claude-opus-4-8

Command running in background with ID: buh3hn553. Output is being written to: /tmp/claude-0/-workspace/25296ac2-340c-4280-bfe7-333d9d814974/tasks/buh3hn553.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": "buh3hn553", "backgroundCwdHint": "Session cwd remains /workspace; directory changes made by the backgrounded command do not apply to subsequent commands."}

Command did not complete within its 120s timeout and was moved to the background (ID: bnnni0ej7). Output is being written to: /tmp/claude-0/-workspace/25296ac2-340c-4280-bfe7-333d9d814974/tasks/bnnni0ej7.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": "bnnni0ej7", "timedOutAfterMs": 120000, "backgroundCwdHint": "Session cwd remains /workspace; directory changes made by the backgrounded command do not apply to subsequent commands."}

=== gated selection dev_eval ===
HELDOUT_PPL 317.399  loss 5.7602  train_s 54  /tmp/dev_train.npy
=== tech scan ===
---timer---
4058

[stdout]
=== gated selection dev_eval ===
HELDOUT_PPL 317.399  loss 5.7602  train_s 54  /tmp/dev_train.npy
=== tech scan ===
---timer---
4058
[metadata] {"noOutputExpected": false}

total 182016
html/code markers (<pre/<code/&lt;/&gt;): 180
brace/semicolon-dense (code-like): 1736
>=5 strong tech tokens: 5093
(if empty, still running)

[stdout]
total 182016
html/code markers (<pre/<code/&lt;/&gt;): 180
brace/semicolon-dense (code-like): 1736
>=5 strong tech tokens: 5093
(if empty, still running)
[metadata] {"noOutputExpected": false}

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

[metadata] {"filePath": "/workspace/submission/curate.py", "oldString": "    qs_path = f\"{CACHE}/pool_qsig.npz\"\n    if os.path.exists(qs_path):\n        z = np.load(qs_path)\n        nw, stop_frac, ascii_ratio, frac_long, is_html = (\n            z[\"nw\"], z[\"stop_frac\"], z[\"ascii_ratio\"], z[\"frac_long\"], z[\"is_html\"])\n    else:\n        nw = np.zeros(N); stop_frac = np.zeros(N); ascii_ratio = np.zeros(N)\n        frac_long = np.zeros(N); is_html = np.zeros(N, dtype=bool)\n        seg_re = re.compile(r\"[\\n|•·\\t]\")\n        for i, t in enumerate(texts):\n            head = t[:4000]\n            low = head.lower()\n            w = WORD.findall(low); nwi = len(w); nw[i] = nwi\n            if nwi:\n                stop_frac[i] = sum(x in STOP for x in w) / nwi\n            letters = [c for c in head if c.isalpha()]\n            if letters:\n                ascii_ratio[i] = sum(\n                    65 <= ord(c) <= 90 or 97 <= ord(c) <= 122 for c in letters\n                ) / len(letters)\n            segs = seg_re.split(head)\n            sc = [len(WORD.findall(s)) for s in segs]; tot = sum(sc)\n            if tot:\n                frac_long[i] = sum(c for c in sc if c >= 8) / tot\n            is_html[i] = (\"<p>\" in low or \"<pre\" in low or \"<code\" in low\n                          or \"</p>\" in low or \"&lt;\" in low or \"&gt;\" in low)\n        np.savez(qs_path, nw=nw, stop_frac=stop_frac, ascii_ratio=ascii_ratio,\n                 frac_long=frac_long, is_html=is_html)\n\n    prose_ok = (nw >= 50) & (ascii_ratio >= 0.92) & (stop_frac >= 0.22) & (frac_long >= 0.50)\n    code_ok  = is_html & (nw >= 20) & (ascii_ratio >= 0.85)\n    ok = (counts >= MIN_TOKENS) & (prose_ok | code_ok)", "newString": "    TECHTOK = re.compile(r\"\\b(function|import|public|private|void|string|int|class|\"\n                         r\"return|null|var|const|def|self|print|java|python|javascript|\"\n                         r\"sql|select|html|div|span|href|json|array|object|method|query|\"\n                         r\"database|server|error|exception|github|stackoverflow|api|\"\n                         r\"boolean|static|namespace|struct|async|await|lambda)\\b\")\n    qs_path = f\"{CACHE}/pool_qsig2.npz\"\n    if os.path.exists(qs_path):\n        z = np.load(qs_path)\n        nw, stop_frac, ascii_ratio, frac_long, is_tech = (\n            z[\"nw\"], z[\"stop_frac\"], z[\"ascii_ratio\"], z[\"frac_long\"], z[\"is_tech\"])\n    else:\n        nw = np.zeros(N); stop_frac = np.zeros(N); ascii_ratio = np.zeros(N)\n        frac_long = np.zeros(N); is_tech = np.zeros(N, dtype=bool)\n        seg_re = re.compile(r\"[\\n|•·\\t]\")\n        for i, t in enumerate(texts):\n            head = t[:4000]\n            low = head.lower()\n            w = WORD.findall(low); nwi = len(w); nw[i] = nwi\n            if nwi:\n                stop_frac[i] = sum(x in STOP for x in w) / nwi\n            letters = [c for c in head if c.isalpha()]\n            if letters:\n                ascii_ratio[i] = sum(\n                    65 <= ord(c) <= 90 or 97 <= ord(c) <= 122 for c in letters\n                ) / len(letters)\n            segs = seg_re.split(head)\n            sc = [len(WORD.findall(s)) for s in segs]; tot = sum(sc)\n            if tot:\n                frac_long[i] = sum(c for c in sc if c >= 8) / tot\n            html = (\"<p>\" in low or \"<pre\" in low or \"<code\" in low or \"</p>\" in low\n                    or \"&lt;\" in low or \"&gt;\" in low)\n            codey = (head.count(\"{\") + head.count(\"}\") + head.count(\";\")\n                     + head.count(\"<\") + head.count(\">\")) >= len(head) / 60\n            is_tech[i] = html or codey or len(TECHTOK.findall(low)) >= 6\n        np.savez(qs_path, nw=nw, stop_frac=stop_frac, ascii_ratio=ascii_ratio,\n                 frac_long=frac_long, is_tech=is_tech)\n\n    # technical Q&A is very rare as raw HTML in this pool, so we source the whole\n    # technical register from any code/markup/programming-vocab doc (gives the model\n    # the syntax + vocabulary it needs to predict the SO-formatted target).\n    prose_ok = (nw >= 50) & (ascii_ratio >= 0.92) & (stop_frac >= 0.22) & (frac_long >= 0.50)\n    code_ok  = is_tech & (nw >= 20) & (ascii_ratio >= 0.85)\n    ok = (counts >= MIN_TOKENS) & (prose_ok | code_ok)", "originalFile": null, "structuredPatch": [{"oldStart": 178, "oldLines": 14, "newStart": 178, "newLines": 19, "lines": ["     # domain scoring *underperforms* random by selecting long word-dense junk. So we", "     # first require each doc to read like fluent English prose (or, for the technical", "     # register, like real HTML/code Q&A), THEN rank by domain match.", "-    qs_path = f\"{CACHE}/pool_qsig.npz\"", "+    TECHTOK = re.compile(r\"\\b(function|import|public|private|void|string|int|class|\"", "+                         r\"return|null|var|const|def|self|print|java|python|javascript|\"", "+                         r\"sql|select|html|div|span|href|json|array|object|method|query|\"", "+                         r\"database|server|error|exception|github|stackoverflow|api|\"", "+                         r\"boolean|static|namespace|struct|async|await|lambda)\\b\")", "+    qs_path = f\"{CACHE}/pool_qsig2.npz\"", "     if os.path.exists(qs_path):", "         z = np.load(qs_path)", "-        nw, stop_frac, ascii_ratio, frac_long, is_html = (", "-            z[\"nw\"], z[\"stop_frac\"], z[\"ascii_ratio\"], z[\"frac_long\"], z[\"is_html\"])", "+        nw, stop_frac, ascii_ratio, frac_long, is_tech = (", "+            z[\"nw\"], z[\"stop_frac\"], z[\"ascii_ratio\"], z[\"frac_long\"], z[\"is_tech\"])", "     else:", "         nw = np.zeros(N); stop_frac = np.zeros(N); ascii_ratio = np.zeros(N)", "-        frac_long = np.zeros(N); is_html = np.zeros(N, dtype=bool)", "+        frac_long = np.zeros(N); is_tech = np.zeros(N, dtype=bool)", "         seg_re = re.compile(r\"[\\n|•·\\t]\")", "         for i, t in enumerate(texts):", "             head = t[:4000]"]}, {"oldStart": 202, "oldLines": 13, "newStart": 207, "newLines": 19, "lines": ["             sc = [len(WORD.findall(s)) for s in segs]; tot = sum(sc)", "             if tot:", "                 frac_long[i] = sum(c for c in sc if c >= 8) / tot", "-            is_html[i] = (\"<p>\" in low or \"<pre\" in low or \"<code\" in low", "-                          or \"</p>\" in low or \"&lt;\" in low or \"&gt;\" in low)", "+            html = (\"<p>\" in low or \"<pre\" in low or \"<code\" in low or \"</p>\" in low", "+                    or \"&lt;\" in low or \"&gt;\" in low)", "+            codey = (head.count(\"{\") + head.count(\"}\") + head.count(\";\")", "+                     + head.count(\"<\") + head.count(\">\")) >= len(head) / 60", "+            is_tech[i] = html or codey or len(TECHTOK.findall(low)) >= 6", "         np.savez(qs_path, nw=nw, stop_frac=stop_frac, ascii_ratio=ascii_ratio,", "-                 frac_long=frac_long, is_html=is_html)", "+                 frac_long=frac_long, is_tech=is_tech)", " ", "+    # technical Q&A is very rare as raw HTML in this pool, so we source the whole", "+    # technical register from any code/markup/programming-vocab doc (gives the model", "+    # the syntax + vocabulary it needs to predict the SO-formatted target).", "     prose_ok = (nw >= 50) & (ascii_ratio >= 0.92) & (stop_frac >= 0.22) & (frac_long >= 0.50)", "-    code_ok  = is_html & (nw >= 20) & (ascii_ratio >= 0.85)", "+    code_ok  = is_tech & (nw >= 20) & (ascii_ratio >= 0.85)", "     ok = (counts >= MIN_TOKENS) & (prose_ok | code_ok)", " ", "     seen, dup = set(), np.zeros(N, dtype=bool)"]}], "userModified": false, "replaceAll": false}

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

[metadata] {"filePath": "/workspace/submission/curate.py", "oldString": "    # ---- assign each kept doc to its best-matching register, rank within ----\n    stack = np.vstack([scores[r] for r in MIX])          # [3, N]\n    reg_names = list(MIX)\n    best_reg = stack.argmax(0)\n    queues = {}\n    for ri, reg in enumerate(reg_names):\n        cand = np.where(ok & (best_reg == ri))[0]\n        cand = cand[np.argsort(-scores[reg][cand])]      # best first\n        queues[reg] = list(cand)\n    for reg in reg_names:\n        print(f\"    {reg}: {len(queues[reg])} docs\", flush=True)", "newString": "    # ---- assign each kept doc to a register, rank within by domain match ----\n    # tech docs -> techqa; everything else split wiki vs webnews by NB score.\n    reg_names = [\"wiki\", \"webnews\", \"techqa\"]\n    assign = {r: [] for r in reg_names}\n    kept = np.where(ok)[0]\n    for i in kept:\n        if is_tech[i]:\n            assign[\"techqa\"].append(i)\n        elif scores[\"wiki\"][i] >= scores[\"webnews\"][i]:\n            assign[\"wiki\"].append(i)\n        else:\n            assign[\"webnews\"].append(i)\n    queues = {}\n    for reg in reg_names:\n        cand = np.array(assign[reg], dtype=np.int64)\n        cand = cand[np.argsort(-scores[reg][cand])] if len(cand) else cand\n        queues[reg] = list(cand)\n        print(f\"    {reg}: {len(queues[reg])} docs\", flush=True)\n    stack = np.vstack([scores[r] for r in reg_names])", "originalFile": null, "structuredPatch": [{"oldStart": 232, "oldLines": 17, "newStart": 232, "newLines": 25, "lines": ["           f\"(prose {int((prose_ok&~dup).sum())}, code {int((code_ok&~dup).sum())}) \"", "           f\"({time.time()-t0:.0f}s)\", flush=True)", " ", "-    # ---- assign each kept doc to its best-matching register, rank within ----", "-    stack = np.vstack([scores[r] for r in MIX])          # [3, N]", "-    reg_names = list(MIX)", "-    best_reg = stack.argmax(0)", "+    # ---- assign each kept doc to a register, rank within by domain match ----", "+    # tech docs -> techqa; everything else split wiki vs webnews by NB score.", "+    reg_names = [\"wiki\", \"webnews\", \"techqa\"]", "+    assign = {r: [] for r in reg_names}", "+    kept = np.where(ok)[0]", "+    for i in kept:", "+        if is_tech[i]:", "+            assign[\"techqa\"].append(i)", "+        elif scores[\"wiki\"][i] >= scores[\"webnews\"][i]:", "+            assign[\"wiki\"].append(i)", "+        else:", "+            assign[\"webnews\"].append(i)", "     queues = {}", "-    for ri, reg in enumerate(reg_names):", "-        cand = np.where(ok & (best_reg == ri))[0]", "-        cand = cand[np.argsort(-scores[reg][cand])]      # best first", "-        queues[reg] = list(cand)", "     for reg in reg_names:", "+        cand = np.array(assign[reg], dtype=np.int64)", "+        cand = cand[np.argsort(-scores[reg][cand])] if len(cand) else cand", "+        queues[reg] = list(cand)", "         print(f\"    {reg}: {len(queues[reg])} docs\", flush=True)", "+    stack = np.vstack([scores[r] for r in reg_names])", " ", "     # ---- interleave by token quota to match MIX in the consumed budget ----", "     pos = {r: 0 for r in reg_names}"]}], "userModified": false, "replaceAll": false}

Command did not complete within its 120s timeout and was moved to the background (ID: b98w4apuw). Output is being written to: /tmp/claude-0/-workspace/25296ac2-340c-4280-bfe7-333d9d814974/tasks/b98w4apuw.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": "b98w4apuw", "timedOutAfterMs": 120000, "backgroundCwdHint": "Session cwd remains /workspace; directory changes made by the backgrounded command do not apply to subsequent commands."}

loading pool ...
  182016 docs
  tokenized (6s)
  featurized (7s)
  scored register wiki (18s)
  scored register webnews (24s)
  scored register techqa (31s)
  quality gate keeps 132633/182016  (prose 129619, code 6204) (132s)
    wiki: 16973 docs
    webnews: 109464 docs
    techqa: 6196 docs
  balanced-portion tokens by register: {'wiki': 3991752, 'webnews': 7983071, 'techqa': 4025558} sum 16000381
wrote 15091 ids (~24.0M tokens) -> /workspace/submission/selection.json (132s)
---timer---
3778

[stdout]
loading pool ...
  182016 docs
  tokenized (6s)
  featurized (7s)
  scored register wiki (18s)
  scored register webnews (24s)
  scored register techqa (31s)
  quality gate keeps 132633/182016  (prose 129619, code 6204) (132s)
    wiki: 16973 docs
    webnews: 109464 docs
    techqa: 6196 docs
  balanced-portion tokens by register: {'wiki': 3991752, 'webnews': 7983071, 'techqa': 4025558} sum 16000381
wrote 15091 ids (~24.0M tokens) -> /workspace/submission/selection.json (132s)
---timer---
3778
[metadata] {"noOutputExpected": false}

Command running in background with ID: baovil0rh. Output is being written to: /tmp/claude-0/-workspace/25296ac2-340c-4280-bfe7-333d9d814974/tasks/baovil0rh.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": "baovil0rh", "backgroundCwdHint": "Session cwd remains /workspace; directory changes made by the backgrounded command do not apply to subsequent commands."}

-rw-r--r-- 1 root root 221657 Aug  4 04:56 /workspace/artifacts/random_sel.json
-rw-r--r-- 1 root root 108067 Aug  4 05:13 /workspace/artifacts/sel_v3_balanced_techqa.json
backed up

[stdout]
-rw-r--r-- 1 root root 221657 Aug  4 04:56 /workspace/artifacts/random_sel.json
-rw-r--r-- 1 root root 108067 Aug  4 05:13 /workspace/artifacts/sel_v3_balanced_techqa.json
backed up
[metadata] {"noOutputExpected": false}

HELDOUT_PPL 409.990  loss 6.0161  train_s 54  /tmp/dev_train.npy
---timer---
3617

[stdout]
HELDOUT_PPL 409.990  loss 6.0161  train_s 54  /tmp/dev_train.npy
---timer---
3617
[metadata] {"noOutputExpected": false}

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

[metadata] {"filePath": "/workspace/submission/curate.py", "oldString": "    json.dump(order, open(OUT, \"w\"))\n    print(f\"wrote {len(order)} ids (~{bt/1e6:.1f}M tokens) -> {OUT} ({time.time()-t0:.0f}s)\")\n\nif __name__ == \"__main__\":\n    main()", "newString": "    json.dump(order, open(OUT, \"w\"))\n    print(f\"wrote {len(order)} ids (~{bt/1e6:.1f}M tokens) -> {OUT} ({time.time()-t0:.0f}s)\")\n\nif __name__ == \"__main__\":\n    # optional CLI mix override for sweeping: curate.py [wiki web tech] [out.json]\n    av = sys.argv[1:]\n    if len(av) >= 3:\n        MIX = {\"wiki\": float(av[0]), \"webnews\": float(av[1]), \"techqa\": float(av[2])}\n        s = sum(MIX.values()); MIX = {k: v/s for k, v in MIX.items()}\n    if len(av) >= 4:\n        OUT = av[3]\n    main()", "originalFile": null, "structuredPatch": [{"oldStart": 283, "oldLines": 4, "newStart": 283, "newLines": 11, "lines": ["     print(f\"wrote {len(order)} ids (~{bt/1e6:.1f}M tokens) -> {OUT} ({time.time()-t0:.0f}s)\")", " ", " if __name__ == \"__main__\":", "+    # optional CLI mix override for sweeping: curate.py [wiki web tech] [out.json]", "+    av = sys.argv[1:]", "+    if len(av) >= 3:", "+        MIX = {\"wiki\": float(av[0]), \"webnews\": float(av[1]), \"techqa\": float(av[2])}", "+        s = sum(MIX.values()); MIX = {k: v/s for k, v in MIX.items()}", "+    if len(av) >= 4:", "+        OUT = av[3]", "     main()"]}], "userModified": false, "replaceAll": false}

=== v4: 0.30/0.60/0.10 ===
  quality gate keeps 132633/182016  (prose 129619, code 6204) (33s)
  balanced-portion tokens by register: {'wiki': 4796083, 'webnews': 9587781, 'techqa': 1616842} sum 16000706
wrote 15140 ids (~24.0M tokens) -> /workspace/artifacts/sel_v4.json (33s)
=== v6: 0.22/0.68/0.10 ===
  quality gate keeps 132633/182016  (prose 129619, code 6204) (33s)
  balanced-portion tokens by register: {'wiki': 3520502, 'webnews': 10863063, 'techqa': 1616842} sum 16000407
wrote 15282 ids (~24.0M tokens) -> /workspace/artifacts/sel_v6.json (33s)
---timer---
3437

[stdout]
=== v4: 0.30/0.60/0.10 ===
  quality gate keeps 132633/182016  (prose 129619, code 6204) (33s)
  balanced-portion tokens by register: {'wiki': 4796083, 'webnews': 9587781, 'techqa': 1616842} sum 16000706
wrote 15140 ids (~24.0M tokens) -> /workspace/artifacts/sel_v4.json (33s)
=== v6: 0.22/0.68/0.10 ===
  quality gate keeps 132633/182016  (prose 129619, code 6204) (33s)
  balanced-portion tokens by register: {'wiki': 3520502, 'webnews': 10863063, 'techqa': 1616842} sum 16000407
wrote 15282 ids (~24.0M tokens) -> /workspace/artifacts/sel_v6.json (33s)
---timer---
3437
[metadata] {"noOutputExpected": false}

Command running in background with ID: b2fjl50zf. Output is being written to: /tmp/claude-0/-workspace/25296ac2-340c-4280-bfe7-333d9d814974/tasks/b2fjl50zf.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": "b2fjl50zf", "backgroundCwdHint": "Session cwd remains /workspace; directory changes made by the backgrounded command do not apply to subsequent commands."}

--- id 156574 (9752c) ---
 ArchiveRecipes by by Tom Kerridge ArchiveRecipes by by Tom Kerridge ArchiveRecipes by by Tom Kerridge ArchiveRecipes by by Tom Kerridge ArchiveRecipes by by Tom Kerridge ArchiveRecipes by by Tom Kerridge ArchiveRecipes 

--- id 45829 (15349c) ---
„Igowithyoutogether!”Qing Xuzithinksto say!„Master, Ialsotogethergo!”Cheng Yureallywantsto seesmallXin'erearlier, opens the mouthto say.„It is not good! Youcannotgo, suchwill only makecontradictions between ourtwoschools

--- id 124335 (3662c) ---
.<|endoftext|>Prime number 189650000 - 189655000 | Prime-Numbers.org | Prime Numbers: 189650000-189655000 | <Home> <Prev> <Next> | 189650003      189650009      189650057      189650063 | 189650093      189650119      189650177   

--- id 66187 (8918c) ---
 Demand that Israel End Crackdown on Palestinian Protesters | Click here to add Abdallah Abu Rahmah as an alert | Disable alert for Abdallah Abu Rahmah, | Click here to add Adeeb Abu Rahmah as an alert | Disable alert for Adeeb 

--- id 73993 (6242c) ---
|PREDECESSORS AND SHORT HISTORY: | in the 16th century by Rao Gopalji fl.1550, 4th son of Raja Prithviraj | Singhji I of Jaipur. Rulers were… | - Rao GOPAL SINGH, | Thakur Saheb of Chomu fl.1550, married | and had issue. | - Thakur 

--- id 23673 (807c) ---
Lahore:In the prospects of stoned to death incident of a Khanewal Woman, Inspector General of Police Punjab, Mohammad Habib-ur-Rehman has recommended the Government of Punjab for Judicial Inquiry into Maryam Bibi’s case.

--- id 98806 (1226c) ---
 hesiall, Loo. Micks. The ness arou shis. I therrying ot! Mmmend bou. Mays 14 he sairry, by norms nexpren I ateseel ing thaver. Waying aroolvere up thishe lie thought wasse whelf evere voinsits tow? | Eve inde withey crace

--- id 32323 (9848c) ---
<|endoftext|>Wallache leads frequently, his coact mercenariamente. Equatorial and intransigent Hilbert peninsulate his stilettos and cosmetics kilocycle titles. gluteus Darcy subscribes astrogeology effective leadership

[stdout]
--- id 156574 (9752c) ---
 ArchiveRecipes by by Tom Kerridge ArchiveRecipes by by Tom Kerridge ArchiveRecipes by by Tom Kerridge ArchiveRecipes by by Tom Kerridge ArchiveRecipes by by Tom Kerridge ArchiveRecipes by by Tom Kerridge ArchiveRecipes 

--- id 45829 (15349c) ---
„Igowithyoutogether!”Qing Xuzithinksto say!„Master, Ialsotogethergo!”Cheng Yureallywantsto seesmallXin'erearlier, opens the mouthto say.„It is not good! Youcannotgo, suchwill only makecontradictions between ourtwoschools

--- id 124335 (3662c) ---
.<|endoftext|>Prime number 189650000 - 189655000 | Prime-Numbers.org | Prime Numbers: 189650000-189655000 | <Home> <Prev> <Next> | 189650003      189650009      189650057      189650063 | 189650093      189650119      189650177   

--- id 66187 (8918c) ---
 Demand that Israel End Crackdown on Palestinian Protesters | Click here to add Abdallah Abu Rahmah as an alert | Disable alert for Abdallah Abu Rahmah, | Click here to add Adeeb Abu Rahmah as an alert | Disable alert for Adeeb 

--- id 73993 (6242c) ---
|PREDECESSORS AND SHORT HISTORY: | in the 16th century by Rao Gopalji fl.1550, 4th son of Raja Prithviraj | Singhji I of Jaipur. Rulers were… | - Rao GOPAL SINGH, | Thakur Saheb of Chomu fl.1550, married | and had issue. | - Thakur 

--- id 23673 (807c) ---
Lahore:In the prospects of stoned to death incident of a Khanewal Woman, Inspector General of Police Punjab, Mohammad Habib-ur-Rehman has recommended the Government of Punjab for Judicial Inquiry into Maryam Bibi’s case.

--- id 98806 (1226c) ---
 hesiall, Loo. Micks. The ness arou shis. I therrying ot! Mmmend bou. Mays 14 he sairry, by norms nexpren I ateseel ing thaver. Waying aroolvere up thishe lie thought wasse whelf evere voinsits tow? | Eve inde withey crace

--- id 32323 (9848c) ---
<|endoftext|>Wallache leads frequently, his coact mercenariamente. Equatorial and intransigent Hilbert peninsulate his stilettos and cosmetics kilocycle titles. gluteus Darcy subscribes astrogeology effective leadership
[metadata] {"noOutputExpected": false}

still running
---timer---
3347

[stdout]
still running
---timer---
3347
[metadata] {"noOutputExpected": false}

175	    # ---- quality gate + dedup ----
176	    # Random web is full of boilerplate (nav menus, classified ads, link farms) and
177	    # non-English pages; a random draw already contains enough real prose that naive
178	    # domain scoring *underperforms* random by selecting long word-dense junk. So we
179	    # first require each doc to read like fluent English prose (or, for the technical
180	    # register, like real HTML/code Q&A), THEN rank by domain match.
181	    TECHTOK = re.compile(r"\b(function|import|public|private|void|string|int|class|"
182	                         r"return|null|var|const|def|self|print|java|python|javascript|"
183	                         r"sql|select|html|div|span|href|json|array|object|method|query|"
184	                         r"database|server|error|exception|github|stackoverflow|api|"
185	                         r"boolean|static|namespace|struct|async|await|lambda)\b")
186	    qs_path = f"{CACHE}/pool_qsig2.npz"
187	    if os.path.exists(qs_path):
188	        z = np.load(qs_path)
189	        nw, stop_frac, ascii_ratio, frac_long, is_tech = (
190	            z["nw"], z["stop_frac"], z["ascii_ratio"], z["frac_long"], z["is_tech"])
191	    else:
192	        nw = np.zeros(N); stop_frac = np.zeros(N); ascii_ratio = np.zeros(N)
193	        frac_long = np.zeros(N); is_tech = np.zeros(N, dtype=bool)
194	        seg_re = re.compile(r"[\n|•·\t]")
195	        for i, t in enumerate(texts):
196	            head = t[:4000]
197	            low = head.lower()
198	            w = WORD.findall(low); nwi = len(w); nw[i] = nwi
199	            if nwi:
200	                stop_frac[i] = sum(x in STOP for x in w) / nwi
201	            letters = [c for c in head if c.isalpha()]
202	            if letters:
203	                ascii_ratio[i] = sum(
204	                    65 <= ord(c) <= 90 or 97 <= ord(c) <= 122 for c in letters
205	                ) / len(letters)
206	            segs = seg_re.split(head)
207	            sc = [len(WORD.findall(s)) for s in segs]; tot = sum(sc)
208	            if tot:
209	                frac_long[i] = sum(c for c in sc if c >= 8) / tot
210	            html = ("<p>" in low or "<pre" in low or "<code" in low or "</p>" in low
211	                    or "&lt;" in low or "&gt;" in low)
212	            codey = (head.count("{") + head.count("}") + head.count(";")
213	                     + head.count("<") + head.count(">")) >= len(head) / 60
214	            is_tech[i] = html or codey or len(TECHTOK.findall(low)) >= 6
215	        np.savez(qs_path, nw=nw, stop_frac=stop_frac, ascii_ratio=ascii_ratio,
216	                 frac_long=frac_long, is_tech=is_tech)
217	
218	    # technical Q&A is very rare as raw HTML in this pool, so we source the whole
219	    # technical register from any code/markup/programming-vocab doc (gives the model
220	    # the syntax + vocabulary it needs to predict the SO-formatted target).
221	    prose_ok = (nw >= 50) & (ascii_ratio >= 0.92) & (stop_frac >= 0.22) & (frac_long >= 0.50)
222	    code_ok  = is_tech & (nw >= 20) & (ascii_ratio >= 0.85)
223	    ok = (counts >= MIN_TOKENS) & (prose_ok | code_ok)
224	
225	    seen, dup = set(), np.zeros(N, dtype=bool)
226	    for i in range(N):
227	        h = zlib.crc32(norm(texts[i])[:512].encode())
228	        if h in seen: dup[i] = True

[metadata] {"type": "text", "file": {"filePath": "/workspace/submission/curate.py", "content": "    # ---- quality gate + dedup ----\n    # Random web is full of boilerplate (nav menus, classified ads, link farms) and\n    # non-English pages; a random draw already contains enough real prose that naive\n    # domain scoring *underperforms* random by selecting long word-dense junk. So we\n    # first require each doc to read like fluent English prose (or, for the technical\n    # register, like real HTML/code Q&A), THEN rank by domain match.\n    TECHTOK = re.compile(r\"\\b(function|import|public|private|void|string|int|class|\"\n                         r\"return|null|var|const|def|self|print|java|python|javascript|\"\n                         r\"sql|select|html|div|span|href|json|array|object|method|query|\"\n                         r\"database|server|error|exception|github|stackoverflow|api|\"\n                         r\"boolean|static|namespace|struct|async|await|lambda)\\b\")\n    qs_path = f\"{CACHE}/pool_qsig2.npz\"\n    if os.path.exists(qs_path):\n        z = np.load(qs_path)\n        nw, stop_frac, ascii_ratio, frac_long, is_tech = (\n            z[\"nw\"], z[\"stop_frac\"], z[\"ascii_ratio\"], z[\"frac_long\"], z[\"is_tech\"])\n    else:\n        nw = np.zeros(N); stop_frac = np.zeros(N); ascii_ratio = np.zeros(N)\n        frac_long = np.zeros(N); is_tech = np.zeros(N, dtype=bool)\n        seg_re = re.compile(r\"[\\n|•·\\t]\")\n        for i, t in enumerate(texts):\n            head = t[:4000]\n            low = head.lower()\n            w = WORD.findall(low); nwi = len(w); nw[i] = nwi\n            if nwi:\n                stop_frac[i] = sum(x in STOP for x in w) / nwi\n            letters = [c for c in head if c.isalpha()]\n            if letters:\n                ascii_ratio[i] = sum(\n                    65 <= ord(c) <= 90 or 97 <= ord(c) <= 122 for c in letters\n                ) / len(letters)\n            segs = seg_re.split(head)\n            sc = [len(WORD.findall(s)) for s in segs]; tot = sum(sc)\n            if tot:\n                frac_long[i] = sum(c for c in sc if c >= 8) / tot\n            html = (\"<p>\" in low or \"<pre\" in low or \"<code\" in low or \"</p>\" in low\n                    or \"&lt;\" in low or \"&gt;\" in low)\n            codey = (head.count(\"{\") + head.count(\"}\") + head.count(\";\")\n                     + head.count(\"<\") + head.count(\">\")) >= len(head) / 60\n            is_tech[i] = html or codey or len(TECHTOK.findall(low)) >= 6\n        np.savez(qs_path, nw=nw, stop_frac=stop_frac, ascii_ratio=ascii_ratio,\n                 frac_long=frac_long, is_tech=is_tech)\n\n    # technical Q&A is very rare as raw HTML in this pool, so we source the whole\n    # technical register from any code/markup/programming-vocab doc (gives the model\n    # the syntax + vocabulary it needs to predict the SO-formatted target).\n    prose_ok = (nw >= 50) & (ascii_ratio >= 0.92) & (stop_frac >= 0.22) & (frac_long >= 0.50)\n    code_ok  = is_tech & (nw >= 20) & (ascii_ratio >= 0.85)\n    ok = (counts >= MIN_TOKENS) & (prose_ok | code_ok)\n\n    seen, dup = set(), np.zeros(N, dtype=bool)\n    for i in range(N):\n        h = zlib.crc32(norm(texts[i])[:512].encode())\n        if h in seen: dup[i] = True", "numLines": 54, "startLine": 175, "totalLines": 294}}

HELDOUT_PPL 412.153  loss 6.0214  train_s 54  /tmp/dev_train.npy

[stdout]
HELDOUT_PPL 412.153  loss 6.0214  train_s 54  /tmp/dev_train.npy
[metadata] {"noOutputExpected": false}

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

[metadata] {"filePath": "/workspace/submission/curate.py", "oldString": "    # ---- quality gate + dedup ----\n    # Random web is full of boilerplate (nav menus, classified ads, link farms) and\n    # non-English pages; a random draw already contains enough real prose that naive\n    # domain scoring *underperforms* random by selecting long word-dense junk. So we\n    # first require each doc to read like fluent English prose (or, for the technical\n    # register, like real HTML/code Q&A), THEN rank by domain match.\n    TECHTOK = re.compile(r\"\\b(function|import|public|private|void|string|int|class|\"\n                         r\"return|null|var|const|def|self|print|java|python|javascript|\"\n                         r\"sql|select|html|div|span|href|json|array|object|method|query|\"\n                         r\"database|server|error|exception|github|stackoverflow|api|\"\n                         r\"boolean|static|namespace|struct|async|await|lambda)\\b\")\n    qs_path = f\"{CACHE}/pool_qsig2.npz\"\n    if os.path.exists(qs_path):\n        z = np.load(qs_path)\n        nw, stop_frac, ascii_ratio, frac_long, is_tech = (\n            z[\"nw\"], z[\"stop_frac\"], z[\"ascii_ratio\"], z[\"frac_long\"], z[\"is_tech\"])\n    else:\n        nw = np.zeros(N); stop_frac = np.zeros(N); ascii_ratio = np.zeros(N)\n        frac_long = np.zeros(N); is_tech = np.zeros(N, dtype=bool)\n        seg_re = re.compile(r\"[\\n|•·\\t]\")\n        for i, t in enumerate(texts):\n            head = t[:4000]\n            low = head.lower()\n            w = WORD.findall(low); nwi = len(w); nw[i] = nwi\n            if nwi:\n                stop_frac[i] = sum(x in STOP for x in w) / nwi\n            letters = [c for c in head if c.isalpha()]\n            if letters:\n                ascii_ratio[i] = sum(\n                    65 <= ord(c) <= 90 or 97 <= ord(c) <= 122 for c in letters\n                ) / len(letters)\n            segs = seg_re.split(head)\n            sc = [len(WORD.findall(s)) for s in segs]; tot = sum(sc)\n            if tot:\n                frac_long[i] = sum(c for c in sc if c >= 8) / tot\n            html = (\"<p>\" in low or \"<pre\" in low or \"<code\" in low or \"</p>\" in low\n                    or \"&lt;\" in low or \"&gt;\" in low)\n            codey = (head.count(\"{\") + head.count(\"}\") + head.count(\";\")\n                     + head.count(\"<\") + head.count(\">\")) >= len(head) / 60\n            is_tech[i] = html or codey or len(TECHTOK.findall(low)) >= 6\n        np.savez(qs_path, nw=nw, stop_frac=stop_frac, ascii_ratio=ascii_ratio,\n                 frac_long=frac_long, is_tech=is_tech)\n\n    # technical Q&A is very rare as raw HTML in this pool, so we source the whole\n    # technical register from any code/markup/programming-vocab doc (gives the model\n    # the syntax + vocabulary it needs to predict the SO-formatted target).\n    prose_ok = (nw >= 50) & (ascii_ratio >= 0.92) & (stop_frac >= 0.22) & (frac_long >= 0.50)\n    code_ok  = is_tech & (nw >= 20) & (ascii_ratio >= 0.85)\n    ok = (counts >= MIN_TOKENS) & (prose_ok | code_ok)", "newString": "    # ---- quality gate + dedup ----\n    # Random web is full of boilerplate (nav menus, classified ads, link farms),\n    # non-English pages, and machine-generated junk (repetition spam, word-salad SEO,\n    # number dumps, de-spaced MT). A naive domain score actively *prefers* keyword-dense\n    # junk, so it underperforms random. We require each doc to read like fluent English\n    # prose (or, for the rare technical register, real HTML Q&A) BEFORE ranking by match.\n    #\n    # `common`: words occurring in >=50 pool documents ~ real English vocabulary.\n    # Fluency = fraction of a doc's tokens that are common words; gibberish / foreign /\n    # de-spaced / number-dump text scores low. Type-token ratio catches repetition spam.\n    cw_path = f\"{CACHE}/common_words.json\"\n    if os.path.exists(cw_path):\n        common = set(json.load(open(cw_path)))\n    else:\n        from collections import Counter\n        dfc = Counter()\n        for t in texts:\n            dfc.update(set(WORD.findall(t[:3000].lower())))\n        common = {w for w, c in dfc.items() if c >= 50}\n        json.dump(sorted(common), open(cw_path, \"w\"))\n    print(f\"  common vocab {len(common)} words ({time.time()-t0:.0f}s)\", flush=True)\n\n    qs_path = f\"{CACHE}/pool_qsig3.npz\"\n    if os.path.exists(qs_path):\n        z = np.load(qs_path)\n        nw, stop_frac, ascii_ratio, frac_long, frac_common, ttr, is_html = (\n            z[\"nw\"], z[\"stop_frac\"], z[\"ascii_ratio\"], z[\"frac_long\"],\n            z[\"frac_common\"], z[\"ttr\"], z[\"is_html\"])\n    else:\n        nw = np.zeros(N); stop_frac = np.zeros(N); ascii_ratio = np.zeros(N)\n        frac_long = np.zeros(N); frac_common = np.zeros(N); ttr = np.zeros(N)\n        is_html = np.zeros(N, dtype=bool)\n        seg_re = re.compile(r\"[\\n|•·\\t]\")\n        for i, t in enumerate(texts):\n            head = t[:4000]\n            low = head.lower()\n            w = WORD.findall(low)[:400]; nwi = len(w); nw[i] = len(WORD.findall(low))\n            if nwi:\n                stop_frac[i]   = sum(x in STOP for x in w) / nwi\n                frac_common[i] = sum(x in common for x in w) / nwi\n                ttr[i]         = len(set(w)) / nwi\n            letters = [c for c in head if c.isalpha()]\n            if letters:\n                ascii_ratio[i] = sum(\n                    65 <= ord(c) <= 90 or 97 <= ord(c) <= 122 for c in letters\n                ) / len(letters)\n            segs = seg_re.split(head)\n            sc = [len(WORD.findall(s)) for s in segs]; tot = sum(sc)\n            if tot:\n                frac_long[i] = sum(c for c in sc if c >= 8) / tot\n            is_html[i] = (\"<p>\" in low or \"<pre\" in low or \"<code\" in low\n                          or \"</p>\" in low or \"&lt;\" in low or \"&gt;\" in low)\n        np.savez(qs_path, nw=nw, stop_frac=stop_frac, ascii_ratio=ascii_ratio,\n                 frac_long=frac_long, frac_common=frac_common, ttr=ttr, is_html=is_html)\n\n    prose_ok = ((nw >= 50) & (ascii_ratio >= 0.92) & (stop_frac >= 0.22)\n                & (frac_long >= 0.50) & (frac_common >= 0.80) & (ttr >= 0.35))\n    # keep the handful of genuine HTML/SO docs so the technical register has real\n    # in-format examples (code-*dense* docs are deliberately NOT sourced: they proved\n    # to be worse-than-random training data).\n    html_ok = is_html & (nw >= 20) & (ascii_ratio >= 0.85) & (frac_common >= 0.40)\n    ok = (counts >= MIN_TOKENS) & (prose_ok | html_ok)", "originalFile": null, "structuredPatch": [{"oldStart": 173, "oldLines": 31, "newStart": 173, "newLines": 46, "lines": ["         print(f\"  scored register {reg} ({time.time()-t0:.0f}s)\", flush=True)", " ", "     # ---- quality gate + dedup ----", "-    # Random web is full of boilerplate (nav menus, classified ads, link farms) and", "-    # non-English pages; a random draw already contains enough real prose that naive", "-    # domain scoring *underperforms* random by selecting long word-dense junk. So we", "-    # first require each doc to read like fluent English prose (or, for the technical", "-    # register, like real HTML/code Q&A), THEN rank by domain match.", "-    TECHTOK = re.compile(r\"\\b(function|import|public|private|void|string|int|class|\"", "-                         r\"return|null|var|const|def|self|print|java|python|javascript|\"", "-                         r\"sql|select|html|div|span|href|json|array|object|method|query|\"", "-                         r\"database|server|error|exception|github|stackoverflow|api|\"", "-                         r\"boolean|static|namespace|struct|async|await|lambda)\\b\")", "-    qs_path = f\"{CACHE}/pool_qsig2.npz\"", "+    # Random web is full of boilerplate (nav menus, classified ads, link farms),", "+    # non-English pages, and machine-generated junk (repetition spam, word-salad SEO,", "+    # number dumps, de-spaced MT). A naive domain score actively *prefers* keyword-dense", "+    # junk, so it underperforms random. We require each doc to read like fluent English", "+    # prose (or, for the rare technical register, real HTML Q&A) BEFORE ranking by match.", "+    #", "+    # `common`: words occurring in >=50 pool documents ~ real English vocabulary.", "+    # Fluency = fraction of a doc's tokens that are common words; gibberish / foreign /", "+    # de-spaced / number-dump text scores low. Type-token ratio catches repetition spam.", "+    cw_path = f\"{CACHE}/common_words.json\"", "+    if os.path.exists(cw_path):", "+        common = set(json.load(open(cw_path)))", "+    else:", "+        from collections import Counter", "+        dfc = Counter()", "+        for t in texts:", "+            dfc.update(set(WORD.findall(t[:3000].lower())))", "+        common = {w for w, c in dfc.items() if c >= 50}", "+        json.dump(sorted(common), open(cw_path, \"w\"))", "+    print(f\"  common vocab {len(common)} words ({time.time()-t0:.0f}s)\", flush=True)", "+", "+    qs_path = f\"{CACHE}/pool_qsig3.npz\"", "     if os.path.exists(qs_path):", "         z = np.load(qs_path)", "-        nw, stop_frac, ascii_ratio, frac_long, is_tech = (", "-            z[\"nw\"], z[\"stop_frac\"], z[\"ascii_ratio\"], z[\"frac_long\"], z[\"is_tech\"])", "+        nw, stop_frac, ascii_ratio, frac_long, frac_common, ttr, is_html = (", "+            z[\"nw\"], z[\"stop_frac\"], z[\"ascii_ratio\"], z[\"frac_long\"],", "+            z[\"frac_common\"], z[\"ttr\"], z[\"is_html\"])", "     else:", "         nw = np.zeros(N); stop_frac = np.zeros(N); ascii_ratio = np.zeros(N)", "-        frac_long = np.zeros(N); is_tech = np.zeros(N, dtype=bool)", "+        frac_long = np.zeros(N); frac_common = np.zeros(N); ttr = np.zeros(N)", "+        is_html = np.zeros(N, dtype=bool)", "         seg_re = re.compile(r\"[\\n|•·\\t]\")", "         for i, t in enumerate(texts):", "             head = t[:4000]", "             low = head.lower()", "-            w = WORD.findall(low); nwi = len(w); nw[i] = nwi", "+            w = WORD.findall(low)[:400]; nwi = len(w); nw[i] = len(WORD.findall(low))", "             if nwi:", "-                stop_frac[i] = sum(x in STOP for x in w) / nwi", "+                stop_frac[i]   = sum(x in STOP for x in w) / nwi", "+                frac_common[i] = sum(x in common for x in w) / nwi", "+                ttr[i]         = len(set(w)) / nwi", "             letters = [c for c in head if c.isalpha()]", "             if letters:", "                 ascii_ratio[i] = sum("]}, {"oldStart": 207, "oldLines": 20, "newStart": 222, "newLines": 18, "lines": ["             sc = [len(WORD.findall(s)) for s in segs]; tot = sum(sc)", "             if tot:", "                 frac_long[i] = sum(c for c in sc if c >= 8) / tot", "-            html = (\"<p>\" in low or \"<pre\" in low or \"<code\" in low or \"</p>\" in low", "-                    or \"&lt;\" in low or \"&gt;\" in low)", "-            codey = (head.count(\"{\") + head.count(\"}\") + head.count(\";\")", "-                     + head.count(\"<\") + head.count(\">\")) >= len(head) / 60", "-            is_tech[i] = html or codey or len(TECHTOK.findall(low)) >= 6", "+            is_html[i] = (\"<p>\" in low or \"<pre\" in low or \"<code\" in low", "+                          or \"</p>\" in low or \"&lt;\" in low or \"&gt;\" in low)", "         np.savez(qs_path, nw=nw, stop_frac=stop_frac, ascii_ratio=ascii_ratio,", "-                 frac_long=frac_long, is_tech=is_tech)", "+                 frac_long=frac_long, frac_common=frac_common, ttr=ttr, is_html=is_html)", " ", "-    # technical Q&A is very rare as raw HTML in this pool, so we source the whole", "-    # technical register from any code/markup/programming-vocab doc (gives the model", "-    # the syntax + vocabulary it needs to predict the SO-formatted target).", "-    prose_ok = (nw >= 50) & (ascii_ratio >= 0.92) & (stop_frac >= 0.22) & (frac_long >= 0.50)", "-    code_ok  = is_tech & (nw >= 20) & (ascii_ratio >= 0.85)", "-    ok = (counts >= MIN_TOKENS) & (prose_ok | code_ok)", "+    prose_ok = ((nw >= 50) & (ascii_ratio >= 0.92) & (stop_frac >= 0.22)", "+                & (frac_long >= 0.50) & (frac_common >= 0.80) & (ttr >= 0.35))", "+    # keep the handful of genuine HTML/SO docs so the technical register has real", "+    # in-format examples (code-*dense* docs are deliberately NOT sourced: they proved", "+    # to be worse-than-random training data).", "+    html_ok = is_html & (nw >= 20) & (ascii_ratio >= 0.85) & (frac_common >= 0.40)", "+    ok = (counts >= MIN_TOKENS) & (prose_ok | html_ok)", " ", "     seen, dup = set(), np.zeros(N, dtype=bool)", "     for i in range(N):"]}], "userModified": false, "replaceAll": false}

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

[metadata] {"filePath": "/workspace/submission/curate.py", "oldString": "    # ---- assign each kept doc to a register, rank within by domain match ----\n    # tech docs -> techqa; everything else split wiki vs webnews by NB score.\n    reg_names = [\"wiki\", \"webnews\", \"techqa\"]\n    assign = {r: [] for r in reg_names}\n    kept = np.where(ok)[0]\n    for i in kept:\n        if is_tech[i]:\n            assign[\"techqa\"].append(i)\n        elif scores[\"wiki\"][i] >= scores[\"webnews\"][i]:\n            assign[\"wiki\"].append(i)\n        else:\n            assign[\"webnews\"].append(i)\n    queues = {}\n    for reg in reg_names:\n        cand = np.array(assign[reg], dtype=np.int64)\n        cand = cand[np.argsort(-scores[reg][cand])] if len(cand) else cand\n        queues[reg] = list(cand)\n        print(f\"    {reg}: {len(queues[reg])} docs\", flush=True)\n    stack = np.vstack([scores[r] for r in reg_names])", "newString": "    # ---- assign each kept doc to its best-matching register, rank within ----\n    reg_names = [\"wiki\", \"webnews\", \"techqa\"]\n    stack = np.vstack([scores[r] for r in reg_names])     # [3, N]\n    best_reg = stack.argmax(0)\n    queues = {}\n    for ri, reg in enumerate(reg_names):\n        cand = np.where(ok & (best_reg == ri))[0]\n        cand = cand[np.argsort(-scores[reg][cand])]       # best domain match first\n        queues[reg] = list(cand)\n        print(f\"    {reg}: {len(queues[reg])} docs\", flush=True)", "originalFile": null, "structuredPatch": [{"oldStart": 245, "oldLines": 25, "newStart": 245, "newLines": 16, "lines": ["           f\"(prose {int((prose_ok&~dup).sum())}, code {int((code_ok&~dup).sum())}) \"", "           f\"({time.time()-t0:.0f}s)\", flush=True)", " ", "-    # ---- assign each kept doc to a register, rank within by domain match ----", "-    # tech docs -> techqa; everything else split wiki vs webnews by NB score.", "+    # ---- assign each kept doc to its best-matching register, rank within ----", "     reg_names = [\"wiki\", \"webnews\", \"techqa\"]", "-    assign = {r: [] for r in reg_names}", "-    kept = np.where(ok)[0]", "-    for i in kept:", "-        if is_tech[i]:", "-            assign[\"techqa\"].append(i)", "-        elif scores[\"wiki\"][i] >= scores[\"webnews\"][i]:", "-            assign[\"wiki\"].append(i)", "-        else:", "-            assign[\"webnews\"].append(i)", "+    stack = np.vstack([scores[r] for r in reg_names])     # [3, N]", "+    best_reg = stack.argmax(0)", "     queues = {}", "-    for reg in reg_names:", "-        cand = np.array(assign[reg], dtype=np.int64)", "-        cand = cand[np.argsort(-scores[reg][cand])] if len(cand) else cand", "+    for ri, reg in enumerate(reg_names):", "+        cand = np.where(ok & (best_reg == ri))[0]", "+        cand = cand[np.argsort(-scores[reg][cand])]       # best domain match first", "         queues[reg] = list(cand)", "         print(f\"    {reg}: {len(queues[reg])} docs\", flush=True)", "-    stack = np.vstack([scores[r] for r in reg_names])", " ", "     # ---- interleave by token quota to match MIX in the consumed budget ----", "     pos = {r: 0 for r in reg_names}"]}], "userModified": false, "replaceAll": false}

Command did not complete within its 120s timeout and was moved to the background (ID: bpi231vlw). Output is being written to: /tmp/claude-0/-workspace/25296ac2-340c-4280-bfe7-333d9d814974/tasks/bpi231vlw.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": "bpi231vlw", "timedOutAfterMs": 120000, "backgroundCwdHint": "Session cwd remains /workspace; directory changes made by the backgrounded command do not apply to subsequent commands."}

loading pool ...
  182016 docs
  tokenized (6s)
  featurized (7s)
  scored register wiki (17s)
  scored register webnews (24s)
  scored register techqa (30s)
  common vocab 30767 words (45s)
Traceback (most recent call last):
  File "/workspace/submission/curate.py", line 297, in <module>
    main()
  File "/workspace/submission/curate.py", line 245, in main
    f"(prose {int((prose_ok&~dup).sum())}, code {int((code_ok&~dup).sum())}) "
NameError: name 'code_ok' is not defined. Did you mean: 'prose_ok'?
---timer---
3077

[stdout]
loading pool ...
  182016 docs
  tokenized (6s)
  featurized (7s)
  scored register wiki (17s)
  scored register webnews (24s)
  scored register techqa (30s)
  common vocab 30767 words (45s)
Traceback (most recent call last):
  File "/workspace/submission/curate.py", line 297, in <module>
    main()
  File "/workspace/submission/curate.py", line 245, in main
    f"(prose {int((prose_ok&~dup).sum())}, code {int((code_ok&~dup).sum())}) "
NameError: name 'code_ok' is not defined. Did you mean: 'prose_ok'?
---timer---
3077
[metadata] {"noOutputExpected": false}

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

[metadata] {"filePath": "/workspace/submission/curate.py", "oldString": "    print(f\"  quality gate keeps {int(ok.sum())}/{N}  \"\n          f\"(prose {int((prose_ok&~dup).sum())}, code {int((code_ok&~dup).sum())}) \"\n          f\"({time.time()-t0:.0f}s)\", flush=True)", "newString": "    print(f\"  quality gate keeps {int(ok.sum())}/{N}  \"\n          f\"(prose {int((prose_ok&~dup).sum())}, html {int((html_ok&~dup).sum())}) \"\n          f\"({time.time()-t0:.0f}s)\", flush=True)", "originalFile": null, "structuredPatch": [{"oldStart": 242, "oldLines": 7, "newStart": 242, "newLines": 7, "lines": ["         else: seen.add(h)", "     ok &= ~dup", "     print(f\"  quality gate keeps {int(ok.sum())}/{N}  \"", "-          f\"(prose {int((prose_ok&~dup).sum())}, code {int((code_ok&~dup).sum())}) \"", "+          f\"(prose {int((prose_ok&~dup).sum())}, html {int((html_ok&~dup).sum())}) \"", "           f\"({time.time()-t0:.0f}s)\", flush=True)", " ", "     # ---- assign each kept doc to its best-matching register, rank within ----"]}], "userModified": false, "replaceAll": false}

loading pool ...
  182016 docs
  tokenized (6s)
  featurized (8s)
  scored register wiki (19s)
  scored register webnews (25s)
  scored register techqa (32s)
  common vocab 30767 words (32s)
  quality gate keeps 128437/182016  (prose 128364, html 171) (35s)
    wiki: 16930 docs
    webnews: 109469 docs
    techqa: 2038 docs
  balanced-portion tokens by register: {'wiki': 4951801, 'webnews': 9814098, 'techqa': 1235948} sum 16001847
wrote 22854 ids (~24.0M tokens) -> /workspace/submission/selection.json (36s)
=== TOP DOCS ===
--- id 10550 (10130c) ---
The Mitchell Highway is a state highway located in the central and south western regions of Queensland and the northern and central western regions of New South Wales in Australia.

--- id 18495 (3114c) ---
Central Govt. & NDRF ignoring Maharashtra Agrarian crisis-Four more Vidarbha Farmers Suicides in a Day | Dated-15th January 2015 | Maharashtra is grilling under severe drought and stat

--- id 46916 (3469c) ---
camlp5o.opt [--] [ other-options ] | camlp5r.opt [--] [ other-options ] | camlp5o, camlp5r and camlp5sch are versions of camlp5 with some files already loaded (see further). | camlp5o.cm

--- id 81859 (1424c) ---
|Rediff India Abroad Home | All the sections| | Bihar: Vigilante justice resurfaces, three people lynched | February 18, 2008 17:20 IST | Fresh incidents of vigilante justice have been r

--- id 3196 (2140c) ---
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 

--- id 71929 (2871c) ---
IGARH: In a major administrative reshuffle, the Punjab government on Wednesday issued transfer and posting orders of 43 IAS and 38 PCS officers with immediate effect. | Several IAS o

--- id 37064 (1021c) ---
The Bharatiya Janata Party (BJP) leaders of the West Bengal unit will meet Union Home Minister Rajnath Singh on Thursday to lodge protest against the vandalising of its party headq

--- id 124083 (10854c) ---
<|endoftext|>Home | ImageMedia | Home | Latest Reviews | Contact | Image-media.com | Search Preview | Home | ImageMedia | www.image-media.com/ | Anbieter für großformatigen Digitaldruck. Riesenp

--- id 36353 (1228c) ---
New Delhi | ANI: Yes Bank promoter Rana Kapoor informed the Enforcement Directorate (ED) that he was 'forced' by a then Congress Union Minister to buy an MF Hussain painting from P

--- id 58452 (1237c) ---
<|endoftext|>New Delhi: Five new Supreme Court judges were today sworn in, taking the top court’s strength to 28, including the Chief Justice of India. Justices Sanjay Kishan Kaul,

[stdout]
loading pool ...
  182016 docs
  tokenized (6s)
  featurized (8s)
  scored register wiki (19s)
  scored register webnews (25s)
  scored register techqa (32s)
  common vocab 30767 words (32s)
  quality gate keeps 128437/182016  (prose 128364, html 171) (35s)
    wiki: 16930 docs
    webnews: 109469 docs
    techqa: 2038 docs
  balanced-portion tokens by register: {'wiki': 4951801, 'webnews': 9814098, 'techqa': 1235948} sum 16001847
wrote 22854 ids (~24.0M tokens) -> /workspace/submission/selection.json (36s)
=== TOP DOCS ===
--- id 10550 (10130c) ---
The Mitchell Highway is a state highway located in the central and south western regions of Queensland and the northern and central western regions of New South Wales in Australia.

--- id 18495 (3114c) ---
Central Govt. & NDRF ignoring Maharashtra Agrarian crisis-Four more Vidarbha Farmers Suicides in a Day | Dated-15th January 2015 | Maharashtra is grilling under severe drought and stat

--- id 46916 (3469c) ---
camlp5o.opt [--] [ other-options ] | camlp5r.opt [--] [ other-options ] | camlp5o, camlp5r and camlp5sch are versions of camlp5 with some files already loaded (see further). | camlp5o.cm

--- id 81859 (1424c) ---
|Rediff India Abroad Home | All the sections| | Bihar: Vigilante justice resurfaces, three people lynched | February 18, 2008 17:20 IST | Fresh incidents of vigilante justice have been r

--- id 3196 (2140c) ---
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 

--- id 71929 (2871c) ---
IGARH: In a major administrative reshuffle, the Punjab government on Wednesday issued transfer and posting orders of 43 IAS and 38 PCS officers with immediate effect. | Several IAS o

--- id 37064 (1021c) ---
The Bharatiya Janata Party (BJP) leaders of the West Bengal unit will meet Union Home Minister Rajnath Singh on Thursday to lodge protest against the vandalising of its party headq

--- id 124083 (10854c) ---
<|endoftext|>Home | ImageMedia | Home | Latest Reviews | Contact | Image-media.com | Search Preview | Home | ImageMedia | www.image-media.com/ | Anbieter für großformatigen Digitaldruck. Riesenp

--- id 36353 (1228c) ---
New Delhi | ANI: Yes Bank promoter Rana Kapoor informed the Enforcement Directorate (ED) that he was 'forced' by a then Congress Union Minister to buy an MF Hussain painting from P

--- id 58452 (1237c) ---
<|endoftext|>New Delhi: Five new Supreme Court judges were today sworn in, taking the top court’s strength to 28, including the Chief Justice of India. Justices Sanjay Kishan Kaul,
[metadata] {"noOutputExpected": false}

Command running in background with ID: b5a2o034d. Output is being written to: /tmp/claude-0/-workspace/25296ac2-340c-4280-bfe7-333d9d814974/tasks/b5a2o034d.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": "b5a2o034d", "backgroundCwdHint": "Session cwd remains /workspace; directory changes made by the backgrounded command do not apply to subsequent commands."}

still running

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