claude-opus-4-8

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

[metadata] {"filePath": "/workspace/curate.py", "oldString": "    if n < 50:                                    # length floor\n        continue\n    head = t[:2000]\n    if sum(c.isalpha() for c in head) / max(1, len(head)) < 0.55:\n        continue                                  # markup / boilerplate gate\n    idx, v = featvec(wds)", "newString": "    if n < 50:                                    # length floor\n        continue\n    head = t[:3000]\n    if sum(c.isalpha() for c in head) / max(1, len(head)) < 0.55:\n        continue                                  # markup / boilerplate gate\n    if english_frac(head) < 0.90:\n        continue                                  # non-English gate (drop Cyrillic etc.)\n    stop_frac = sum(1 for x in wds if x in STOP) / n\n    if stop_frac < 0.20:\n        continue                                  # fluency gate: kill list/nav spam\n    idx, v = featvec(wds)", "originalFile": "\"\"\"Curate the raw web pool for a small LM whose target is a BROAD, multi-domain\nhigh-quality English distribution (Wikipedia + high-quality web prose + news +\ntechnical Q&A).\n\nStated, reproducible criterion: a QUALITY/DOMAIN classifier.\nWe train a logistic-regression classifier on L2-normalized hashed unigram+bigram\nword features to separate\n  positive = the disclosed HQ target (multi_dev.npy decoded into documents), from\n  negative = random raw pool documents.\nEvery pool document is scored by the classifier's decision value = \"how\ntarget-like\". Documents are ranked highest-first, subject to light quality gates\n(length floor, alpha-fraction) and near-duplicate removal, then emitted in\npriority order until well past the 12M-token training budget.\n\nLength-robustness: features are L2-normalized, so score reflects the *direction*\nof a document's n-gram profile, not its length or raw repetition — this avoids\nthe failure mode of raw log-likelihood-ratio ranking, which floats generic\nnavigation/list spam to the top.\n\"\"\"\nimport json, re, zlib, numpy as np\nfrom transformers import AutoTokenizer\n\nPOOL = \"/workspace/data/pool.jsonl\"\nDEV  = \"/workspace/data/multi_dev.npy\"\nOUT  = \"/workspace/submission/selection.json\"\n\nB = 1 << 20                 # hash buckets\nNEG_SAMPLE = 24000          # random pool docs used as negatives\nEPOCHS = 6\nLR = 0.5\nL2 = 1e-6\nSEED = 0\n\n_word = re.compile(r\"[a-z0-9]+\")\ndef words_of(s):\n    return _word.findall(s.lower())\n\n# small English function-word set — fluent prose is rich in these; navigation\n# menus / name lists / license-plate spam are not. A cheap, robust fluency gate.\nSTOP = set(\"the of and to a in is that it for was on as with by at be this from or \"\n           \"an are not but he she they we you his her their which have has had were \"\n           \"been i who what when where how all would there been more one about\".split())\n\ndef english_frac(s):\n    \"\"\"Fraction of ASCII latin letters among all alphabetic characters (0..1).\"\"\"\n    a = ascii_ = 0\n    for c in s:\n        if c.isalpha():\n            a += 1\n            if 'a' <= c <= 'z' or 'A' <= c <= 'Z':\n                ascii_ += 1\n    return ascii_ / max(1, a)\n\ndef crc(b):\n    return zlib.crc32(b) & (B - 1)\n\ndef featvec(words):\n    \"\"\"Return (unique_bucket_ids int32, L2-normalized float32 values) for\n    unigram+bigram hashed features of a word list.\"\"\"\n    if not words:\n        return np.empty(0, np.int32), np.empty(0, np.float32)\n    h = [crc(w.encode()) for w in words]\n    for i in range(len(words) - 1):\n        h.append(crc((words[i] + \"\\x00\" + words[i+1]).encode()))\n    h = np.asarray(h, dtype=np.int64)\n    idx, cnt = np.unique(h, return_counts=True)\n    v = cnt.astype(np.float32)\n    v /= np.sqrt((v * v).sum())\n    return idx.astype(np.int32), v\n\n# ---------- 1. build training features ----------\nprint(\"decoding target ...\")\ntok = AutoTokenizer.from_pretrained(\"gpt2\")\ndev = np.load(DEV)\neos = tok.eos_token_id\narr = dev.tolist()\n# split target into documents on EOS\npos = [i for i, t in enumerate(arr) if t == eos]\nsegs, prev = [], 0\nfor p in pos:\n    if p - prev > 5:\n        segs.append(arr[prev:p])\n    prev = p + 1\nif len(arr) - prev > 5:\n    segs.append(arr[prev:])\npos_feats = [featvec(words_of(tok.decode(s))) for s in segs]\nprint(\"target docs:\", len(pos_feats))\n\nprint(\"loading pool ...\")\nids, texts = [], []\nfor line in open(POOL):\n    r = json.loads(line)\n    ids.append(r[\"id\"]); texts.append(r[\"text\"])\nN = len(ids)\nprint(\"pool docs:\", N)\n\nrng = np.random.default_rng(SEED)\nneg_idx = rng.choice(N, size=min(NEG_SAMPLE, N), replace=False)\nprint(\"building negative features ...\")\nneg_feats = [featvec(words_of(texts[j])) for j in neg_idx]\n\n# ---------- 2. train logistic regression (sparse SGD) ----------\nprint(\"training classifier ...\")\nw = np.zeros(B, dtype=np.float32)\nb = 0.0\ntrain = [(f, 1.0) for f in pos_feats] + [(f, 0.0) for f in neg_feats]\norder = np.arange(len(train))\nfor ep in range(EPOCHS):\n    rng.shuffle(order)\n    lr = LR * (1.0 - ep / (EPOCHS + 1))\n    loss = 0.0\n    for oi in order:\n        (idx, v), y = train[oi]\n        if idx.size == 0:\n            continue\n        z = float(w[idx] @ v) + b\n        p = 1.0 / (1.0 + np.exp(-z))\n        g = p - y\n        w[idx] -= lr * (g * v + L2 * w[idx])\n        b -= lr * g\n        loss += -(y*np.log(p+1e-9) + (1-y)*np.log(1-p+1e-9))\n    print(f\"  epoch {ep} loss {loss/len(train):.4f}\")\n\n# ---------- 3. score every document, with gates + dedup ----------\nprint(\"scoring documents ...\")\nscores = np.full(N, -1e9, dtype=np.float64)\nntok = np.zeros(N, dtype=np.int32)\nfor j in range(N):\n    t = texts[j]\n    wds = words_of(t)\n    n = len(wds)\n    ntok[j] = n\n    if n < 50:                                    # length floor\n        continue\n    head = t[:2000]\n    if sum(c.isalpha() for c in head) / max(1, len(head)) < 0.55:\n        continue                                  # markup / boilerplate gate\n    idx, v = featvec(wds)\n    scores[j] = float(w[idx] @ v) + b\n    if j % 40000 == 0:\n        print(f\"  {j}/{N}\")\n\n# ---------- 4. rank, remove near-duplicates, emit priority order ----------\nprint(\"ranking + dedup ...\")\norder = np.argsort(-scores)\nseen = set()\nselection, tot = [], 0\nBUDGET = 12_000_000\nfor j in order:\n    if scores[j] <= -1e8:\n        continue\n    t = texts[j]\n    wl = words_of(t)\n    # near-duplicate key: first 60 tokens + total length bucket\n    key = (hash(\" \".join(wl[:60])), len(wl) // 40)\n    if key in seen:\n        continue\n    seen.add(key)\n    selection.append(int(ids[j]))\n    tot += int(ntok[j] * 1.3) + 1\n    if tot >= BUDGET * 2.5:\n        break\n\njson.dump(selection, open(OUT, \"w\"))\nprint(f\"wrote {len(selection)} ids (~{tot} est tokens) -> {OUT}\")\nsrt = scores[order]\nprint(\"score p99/p50/min-selected:\", float(srt[0]),\n      float(scores[order[len(selection)//2]]))\n", "structuredPatch": [{"oldStart": 132, "oldLines": 9, "newStart": 132, "newLines": 14, "lines": ["     ntok[j] = n", "     if n < 50:                                    # length floor", "         continue", "-    head = t[:2000]", "+    head = t[:3000]", "     if sum(c.isalpha() for c in head) / max(1, len(head)) < 0.55:", "         continue                                  # markup / boilerplate gate", "+    if english_frac(head) < 0.90:", "+        continue                                  # non-English gate (drop Cyrillic etc.)", "+    stop_frac = sum(1 for x in wds if x in STOP) / n", "+    if stop_frac < 0.20:", "+        continue                                  # fluency gate: kill list/nav spam", "     idx, v = featvec(wds)", "     scores[j] = float(w[idx] @ v) + b", "     if j % 40000 == 0:"]}], "userModified": false, "replaceAll": false}

Command running in background with ID: b7e7bcgc1. Output is being written to: /tmp/claude-0/-workspace/b3699ac4-1c9f-4c54-b65c-10b5011c1085/tasks/b7e7bcgc1.output. You will be notified when it completes. To check interim output, use Read on that file path.
Session cwd remains /workspace; directory changes made by the backgrounded command do not apply to subsequent commands.

[metadata] {"noOutputExpected": false, "backgroundTaskId": "b7e7bcgc1", "backgroundCwdHint": "Session cwd remains /workspace; directory changes made by the backgrounded command do not apply to subsequent commands."}

---
5

[stdout]
---
5
[metadata] {"noOutputExpected": false}

root      1564  0.0  0.0  12564  4484 ?        Ss   04:22   0:00 /bin/bash -c source /logs/agent/sessions/shell-snapshots/snapshot-bash-1785816393965-01djwo.sh 2>/dev/null || true && shopt -u extglob 2>/dev/null || true && { \builtin unalias -- 'unsetenv'; \builtin unset -f -- 'unsetenv'; } >/dev/null 2>&1 || true && eval 'cd /workspace; python3 curate.py > /tmp/curate2.log 2>&1; tail -6 /tmp/curate2.log' < /dev/null && pwd -P >| /tmp/claude-f0fc-cwd
root      1566 94.4  0.5 6853884 6238204 ?     Rl   04:22   2:30 python3 curate.py
===

[stdout]
root      1564  0.0  0.0  12564  4484 ?        Ss   04:22   0:00 /bin/bash -c source /logs/agent/sessions/shell-snapshots/snapshot-bash-1785816393965-01djwo.sh 2>/dev/null || true && shopt -u extglob 2>/dev/null || true && { \builtin unalias -- 'unsetenv'; \builtin unset -f -- 'unsetenv'; } >/dev/null 2>&1 || true && eval 'cd /workspace; python3 curate.py > /tmp/curate2.log 2>&1; tail -6 /tmp/curate2.log' < /dev/null && pwd -P >| /tmp/claude-f0fc-cwd
root      1566 94.4  0.5 6853884 6238204 ?     Rl   04:22   2:30 python3 curate.py
===
[metadata] {"noOutputExpected": false}

epoch 5 loss 0.0873
scoring documents ...
  0/182016
  40000/182016
  80000/182016
ranking + dedup ...
wrote 39589 ids (~30003733 est tokens) -> /workspace/submission/selection.json
score p99/p50/min-selected: 2.872614090604287 -2.2426965783410253

[stdout]
  epoch 5 loss 0.0873
scoring documents ...
  0/182016
  40000/182016
  80000/182016
ranking + dedup ...
wrote 39589 ids (~30003733 est tokens) -> /workspace/submission/selection.json
score p99/p50/min-selected: 2.872614090604287 -2.2426965783410253
[metadata] {"noOutputExpected": false}

=== id 57336 ===
Islamabad, December 25: Indian death row prisoner Kulbhushan Jadhav’s wife and mother arrived in Islamabad for a meeting with him at the Pakistan foreign affairs ministry, officials said. TV footage showed a convoy of ar
=== id 7849 ===
Solar radiation-nutrient interaction enhances the resource and predation algal control on bacterioplankton: A short-term experimental study Limnol. Oceanogr., 51(2), 2006, 913-924 | DOI: 10.4319/lo.2006.51.2.0913 ABSTRAC
=== id 79653 ===
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 decisi
=== id 49061 ===
Barbara Bush, the matriarch of a Republican political dynasty and a first lady who elevated the cause of literacy, died Tuesday, a family spokesman said. She was 92. In 2001, when George W. Bush took office, Barbara Bush
=== id 109550 ===
.<|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 
=== id 3196 ===
DMK chief MK Stalin on Sunday hit out at the Election Commission for not announcing by-elections to two Tamil Nadu assembly constituencies citing monsoon as the reason and said it was not a ‘justifiable’ decision. Expres
n sel 39589

[stdout]
=== id 57336 ===
Islamabad, December 25: Indian death row prisoner Kulbhushan Jadhav’s wife and mother arrived in Islamabad for a meeting with him at the Pakistan foreign affairs ministry, officials said. TV footage showed a convoy of ar
=== id 7849 ===
Solar radiation-nutrient interaction enhances the resource and predation algal control on bacterioplankton: A short-term experimental study Limnol. Oceanogr., 51(2), 2006, 913-924 | DOI: 10.4319/lo.2006.51.2.0913 ABSTRAC
=== id 79653 ===
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 decisi
=== id 49061 ===
Barbara Bush, the matriarch of a Republican political dynasty and a first lady who elevated the cause of literacy, died Tuesday, a family spokesman said. She was 92. In 2001, when George W. Bush took office, Barbara Bush
=== id 109550 ===
.<|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 
=== id 3196 ===
DMK chief MK Stalin on Sunday hit out at the Election Commission for not announcing by-elections to two Tamil Nadu assembly constituencies citing monsoon as the reason and said it was not a ‘justifiable’ decision. Expres
n sel 39589
[metadata] {"noOutputExpected": false}

Command running in background with ID: b170fqffk. Output is being written to: /tmp/claude-0/-workspace/b3699ac4-1c9f-4c54-b65c-10b5011c1085/tasks/b170fqffk.output. You will be notified when it completes. To check interim output, use Read on that file path.
Session cwd remains /workspace; directory changes made by the backgrounded command do not apply to subsequent commands.

[metadata] {"noOutputExpected": false, "backgroundTaskId": "b170fqffk", "backgroundCwdHint": "Session cwd remains /workspace; directory changes made by the backgrounded command do not apply to subsequent commands."}

packed 12000000 tokens from 39589 ids -> /tmp/dev_train.npy
HELDOUT_PPL 430.630  loss 6.0652  train_s 56  /tmp/dev_train.npy
4118
4088

[stdout]
packed 12000000 tokens from 39589 ids -> /tmp/dev_train.npy
HELDOUT_PPL 430.630  loss 6.0652  train_s 56  /tmp/dev_train.npy
4118
4088
[metadata] {"noOutputExpected": false}

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

[metadata] {"filePath": "/workspace/curate.py", "oldString": "    if english_frac(head) < 0.90:\n        continue                                  # non-English gate (drop Cyrillic etc.)\n    stop_frac = sum(1 for x in wds if x in STOP) / n\n    if stop_frac < 0.20:\n        continue                                  # fluency gate: kill list/nav spam\n    idx, v = featvec(wds)", "newString": "    if english_frac(head) < 0.85:\n        continue                                  # non-English gate (drop Cyrillic etc.)\n    idx, v = featvec(wds)", "originalFile": "\"\"\"Curate the raw web pool for a small LM whose target is a BROAD, multi-domain\nhigh-quality English distribution (Wikipedia + high-quality web prose + news +\ntechnical Q&A).\n\nStated, reproducible criterion: a QUALITY/DOMAIN classifier.\nWe train a logistic-regression classifier on L2-normalized hashed unigram+bigram\nword features to separate\n  positive = the disclosed HQ target (multi_dev.npy decoded into documents), from\n  negative = random raw pool documents.\nEvery pool document is scored by the classifier's decision value = \"how\ntarget-like\". Documents are ranked highest-first, subject to light quality gates\n(length floor, alpha-fraction) and near-duplicate removal, then emitted in\npriority order until well past the 12M-token training budget.\n\nLength-robustness: features are L2-normalized, so score reflects the *direction*\nof a document's n-gram profile, not its length or raw repetition — this avoids\nthe failure mode of raw log-likelihood-ratio ranking, which floats generic\nnavigation/list spam to the top.\n\"\"\"\nimport json, re, zlib, numpy as np\nfrom transformers import AutoTokenizer\n\nPOOL = \"/workspace/data/pool.jsonl\"\nDEV  = \"/workspace/data/multi_dev.npy\"\nOUT  = \"/workspace/submission/selection.json\"\n\nB = 1 << 20                 # hash buckets\nNEG_SAMPLE = 24000          # random pool docs used as negatives\nEPOCHS = 6\nLR = 0.5\nL2 = 1e-6\nSEED = 0\n\n_word = re.compile(r\"[a-z0-9]+\")\ndef words_of(s):\n    return _word.findall(s.lower())\n\n# small English function-word set — fluent prose is rich in these; navigation\n# menus / name lists / license-plate spam are not. A cheap, robust fluency gate.\nSTOP = set(\"the of and to a in is that it for was on as with by at be this from or \"\n           \"an are not but he she they we you his her their which have has had were \"\n           \"been i who what when where how all would there been more one about\".split())\n\ndef english_frac(s):\n    \"\"\"Fraction of ASCII latin letters among all alphabetic characters (0..1).\"\"\"\n    a = ascii_ = 0\n    for c in s:\n        if c.isalpha():\n            a += 1\n            if 'a' <= c <= 'z' or 'A' <= c <= 'Z':\n                ascii_ += 1\n    return ascii_ / max(1, a)\n\ndef crc(b):\n    return zlib.crc32(b) & (B - 1)\n\ndef featvec(words):\n    \"\"\"Return (unique_bucket_ids int32, L2-normalized float32 values) for\n    unigram+bigram hashed features of a word list.\"\"\"\n    if not words:\n        return np.empty(0, np.int32), np.empty(0, np.float32)\n    h = [crc(w.encode()) for w in words]\n    for i in range(len(words) - 1):\n        h.append(crc((words[i] + \"\\x00\" + words[i+1]).encode()))\n    h = np.asarray(h, dtype=np.int64)\n    idx, cnt = np.unique(h, return_counts=True)\n    v = cnt.astype(np.float32)\n    v /= np.sqrt((v * v).sum())\n    return idx.astype(np.int32), v\n\n# ---------- 1. build training features ----------\nprint(\"decoding target ...\")\ntok = AutoTokenizer.from_pretrained(\"gpt2\")\ndev = np.load(DEV)\neos = tok.eos_token_id\narr = dev.tolist()\n# split target into documents on EOS\npos = [i for i, t in enumerate(arr) if t == eos]\nsegs, prev = [], 0\nfor p in pos:\n    if p - prev > 5:\n        segs.append(arr[prev:p])\n    prev = p + 1\nif len(arr) - prev > 5:\n    segs.append(arr[prev:])\npos_feats = [featvec(words_of(tok.decode(s))) for s in segs]\nprint(\"target docs:\", len(pos_feats))\n\nprint(\"loading pool ...\")\nids, texts = [], []\nfor line in open(POOL):\n    r = json.loads(line)\n    ids.append(r[\"id\"]); texts.append(r[\"text\"])\nN = len(ids)\nprint(\"pool docs:\", N)\n\nrng = np.random.default_rng(SEED)\nneg_idx = rng.choice(N, size=min(NEG_SAMPLE, N), replace=False)\nprint(\"building negative features ...\")\nneg_feats = [featvec(words_of(texts[j])) for j in neg_idx]\n\n# ---------- 2. train logistic regression (sparse SGD) ----------\nprint(\"training classifier ...\")\nw = np.zeros(B, dtype=np.float32)\nb = 0.0\ntrain = [(f, 1.0) for f in pos_feats] + [(f, 0.0) for f in neg_feats]\norder = np.arange(len(train))\nfor ep in range(EPOCHS):\n    rng.shuffle(order)\n    lr = LR * (1.0 - ep / (EPOCHS + 1))\n    loss = 0.0\n    for oi in order:\n        (idx, v), y = train[oi]\n        if idx.size == 0:\n            continue\n        z = float(w[idx] @ v) + b\n        p = 1.0 / (1.0 + np.exp(-z))\n        g = p - y\n        w[idx] -= lr * (g * v + L2 * w[idx])\n        b -= lr * g\n        loss += -(y*np.log(p+1e-9) + (1-y)*np.log(1-p+1e-9))\n    print(f\"  epoch {ep} loss {loss/len(train):.4f}\")\n\n# ---------- 3. score every document, with gates + dedup ----------\nprint(\"scoring documents ...\")\nscores = np.full(N, -1e9, dtype=np.float64)\nntok = np.zeros(N, dtype=np.int32)\nfor j in range(N):\n    t = texts[j]\n    wds = words_of(t)\n    n = len(wds)\n    ntok[j] = n\n    if n < 50:                                    # length floor\n        continue\n    head = t[:3000]\n    if sum(c.isalpha() for c in head) / max(1, len(head)) < 0.55:\n        continue                                  # markup / boilerplate gate\n    if english_frac(head) < 0.90:\n        continue                                  # non-English gate (drop Cyrillic etc.)\n    stop_frac = sum(1 for x in wds if x in STOP) / n\n    if stop_frac < 0.20:\n        continue                                  # fluency gate: kill list/nav spam\n    idx, v = featvec(wds)\n    scores[j] = float(w[idx] @ v) + b\n    if j % 40000 == 0:\n        print(f\"  {j}/{N}\")\n\n# ---------- 4. rank, remove near-duplicates, emit priority order ----------\nprint(\"ranking + dedup ...\")\norder = np.argsort(-scores)\nseen = set()\nselection, tot = [], 0\nBUDGET = 12_000_000\nfor j in order:\n    if scores[j] <= -1e8:\n        continue\n    t = texts[j]\n    wl = words_of(t)\n    # near-duplicate key: first 60 tokens + total length bucket\n    key = (hash(\" \".join(wl[:60])), len(wl) // 40)\n    if key in seen:\n        continue\n    seen.add(key)\n    selection.append(int(ids[j]))\n    tot += int(ntok[j] * 1.3) + 1\n    if tot >= BUDGET * 2.5:\n        break\n\njson.dump(selection, open(OUT, \"w\"))\nprint(f\"wrote {len(selection)} ids (~{tot} est tokens) -> {OUT}\")\nsrt = scores[order]\nprint(\"score p99/p50/min-selected:\", float(srt[0]),\n      float(scores[order[len(selection)//2]]))\n", "structuredPatch": [{"oldStart": 135, "oldLines": 11, "newStart": 135, "newLines": 8, "lines": ["     head = t[:3000]", "     if sum(c.isalpha() for c in head) / max(1, len(head)) < 0.55:", "         continue                                  # markup / boilerplate gate", "-    if english_frac(head) < 0.90:", "+    if english_frac(head) < 0.85:", "         continue                                  # non-English gate (drop Cyrillic etc.)", "-    stop_frac = sum(1 for x in wds if x in STOP) / n", "-    if stop_frac < 0.20:", "-        continue                                  # fluency gate: kill list/nav spam", "     idx, v = featvec(wds)", "     scores[j] = float(w[idx] @ v) + b", "     if j % 40000 == 0:"]}], "userModified": false, "replaceAll": false}

Command running in background with ID: byxqxr5t6. Output is being written to: /tmp/claude-0/-workspace/b3699ac4-1c9f-4c54-b65c-10b5011c1085/tasks/byxqxr5t6.output. You will be notified when it completes. To check interim output, use Read on that file path.
Session cwd remains /workspace; directory changes made by the backgrounded command do not apply to subsequent commands.

[metadata] {"noOutputExpected": false, "backgroundTaskId": "byxqxr5t6", "backgroundCwdHint": "Session cwd remains /workspace; directory changes made by the backgrounded command do not apply to subsequent commands."}

2

[stdout]
2
[metadata] {"noOutputExpected": false}

ranking + dedup ...
wrote 36159 ids (~30000042 est tokens) -> /workspace/submission/selection.json
score p99/p50/min-selected: 4.30266970889042 -2.054031903582114
0

[stdout]
ranking + dedup ...
wrote 36159 ids (~30000042 est tokens) -> /workspace/submission/selection.json
score p99/p50/min-selected: 4.30266970889042 -2.054031903582114
0
[metadata] {"noOutputExpected": false}

Command running in background with ID: bqjq9q0mt. Output is being written to: /tmp/claude-0/-workspace/b3699ac4-1c9f-4c54-b65c-10b5011c1085/tasks/bqjq9q0mt.output. You will be notified when it completes. To check interim output, use Read on that file path.
Session cwd remains /workspace; directory changes made by the backgrounded command do not apply to subsequent commands.

[metadata] {"noOutputExpected": false, "backgroundTaskId": "bqjq9q0mt", "backgroundCwdHint": "Session cwd remains /workspace; directory changes made by the backgrounded command do not apply to subsequent commands."}

packed 12000000 tokens from 36159 ids -> /tmp/dev_train.npy
HELDOUT_PPL 410.756  loss 6.0180  train_s 55  /tmp/dev_train.npy
3747
3727

[stdout]
packed 12000000 tokens from 36159 ids -> /tmp/dev_train.npy
HELDOUT_PPL 410.756  loss 6.0180  train_s 55  /tmp/dev_train.npy
3747
3727
[metadata] {"noOutputExpected": false}

top-11k composition: Counter({'prose/other': 5690, 'news': 5295, 'code/tech': 15})

[stdout]
top-11k composition: Counter({'prose/other': 5690, 'news': 5295, 'code/tech': 15})
[metadata] {"noOutputExpected": false}

sig>= 1 17932
sig>= 2 2747
sig>= 3 1572
sig>= 4 1111
=== id 12535 ===
So let’s say you’ve got some particles in 2-dimensional space. There they are. Now let’s say you want any two particles which are very close to each other to interact. You need to know which pairs are close enough. You could take all possible pairs o
=== id 18070 ===
Node.JS Easy 3rd party OAuth & REST API Consumer Make consuming RESTful apis waaaaay easier in Node.js Ever notice how all of these APIs seem to have a high degree of consistency? They're just a bunch of URLs that accept different parameters you pass
=== id 38096 ===
<|endoftext|>OSSL_ESS_signing_cert_new_init, OSSL_ESS_signing_cert_v2_new_init, OSSL_ESS_check_signing_certs - Enhanced Security Services (ESS) functions #include <openssl/ess.h> ESS_SIGNING_CERT *OSSL_ESS_signing_cert_new_init(const X509 *signcert,

[stdout]
sig>= 1 17932
sig>= 2 2747
sig>= 3 1572
sig>= 4 1111
=== id 12535 ===
So let’s say you’ve got some particles in 2-dimensional space. There they are. Now let’s say you want any two particles which are very close to each other to interact. You need to know which pairs are close enough. You could take all possible pairs o
=== id 18070 ===
Node.JS Easy 3rd party OAuth & REST API Consumer Make consuming RESTful apis waaaaay easier in Node.js Ever notice how all of these APIs seem to have a high degree of consistency? They're just a bunch of URLs that accept different parameters you pass
=== id 38096 ===
<|endoftext|>OSSL_ESS_signing_cert_new_init, OSSL_ESS_signing_cert_v2_new_init, OSSL_ESS_check_signing_certs - Enhanced Security Services (ESS) functions #include <openssl/ess.h> ESS_SIGNING_CERT *OSSL_ESS_signing_cert_new_init(const X509 *signcert,
[metadata] {"noOutputExpected": false}

108	for ep in range(EPOCHS):
109	    rng.shuffle(order)
110	    lr = LR * (1.0 - ep / (EPOCHS + 1))
111	    loss = 0.0
112	    for oi in order:
113	        (idx, v), y = train[oi]
114	        if idx.size == 0:
115	            continue
116	        z = float(w[idx] @ v) + b
117	        p = 1.0 / (1.0 + np.exp(-z))
118	        g = p - y
119	        w[idx] -= lr * (g * v + L2 * w[idx])
120	        b -= lr * g
121	        loss += -(y*np.log(p+1e-9) + (1-y)*np.log(1-p+1e-9))
122	    print(f"  epoch {ep} loss {loss/len(train):.4f}")
123	
124	# ---------- 3. score every document, with gates + dedup ----------
125	print("scoring documents ...")
126	scores = np.full(N, -1e9, dtype=np.float64)
127	ntok = np.zeros(N, dtype=np.int32)
128	for j in range(N):
129	    t = texts[j]
130	    wds = words_of(t)
131	    n = len(wds)
132	    ntok[j] = n
133	    if n < 50:                                    # length floor
134	        continue
135	    head = t[:3000]
136	    if sum(c.isalpha() for c in head) / max(1, len(head)) < 0.55:
137	        continue                                  # markup / boilerplate gate
138	    if english_frac(head) < 0.85:
139	        continue                                  # non-English gate (drop Cyrillic etc.)
140	    idx, v = featvec(wds)
141	    scores[j] = float(w[idx] @ v) + b
142	    if j % 40000 == 0:
143	        print(f"  {j}/{N}")
144	
145	# ---------- 4. rank, remove near-duplicates, emit priority order ----------
146	print("ranking + dedup ...")
147	order = np.argsort(-scores)
148	seen = set()
149	selection, tot = [], 0
150	BUDGET = 12_000_000
151	for j in order:
152	    if scores[j] <= -1e8:
153	        continue
154	    t = texts[j]
155	    wl = words_of(t)
156	    # near-duplicate key: first 60 tokens + total length bucket
157	    key = (hash(" ".join(wl[:60])), len(wl) // 40)
158	    if key in seen:
159	        continue
160	    seen.add(key)
161	    selection.append(int(ids[j]))
162	    tot += int(ntok[j] * 1.3) + 1
163	    if tot >= BUDGET * 2.5:
164	        break
165	
166	json.dump(selection, open(OUT, "w"))
167	print(f"wrote {len(selection)} ids (~{tot} est tokens) -> {OUT}")

[metadata] {"type": "text", "file": {"filePath": "/workspace/curate.py", "content": "for ep in range(EPOCHS):\n    rng.shuffle(order)\n    lr = LR * (1.0 - ep / (EPOCHS + 1))\n    loss = 0.0\n    for oi in order:\n        (idx, v), y = train[oi]\n        if idx.size == 0:\n            continue\n        z = float(w[idx] @ v) + b\n        p = 1.0 / (1.0 + np.exp(-z))\n        g = p - y\n        w[idx] -= lr * (g * v + L2 * w[idx])\n        b -= lr * g\n        loss += -(y*np.log(p+1e-9) + (1-y)*np.log(1-p+1e-9))\n    print(f\"  epoch {ep} loss {loss/len(train):.4f}\")\n\n# ---------- 3. score every document, with gates + dedup ----------\nprint(\"scoring documents ...\")\nscores = np.full(N, -1e9, dtype=np.float64)\nntok = np.zeros(N, dtype=np.int32)\nfor j in range(N):\n    t = texts[j]\n    wds = words_of(t)\n    n = len(wds)\n    ntok[j] = n\n    if n < 50:                                    # length floor\n        continue\n    head = t[:3000]\n    if sum(c.isalpha() for c in head) / max(1, len(head)) < 0.55:\n        continue                                  # markup / boilerplate gate\n    if english_frac(head) < 0.85:\n        continue                                  # non-English gate (drop Cyrillic etc.)\n    idx, v = featvec(wds)\n    scores[j] = float(w[idx] @ v) + b\n    if j % 40000 == 0:\n        print(f\"  {j}/{N}\")\n\n# ---------- 4. rank, remove near-duplicates, emit priority order ----------\nprint(\"ranking + dedup ...\")\norder = np.argsort(-scores)\nseen = set()\nselection, tot = [], 0\nBUDGET = 12_000_000\nfor j in order:\n    if scores[j] <= -1e8:\n        continue\n    t = texts[j]\n    wl = words_of(t)\n    # near-duplicate key: first 60 tokens + total length bucket\n    key = (hash(\" \".join(wl[:60])), len(wl) // 40)\n    if key in seen:\n        continue\n    seen.add(key)\n    selection.append(int(ids[j]))\n    tot += int(ntok[j] * 1.3) + 1\n    if tot >= BUDGET * 2.5:\n        break\n\njson.dump(selection, open(OUT, \"w\"))\nprint(f\"wrote {len(selection)} ids (~{tot} est tokens) -> {OUT}\")", "numLines": 60, "startLine": 108, "totalLines": 171}}

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

[metadata] {"filePath": "/workspace/curate.py", "oldString": "# ---------- 3. score every document, with gates + dedup ----------\nprint(\"scoring documents ...\")\nscores = np.full(N, -1e9, dtype=np.float64)\nntok = np.zeros(N, dtype=np.int32)\nfor j in range(N):\n    t = texts[j]\n    wds = words_of(t)\n    n = len(wds)\n    ntok[j] = n\n    if n < 50:                                    # length floor\n        continue\n    head = t[:3000]\n    if sum(c.isalpha() for c in head) / max(1, len(head)) < 0.55:\n        continue                                  # markup / boilerplate gate\n    if english_frac(head) < 0.85:\n        continue                                  # non-English gate (drop Cyrillic etc.)\n    idx, v = featvec(wds)\n    scores[j] = float(w[idx] @ v) + b\n    if j % 40000 == 0:\n        print(f\"  {j}/{N}\")\n\n# ---------- 4. rank, remove near-duplicates, emit priority order ----------\nprint(\"ranking + dedup ...\")\norder = np.argsort(-scores)\nseen = set()\nselection, tot = [], 0\nBUDGET = 12_000_000\nfor j in order:\n    if scores[j] <= -1e8:\n        continue\n    t = texts[j]\n    wl = words_of(t)\n    # near-duplicate key: first 60 tokens + total length bucket\n    key = (hash(\" \".join(wl[:60])), len(wl) // 40)\n    if key in seen:\n        continue\n    seen.add(key)\n    selection.append(int(ids[j]))\n    tot += int(ntok[j] * 1.3) + 1\n    if tot >= BUDGET * 2.5:\n        break\n\njson.dump(selection, open(OUT, \"w\"))\nprint(f\"wrote {len(selection)} ids (~{tot} est tokens) -> {OUT}\")", "newString": "# The target is EQUAL PARTS four registers incl. technical Q&A / code. Detect the\n# technical register so we can guarantee it a token quota — otherwise the plain\n# classifier floats news/prose to the top and code is starved (<0.2% of budget),\n# leaving a whole quarter of the target badly modeled.\nTECH_SIG = ['<pre', '<code', 'stack overflow', 'stackoverflow', 'function(', 'var ',\n            'import ', 'public static', '#include', 'console.log', 'println', '</code',\n            '&lt;', 'def ', 'npm ', 'printf', 'const ', 'return ', ');']\ndef tech_score(tl):\n    return sum(1 for m in TECH_SIG if m in tl)\n\n# ---------- 3. score every document, with register-aware gates ----------\nprint(\"scoring documents ...\")\nscores = np.full(N, -1e9, dtype=np.float64)\nntok = np.zeros(N, dtype=np.int32)\nis_tech = np.zeros(N, dtype=bool)\nfor j in range(N):\n    t = texts[j]\n    wds = words_of(t)\n    n = len(wds)\n    ntok[j] = n\n    if n < 50:                                    # length floor\n        continue\n    head = t[:3000]\n    tech = tech_score(t.lower()) >= 2\n    is_tech[j] = tech\n    alpha = sum(c.isalpha() for c in head) / max(1, len(head))\n    if tech:\n        if alpha < 0.30:                          # code is symbol-heavy: relax gate\n            continue\n    else:\n        if alpha < 0.55 or english_frac(head) < 0.85:\n            continue                              # prose gates: clean English only\n    idx, v = featvec(wds)\n    scores[j] = float(w[idx] @ v) + b\n    if j % 40000 == 0:\n        print(f\"  {j}/{N}\")\n\n# ---------- 4. rank each register, merge to a balanced priority order ----------\nprint(\"ranking + balancing + dedup ...\")\nBUDGET = 12_000_000\nTECH_SHARE = 0.25            # technical register's share of the 12M budget\norder = np.argsort(-scores)\ntech_q = [j for j in order if scores[j] > -1e8 and is_tech[j]]\nrest_q = [j for j in order if scores[j] > -1e8 and not is_tech[j]]\n\ndef est(j):\n    return int(ntok[j] * 1.3) + 1\n\nseen = set()\ndef fresh(j):\n    wl = words_of(texts[j])\n    key = (hash(\" \".join(wl[:60])), len(wl) // 40)\n    if key in seen:\n        return False\n    seen.add(key)\n    return True\n\nselection, tot, tech_tok = [], 0, 0\nti = ri = 0\n# interleave: pull from tech whenever its running token share is below target\nwhile tot < BUDGET * 2.5 and (ti < len(tech_q) or ri < len(rest_q)):\n    want_tech = (tech_tok < TECH_SHARE * max(1, tot)) and ti < len(tech_q)\n    if want_tech:\n        j = tech_q[ti]; ti += 1\n        if not fresh(j):\n            continue\n        selection.append(int(ids[j])); e = est(j); tot += e; tech_tok += e\n    else:\n        if ri >= len(rest_q):\n            if ti >= len(tech_q):\n                break\n            continue\n        j = rest_q[ri]; ri += 1\n        if not fresh(j):\n            continue\n        selection.append(int(ids[j])); tot += est(j)\n\njson.dump(selection, open(OUT, \"w\"))\nprint(f\"wrote {len(selection)} ids (~{tot} est tokens, tech~{tech_tok}) -> {OUT}\")", "originalFile": "\"\"\"Curate the raw web pool for a small LM whose target is a BROAD, multi-domain\nhigh-quality English distribution (Wikipedia + high-quality web prose + news +\ntechnical Q&A).\n\nStated, reproducible criterion: a QUALITY/DOMAIN classifier.\nWe train a logistic-regression classifier on L2-normalized hashed unigram+bigram\nword features to separate\n  positive = the disclosed HQ target (multi_dev.npy decoded into documents), from\n  negative = random raw pool documents.\nEvery pool document is scored by the classifier's decision value = \"how\ntarget-like\". Documents are ranked highest-first, subject to light quality gates\n(length floor, alpha-fraction) and near-duplicate removal, then emitted in\npriority order until well past the 12M-token training budget.\n\nLength-robustness: features are L2-normalized, so score reflects the *direction*\nof a document's n-gram profile, not its length or raw repetition — this avoids\nthe failure mode of raw log-likelihood-ratio ranking, which floats generic\nnavigation/list spam to the top.\n\"\"\"\nimport json, re, zlib, numpy as np\nfrom transformers import AutoTokenizer\n\nPOOL = \"/workspace/data/pool.jsonl\"\nDEV  = \"/workspace/data/multi_dev.npy\"\nOUT  = \"/workspace/submission/selection.json\"\n\nB = 1 << 20                 # hash buckets\nNEG_SAMPLE = 24000          # random pool docs used as negatives\nEPOCHS = 6\nLR = 0.5\nL2 = 1e-6\nSEED = 0\n\n_word = re.compile(r\"[a-z0-9]+\")\ndef words_of(s):\n    return _word.findall(s.lower())\n\n# small English function-word set — fluent prose is rich in these; navigation\n# menus / name lists / license-plate spam are not. A cheap, robust fluency gate.\nSTOP = set(\"the of and to a in is that it for was on as with by at be this from or \"\n           \"an are not but he she they we you his her their which have has had were \"\n           \"been i who what when where how all would there been more one about\".split())\n\ndef english_frac(s):\n    \"\"\"Fraction of ASCII latin letters among all alphabetic characters (0..1).\"\"\"\n    a = ascii_ = 0\n    for c in s:\n        if c.isalpha():\n            a += 1\n            if 'a' <= c <= 'z' or 'A' <= c <= 'Z':\n                ascii_ += 1\n    return ascii_ / max(1, a)\n\ndef crc(b):\n    return zlib.crc32(b) & (B - 1)\n\ndef featvec(words):\n    \"\"\"Return (unique_bucket_ids int32, L2-normalized float32 values) for\n    unigram+bigram hashed features of a word list.\"\"\"\n    if not words:\n        return np.empty(0, np.int32), np.empty(0, np.float32)\n    h = [crc(w.encode()) for w in words]\n    for i in range(len(words) - 1):\n        h.append(crc((words[i] + \"\\x00\" + words[i+1]).encode()))\n    h = np.asarray(h, dtype=np.int64)\n    idx, cnt = np.unique(h, return_counts=True)\n    v = cnt.astype(np.float32)\n    v /= np.sqrt((v * v).sum())\n    return idx.astype(np.int32), v\n\n# ---------- 1. build training features ----------\nprint(\"decoding target ...\")\ntok = AutoTokenizer.from_pretrained(\"gpt2\")\ndev = np.load(DEV)\neos = tok.eos_token_id\narr = dev.tolist()\n# split target into documents on EOS\npos = [i for i, t in enumerate(arr) if t == eos]\nsegs, prev = [], 0\nfor p in pos:\n    if p - prev > 5:\n        segs.append(arr[prev:p])\n    prev = p + 1\nif len(arr) - prev > 5:\n    segs.append(arr[prev:])\npos_feats = [featvec(words_of(tok.decode(s))) for s in segs]\nprint(\"target docs:\", len(pos_feats))\n\nprint(\"loading pool ...\")\nids, texts = [], []\nfor line in open(POOL):\n    r = json.loads(line)\n    ids.append(r[\"id\"]); texts.append(r[\"text\"])\nN = len(ids)\nprint(\"pool docs:\", N)\n\nrng = np.random.default_rng(SEED)\nneg_idx = rng.choice(N, size=min(NEG_SAMPLE, N), replace=False)\nprint(\"building negative features ...\")\nneg_feats = [featvec(words_of(texts[j])) for j in neg_idx]\n\n# ---------- 2. train logistic regression (sparse SGD) ----------\nprint(\"training classifier ...\")\nw = np.zeros(B, dtype=np.float32)\nb = 0.0\ntrain = [(f, 1.0) for f in pos_feats] + [(f, 0.0) for f in neg_feats]\norder = np.arange(len(train))\nfor ep in range(EPOCHS):\n    rng.shuffle(order)\n    lr = LR * (1.0 - ep / (EPOCHS + 1))\n    loss = 0.0\n    for oi in order:\n        (idx, v), y = train[oi]\n        if idx.size == 0:\n            continue\n        z = float(w[idx] @ v) + b\n        p = 1.0 / (1.0 + np.exp(-z))\n        g = p - y\n        w[idx] -= lr * (g * v + L2 * w[idx])\n        b -= lr * g\n        loss += -(y*np.log(p+1e-9) + (1-y)*np.log(1-p+1e-9))\n    print(f\"  epoch {ep} loss {loss/len(train):.4f}\")\n\n# ---------- 3. score every document, with gates + dedup ----------\nprint(\"scoring documents ...\")\nscores = np.full(N, -1e9, dtype=np.float64)\nntok = np.zeros(N, dtype=np.int32)\nfor j in range(N):\n    t = texts[j]\n    wds = words_of(t)\n    n = len(wds)\n    ntok[j] = n\n    if n < 50:                                    # length floor\n        continue\n    head = t[:3000]\n    if sum(c.isalpha() for c in head) / max(1, len(head)) < 0.55:\n        continue                                  # markup / boilerplate gate\n    if english_frac(head) < 0.85:\n        continue                                  # non-English gate (drop Cyrillic etc.)\n    idx, v = featvec(wds)\n    scores[j] = float(w[idx] @ v) + b\n    if j % 40000 == 0:\n        print(f\"  {j}/{N}\")\n\n# ---------- 4. rank, remove near-duplicates, emit priority order ----------\nprint(\"ranking + dedup ...\")\norder = np.argsort(-scores)\nseen = set()\nselection, tot = [], 0\nBUDGET = 12_000_000\nfor j in order:\n    if scores[j] <= -1e8:\n        continue\n    t = texts[j]\n    wl = words_of(t)\n    # near-duplicate key: first 60 tokens + total length bucket\n    key = (hash(\" \".join(wl[:60])), len(wl) // 40)\n    if key in seen:\n        continue\n    seen.add(key)\n    selection.append(int(ids[j]))\n    tot += int(ntok[j] * 1.3) + 1\n    if tot >= BUDGET * 2.5:\n        break\n\njson.dump(selection, open(OUT, \"w\"))\nprint(f\"wrote {len(selection)} ids (~{tot} est tokens) -> {OUT}\")\nsrt = scores[order]\nprint(\"score p99/p50/min-selected:\", float(srt[0]),\n      float(scores[order[len(selection)//2]]))\n", "structuredPatch": [{"oldStart": 121, "oldLines": 10, "newStart": 121, "newLines": 21, "lines": ["         loss += -(y*np.log(p+1e-9) + (1-y)*np.log(1-p+1e-9))", "     print(f\"  epoch {ep} loss {loss/len(train):.4f}\")", " ", "-# ---------- 3. score every document, with gates + dedup ----------", "+# The target is EQUAL PARTS four registers incl. technical Q&A / code. Detect the", "+# technical register so we can guarantee it a token quota — otherwise the plain", "+# classifier floats news/prose to the top and code is starved (<0.2% of budget),", "+# leaving a whole quarter of the target badly modeled.", "+TECH_SIG = ['<pre', '<code', 'stack overflow', 'stackoverflow', 'function(', 'var ',", "+            'import ', 'public static', '#include', 'console.log', 'println', '</code',", "+            '&lt;', 'def ', 'npm ', 'printf', 'const ', 'return ', ');']", "+def tech_score(tl):", "+    return sum(1 for m in TECH_SIG if m in tl)", "+", "+# ---------- 3. score every document, with register-aware gates ----------", " print(\"scoring documents ...\")", " scores = np.full(N, -1e9, dtype=np.float64)", " ntok = np.zeros(N, dtype=np.int32)", "+is_tech = np.zeros(N, dtype=bool)", " for j in range(N):", "     t = texts[j]", "     wds = words_of(t)"]}, {"oldStart": 133, "oldLines": 38, "newStart": 144, "newLines": 62, "lines": ["     if n < 50:                                    # length floor", "         continue", "     head = t[:3000]", "-    if sum(c.isalpha() for c in head) / max(1, len(head)) < 0.55:", "-        continue                                  # markup / boilerplate gate", "-    if english_frac(head) < 0.85:", "-        continue                                  # non-English gate (drop Cyrillic etc.)", "+    tech = tech_score(t.lower()) >= 2", "+    is_tech[j] = tech", "+    alpha = sum(c.isalpha() for c in head) / max(1, len(head))", "+    if tech:", "+        if alpha < 0.30:                          # code is symbol-heavy: relax gate", "+            continue", "+    else:", "+        if alpha < 0.55 or english_frac(head) < 0.85:", "+            continue                              # prose gates: clean English only", "     idx, v = featvec(wds)", "     scores[j] = float(w[idx] @ v) + b", "     if j % 40000 == 0:", "         print(f\"  {j}/{N}\")", " ", "-# ---------- 4. rank, remove near-duplicates, emit priority order ----------", "-print(\"ranking + dedup ...\")", "+# ---------- 4. rank each register, merge to a balanced priority order ----------", "+print(\"ranking + balancing + dedup ...\")", "+BUDGET = 12_000_000", "+TECH_SHARE = 0.25            # technical register's share of the 12M budget", " order = np.argsort(-scores)", "+tech_q = [j for j in order if scores[j] > -1e8 and is_tech[j]]", "+rest_q = [j for j in order if scores[j] > -1e8 and not is_tech[j]]", "+", "+def est(j):", "+    return int(ntok[j] * 1.3) + 1", "+", " seen = set()", "-selection, tot = [], 0", "-BUDGET = 12_000_000", "-for j in order:", "-    if scores[j] <= -1e8:", "-        continue", "-    t = texts[j]", "-    wl = words_of(t)", "-    # near-duplicate key: first 60 tokens + total length bucket", "+def fresh(j):", "+    wl = words_of(texts[j])", "     key = (hash(\" \".join(wl[:60])), len(wl) // 40)", "     if key in seen:", "-        continue", "+        return False", "     seen.add(key)", "-    selection.append(int(ids[j]))", "-    tot += int(ntok[j] * 1.3) + 1", "-    if tot >= BUDGET * 2.5:", "-        break", "+    return True", " ", "+selection, tot, tech_tok = [], 0, 0", "+ti = ri = 0", "+# interleave: pull from tech whenever its running token share is below target", "+while tot < BUDGET * 2.5 and (ti < len(tech_q) or ri < len(rest_q)):", "+    want_tech = (tech_tok < TECH_SHARE * max(1, tot)) and ti < len(tech_q)", "+    if want_tech:", "+        j = tech_q[ti]; ti += 1", "+        if not fresh(j):", "+            continue", "+        selection.append(int(ids[j])); e = est(j); tot += e; tech_tok += e", "+    else:", "+        if ri >= len(rest_q):", "+            if ti >= len(tech_q):", "+                break", "+            continue", "+        j = rest_q[ri]; ri += 1", "+        if not fresh(j):", "+            continue", "+        selection.append(int(ids[j])); tot += est(j)", "+", " json.dump(selection, open(OUT, \"w\"))", "-print(f\"wrote {len(selection)} ids (~{tot} est tokens) -> {OUT}\")", "+print(f\"wrote {len(selection)} ids (~{tot} est tokens, tech~{tech_tok}) -> {OUT}\")", " srt = scores[order]", " print(\"score p99/p50/min-selected:\", float(srt[0]),", "       float(scores[order[len(selection)//2]]))"]}], "userModified": false, "replaceAll": false}

Command running in background with ID: b0vb4wet2. Output is being written to: /tmp/claude-0/-workspace/b3699ac4-1c9f-4c54-b65c-10b5011c1085/tasks/b0vb4wet2.output. You will be notified when it completes. To check interim output, use Read on that file path.
Session cwd remains /workspace; directory changes made by the backgrounded command do not apply to subsequent commands.

[metadata] {"noOutputExpected": false, "backgroundTaskId": "b0vb4wet2", "backgroundCwdHint": "Session cwd remains /workspace; directory changes made by the backgrounded command do not apply to subsequent commands."}

2

[stdout]
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[metadata] {"noOutputExpected": false}