6abrm3.output", "content": "", "numLines": 1, "startLine": 1, "totalLines": 1}}

Command running in background with ID: bjkmrl537. Output is being written to: /tmp/claude-0/-workspace/efeaea96-281e-4798-9cb0-f58a908f0340/tasks/bjkmrl537.output. You will be notified when it completes. To check interim output, use Read on that file path.

[metadata] {"noOutputExpected": false, "backgroundTaskId": "bjkmrl537"}

total 10
drwxr-xr-x 1 root root  120 Aug  4 01:12 .
drwxr-xr-x 1 root root  200 Aug  4 01:13 ..
-rw-r--r-- 1 root root 9367 Aug  4 01:12 curate.py
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drwxr-xr-x 1 root root  120 Aug  4 01:12 .
drwxr-xr-x 1 root root  200 Aug  4 01:13 ..
-rw-r--r-- 1 root root 9367 Aug  4 01:12 curate.py
STILL
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[metadata] {"noOutputExpected": false}

Command running in background with ID: b4b0mdowf. Output is being written to: /tmp/claude-0/-workspace/efeaea96-281e-4798-9cb0-f58a908f0340/tasks/b4b0mdowf.output. You will be notified when it completes. To check interim output, use Read on that file path.

[metadata] {"noOutputExpected": false, "backgroundTaskId": "b4b0mdowf"}

<system-reminder>Warning: the file exists but the contents are empty.</system-reminder>

[metadata] {"type": "text", "file": {"filePath": "/tmp/claude-0/-workspace/efeaea96-281e-4798-9cb0-f58a908f0340/tasks/b4b0mdowf.output", "content": "", "numLines": 1, "startLine": 1, "totalLines": 1}}

Command running in background with ID: bawh1e91p. Output is being written to: /tmp/claude-0/-workspace/efeaea96-281e-4798-9cb0-f58a908f0340/tasks/bawh1e91p.output. You will be notified when it completes. To check interim output, use Read on that file path.

[metadata] {"noOutputExpected": false, "backgroundTaskId": "bawh1e91p"}

STILL
total 10
drwxr-xr-x 1 root root  120 Aug  4 01:12 .
drwxr-xr-x 1 root root  200 Aug  4 01:13 ..
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STILL
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drwxr-xr-x 1 root root  120 Aug  4 01:12 .
drwxr-xr-x 1 root root  200 Aug  4 01:13 ..
-rw-r--r-- 1 root root 9367 Aug  4 01:12 curate.py
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[metadata] {"noOutputExpected": false}

PID USER      PR  NI    VIRT    RES    SHR S  %CPU  %MEM     TIME+ COMMAND
  460 root      20   0 6526480   5.6g      0 R 100.0   0.6   2:04.53 python
memory.used [MiB], utilization.gpu [%]
4 MiB, 0 %

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  PID USER      PR  NI    VIRT    RES    SHR S  %CPU  %MEM     TIME+ COMMAND
  460 root      20   0 6526480   5.6g      0 R 100.0   0.6   2:04.53 python
memory.used [MiB], utilization.gpu [%]
4 MiB, 0 %
[metadata] {"noOutputExpected": false}

utime ticks: 13776 -> 15657

[stdout]
utime ticks: 13776 -> 15657
[metadata] {"noOutputExpected": false}

56:PREFIX = 4000  # all surface features computed on the first 4000 chars (speed)
59:    t = full[:PREFIX]
63:    nb = len(t.encode("utf-8", "ignore"))
82:    nalpha = alpha_frac * n
83:    upper_frac = sum(map(str.isupper, t)) / max(1.0, nalpha)

[stdout]
56:PREFIX = 4000  # all surface features computed on the first 4000 chars (speed)
59:    t = full[:PREFIX]
63:    nb = len(t.encode("utf-8", "ignore"))
82:    nalpha = alpha_frac * n
83:    upper_frac = sum(map(str.isupper, t)) / max(1.0, nalpha)
[metadata] {"noOutputExpected": false}

86:    return dict(n=len(full), nw=nw, nw_full=nw * len(full) / n, ascii_frac=ascii_frac, alpha_frac=alpha_frac,
202:    final[i] += 0.35 * min(1.0, math.log1p(f["nw_full"] / 150.0) / math.log(6.0))
pool: 182016 docs

[stdout]
86:    return dict(n=len(full), nw=nw, nw_full=nw * len(full) / n, ascii_frac=ascii_frac, alpha_frac=alpha_frac,
202:    final[i] += 0.35 * min(1.0, math.log1p(f["nw_full"] / 150.0) / math.log(6.0))
pool: 182016 docs
[metadata] {"noOutputExpected": false}

pool: 182016 docs

[stdout]
pool: 182016 docs
[metadata] {"noOutputExpected": false}

pool: 182016 docs

[stdout]
pool: 182016 docs
[metadata] {"noOutputExpected": false}

scored 20000/133278
scored 40000/133278
scored 60000/133278
scored 80000/133278

[stdout]
scored 20000/133278
scored 40000/133278
scored 60000/133278
scored 80000/133278
[metadata] {"noOutputExpected": false}

scored 20000/133278
scored 40000/133278
scored 60000/133278
scored 80000/133278
scored 100000/133278
scored 120000/133278
scored 133278/133278
wrote 45034 ids -> /workspace/submission/selection.json (~48M tokens of text)
3:after hard filters: 133278 docs (73.2%)
4:positives: 2520 chunks from 2597 dev docs
5:negatives: 7560
6:  step 0 loss 0.6931 acc 0.750
7:  step 100 loss 0.2505 acc 0.970
8:  step 200 loss 0.2384 acc 0.970
9:  step 300 loss 0.2328 acc 0.972

[stdout]
scored 20000/133278
scored 40000/133278
scored 60000/133278
scored 80000/133278
scored 100000/133278
scored 120000/133278
scored 133278/133278
wrote 45034 ids -> /workspace/submission/selection.json (~48M tokens of text)
3:after hard filters: 133278 docs (73.2%)
4:positives: 2520 chunks from 2597 dev docs
5:negatives: 7560
6:  step 0 loss 0.6931 acc 0.750
7:  step 100 loss 0.2505 acc 0.970
8:  step 200 loss 0.2384 acc 0.970
9:  step 300 loss 0.2328 acc 0.972
[metadata] {"noOutputExpected": false}

45034 45034 1 182015
==== 109550 4176 :: .<|endoftext|>Amid calls for war by the state-run media, China accused India of lying to the public on the border row and again warned New Delhi to withdraw troops to “avoid worsening of the situation”….A special report by Gaurav Sharma for Asian Lite News As Beijing raised the d
==== 91094 2410 :: <|endoftext|>|courtesy of singtao.com| |Jackie Chan takes off as soon as he hears questions about Ng Cheuk Lam| |Ella Chen takes the chance to promote her new record| courtesy of on.cc The 19th China Music Award took place two nights ago in Macau. 45 awards were presented and Jay
==== 27123 59829 ::  the loft, Dante awakened Lulu, who was agitated by a disturbing dream. In the dream, Lulu had berated Luke and told him angrily that he loved alcohol more than life. When she told this to Dante, she said that the dream was the opposite of how she felt. Lulu said that she felt as
==== 79653 2170 :: bourne: Visiting US President Barak Obama has denied speculations that America influenced Australia to overturn its policy of not selling uranium to India. “We have not had any influence, I suspect, on Australia’s decision to explore what its relationship in terms of the peaceful
==== 45958 3557 ::  investigations into Russian interference in the presidential election continue, the revelation that Donald Trump Jr. and other senior members of the Trump campaign met with a Kremlin-affiliated lawyer last year has plagued the White House in recent weeks. Here’s how the story fr

[stdout]
45034 45034 1 182015
==== 109550 4176 :: .<|endoftext|>Amid calls for war by the state-run media, China accused India of lying to the public on the border row and again warned New Delhi to withdraw troops to “avoid worsening of the situation”….A special report by Gaurav Sharma for Asian Lite News As Beijing raised the d
==== 91094 2410 :: <|endoftext|>|courtesy of singtao.com| |Jackie Chan takes off as soon as he hears questions about Ng Cheuk Lam| |Ella Chen takes the chance to promote her new record| courtesy of on.cc The 19th China Music Award took place two nights ago in Macau. 45 awards were presented and Jay
==== 27123 59829 ::  the loft, Dante awakened Lulu, who was agitated by a disturbing dream. In the dream, Lulu had berated Luke and told him angrily that he loved alcohol more than life. When she told this to Dante, she said that the dream was the opposite of how she felt. Lulu said that she felt as
==== 79653 2170 :: bourne: Visiting US President Barak Obama has denied speculations that America influenced Australia to overturn its policy of not selling uranium to India. “We have not had any influence, I suspect, on Australia’s decision to explore what its relationship in terms of the peaceful
==== 45958 3557 ::  investigations into Russian interference in the presidential election continue, the revelation that Donald Trump Jr. and other senior members of the Trump campaign met with a Kremlin-affiliated lawyer last year has plagued the White House in recent weeks. Here’s how the story fr
[metadata] {"noOutputExpected": false}

Token indices sequence length is longer than the specified maximum sequence length for this model (12483 > 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 (12483 > 1024). Running this sequence through the model will result in indexing errors
[metadata] {"noOutputExpected": false}

File created successfully at: /workspace/curate_balanced.py (file state is current in your context — no need to Read it back)

[metadata] {"type": "create", "filePath": "/workspace/curate_balanced.py", "content": "\"\"\"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\", \"6\"))\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\nactive = [k for k in range(K) if ranks[k]]\nwhile active and chars < LIM:\n    for k in list(active):\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])); chars += len(texts[i]) or 1\n            adv = True; break\n        if not adv: active.remove(k)\njson.dump(order, open(\"/tmp/selB.json\", \"w\"))\nprint(f\"wrote {len(order)} ids -> /tmp/selB.json\", flush=True)\n", "structuredPatch": [], "originalFile": null, "userModified": false}

Token indices sequence length is longer than the specified maximum sequence length for this model (12483 > 1024). Running this sequence through the model will result in indexing errors
packed 12000000 tokens from 45034 ids -> /tmp/A.npy

[stdout]
Token indices sequence length is longer than the specified maximum sequence length for this model (12483 > 1024). Running this sequence through the model will result in indexing errors
packed 12000000 tokens from 45034 ids -> /tmp/A.npy
[metadata] {"noOutputExpected": false}

packed 12000000 tokens from 45034 ids -> /tmp/A.npy
HELDOUT_PPL 389.527  loss 5.9649  train_s 55  /tmp/A.npy

[stdout]
packed 12000000 tokens from 45034 ids -> /tmp/A.npy
HELDOUT_PPL 389.527  loss 5.9649  train_s 55  /tmp/A.npy
[metadata] {"noOutputExpected": false}

Token indices sequence length is longer than the specified maximum sequence length for this model (1098 > 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 (1098 > 1024). Running this sequence through the model will result in indexing errors
[metadata] {"noOutputExpected": false}