claude-opus-4-8

5400
---
total 14
drwxr-xr-x 1 root root  100 Aug  4 03:22 .
drwxr-xr-x 1 root root  160 Aug  4 03:22 ..
drwxr-xr-x 1 root root   60 Aug  4 03:22 .live
drwxr-xr-x 1 root root  100 Aug  4 03:22 .metrics
drwxr-xr-x 1 root root  140 Aug  4 03:22 .timer
drwxr-xr-x 1 root root   57 Aug  3 22:37 data
-rwxrwxr-x 1 root root  714 Aug  3 22:34 dev_eval.sh
-rw-rw-r-- 1 root root 5706 Aug  3 22:34 model.py
-rw-rw-r-- 1 root root  869 Aug  3 22:34 pack_selection.py
drwxr-xr-x 1 root root   10 Aug  3 22:37 submission
-rwxrwxr-x 1 root root 1509 Aug  3 22:34 timer.sh
-rw-rw-r-- 1 root root 2962 Aug  3 22:34 train_nano.py
---
total 792657
drwxr-xr-x 1 root root        57 Aug  3 22:37 .
drwxr-xr-x 1 root root       100 Aug  4 03:22 ..
-r--r--r-- 1 root root   2000128 Aug  3 22:34 multi_dev.npy
-r--r--r-- 1 root root 809679384 Aug  3 22:35 pool.jsonl
---
total 1
drwxr-xr-x 1 root root  10 Aug  3 22:37 .
drwxr-xr-x 1 root root 100 Aug  4 03:22 ..
---nvidia---
name, memory.total [MiB], memory.used [MiB]
NVIDIA H200, 143771 MiB, 4 MiB

[stdout]
5400
---
total 14
drwxr-xr-x 1 root root  100 Aug  4 03:22 .
drwxr-xr-x 1 root root  160 Aug  4 03:22 ..
drwxr-xr-x 1 root root   60 Aug  4 03:22 .live
drwxr-xr-x 1 root root  100 Aug  4 03:22 .metrics
drwxr-xr-x 1 root root  140 Aug  4 03:22 .timer
drwxr-xr-x 1 root root   57 Aug  3 22:37 data
-rwxrwxr-x 1 root root  714 Aug  3 22:34 dev_eval.sh
-rw-rw-r-- 1 root root 5706 Aug  3 22:34 model.py
-rw-rw-r-- 1 root root  869 Aug  3 22:34 pack_selection.py
drwxr-xr-x 1 root root   10 Aug  3 22:37 submission
-rwxrwxr-x 1 root root 1509 Aug  3 22:34 timer.sh
-rw-rw-r-- 1 root root 2962 Aug  3 22:34 train_nano.py
---
total 792657
drwxr-xr-x 1 root root        57 Aug  3 22:37 .
drwxr-xr-x 1 root root       100 Aug  4 03:22 ..
-r--r--r-- 1 root root   2000128 Aug  3 22:34 multi_dev.npy
-r--r--r-- 1 root root 809679384 Aug  3 22:35 pool.jsonl
---
total 1
drwxr-xr-x 1 root root  10 Aug  3 22:37 .
drwxr-xr-x 1 root root 100 Aug  4 03:22 ..
---nvidia---
name, memory.total [MiB], memory.used [MiB]
NVIDIA H200, 143771 MiB, 4 MiB
[metadata] {"noOutputExpected": false}

===train_nano.py===
"""From-scratch nano-GPT (30M) LM training on a 1D token .npy; report held-out perplexity.
Frozen recipe — the ONLY thing that varies between runs is the training token set."""
import argparse, math, time, numpy as np, torch
from model import GPT, GPTConfig

ap = argparse.ArgumentParser()
ap.add_argument("--train_npy", required=True)
ap.add_argument("--heldout_npy", required=True)
ap.add_argument("--out_json", required=True)
ap.add_argument("--max_iters", type=int, default=3000)
ap.add_argument("--warmup", type=int, default=150)
ap.add_argument("--batch", type=int, default=32)
ap.add_argument("--block", type=int, default=256)
ap.add_argument("--lr", type=float, default=6e-4)
ap.add_argument("--seed", type=int, default=1337)
a = ap.parse_args()

torch.manual_seed(a.seed); np.random.seed(a.seed)
dev = "cuda"
tr = np.load(a.train_npy); ho = np.load(a.heldout_npy)
tr = torch.from_numpy(tr.astype(np.int64)); ho = torch.from_numpy(ho.astype(np.int64))
rng = np.random.default_rng(a.seed)

model = GPT(GPTConfig(block_size=a.block, vocab_size=50257, n_layer=6, n_head=6,
                      n_embd=384, dropout=0.0, bias=False)).to(dev)
opt = model.configure_optimizers(0.1, a.lr, (0.9, 0.95), "cuda")

def lr_at(it):
    if it < a.warmup: return a.lr * (it + 1) / (a.warmup + 1)
    r = (it - a.warmup) / max(1, a.max_iters - a.warmup)
    return 0.1 * a.lr + 0.5 * (1 + math.cos(math.pi * r)) * (a.lr - 0.1 * a.lr)

def get_batch(src):
    ix = rng.integers(0, len(src) - a.block - 1, size=a.batch)
    x = torch.stack([src[i:i+a.block] for i in ix]).to(dev)
    y = torch.stack([src[i+1:i+1+a.block] for i in ix]).to(dev)
    return x, y

t0 = time.time()
model.train()
for it in range(a.max_iters):
    for g in opt.param_groups: g["lr"] = lr_at(it)
    x, y = get_batch(tr)
    with torch.autocast("cuda", dtype=torch.bfloat16):
        _, loss = model(x, y)
    opt.zero_grad(set_to_none=True); loss.backward()
    torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0); opt.step()
train_s = time.time() - t0

# held-out perplexity: mean next-token CE over non-overlapping windows (window-weighted)
import json
model.eval()
nwin = (len(ho) - 1) // a.block
starts = [j * a.block for j in range(nwin)]
sum_loss, nseen = 0.0, 0
with torch.no_grad():
    for i in range(0, nwin, a.batch):
        bs = starts[i:i+a.batch]
        x = torch.stack([ho[s:s+a.block] for s in bs]).to(dev)
        y = torch.stack([ho[s+1:s+1+a.block] for s in bs]).to(dev)
        with torch.autocast("cuda", dtype=torch.bfloat16):
            _, l = model(x, y)
        sum_loss += l.item() * len(bs); nseen += len(bs)
mean_loss = sum_loss / max(1, nseen)
ppl = math.exp(mean_loss)
out = {"train_npy": a.train_npy, "heldout_ppl": ppl, "mean_loss": mean_loss,
       "max_iters": a.max_iters, "seed": a.seed, "train_s": round(train_s, 1)}
json.dump(out, open(a.out_json, "w"))
print(f"HELDOUT_PPL {ppl:.3f}  loss {mean_loss:.4f}  train_s {train_s:.0f}  {a.train_npy}")
===pack_selection.py===
"""Solver-side: pack a selection.json (ordered ids) into a 12M-token train array from pool.jsonl."""
import json, sys, numpy as np
from transformers import AutoTokenizer

POOL = "/workspace/data/pool.jsonl"
SEL = sys.argv[1] if len(sys.argv) > 1 else "/workspace/submission/selection.json"
OUT = sys.argv[2] if len(sys.argv) > 2 else "/tmp/dev_train.npy"
BUDGET = 12_000_000

tok = AutoTokenizer.from_pretrained("gpt2"); EOS = tok.eos_token_id
text = {}
for line in open(POOL):
    r = json.loads(line); text[r["id"]] = r["text"]
sel = json.load(open(SEL))
parts, tot = [], 0
for i in sel:
    ids = tok(text[i], add_special_tokens=False).input_ids
    parts.extend(ids); parts.append(EOS); tot += len(ids) + 1
    if tot >= BUDGET: break
arr = np.array(parts[:BUDGET], dtype=np.uint16)
np.save(OUT, arr)
print(f"packed {len(arr)} tokens from {len(sel)} ids -> {OUT}")
===dev_eval.sh===
#!/bin/bash
# Diagnostic: train the frozen 30M GPT on your selection and report DEV perplexity.
# Usage: bash dev_eval.sh [selection.json]   (default /workspace/submission/selection.json)
# NOTE: the official target is a DIFFERENT (hidden) sample from the same HQ domain.
set -euo pipefail
export TOKENIZERS_PARALLELISM=false
SEL="${1:-/workspace/submission/selection.json}"
cd /workspace
python3 pack_selection.py "$SEL" /tmp/dev_train.npy
python3 train_nano.py --train_npy /tmp/dev_train.npy --heldout_npy /workspace/data/multi_dev.npy \
    --out_json /tmp/dev.json --max_iters 3000 --seed 1337 2>&1 | grep HELDOUT_PPL
echo "(dev PPL on multi_dev; official uses a hidden disjoint sample of the same HQ domain)"
===timer.sh===
#!/usr/bin/env bash
# Budget timer daemon — writes /workspace/.timer/remaining_secs every 10s so the
# solver can poll time left instead of doing wall-clock arithmetic.
#
# Budget (seconds) comes from $TASK_BUDGET_SECS (set by the runner to the agent
# timeout). Idempotent via a PID file. On ScaleTrain the trial runner starts its
# own timer; this file is the fallback for the Modal/local harbor backends, which
# bootstrap /workspace/timer.sh on environment start. The fallback default below
# (7200 = 2 h) matches this task's [agent] timeout_sec and is only used if
# $TASK_BUDGET_SECS is unset.
set -u
TIMER_DIR="/workspace/.timer"
PID_FILE="$TIMER_DIR/timer.pid"
mkdir -p "$TIMER_DIR"

if [ -s "$PID_FILE" ] && kill -0 "$(cat "$PID_FILE" 2>/dev/null)" 2>/dev/null; then
    exit 0   # already running
fi
echo $$ > "$PID_FILE"
trap 'rm -f "$PID_FILE"' EXIT INT TERM

START=$(date +%s)
BUDGET="${TASK_BUDGET_SECS:-7200}"
echo "$START" > "$TIMER_DIR/start_epoch"
echo "$BUDGET" > "$TIMER_DIR/budget_secs"

while true; do
    NOW=$(date +%s)
    ELAPSED=$((NOW - START))
    REMAINING=$((BUDGET - ELAPSED))
    [ "$REMAINING" -lt 0 ] && REMAINING=0
    echo "$REMAINING" > "$TIMER_DIR/remaining_secs"
    echo "$ELAPSED" > "$TIMER_DIR/elapsed_secs"
    [ "$REMAINING" -le 1800 ] && [ ! -f "$TIMER_DIR/alert_30min" ] && touch "$TIMER_DIR/alert_30min"
    [ "$REMAINING" -le 600 ]  && [ ! -f "$TIMER_DIR/alert_10min" ] && touch "$TIMER_DIR/alert_10min"
    [ "$REMAINING" -le 0 ] && break
    sleep 10
done

[stdout]
===train_nano.py===
"""From-scratch nano-GPT (30M) LM training on a 1D token .npy; report held-out perplexity.
Frozen recipe — the ONLY thing that varies between runs is the training token set."""
import argparse, math, time, numpy as np, torch
from model import GPT, GPTConfig

ap = argparse.ArgumentParser()
ap.add_argument("--train_npy", required=True)
ap.add_argument("--heldout_npy", required=True)
ap.add_argument("--out_json", required=True)
ap.add_argument("--max_iters", type=int, default=3000)
ap.add_argument("--warmup", type=int, default=150)
ap.add_argument("--batch", type=int, default=32)
ap.add_argument("--block", type=int, default=256)
ap.add_argument("--lr", type=float, default=6e-4)
ap.add_argument("--seed", type=int, default=1337)
a = ap.parse_args()

torch.manual_seed(a.seed); np.random.seed(a.seed)
dev = "cuda"
tr = np.load(a.train_npy); ho = np.load(a.heldout_npy)
tr = torch.from_numpy(tr.astype(np.int64)); ho = torch.from_numpy(ho.astype(np.int64))
rng = np.random.default_rng(a.seed)

model = GPT(GPTConfig(block_size=a.block, vocab_size=50257, n_layer=6, n_head=6,
                      n_embd=384, dropout=0.0, bias=False)).to(dev)
opt = model.configure_optimizers(0.1, a.lr, (0.9, 0.95), "cuda")

def lr_at(it):
    if it < a.warmup: return a.lr * (it + 1) / (a.warmup + 1)
    r = (it - a.warmup) / max(1, a.max_iters - a.warmup)
    return 0.1 * a.lr + 0.5 * (1 + math.cos(math.pi * r)) * (a.lr - 0.1 * a.lr)

def get_batch(src):
    ix = rng.integers(0, len(src) - a.block - 1, size=a.batch)
    x = torch.stack([src[i:i+a.block] for i in ix]).to(dev)
    y = torch.stack([src[i+1:i+1+a.block] for i in ix]).to(dev)
    return x, y

t0 = time.time()
model.train()
for it in range(a.max_iters):
    for g in opt.param_groups: g["lr"] = lr_at(it)
    x, y = get_batch(tr)
    with torch.autocast("cuda", dtype=torch.bfloat16):
        _, loss = model(x, y)
    opt.zero_grad(set_to_none=True); loss.backward()
    torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0); opt.step()
train_s = time.time() - t0

# held-out perplexity: mean next-token CE over non-overlapping windows (window-weighted)
import json
model.eval()
nwin = (len(ho) - 1) // a.block
starts = [j * a.block for j in range(nwin)]
sum_loss, nseen = 0.0, 0
with torch.no_grad():
    for i in range(0, nwin, a.batch):
        bs = starts[i:i+a.batch]
        x = torch.stack([ho[s:s+a.block] for s in bs]).to(dev)
        y = torch.stack([ho[s+1:s+1+a.block] for s in bs]).to(dev)
        with torch.autocast("cuda", dtype=torch.bfloat16):
            _, l = model(x, y)
        sum_loss += l.item() * len(bs); nseen += len(bs)
mean_loss = sum_loss / max(1, nseen)
ppl = math.exp(mean_loss)
out = {"train_npy": a.train_npy, "heldout_ppl": ppl, "mean_loss": mean_loss,
       "max_iters": a.max_iters, "seed": a.seed, "train_s": round(train_s, 1)}
json.dump(out, open(a.out_json, "w"))
print(f"HELDOUT_PPL {ppl:.3f}  loss {mean_loss:.4f}  train_s {train_s:.0f}  {a.train_npy}")
===pack_selection.py===
"""Solver-side: pack a selection.json (ordered ids) into a 12M-token train array from pool.jsonl."""
import json, sys, numpy as np
from transformers import AutoTokenizer

POOL = "/workspace/data/pool.jsonl"
SEL = sys.argv[1] if len(sys.argv) > 1 else "/workspace/submission/selection.json"
OUT = sys.argv[2] if len(sys.argv) > 2 else "/tmp/dev_train.npy"
BUDGET = 12_000_000

tok = AutoTokenizer.from_pretrained("gpt2"); EOS = tok.eos_token_id
text = {}
for line in open(POOL):
    r = json.loads(line); text[r["id"]] = r["text"]
sel = json.load(open(SEL))
parts, tot = [], 0
for i in sel:
    ids = tok(text[i], add_special_tokens=False).input_ids
    parts.extend(ids); parts.append(EOS); tot += len(ids) + 1
    if tot >= BUDGET: break
arr = np.array(parts[:BUDGET], dtype=np.uint16)
np.save(OUT, arr)
print(f"packed {len(arr)} tokens from {len(sel)} ids -> {OUT}")
===dev_eval.sh===
#!/bin/bash
# Diagnostic: train the frozen 30M GPT on your selection and report DEV perplexity.
# Usage: bash dev_eval.sh [selection.json]   (default /workspace/submission/selection.json)
# NOTE: the official target is a DIFFERENT (hidden) sample from the same HQ domain.
set -euo pipefail
export TOKENIZERS_PARALLELISM=false
SEL="${1:-/workspace/submission/selection.json}"
cd /workspace
python3 pack_selection.py "$SEL" /tmp/dev_train.npy
python3 train_nano.py --train_npy /tmp/dev_train.npy --heldout_npy /workspace/data/multi_dev.npy \
    --out_json /tmp/dev.json --max_iters 3000 --seed 1337 2>&1 | grep HELDOUT_PPL
echo "(dev PPL on multi_dev; official uses a hidden disjoint sample of the same HQ domain)"
===timer.sh===
#!/usr/bin/env bash
# Budget timer daemon — writes /workspace/.timer/remaining_secs every 10s so the
# solver can poll time left instead of doing wall-clock arithmetic.
#
# Budget (seconds) comes from $TASK_BUDGET_SECS (set by the runner to the agent
# timeout). Idempotent via a PID file. On ScaleTrain the trial runner starts its
# own timer; this file is the fallback for the Modal/local harbor backends, which
# bootstrap /workspace/timer.sh on environment start. The fallback default below
# (7200 = 2 h) matches this task's [agent] timeout_sec and is only used if
# $TASK_BUDGET_SECS is unset.
set -u
TIMER_DIR="/workspace/.timer"
PID_FILE="$TIMER_DIR/timer.pid"
mkdir -p "$TIMER_DIR"

if [ -s "$PID_FILE" ] && kill -0 "$(cat "$PID_FILE" 2>/dev/null)" 2>/dev/null; then
    exit 0   # already running
fi
echo $$ > "$PID_FILE"
trap 'rm -f "$PID_FILE"' EXIT INT TERM

START=$(date +%s)
BUDGET="${TASK_BUDGET_SECS:-7200}"
echo "$START" > "$TIMER_DIR/start_epoch"
echo "$BUDGET" > "$TIMER_DIR/budget_secs"

while true; do
    NOW=$(date +%s)
    ELAPSED=$((NOW - START))
    REMAINING=$((BUDGET - ELAPSED))
    [ "$REMAINING" -lt 0 ] && REMAINING=0
    echo "$REMAINING" > "$TIMER_DIR/remaining_secs"
    echo "$ELAPSED" > "$TIMER_DIR/elapsed_secs"
    [ "$REMAINING" -le 1800 ] && [ ! -f "$TIMER_DIR/alert_30min" ] && touch "$TIMER_DIR/alert_30min"
    [ "$REMAINING" -le 600 ]  && [ ! -f "$TIMER_DIR/alert_10min" ] && touch "$TIMER_DIR/alert_10min"
    [ "$REMAINING" -le 0 ] && break
    sleep 10
done
[metadata] {"noOutputExpected": false}

dev tokens: (1000000,) uint16
eos id 50256 num eos 2704
==== dev sample ====
 Beardmore was sufficiently impressed with Shackleton to offer financial support , but other donations proved hard to come by . Nevertheless , in February 1907 , Shackleton presented to the Royal Geographic Society his plans for an Antarctic expedition , the details of which , under the name British Antarctic Expedition , were published in the Royal Society 's newsletter , Geographic Journal . The aim was the conquest of both the geographical South Pole and the South Magnetic Pole . Shackleton then worked hard to persuade others of his wealthy friends and acquaintances to contribute , including Sir Philip Lee Brocklehurst , who subscribed £ 2 @,@ 000 ( 2011 equivalent £ 157 @,@ 000 ) to secure a place on the expedition ; author Campbell Mackellar ; and Guinness baron Lord Iveagh , whose contribution was secured less than two weeks before the departure of the expedition ship Nimrod . On 4 August 1907 , Shackleton was appointed a Member of the Royal Victorian Order , 4th Class ( MVO ; the present @-@ day grade of Lieutenant ) . 
<|endoftext|> On 1 January 1908 , Nimrod sailed for the Antarctic from Lyttelton Harbour , New Zealand . Shackleton 's original plans had envisaged using the old Discovery base in McMurdo Sound to launch his attempts on the South Pole and South Magnetic Pole . However , before leaving England , he had been pressured to give an undertaking to Scott that he would not base himself in the McMurdo area , which Scott was claiming as his own field of work . Shackleton reluctantly agreed to look for winter quarters at either the Barrier Inlet ( which Discovery had briefly visited in 1902 ) or King Edward VII Land . 
<|endoftext|> To conserve coal , the ship was towed 1 @,@ 650 miles ( 2 @,@ 655 km ) by the steamer Koonya to the Antarctic ice , after Shackleton had persuaded the New Zealand government and the Union Steamship Company to share the cost . In accordance with Shackleton 's promise to Scott , the ship headed for the eastern sector of the Great Ice Barrier , arriving there on 21 January 1908 . They found that the Barrier Inlet had expanded to form a large bay , in which were hundreds of whales , which led to the immediate christening of the area as the Bay of Whales . It was noted that ice conditions were unstable , precluding the establishment of a safe base there . An extended search for an anchorage at King Edward VII Land proved equally fruitless , so Shackleton was forced to break his undertaking to Scott and set sail for McMurdo Sound , a decision which , according to second officer Arthur Harbord , was " dictated by common sense " in view of the difficulties of ice pressure , coal shortage and the lack of any nearer known base . 
<|endoftext|> Nimrod arrived at McMurdo Sound on 29 January , but was stopped by ice 16 miles ( 26 km ) north of Discovery 's old base at Hut Point . After considerable weather delays , Shackleton 's base was eventually established at Cape Royds , about 24 miles ( 39 km ) north of Hut Point . The party was in high spirits , despite the difficult conditions ; Shackleton 's ability to communicate with each man kept the party happy and focused . 
<|endoftext|> The " Great Southern Journey " , as Frank Wild called it , began on 29 October 1908 . On 9 January 1909 , Shackleton and three companions ( Wild , Eric Marshall and Jameson Adams ) reached a new Farthest South latitude of 88 ° 23 ' S , a point only 112 miles ( 180 km ) from the Pole . En route the South Pole party discovered th

[stdout]
dev tokens: (1000000,) uint16
eos id 50256 num eos 2704
==== dev sample ====
 Beardmore was sufficiently impressed with Shackleton to offer financial support , but other donations proved hard to come by . Nevertheless , in February 1907 , Shackleton presented to the Royal Geographic Society his plans for an Antarctic expedition , the details of which , under the name British Antarctic Expedition , were published in the Royal Society 's newsletter , Geographic Journal . The aim was the conquest of both the geographical South Pole and the South Magnetic Pole . Shackleton then worked hard to persuade others of his wealthy friends and acquaintances to contribute , including Sir Philip Lee Brocklehurst , who subscribed £ 2 @,@ 000 ( 2011 equivalent £ 157 @,@ 000 ) to secure a place on the expedition ; author Campbell Mackellar ; and Guinness baron Lord Iveagh , whose contribution was secured less than two weeks before the departure of the expedition ship Nimrod . On 4 August 1907 , Shackleton was appointed a Member of the Royal Victorian Order , 4th Class ( MVO ; the present @-@ day grade of Lieutenant ) . 
<|endoftext|> On 1 January 1908 , Nimrod sailed for the Antarctic from Lyttelton Harbour , New Zealand . Shackleton 's original plans had envisaged using the old Discovery base in McMurdo Sound to launch his attempts on the South Pole and South Magnetic Pole . However , before leaving England , he had been pressured to give an undertaking to Scott that he would not base himself in the McMurdo area , which Scott was claiming as his own field of work . Shackleton reluctantly agreed to look for winter quarters at either the Barrier Inlet ( which Discovery had briefly visited in 1902 ) or King Edward VII Land . 
<|endoftext|> To conserve coal , the ship was towed 1 @,@ 650 miles ( 2 @,@ 655 km ) by the steamer Koonya to the Antarctic ice , after Shackleton had persuaded the New Zealand government and the Union Steamship Company to share the cost . In accordance with Shackleton 's promise to Scott , the ship headed for the eastern sector of the Great Ice Barrier , arriving there on 21 January 1908 . They found that the Barrier Inlet had expanded to form a large bay , in which were hundreds of whales , which led to the immediate christening of the area as the Bay of Whales . It was noted that ice conditions were unstable , precluding the establishment of a safe base there . An extended search for an anchorage at King Edward VII Land proved equally fruitless , so Shackleton was forced to break his undertaking to Scott and set sail for McMurdo Sound , a decision which , according to second officer Arthur Harbord , was " dictated by common sense " in view of the difficulties of ice pressure , coal shortage and the lack of any nearer known base . 
<|endoftext|> Nimrod arrived at McMurdo Sound on 29 January , but was stopped by ice 16 miles ( 26 km ) north of Discovery 's old base at Hut Point . After considerable weather delays , Shackleton 's base was eventually established at Cape Royds , about 24 miles ( 39 km ) north of Hut Point . The party was in high spirits , despite the difficult conditions ; Shackleton 's ability to communicate with each man kept the party happy and focused . 
<|endoftext|> The " Great Southern Journey " , as Frank Wild called it , began on 29 October 1908 . On 9 January 1909 , Shackleton and three companions ( Wild , Eric Marshall and Jameson Adams ) reached a new Farthest South latitude of 88 ° 23 ' S , a point only 112 miles ( 180 km ) from the Pole . En route the South Pole party discovered th
[metadata] {"noOutputExpected": false}

==== dev @ 0.25 ====
Description of a very high speed transit (VHST) system operating in its own rarefied atmosphere in evacuated tubes in underground tunnels. Most cases considered took less time to go coast-to-coast (e.g., 21 min) than it takes an aircraft to climb to an efficient operating altitude. VHST's tubecraft ride on, and are driven by, electromagnetic (EM) waves. In accelerating, it employs the energy of the surrounding EM field; in decelerating, it returns most of this energy to the system. Tunnel systems would be shared by oil, water, and gas pipelines; channels for laser and microwave waveguides; electric power lines including superconducting ones; and freight systems. Environmental and economic benefits are substantial, and the technology for building and operating the system exists.

This report is part of the RAND Corporation paper series. The paper was a product of the RAND Corporation from 1948 to 2003 that captured speeches, memorials, and derivative research, usually prepared on authors' own time and meant to be the scholarly or scientific contribution of individual authors to their professional fields. Papers were less formal than reports and did not require rigorous peer review.

Permission is given to duplicate this electronic document for personal use only, as long as it is unaltered and complete. Copies may not be duplicated for commercial purposes. Unauthorized posting of

==== dev @ 0.5 ====
I fucking hate when he does this shit pic.twitter.com/kpmcHnW4Cz — Miley Ray Cyrus (@MileyCyrus) April 22, 2018
Singer-actress Miley Cyrus has shared a rare video of herself with her partner, actor Liam Hemsworth, on social media. She shared a video of them both in a car, which showed Hemsworth listening to rap music as he drove them to their destination.Cyrus captioned the image: "I f***ing hate it when he does this s**t."In the clip, she was sporting a top which had the word 'Sunday' emblazoned across the front as she sat in the passenger seat. With his music blaring out, she filmed herself dancing and bopping her head to the music. Then all of a sudden, he made Cyrus jump as he suddenly screamed at her, causing to drop her phone in panic.(With IANS inputs)<|endoftext|>Uttar Pradesh chief minister Yogi Adityanath has maintained that 63 children in government-run Gorakhpur hospital died because of their ailments and not oxygen shortage, but grieving parents say their children were fine till the oxygen supply was cut.Some like Mohd Zahid, father of a five-year-old, alleged that BRD hospital authorities refused to declare their children dead, even as their bodies had turned ice cold as this would have further taken up the death toll.Another father said that while his son started bleeding from the nose, the hospital staff dismissed it saying, “Kachra nikal raha hai” (It’s just bo

==== dev @ 0.75 ====
<p>I found the platform module but it says it returns 'Windows' and it's returning 'Microsoft' on my machine. I notice in another thread here on stackoverflow it returns 'Vista' sometimes.</p>
<p>So, the question is, how do implemement?</p>
<pre><code>if is_windows():
  ...
</code></pre>
<p>In a forward compatible way? If I have to check for things like 'Vista' then it will break when the next version of windows comes out.</p>
<hr />
<p>Note: The answers claiming this is a duplicate question do not actually answer the question <code>is_windows</code>. They answer the question &quot;what platform&quot;. Since many flavors of windows exist none of them comprehensively describe how to get an answer of <code>isWindows</code>.</p>

<p>Python <a href="http://docs.python.org/library/os.html" rel="noreferrer">os</a> module</p>

<p>Specifically for Python 3.6/3.7:</p>

<blockquote>
  <p><code>os.name</code>: The name of the operating
  system dependent module imported. The
  following names have currently been
  registered: 'posix', 'nt', 'java'.</p>
</blockquote>

<p>In your case, you want to check for 'nt' as <code>os.name</code> output:</p>

<pre><code>import os

if os.name == 'nt':
     ...
</code></pre>

<p>There is also a note on <code>os.name</

==== dev @ 0.9 ====
 application (not an applet) that needs to access a web service. Proxies for the web service have been generated with JAX-WS, and seem to work fine. In one scenario it needs to talk through a web proxy server (actually Squid 3.0), which is set to require NTLM authentication.</p>

<p>Running on Sun's JRE 1.6.0_14, everything works fine for accessing HTTP URLs, without requiring any changes: the built-in NTLM authenticator kicks in and it all works seemlessly. If, however, the web service URL is a HTTPS URL, the web service call fails deep inside Sun's code:</p>

<pre><code>com.sun.xml.internal.ws.client.ClientTransportException: HTTP transport error: java.lang.NullPointerException
        at com.sun.xml.internal.ws.transport.http.client.HttpClientTransport.getOutput(HttpClientTransport.java:121)
        at com.sun.xml.internal.ws.transport.http.client.HttpTransportPipe.process(HttpTransportPipe.java:142)
        at com.sun.xml.internal.ws.transport.http.client.HttpTransportPipe.processRequest(HttpTransportPipe.java:83)
        at com.sun.xml.internal.ws.transport.DeferredTransportPipe.processRequest(DeferredTransportPipe.java:105)
        at com.sun.xml.internal.ws.api.pipe.Fiber.__doRun(Fiber.java:587)

[stdout]
==== dev @ 0.25 ====
Description of a very high speed transit (VHST) system operating in its own rarefied atmosphere in evacuated tubes in underground tunnels. Most cases considered took less time to go coast-to-coast (e.g., 21 min) than it takes an aircraft to climb to an efficient operating altitude. VHST's tubecraft ride on, and are driven by, electromagnetic (EM) waves. In accelerating, it employs the energy of the surrounding EM field; in decelerating, it returns most of this energy to the system. Tunnel systems would be shared by oil, water, and gas pipelines; channels for laser and microwave waveguides; electric power lines including superconducting ones; and freight systems. Environmental and economic benefits are substantial, and the technology for building and operating the system exists.

This report is part of the RAND Corporation paper series. The paper was a product of the RAND Corporation from 1948 to 2003 that captured speeches, memorials, and derivative research, usually prepared on authors' own time and meant to be the scholarly or scientific contribution of individual authors to their professional fields. Papers were less formal than reports and did not require rigorous peer review.

Permission is given to duplicate this electronic document for personal use only, as long as it is unaltered and complete. Copies may not be duplicated for commercial purposes. Unauthorized posting of

==== dev @ 0.5 ====
I fucking hate when he does this shit pic.twitter.com/kpmcHnW4Cz — Miley Ray Cyrus (@MileyCyrus) April 22, 2018
Singer-actress Miley Cyrus has shared a rare video of herself with her partner, actor Liam Hemsworth, on social media. She shared a video of them both in a car, which showed Hemsworth listening to rap music as he drove them to their destination.Cyrus captioned the image: "I f***ing hate it when he does this s**t."In the clip, she was sporting a top which had the word 'Sunday' emblazoned across the front as she sat in the passenger seat. With his music blaring out, she filmed herself dancing and bopping her head to the music. Then all of a sudden, he made Cyrus jump as he suddenly screamed at her, causing to drop her phone in panic.(With IANS inputs)<|endoftext|>Uttar Pradesh chief minister Yogi Adityanath has maintained that 63 children in government-run Gorakhpur hospital died because of their ailments and not oxygen shortage, but grieving parents say their children were fine till the oxygen supply was cut.Some like Mohd Zahid, father of a five-year-old, alleged that BRD hospital authorities refused to declare their children dead, even as their bodies had turned ice cold as this would have further taken up the death toll.Another father said that while his son started bleeding from the nose, the hospital staff dismissed it saying, “Kachra nikal raha hai” (It’s just bo

==== dev @ 0.75 ====
<p>I found the platform module but it says it returns 'Windows' and it's returning 'Microsoft' on my machine. I notice in another thread here on stackoverflow it returns 'Vista' sometimes.</p>
<p>So, the question is, how do implemement?</p>
<pre><code>if is_windows():
  ...
</code></pre>
<p>In a forward compatible way? If I have to check for things like 'Vista' then it will break when the next version of windows comes out.</p>
<hr />
<p>Note: The answers claiming this is a duplicate question do not actually answer the question <code>is_windows</code>. They answer the question &quot;what platform&quot;. Since many flavors of windows exist none of them comprehensively describe how to get an answer of <code>isWindows</code>.</p>

<p>Python <a href="http://docs.python.org/library/os.html" rel="noreferrer">os</a> module</p>

<p>Specifically for Python 3.6/3.7:</p>

<blockquote>
  <p><code>os.name</code>: The name of the operating
  system dependent module imported. The
  following names have currently been
  registered: 'posix', 'nt', 'java'.</p>
</blockquote>

<p>In your case, you want to check for 'nt' as <code>os.name</code> output:</p>

<pre><code>import os

if os.name == 'nt':
     ...
</code></pre>

<p>There is also a note on <code>os.name</

==== dev @ 0.9 ====
 application (not an applet) that needs to access a web service. Proxies for the web service have been generated with JAX-WS, and seem to work fine. In one scenario it needs to talk through a web proxy server (actually Squid 3.0), which is set to require NTLM authentication.</p>

<p>Running on Sun's JRE 1.6.0_14, everything works fine for accessing HTTP URLs, without requiring any changes: the built-in NTLM authenticator kicks in and it all works seemlessly. If, however, the web service URL is a HTTPS URL, the web service call fails deep inside Sun's code:</p>

<pre><code>com.sun.xml.internal.ws.client.ClientTransportException: HTTP transport error: java.lang.NullPointerException
        at com.sun.xml.internal.ws.transport.http.client.HttpClientTransport.getOutput(HttpClientTransport.java:121)
        at com.sun.xml.internal.ws.transport.http.client.HttpTransportPipe.process(HttpTransportPipe.java:142)
        at com.sun.xml.internal.ws.transport.http.client.HttpTransportPipe.processRequest(HttpTransportPipe.java:83)
        at com.sun.xml.internal.ws.transport.DeferredTransportPipe.processRequest(DeferredTransportPipe.java:105)
        at com.sun.xml.internal.ws.api.pipe.Fiber.__doRun(Fiber.java:587)
[metadata] {"noOutputExpected": false}

total docs sampled loop first 8 shown
==== doc 0 len=414 ====
|Viewing Single Post From: Spoilers for the Week of February 11th| |Lil||Feb 1 2013, 09:58 AM| Don't care about Chloe/Taniel/Jen-Jen. Don't care about Sami, really, but hoping that we get some good "SAMANTHA GENE!!" Marlena Death-Stares out of it. And "newfound" feelings. Please. If only. STEFANO!! STEFANO, STEFANO, STEFANO!!!! :cheer: |Spoilers for the Week of February 11th · DAYS: News, Spoilers & Discussion|

==== doc 1 len=3018 ====
*sigh* Fundamentalist community, let me pass on some advice to you I learned from the atheistic community: If you have set yourself on fire, do not run. Okay? Okay?? Please? Look, D, you had two months to say to Harvard in private emails, "Im sorry, I shouldnt have been using that animation in my paid presentations. I wont use it again. I really do like 'Inner Life', though, and would love to use it in classroom presentations, from the BioVisions site, if that is acceptable." I sat here, for two months, waiting for that to happen, anything to happen, and it didnt. Two months, on your own terms

==== doc 2 len=2825 ====
A novel two-step immunotherapy approach has shown clinically beneficial responses in patients with advanced ovarian cancer. Following Lifestyle Recommendations Reduces Risk of Cancer Death People who follow the diet and lifestyle recommendations laid out by the WCRF and the AICR have a 20 percent reduced risk of dying from cancer. UCSF Launches Social Networking Site for Patients and Families with Hereditary Cancers For Immediate Release May 14, 2013 UCSF Launches Social Networking Site for Patients and Families... Genomic Test May Help Guide Prostate Cancer Treatment The Oncotype DX® Prostate

==== doc 3 len=2467 ====
Free the Cans! Working Together to Reduce Waste In a blog about how people share, it’s worth the occasional reference to the bizarre ways that people DON’T SHARE. Is it safe to say we live in a society that places great value on independence, private property, personal space, and privacy? Even sometimes extreme value? Is that why people at an 8-unit apartment building in Oakland, CA have separate caged stalls for eight separate trash cans? I know it’s not nice to stare, but I walked by these incarcerated cans and could not help myself. I returned with my camera, so that I could share my questi

==== doc 4 len=3303 ====
ORLANDO, Fla. — While the Rapid Recall Exchange, the 2-year-old industry recall portal, has signed up more than 600 manufacturer and retailer subscribers, it still lacks the “critical mass” of suppliers that would make it a primary source of recall information, according to trade association officials and retailers. Manufacturers use the exchange to communicate timely and accurate product recall and withdrawal notifications to retailer and wholesaler headquarters, which in turn share the information with individual stores. The exchange's retail membership represents 85% of U.S. grocery volume 

==== doc 5 len=2744 ====
September 28, 2010 2010 Season - Bowman pulls down CCIW honor |Matt Bowman was named CCIW "Runner of the Week" after his fourth place finish at the Brissman-Lundeen Invitational in Rock Island, Illinois on September 24.| Augustana senior Matt Bowman (Geneva HS, Elburn, Ill.) was selected as the “Runner of the Week” in the College Conference of Illinois & Wisconsin. Bowman’s strong performance helped the Vikings finish second at the Brissman-Lundeen Invitational at Augustana College in Rock Island, Illinois on Saturday, September 24. It was an impressive second place finish for head coach Paul 

==== doc 6 len=1544 ====
Kraft Foods has taken the Cadbury chocolate brand in a new direction, by combining it with cheese for the first time. The company is bringing together two of its brands and launching Philadelphia with Cadbury, a chilled chocolate spread made from Philadelphia Light and Cadbury chocolate. Kraft believes the new product has the potential to do very well and is targeting £10m in sales in the first year. The new cheese and chocolate spread is being launched on 1 February and will be appear in the chilled dairy aisle next to plain Philadelphia Light. It is launching in a 160g tub and a 120g four-pa

==== doc 7 len=417 ====
You must be a registered member to view this page.| If you are already a member, sign in now. To register for your own account, sign up now. Signing up will REMOVE MOST OF THE ANNOYING ADS from your screen. CLICK HERE TO CREATE YOUR ACCOUNT - Get advice - Make friends - Share your expertise - Post in our forums - Send private messages - Join interest groups - Be a community leader - Track your mood - Upload photos

[stdout]
total docs sampled loop first 8 shown
==== doc 0 len=414 ====
|Viewing Single Post From: Spoilers for the Week of February 11th| |Lil||Feb 1 2013, 09:58 AM| Don't care about Chloe/Taniel/Jen-Jen. Don't care about Sami, really, but hoping that we get some good "SAMANTHA GENE!!" Marlena Death-Stares out of it. And "newfound" feelings. Please. If only. STEFANO!! STEFANO, STEFANO, STEFANO!!!! :cheer: |Spoilers for the Week of February 11th · DAYS: News, Spoilers & Discussion|

==== doc 1 len=3018 ====
*sigh* Fundamentalist community, let me pass on some advice to you I learned from the atheistic community: If you have set yourself on fire, do not run. Okay? Okay?? Please? Look, D, you had two months to say to Harvard in private emails, "Im sorry, I shouldnt have been using that animation in my paid presentations. I wont use it again. I really do like 'Inner Life', though, and would love to use it in classroom presentations, from the BioVisions site, if that is acceptable." I sat here, for two months, waiting for that to happen, anything to happen, and it didnt. Two months, on your own terms

==== doc 2 len=2825 ====
A novel two-step immunotherapy approach has shown clinically beneficial responses in patients with advanced ovarian cancer. Following Lifestyle Recommendations Reduces Risk of Cancer Death People who follow the diet and lifestyle recommendations laid out by the WCRF and the AICR have a 20 percent reduced risk of dying from cancer. UCSF Launches Social Networking Site for Patients and Families with Hereditary Cancers For Immediate Release May 14, 2013 UCSF Launches Social Networking Site for Patients and Families... Genomic Test May Help Guide Prostate Cancer Treatment The Oncotype DX® Prostate

==== doc 3 len=2467 ====
Free the Cans! Working Together to Reduce Waste In a blog about how people share, it’s worth the occasional reference to the bizarre ways that people DON’T SHARE. Is it safe to say we live in a society that places great value on independence, private property, personal space, and privacy? Even sometimes extreme value? Is that why people at an 8-unit apartment building in Oakland, CA have separate caged stalls for eight separate trash cans? I know it’s not nice to stare, but I walked by these incarcerated cans and could not help myself. I returned with my camera, so that I could share my questi

==== doc 4 len=3303 ====
ORLANDO, Fla. — While the Rapid Recall Exchange, the 2-year-old industry recall portal, has signed up more than 600 manufacturer and retailer subscribers, it still lacks the “critical mass” of suppliers that would make it a primary source of recall information, according to trade association officials and retailers. Manufacturers use the exchange to communicate timely and accurate product recall and withdrawal notifications to retailer and wholesaler headquarters, which in turn share the information with individual stores. The exchange's retail membership represents 85% of U.S. grocery volume 

==== doc 5 len=2744 ====
September 28, 2010 2010 Season - Bowman pulls down CCIW honor |Matt Bowman was named CCIW "Runner of the Week" after his fourth place finish at the Brissman-Lundeen Invitational in Rock Island, Illinois on September 24.| Augustana senior Matt Bowman (Geneva HS, Elburn, Ill.) was selected as the “Runner of the Week” in the College Conference of Illinois & Wisconsin. Bowman’s strong performance helped the Vikings finish second at the Brissman-Lundeen Invitational at Augustana College in Rock Island, Illinois on Saturday, September 24. It was an impressive second place finish for head coach Paul 

==== doc 6 len=1544 ====
Kraft Foods has taken the Cadbury chocolate brand in a new direction, by combining it with cheese for the first time. The company is bringing together two of its brands and launching Philadelphia with Cadbury, a chilled chocolate spread made from Philadelphia Light and Cadbury chocolate. Kraft believes the new product has the potential to do very well and is targeting £10m in sales in the first year. The new cheese and chocolate spread is being launched on 1 February and will be appear in the chilled dairy aisle next to plain Philadelphia Light. It is launching in a 160g tub and a 120g four-pa

==== doc 7 len=417 ====
You must be a registered member to view this page.| If you are already a member, sign in now. To register for your own account, sign up now. Signing up will REMOVE MOST OF THE ANNOYING ADS from your screen. CLICK HERE TO CREATE YOUR ACCOUNT - Get advice - Make friends - Share your expertise - Post in our forums - Send private messages - Join interest groups - Be a community leader - Track your mood - Upload photos
[metadata] {"noOutputExpected": false}

Task #1 created successfully: Build classifier-based curation

[metadata] {"task": {"id": "1", "subject": "Build classifier-based curation"}}

Task #2 created successfully: Evaluate selection via dev_eval

[metadata] {"task": {"id": "2", "subject": "Evaluate selection via dev_eval"}}

Task #3 created successfully: Write deliverables

[metadata] {"task": {"id": "3", "subject": "Write deliverables"}}

Updated task #1 status

[metadata] {"success": true, "taskId": "1", "updatedFields": ["status"], "statusChange": {"from": "pending", "to": "in_progress"}}

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

[metadata] {"type": "create", "filePath": "/workspace/submission/curate.py", "content": "\"\"\"Curate a raw web pool for small-LM pretraining toward a BROAD multi-domain\nhigh-quality English target (encyclopedic / web prose / news / technical Q&A).\n\nStated criterion\n----------------\nSelect the pool documents that are most *target-like* under a quality classifier,\nsubject to light junk gating. The classifier is a TF-IDF (word 1-2gram +\nchar 3-5gram) logistic regression trained to separate:\n  positive := chunks of the disclosed target distribution (data/multi_dev.npy,\n              GPT-2 tokens decoded back to text, split on <|endoftext|>)\n  negative := a random sample of the raw pool (the \"do-nothing\" distribution)\nDocuments are ranked by P(target). We emit ids in descending score (best first);\nthe frozen pipeline consumes them in priority order until the 12M-token budget\nis full. This is DSIR/quality-classifier-style curation: pick pool docs whose\nregister+vocabulary match the target, and are clean enough to be low-entropy.\n\nReproducible: run `python3 curate.py` -> writes submission/selection.json.\n\"\"\"\nimport json, re, sys, numpy as np\nfrom transformers import AutoTokenizer\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.linear_model import LogisticRegression\nfrom scipy.sparse import hstack\n\nPOOL = \"/workspace/data/pool.jsonl\"\nDEV  = \"/workspace/data/multi_dev.npy\"\nOUT  = \"/workspace/submission/selection.json\"\nSEED = 0\nN_NEG = 12000          # random pool docs as negatives\nN_EMIT = 60000         # emit far more ids than the budget needs (~well over 12M tokens)\n\nrng = np.random.default_rng(SEED)\n\n# ---------- 1. positives: decode the disclosed target back to text ----------\ntok = AutoTokenizer.from_pretrained(\"gpt2\")\nEOS = tok.eos_token_id\ndev = np.load(DEV).astype(np.int64)\npos_texts = []\ncur = []\nfor t in dev:\n    if t == EOS:\n        if cur:\n            pos_texts.append(tok.decode(cur))\n        cur = []\n    else:\n        cur.append(int(t))\nif cur:\n    pos_texts.append(tok.decode(cur))\n\n# strip WikiText-specific surface artifacts so the classifier learns register,\n# not tokenization quirks the pool can't contain.\ndef deartifact(s):\n    s = s.replace(\" @,@ \", \",\").replace(\" @-@ \", \"-\").replace(\" @.@ \", \".\")\n    return s\npos_texts = [deartifact(s) for s in pos_texts if len(s) > 200]\n\n# ---------- 2. load pool ----------\nids, texts = [], []\nwith open(POOL) as f:\n    for line in f:\n        r = json.loads(line)\n        ids.append(r[\"id\"]); texts.append(r[\"text\"])\nids = np.array(ids)\nN = len(ids)\nprint(f\"pool docs: {N}  positives: {len(pos_texts)}\", file=sys.stderr)\n\n# ---------- 3. negatives: random pool sample ----------\nneg_idx = rng.choice(N, size=min(N_NEG, N), replace=False)\nneg_texts = [texts[i] for i in neg_idx]\n\n# ---------- 4. build TF-IDF features (word + char) and train logreg ----------\ntrain_texts = pos_texts + neg_texts\ny = np.r_[np.ones(len(pos_texts)), np.zeros(len(neg_texts))]\n\nword_vec = TfidfVectorizer(lowercase=True, ngram_range=(1, 2), min_df=3,\n                           max_features=80000, sublinear_tf=True,\n                           strip_accents=\"unicode\")\nchar_vec = TfidfVectorizer(lowercase=True, analyzer=\"char_wb\", ngram_range=(3, 5),\n                           min_df=5, max_features=80000, sublinear_tf=True)\nXw = word_vec.fit_transform(train_texts)\nXc = char_vec.fit_transform(train_texts)\nX = hstack([Xw, Xc]).tocsr()\nclf = LogisticRegression(C=1.0, max_iter=400, class_weight=\"balanced\")\nclf.fit(X, y)\nprint(\"train acc:\", clf.score(X, y), file=sys.stderr)\n\n# ---------- 5. score the whole pool in batches ----------\nscores = np.empty(N, dtype=np.float32)\nB = 20000\nfor s in range(0, N, B):\n    e = min(N, s + B)\n    xw = word_vec.transform(texts[s:e])\n    xc = char_vec.transform(texts[s:e])\n    xb = hstack([xw, xc]).tocsr()\n    scores[s:e] = clf.decision_function(xb)\n\n# ---------- 6. light junk gating ----------\n# Reject docs that are too short (little signal / mostly boilerplate) or that look\n# like pure link/menu spam. Kept intentionally light — the classifier does the work.\ndef junk(t):\n    n = len(t)\n    if n < 300:\n        return True\n    # ratio of non-alnum-space \"symbol\" noise\n    letters = sum(c.isalpha() for c in t)\n    if letters / n < 0.55:\n        return True\n    return False\n\ngate = np.array([not junk(t) for t in texts])\nscores_eff = np.where(gate, scores, -1e9)\n\n# ---------- 7. emit ids by descending score ----------\norder = np.argsort(-scores_eff)\nsel = [int(ids[i]) for i in order[:N_EMIT]]\njson.dump(sel, open(OUT, \"w\"))\n\n# persist scores for analysis / re-ranking\nnp.save(\"/tmp/pool_scores.npy\", scores)\nnp.save(\"/tmp/pool_ids.npy\", ids)\nprint(f\"wrote {len(sel)} ids -> {OUT}\", file=sys.stderr)\nprint(\"score pctiles:\", np.percentile(scores, [50, 90, 99, 99.9]), file=sys.stderr)\n", "structuredPatch": [], "originalFile": null, "userModified": false}

Traceback (most recent call last):
  File "/workspace/submission/curate.py", line 21, in <module>
    from sklearn.feature_extraction.text import TfidfVectorizer
ModuleNotFoundError: No module named 'sklearn'

real	0m4.175s
user	0m4.750s
sys	0m1.130s

[stdout]
Traceback (most recent call last):
  File "/workspace/submission/curate.py", line 21, in <module>
    from sklearn.feature_extraction.text import TfidfVectorizer
ModuleNotFoundError: No module named 'sklearn'

real	0m4.175s
user	0m4.750s
sys	0m1.130s
[metadata] {"noOutputExpected": false}

Command did not complete within its 120s timeout and was moved to the background (ID: b1fqx7jh5). Output is being written to: /tmp/claude-0/-workspace/c504c517-4369-451f-ac2a-a4737203bba0/tasks/b1fqx7jh5.output. You will be notified when it completes. To check interim output, use Read on that file path.

[metadata] {"noOutputExpected": false, "backgroundTaskId": "b1fqx7jh5", "timedOutAfterMs": 120000}

---check---
ModuleNotFoundError: No module named 'sklearn'

[stdout]
---check---
ModuleNotFoundError: No module named 'sklearn'
[metadata] {"noOutputExpected": false}

ModuleNotFoundError: No module named 'scipy'
numpy 1.26.4
torch 2.8.0+cu128
---pip cache---

[stdout]
ModuleNotFoundError: No module named 'scipy'
numpy 1.26.4
torch 2.8.0+cu128
---pip cache---
[metadata] {"returnCodeInterpretation": "No matches found", "noOutputExpected": false}

(Bash completed with no output)

[metadata] {"noOutputExpected": false}

Command did not complete within its 120s timeout and was moved to the background (ID: b1g5nw06k). Output is being written to: /tmp/claude-0/-workspace/c504c517-4369-451f-ac2a-a4737203bba0/tasks/b1g5nw06k.output. You will be notified when it completes. To check interim output, use Read on that file path.

[metadata] {"noOutputExpected": false, "backgroundTaskId": "b1g5nw06k", "timedOutAfterMs": 120000}

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

[metadata] {"type": "update", "filePath": "/workspace/submission/curate.py", "content": "\"\"\"Curate a raw web pool for small-LM pretraining toward a BROAD multi-domain\nhigh-quality English target (encyclopedic / web prose / news / technical Q&A).\n\nStated criterion\n----------------\nSelect the pool documents most *target-like* under a quality classifier, subject\nto light junk gating. The classifier is a feature-hashed bag-of-ngrams logistic\nregression (implemented as a torch EmbeddingBag on GPU, no sklearn dependency)\ntrained to separate:\n  positive := chunks of the disclosed target distribution (data/multi_dev.npy,\n              GPT-2 tokens decoded to text, split on <|endoftext|>)\n  negative := a random sample of the raw pool (the \"do-nothing\" distribution)\nEach pool doc is scored by the classifier logit (length-normalized via mean-pool);\nwe emit ids in descending score (best first). The frozen pipeline consumes them in\npriority order until the 12M-token budget is full. This is quality-classifier /\nDSIR-style curation: keep pool docs whose register + vocabulary match the target\nand that are clean enough to be low-entropy for the LM.\n\nReproducible: `python3 curate.py` -> writes submission/selection.json.\n\"\"\"\nimport json, re, sys, numpy as np, torch\nfrom transformers import AutoTokenizer\n\nPOOL = \"/workspace/data/pool.jsonl\"\nDEV  = \"/workspace/data/multi_dev.npy\"\nOUT  = \"/workspace/submission/selection.json\"\nSEED = 0\nN_NEG = 15000          # random pool docs as negatives\nN_EMIT = 60000         # emit far more ids than the 12M-token budget needs\nD = 1 << 20            # hashed feature buckets\nEPOCHS = 60\ndev_t = \"cuda\" if torch.cuda.is_available() else \"cpu\"\ntorch.manual_seed(SEED); rng = np.random.default_rng(SEED)\n\n_word = re.compile(r\"[a-z0-9']+\")\ndef ngram_buckets(text):\n    \"\"\"Hash word unigrams + bigrams to buckets in [0, D).\"\"\"\n    toks = _word.findall(text.lower())\n    if not toks:\n        return [0]\n    out = [(hash(t) & (D - 1)) for t in toks]\n    for i in range(len(toks) - 1):\n        out.append((hash(toks[i] + \" \" + toks[i + 1]) & (D - 1)))\n    return out\n\n# ---------- 1. positives: decode the disclosed target back to text ----------\ntok = AutoTokenizer.from_pretrained(\"gpt2\")\nEOS = tok.eos_token_id\ndev = np.load(DEV).astype(np.int64)\npos_texts, cur = [], []\nfor t in dev:\n    if t == EOS:\n        if cur: pos_texts.append(tok.decode(cur))\n        cur = []\n    else:\n        cur.append(int(t))\nif cur: pos_texts.append(tok.decode(cur))\n\ndef deartifact(s):  # strip WikiText tokenization quirks the pool can't contain\n    return s.replace(\" @,@ \", \",\").replace(\" @-@ \", \"-\").replace(\" @.@ \", \".\")\npos_texts = [deartifact(s) for s in pos_texts if len(s) > 200]\n\n# ---------- 2. load pool ----------\nids, texts = [], []\nwith open(POOL) as f:\n    for line in f:\n        r = json.loads(line)\n        ids.append(r[\"id\"]); texts.append(r[\"text\"])\nids = np.array(ids); N = len(ids)\nprint(f\"pool docs: {N}  positives: {len(pos_texts)}\", file=sys.stderr)\n\n# ---------- 3. negatives: random pool sample ----------\nneg_idx = rng.choice(N, size=min(N_NEG, N), replace=False)\nneg_texts = [texts[i] for i in neg_idx]\n\n# ---------- 4. featurize + train logistic regression (EmbeddingBag on GPU) ----------\ndef build_bag(text_list):\n    flat, offsets, off = [], [], 0\n    for t in text_list:\n        b = ngram_buckets(t)\n        offsets.append(off); flat.extend(b); off += len(b)\n    return (torch.tensor(flat, dtype=torch.long, device=dev_t),\n            torch.tensor(offsets, dtype=torch.long, device=dev_t))\n\ntrain_texts = pos_texts + neg_texts\ny = torch.tensor([1.0] * len(pos_texts) + [0.0] * len(neg_texts), device=dev_t)\ntin, toff = build_bag(train_texts)\n\nemb = torch.nn.EmbeddingBag(D, 1, mode=\"mean\").to(dev_t)\ntorch.nn.init.zeros_(emb.weight)\nbias = torch.zeros(1, requires_grad=True, device=dev_t)\nopt = torch.optim.Adam(list(emb.parameters()) + [bias], lr=0.05, weight_decay=1e-6)\n# class-balance weighting\nw_pos = len(train_texts) / (2 * len(pos_texts)); w_neg = len(train_texts) / (2 * len(neg_texts))\nwt = torch.where(y > 0.5, torch.tensor(w_pos, device=dev_t), torch.tensor(w_neg, device=dev_t))\nfor ep in range(EPOCHS):\n    opt.zero_grad()\n    logit = emb(tin, toff).squeeze(1) + bias\n    loss = torch.nn.functional.binary_cross_entropy_with_logits(logit, y, weight=wt)\n    loss.backward(); opt.step()\nwith torch.no_grad():\n    pred = (torch.sigmoid(emb(tin, toff).squeeze(1) + bias) > 0.5).float()\n    acc = (pred == y).float().mean().item()\nprint(f\"train loss {loss.item():.4f}  acc {acc:.3f}\", file=sys.stderr)\n\n# ---------- 5. score whole pool ----------\nscores = np.empty(N, dtype=np.float32)\nB = 8000\nemb.eval()\nwith torch.no_grad():\n    for s in range(0, N, B):\n        e = min(N, s + B)\n        bin_, boff = build_bag(texts[s:e])\n        scores[s:e] = (emb(bin_, boff).squeeze(1) + bias).cpu().numpy()\n\n# ---------- 6. light junk gating ----------\ndef junk(t):\n    n = len(t)\n    if n < 300: return True\n    if sum(c.isalpha() for c in t) / n < 0.55: return True\n    return False\ngate = np.array([not junk(t) for t in texts])\nscores_eff = np.where(gate, scores, -1e9)\n\n# ---------- 7. emit ids by descending score ----------\norder = np.argsort(-scores_eff)\nsel = [int(ids[i]) for i in order[:N_EMIT]]\njson.dump(sel, open(OUT, \"w\"))\nnp.save(\"/tmp/pool_scores.npy\", scores); np.save(\"/tmp/pool_ids.npy\", ids)\nprint(f\"wrote {len(sel)} ids -> {OUT}\", file=sys.stderr)\nprint(\"kept by gate:\", int(gate.sum()), \"/\", N, file=sys.stderr)\nprint(\"score pctiles:\", np.percentile(scores, [50, 90, 99, 99.9]), file=sys.stderr)\n", "structuredPatch": [{"oldStart": 3, "oldLines": 55, "newStart": 3, "newLines": 61, "lines": [" ", " Stated criterion", " ----------------", "-Select the pool documents that are most *target-like* under a quality classifier,", "-subject to light junk gating. The classifier is a TF-IDF (word 1-2gram +", "-char 3-5gram) logistic regression trained to separate:", "+Select the pool documents most *target-like* under a quality classifier, subject", "+to light junk gating. The classifier is a feature-hashed bag-of-ngrams logistic", "+regression (implemented as a torch EmbeddingBag on GPU, no sklearn dependency)", "+trained to separate:", "   positive := chunks of the disclosed target distribution (data/multi_dev.npy,", "-              GPT-2 tokens decoded back to text, split on <|endoftext|>)", "+              GPT-2 tokens decoded to text, split on <|endoftext|>)", "   negative := a random sample of the raw pool (the \"do-nothing\" distribution)", "-Documents are ranked by P(target). We emit ids in descending score (best first);", "-the frozen pipeline consumes them in priority order until the 12M-token budget", "-is full. This is DSIR/quality-classifier-style curation: pick pool docs whose", "-register+vocabulary match the target, and are clean enough to be low-entropy.", "+Each pool doc is scored by the classifier logit (length-normalized via mean-pool);", "+we emit ids in descending score (best first). The frozen pipeline consumes them in", "+priority order until the 12M-token budget is full. This is quality-classifier /", "+DSIR-style curation: keep pool docs whose register + vocabulary match the target", "+and that are clean enough to be low-entropy for the LM.", " ", "-Reproducible: run `python3 curate.py` -> writes submission/selection.json.", "+Reproducible: `python3 curate.py` -> writes submission/selection.json.", " \"\"\"", "-import json, re, sys, numpy as np", "+import json, re, sys, numpy as np, torch", " from transformers import AutoTokenizer", "-from sklearn.feature_extraction.text import TfidfVectorizer", "-from sklearn.linear_model import LogisticRegression", "-from scipy.sparse import hstack", " ", " POOL = \"/workspace/data/pool.jsonl\"", " DEV  = \"/workspace/data/multi_dev.npy\"", " OUT  = \"/workspace/submission/selection.json\"", " SEED = 0", "-N_NEG = 12000          # random pool docs as negatives", "-N_EMIT = 60000         # emit far more ids than the budget needs (~well over 12M tokens)", "+N_NEG = 15000          # random pool docs as negatives", "+N_EMIT = 60000         # emit far more ids than the 12M-token budget needs", "+D = 1 << 20            # hashed feature buckets", "+EPOCHS = 60", "+dev_t = \"cuda\" if torch.cuda.is_available() else \"cpu\"", "+torch.manual_seed(SEED); rng = np.random.default_rng(SEED)", " ", "-rng = np.random.default_rng(SEED)", "+_word = re.compile(r\"[a-z0-9']+\")", "+def ngram_buckets(text):", "+    \"\"\"Hash word unigrams + bigrams to buckets in [0, D).\"\"\"", "+    toks = _word.findall(text.lower())", "+    if not toks:", "+        return [0]", "+    out = [(hash(t) & (D - 1)) for t in toks]", "+    for i in range(len(toks) - 1):", "+        out.append((hash(toks[i] + \" \" + toks[i + 1]) & (D - 1)))", "+    return out", " ", " # ---------- 1. positives: decode the disclosed target back to text ----------", " tok = AutoTokenizer.from_pretrained(\"gpt2\")", " EOS = tok.eos_token_id", " dev = np.load(DEV).astype(np.int64)", "-pos_texts = []", "-cur = []", "+pos_texts, cur = [], []", " for t in dev:", "     if t == EOS:", "-        if cur:", "-            pos_texts.append(tok.decode(cur))", "+        if cur: pos_texts.append(tok.decode(cur))", "         cur = []", "     else:", "         cur.append(int(t))", "-if cur:", "-    pos_texts.append(tok.decode(cur))", "+if cur: pos_texts.append(tok.decode(cur))", " ", "-# strip WikiText-specific surface artifacts so the classifier learns register,", "-# not tokenization quirks the pool can't contain.", "-def deartifact(s):", "-    s = s.replace(\" @,@ \", \",\").replace(\" @-@ \", \"-\").replace(\" @.@ \", \".\")", "-    return s", "+def deartifact(s):  # strip WikiText tokenization quirks the pool can't contain", "+    return s.replace(\" @,@ \", \",\").replace(\" @-@ \", \"-\").replace(\" @.@ \", \".\")", " pos_texts = [deartifact(s) for s in pos_texts if len(s) > 200]", " ", " # ---------- 2. load pool ----------"]}, {"oldStart": 60, "oldLines": 53, "newStart": 66, "newLines": 59, "lines": ["     for line in f:", "         r = json.loads(line)", "         ids.append(r[\"id\"]); texts.append(r[\"text\"])", "-ids = np.array(ids)", "-N = len(ids)", "+ids = np.array(ids); N = len(ids)", " print(f\"pool docs: {N}  positives: {len(pos_texts)}\", file=sys.stderr)", " ", " # ---------- 3. negatives: random pool sample ----------", " neg_idx = rng.choice(N, size=min(N_NEG, N), replace=False)", " neg_texts = [texts[i] for i in neg_idx]", " ", "-# ---------- 4. build TF-IDF features (word + char) and train logreg ----------", "+# ---------- 4. featurize + train logistic regression (EmbeddingBag on GPU) ----------", "+def build_bag(text_list):", "+    flat, offsets, off = [], [], 0", "+    for t in text_list:", "+        b = ngram_buckets(t)", "+        offsets.append(off); flat.extend(b); off += len(b)", "+    return (torch.tensor(flat, dtype=torch.long, device=dev_t),", "+            torch.tensor(offsets, dtype=torch.long, device=dev_t))", "+", " train_texts = pos_texts + neg_texts", "-y = np.r_[np.ones(len(pos_texts)), np.zeros(len(neg_texts))]", "+y = torch.tensor([1.0] * len(pos_texts) + [0.0] * len(neg_texts), device=dev_t)", "+tin, toff = build_bag(train_texts)", " ", "-word_vec = TfidfVectorizer(lowercase=True, ngram_range=(1, 2), min_df=3,", "-                           max_features=80000, sublinear_tf=True,", "-                           strip_accents=\"unicode\")", "-char_vec = TfidfVectorizer(lowercase=True, analyzer=\"char_wb\", ngram_range=(3, 5),", "-                           min_df=5, max_features=80000, sublinear_tf=True)", "-Xw = word_vec.fit_transform(train_texts)", "-Xc = char_vec.fit_transform(train_texts)", "-X = hstack([Xw, Xc]).tocsr()", "-clf = LogisticRegression(C=1.0, max_iter=400, class_weight=\"balanced\")", "-clf.fit(X, y)", "-print(\"train acc:\", clf.score(X, y), file=sys.stderr)", "+emb = torch.nn.EmbeddingBag(D, 1, mode=\"mean\").to(dev_t)", "+torch.nn.init.zeros_(emb.weight)", "+bias = torch.zeros(1, requires_grad=True, device=dev_t)", "+opt = torch.optim.Adam(list(emb.parameters()) + [bias], lr=0.05, weight_decay=1e-6)", "+# class-balance weighting", "+w_pos = len(train_texts) / (2 * len(pos_texts)); w_neg = len(train_texts) / (2 * len(neg_texts))", "+wt = torch.where(y > 0.5, torch.tensor(w_pos, device=dev_t), torch.tensor(w_neg, device=dev_t))", "+for ep in range(EPOCHS):", "+    opt.zero_grad()", "+    logit = emb(tin, toff).squeeze(1) + bias", "+    loss = torch.nn.functional.binary_cross_entropy_with_logits(logit, y, weight=wt)", "+    loss.backward(); opt.step()", "+with torch.no_grad():", "+    pred = (torch.sigmoid(emb(tin, toff).squeeze(1) + bias) > 0.5).float()", "+    acc = (pred == y).float().mean().item()", "+print(f\"train loss {loss.item():.4f}  acc {acc:.3f}\", file=sys.stderr)", " ", "-# ---------- 5. score the whole pool in batches ----------", "+# ---------- 5. score whole pool ----------", " scores = np.empty(N, dtype=np.float32)", "-B = 20000", "-for s in range(0, N, B):", "-    e = min(N, s + B)", "-    xw = word_vec.transform(texts[s:e])", "-    xc = char_vec.transform(texts[s:e])", "-    xb = hstack([xw, xc]).tocsr()", "-    scores[s:e] = clf.decision_function(xb)", "+B = 8000", "+emb.eval()", "+with torch.no_grad():", "+    for s in range(0, N, B):", "+        e = min(N, s + B)", "+        bin_, boff = build_bag(texts[s:e])", "+        scores[s:e] = (emb(bin_, boff).squeeze(1) + bias).cpu().numpy()", " ", " # ---------- 6. light junk gating ----------", "-# Reject docs that are too short (little signal / mostly boilerplate) or that look", "-# like pure link/menu spam. Kept intentionally light — the classifier does the work.", " def junk(t):", "     n = len(t)", "-    if n < 300:", "-        return True", "-    # ratio of non-alnum-space \"symbol\" noise", "-    letters = sum(c.isalpha() for c in t)", "-    if letters / n < 0.55:", "-        return True", "+    if n < 300: return True", "+    if sum(c.isalpha() for c in t) / n < 0.55: return True", "     return False", "-", " gate = np.array([not junk(t) for t in texts])", " scores_eff = np.where(gate, scores, -1e9)", " "]}, {"oldStart": 114, "oldLines": 9, "newStart": 126, "newLines": 7, "lines": [" order = np.argsort(-scores_eff)", " sel = [int(ids[i]) for i in order[:N_EMIT]]", " json.dump(sel, open(OUT, \"w\"))", "-", "-# persist scores for analysis / re-ranking", "-np.save(\"/tmp/pool_scores.npy\", scores)", "-np.save(\"/tmp/pool_ids.npy\", ids)", "+np.save(\"/tmp/pool_scores.npy\", scores); np.save(\"/tmp/pool_ids.npy\", ids)", " print(f\"wrote {len(sel)} ids -> {OUT}\", file=sys.stderr)", "+print(\"kept by gate:\", int(gate.sum()), \"/\", N, file=sys.stderr)", " print(\"score pctiles:\", np.percentile(scores, [50, 90, 99, 99.9]), file=sys.stderr)"]}], "originalFile": "\"\"\"Curate a raw web pool for small-LM pretraining toward a BROAD multi-domain\nhigh-quality English target (encyclopedic / web prose / news / technical Q&A).\n\nStated criterion\n----------------\nSelect the pool documents that are most *target-like* under a quality classifier,\nsubject to light junk gating. The classifier is a TF-IDF (word 1-2gram +\nchar 3-5gram) logistic regression trained to separate:\n  positive := chunks of the disclosed target distribution (data/multi_dev.npy,\n              GPT-2 tokens decoded back to text, split on <|endoftext|>)\n  negative := a random sample of the raw pool (the \"do-nothing\" distribution)\nDocuments are ranked by P(target). We emit ids in descending score (best first);\nthe frozen pipeline consumes them in priority order until the 12M-token budget\nis full. This is DSIR/quality-classifier-style curation: pick pool docs whose\nregister+vocabulary match the target, and are clean enough to be low-entropy.\n\nReproducible: run `python3 curate.py` -> writes submission/selection.json.\n\"\"\"\nimport json, re, sys, numpy as np\nfrom transformers import AutoTokenizer\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.linear_model import LogisticRegression\nfrom scipy.sparse import hstack\n\nPOOL = \"/workspace/data/pool.jsonl\"\nDEV  = \"/workspace/data/multi_dev.npy\"\nOUT  = \"/workspace/submission/selection.json\"\nSEED = 0\nN_NEG = 12000          # random pool docs as negatives\nN_EMIT = 60000         # emit far more ids than the budget needs (~well over 12M tokens)\n\nrng = np.random.default_rng(SEED)\n\n# ---------- 1. positives: decode the disclosed target back to text ----------\ntok = AutoTokenizer.from_pretrained(\"gpt2\")\nEOS = tok.eos_token_id\ndev = np.load(DEV).astype(np.int64)\npos_texts = []\ncur = []\nfor t in dev:\n    if t == EOS:\n        if cur:\n            pos_texts.append(tok.decode(cur))\n        cur = []\n    else:\n        cur.append(int(t))\nif cur:\n    pos_texts.append(tok.decode(cur))\n\n# strip WikiText-specific surface artifacts so the classifier learns register,\n# not tokenization quirks the pool can't contain.\ndef deartifact(s):\n    s = s.replace(\" @,@ \", \",\").replace(\" @-@ \", \"-\").replace(\" @.@ \", \".\")\n    return s\npos_texts = [deartifact(s) for s in pos_texts if len(s) > 200]\n\n# ---------- 2. load pool ----------\nids, texts = [], []\nwith open(POOL) as f:\n    for line in f:\n        r = json.loads(line)\n        ids.append(r[\"id\"]); texts.append(r[\"text\"])\nids = np.array(ids)\nN = len(ids)\nprint(f\"pool docs: {N}  positives: {len(pos_texts)}\", file=sys.stderr)\n\n# ---------- 3. negatives: random pool sample ----------\nneg_idx = rng.choice(N, size=min(N_NEG, N), replace=False)\nneg_texts = [texts[i] for i in neg_idx]\n\n# ---------- 4. build TF-IDF features (word + char) and train logreg ----------\ntrain_texts = pos_texts + neg_texts\ny = np.r_[np.ones(len(pos_texts)), np.zeros(len(neg_texts))]\n\nword_vec = TfidfVectorizer(lowercase=True, ngram_range=(1, 2), min_df=3,\n                           max_features=80000, sublinear_tf=True,\n                           strip_accents=\"unicode\")\nchar_vec = TfidfVectorizer(lowercase=True, analyzer=\"char_wb\", ngram_range=(3, 5),\n                           min_df=5, max_features=80000, sublinear_tf=True)\nXw = word_vec.fit_transform(train_texts)\nXc = char_vec.fit_transform(train_texts)\nX = hstack([Xw, Xc]).tocsr()\nclf = LogisticRegression(C=1.0, max_iter=400, class_weight=\"balanced\")\nclf.fit(X, y)\nprint(\"train acc:\", clf.score(X, y), file=sys.stderr)\n\n# ---------- 5. score the whole pool in batches ----------\nscores = np.empty(N, dtype=np.float32)\nB = 20000\nfor s in range(0, N, B):\n    e = min(N, s + B)\n    xw = word_vec.transform(texts[s:e])\n    xc = char_vec.transform(texts[s:e])\n    xb = hstack([xw, xc]).tocsr()\n    scores[s:e] = clf.decision_function(xb)\n\n# ---------- 6. light junk gating ----------\n# Reject docs that are too short (little signal / mostly boilerplate) or that look\n# like pure link/menu spam. Kept intentionally light — the classifier does the work.\ndef junk(t):\n    n = len(t)\n    if n < 300:\n        return True\n    # ratio of non-alnum-space \"symbol\" noise\n    letters = sum(c.isalpha() for c in t)\n    if letters / n < 0.55:\n        return True\n    return False\n\ngate = np.array([not junk(t) for t in texts])\nscores_eff = np.where(gate, scores, -1e9)\n\n# ---------- 7. emit ids by descending score ----------\norder = np.argsort(-scores_eff)\nsel = [int(ids[i]) for i in order[:N_EMIT]]\njson.dump(sel, open(OUT, \"w\"))\n\n# persist scores for analysis / re-ranking\nnp.save(\"/tmp/pool_scores.npy\", scores)\nnp.save(\"/tmp/pool_ids.npy\", ids)\nprint(f\"wrote {len(sel)} ids -> {OUT}\", file=sys.stderr)\nprint(\"score pctiles:\", np.percentile(scores, [50, 90, 99, 99.9]), file=sys.stderr)\n", "userModified": false}

Command did not complete within its 120s timeout and was moved to the background (ID: bb7q0rs8f). Output is being written to: /tmp/claude-0/-workspace/c504c517-4369-451f-ac2a-a4737203bba0/tasks/bb7q0rs8f.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": "bb7q0rs8f", "timedOutAfterMs": 120000, "backgroundCwdHint": "Session cwd remains /workspace; directory changes made by the backgrounded command do not apply to subsequent commands."}

pool docs: 182016  positives: 2557
train loss 0.4718  acc 0.926
wrote 60000 ids -> /workspace/submission/selection.json
kept by gate: 177103 / 182016
score pctiles: [-0.53728476 -0.03041644  0.30954432  0.5115409 ]

real	2m18.674s
user	2m8.340s
sys	0m4.220s
---remaining---
4709

[stdout]
pool docs: 182016  positives: 2557
train loss 0.4718  acc 0.926
wrote 60000 ids -> /workspace/submission/selection.json
kept by gate: 177103 / 182016
score pctiles: [-0.53728476 -0.03041644  0.30954432  0.5115409 ]

real	2m18.674s
user	2m8.340s
sys	0m4.220s
---remaining---
4709
[metadata] {"noOutputExpected": false}

=== TOP 4 ===
[score 2.35]  it.

[score 2.35]  it.

[score 1.58]  belong to their

[score 0.94]  for her role as Brittany on the BET comedy-drama series The Game. She appeared in 16 episodes of the series between 2006 and 2009. She earned her first professional acting credit on the show Girlfriends, which was the inspiration for the spin-off series The Game. She appeared on

=== MEDIAN 2 ===
[score -0.54]  Reserved. To Top<|endoftext|>More Reviews: “The Classic Will Make BlackBerry Diehards Swoon” New BlackBerry Enterprise of Things BlackBerry Secure BlackBerry Fact Check News Events Software Solutions

[score -0.54] <|endoftext|>- Air Conditioning - Home Performance - Maintenance Plans - Builder Services A properly insulated basement in Omaha can significantly reduce your energy costs and increase your home comfo

[stdout]
=== TOP 4 ===
[score 2.35]  it.

[score 2.35]  it.

[score 1.58]  belong to their

[score 0.94]  for her role as Brittany on the BET comedy-drama series The Game. She appeared in 16 episodes of the series between 2006 and 2009. She earned her first professional acting credit on the show Girlfriends, which was the inspiration for the spin-off series The Game. She appeared on

=== MEDIAN 2 ===
[score -0.54]  Reserved. To Top<|endoftext|>More Reviews: “The Classic Will Make BlackBerry Diehards Swoon” New BlackBerry Enterprise of Things BlackBerry Secure BlackBerry Fact Check News Events Software Solutions

[score -0.54] <|endoftext|>- Air Conditioning - Home Performance - Maintenance Plans - Builder Services A properly insulated basement in Omaha can significantly reduce your energy costs and increase your home comfo
[metadata] {"noOutputExpected": false}

Updated task #1 status

[metadata] {"success": true, "taskId": "1", "updatedFields": ["status"], "statusChange": {"from": "in_progress", "to": "completed"}}

Updated task #2 status

[metadata] {"success": true, "taskId": "2", "updatedFields": ["status"], "statusChange": {"from": "pending", "to": "in_progress"}}

Token indices sequence length is longer than the specified maximum sequence length for this model (1657 > 1024). Running this sequence through the model will result in indexing errors
packed 12000000 tokens from 60000 ids -> /tmp/dev_train.npy
HELDOUT_PPL 502.007  loss 6.2186  train_s 50  /tmp/dev_train.npy
(dev PPL on multi_dev; official uses a hidden disjoint sample of the same HQ domain)

real	1m30.002s
user	1m26.650s
sys	0m2.960s

[stdout]
Token indices sequence length is longer than the specified maximum sequence length for this model (1657 > 1024). Running this sequence through the model will result in indexing errors
packed 12000000 tokens from 60000 ids -> /tmp/dev_train.npy
HELDOUT_PPL 502.007  loss 6.2186  train_s 50  /tmp/dev_train.npy
(dev PPL on multi_dev; official uses a hidden disjoint sample of the same HQ domain)

real	1m30.002s
user	1m26.650s
sys	0m2.960s
[metadata] {"noOutputExpected": false}

random sel 60000
HELDOUT_PPL 476.685  loss 6.1669  train_s 50  /tmp/dev_train.npy

real	1m28.393s
user	1m25.440s
sys	0m3.000s

[stdout]
random sel 60000
HELDOUT_PPL 476.685  loss 6.1669  train_s 50  /tmp/dev_train.npy

real	1m28.393s
user	1m25.440s
sys	0m3.000s
[metadata] {"noOutputExpected": false}