claude-opus-5

5390
---
total 14
drwxr-xr-x 1 root root  100 Aug  3 22:43 .
drwxr-xr-x 1 root root  160 Aug  3 22:43 ..
drwxr-xr-x 1 root root   60 Aug  3 22:43 .live
drwxr-xr-x 1 root root  100 Aug  3 22:43 .metrics
drwxr-xr-x 1 root root  140 Aug  3 22:43 .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  3 22:43 ..
-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

[stdout]
5390
---
total 14
drwxr-xr-x 1 root root  100 Aug  3 22:43 .
drwxr-xr-x 1 root root  160 Aug  3 22:43 ..
drwxr-xr-x 1 root root   60 Aug  3 22:43 .live
drwxr-xr-x 1 root root  100 Aug  3 22:43 .metrics
drwxr-xr-x 1 root root  140 Aug  3 22:43 .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  3 22:43 ..
-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
[metadata] {"noOutputExpected": false}

Mon Aug  3 22:43:25 2026       
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 580.95.05              Driver Version: 580.95.05      CUDA Version: 13.0     |
+-----------------------------------------+------------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
|                                         |                        |               MIG M. |
|=========================================+========================+======================|
|   0  NVIDIA H200                    On  |   00000000:96:00.0 Off |                    0 |
| N/A   32C    P0             78W /  700W |       4MiB / 143771MiB |      0%      Default |
|                                         |                        |             Disabled |
+-----------------------------------------+------------------------+----------------------+

+-----------------------------------------------------------------------------------------+
| Processes:                                                                              |
|  GPU   GI   CI              PID   Type   Process name                        GPU Memory |
|        ID   ID                                                               Usage      |
|=========================================================================================|
|  No running processes found                                                             |
+-----------------------------------------------------------------------------------------+
---
2.8.0+cu128 4.57.1 True
---
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Mem:            1024           0        1023           0           0        1023
Swap:              0           0           0

[stdout]
Mon Aug  3 22:43:25 2026       
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 580.95.05              Driver Version: 580.95.05      CUDA Version: 13.0     |
+-----------------------------------------+------------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
|                                         |                        |               MIG M. |
|=========================================+========================+======================|
|   0  NVIDIA H200                    On  |   00000000:96:00.0 Off |                    0 |
| N/A   32C    P0             78W /  700W |       4MiB / 143771MiB |      0%      Default |
|                                         |                        |             Disabled |
+-----------------------------------------+------------------------+----------------------+

+-----------------------------------------------------------------------------------------+
| Processes:                                                                              |
|  GPU   GI   CI              PID   Type   Process name                        GPU Memory |
|        ID   ID                                                               Usage      |
|=========================================================================================|
|  No running processes found                                                             |
+-----------------------------------------------------------------------------------------+
---
2.8.0+cu128 4.57.1 True
---
16
               total        used        free      shared  buff/cache   available
Mem:            1024           0        1023           0           0        1023
Swap:              0           0           0
[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
===== model.py =====
"""A small GPT (decoder-only Transformer) used as the distillation *student*.

Self-contained re-implementation in this repo's own idiom (no external GPT code).
GPT-2 byte-level BPE vocabulary (50257) so the student shares the teacher's
tokenizer. Standard pre-norm Transformer with weight-tied embeddings.
"""
from dataclasses import dataclass
import math
import torch
import torch.nn as nn
import torch.nn.functional as F


@dataclass
class GPTConfig:
    block_size: int = 256
    vocab_size: int = 50257
    n_layer: int = 6
    n_head: int = 6
    n_embd: int = 384
    dropout: float = 0.0
    bias: bool = False


class LayerNorm(nn.Module):
    def __init__(self, ndim, bias):
        super().__init__()
        self.weight = nn.Parameter(torch.ones(ndim))
        self.bias = nn.Parameter(torch.zeros(ndim)) if bias else None

    def forward(self, x):
        return F.layer_norm(x, self.weight.shape, self.weight, self.bias, 1e-5)


class CausalSelfAttention(nn.Module):
    def __init__(self, cfg: GPTConfig):
        super().__init__()
        assert cfg.n_embd % cfg.n_head == 0
        self.c_attn = nn.Linear(cfg.n_embd, 3 * cfg.n_embd, bias=cfg.bias)
        self.c_proj = nn.Linear(cfg.n_embd, cfg.n_embd, bias=cfg.bias)
        self.n_head = cfg.n_head
        self.n_embd = cfg.n_embd
        self.dropout = cfg.dropout

    def forward(self, x):
        B, T, C = x.size()
        q, k, v = self.c_attn(x).split(self.n_embd, dim=2)
        q = q.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)
        k = k.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)
        v = v.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)
        y = F.scaled_dot_product_attention(
            q, k, v, dropout_p=self.dropout if self.training else 0.0, is_causal=True
        )
        y = y.transpose(1, 2).contiguous().view(B, T, C)
        return self.c_proj(y)


class MLP(nn.Module):
    def __init__(self, cfg: GPTConfig):
        super().__init__()
        self.c_fc = nn.Linear(cfg.n_embd, 4 * cfg.n_embd, bias=cfg.bias)
        self.c_proj = nn.Linear(4 * cfg.n_embd, cfg.n_embd, bias=cfg.bias)
        self.dropout = nn.Dropout(cfg.dropout)

    def forward(self, x):
        return self.dropout(self.c_proj(F.gelu(self.c_fc(x))))


class Block(nn.Module):
    def __init__(self, cfg: GPTConfig):
        super().__init__()
        self.ln_1 = LayerNorm(cfg.n_embd, cfg.bias)
        self.attn = CausalSelfAttention(cfg)
        self.ln_2 = LayerNorm(cfg.n_embd, cfg.bias)
        self.mlp = MLP(cfg)

    def forward(self, x):
        x = x + self.attn(self.ln_1(x))
        x = x + self.mlp(self.ln_2(x))
        return x


class GPT(nn.Module):
    def __init__(self, cfg: GPTConfig):
        super().__init__()
        self.cfg = cfg
        self.transformer = nn.ModuleDict(dict(
            wte=nn.Embedding(cfg.vocab_size, cfg.n_embd),
            wpe=nn.Embedding(cfg.block_size, cfg.n_embd),
            drop=nn.Dropout(cfg.dropout),
            h=nn.ModuleList([Block(cfg) for _ in range(cfg.n_layer)]),
            ln_f=LayerNorm(cfg.n_embd, cfg.bias),
        ))
        self.lm_head = nn.Linear(cfg.n_embd, cfg.vocab_size, bias=False)
        self.transformer.wte.weight = self.lm_head.weight  # weight tying

        self.apply(self._init_weights)
        # GPT-2 style scaled init on residual projections
        for pn, p in self.named_parameters():
            if pn.endswith('c_proj.weight'):
                nn.init.normal_(p, mean=0.0, std=0.02 / math.sqrt(2 * cfg.n_layer))

    def _init_weights(self, module):
        if isinstance(module, nn.Linear):
            nn.init.normal_(module.weight, mean=0.0, std=0.02)
            if module.bias is not None:
                nn.init.zeros_(module.bias)
        elif isinstance(module, nn.Embedding):
            nn.init.normal_(module.weight, mean=0.0, std=0.02)

    def forward(self, idx, targets=None):
        """Return (logits, ce_loss). logits: [B, T, vocab]. ce_loss is the plain
        next-token cross-entropy if `targets` given, else None. The distillation
        loss is computed separately in kd_trainer.kd_loss."""
        B, T = idx.size()
        pos = torch.arange(0, T, dtype=torch.long, device=idx.device)
        x = self.transformer.drop(self.transformer.wte(idx) + self.transformer.wpe(pos))
        for block in self.transformer.h:
            x = block(x)
        x = self.transformer.ln_f(x)
        logits = self.lm_head(x)
        ce_loss = None
        if targets is not None:
            ce_loss = F.cross_entropy(
                logits.view(-1, logits.size(-1)), targets.reshape(-1), ignore_index=-1
            )
        return logits, ce_loss

    def configure_optimizers(self, weight_decay, lr, betas, device_type):
        decay, no_decay = [], []
        for n, p in self.named_parameters():
            if not p.requires_grad:
                continue
            (decay if p.dim() >= 2 else no_decay).append(p)
        groups = [
            {'params': decay, 'weight_decay': weight_decay},
            {'params': no_decay, 'weight_decay': 0.0},
        ]
        fused = device_type == 'cuda'
        return torch.optim.AdamW(groups, lr=lr, betas=betas, fused=fused)

    @torch.no_grad()
    def estimate_ppl(self, get_eval_batch, n_batches):
        """Mean per-token NLL over `n_batches` eval batches -> perplexity."""
        was_training = self.training
        self.eval()
        losses = torch.zeros(n_batches)
        for i in range(n_batches):
            x, y = get_eval_batch()
            _, loss = self(x, y)
            losses[i] = loss.item()
        if was_training:
            self.train()
        return math.exp(losses.mean().item())

[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
===== model.py =====
"""A small GPT (decoder-only Transformer) used as the distillation *student*.

Self-contained re-implementation in this repo's own idiom (no external GPT code).
GPT-2 byte-level BPE vocabulary (50257) so the student shares the teacher's
tokenizer. Standard pre-norm Transformer with weight-tied embeddings.
"""
from dataclasses import dataclass
import math
import torch
import torch.nn as nn
import torch.nn.functional as F


@dataclass
class GPTConfig:
    block_size: int = 256
    vocab_size: int = 50257
    n_layer: int = 6
    n_head: int = 6
    n_embd: int = 384
    dropout: float = 0.0
    bias: bool = False


class LayerNorm(nn.Module):
    def __init__(self, ndim, bias):
        super().__init__()
        self.weight = nn.Parameter(torch.ones(ndim))
        self.bias = nn.Parameter(torch.zeros(ndim)) if bias else None

    def forward(self, x):
        return F.layer_norm(x, self.weight.shape, self.weight, self.bias, 1e-5)


class CausalSelfAttention(nn.Module):
    def __init__(self, cfg: GPTConfig):
        super().__init__()
        assert cfg.n_embd % cfg.n_head == 0
        self.c_attn = nn.Linear(cfg.n_embd, 3 * cfg.n_embd, bias=cfg.bias)
        self.c_proj = nn.Linear(cfg.n_embd, cfg.n_embd, bias=cfg.bias)
        self.n_head = cfg.n_head
        self.n_embd = cfg.n_embd
        self.dropout = cfg.dropout

    def forward(self, x):
        B, T, C = x.size()
        q, k, v = self.c_attn(x).split(self.n_embd, dim=2)
        q = q.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)
        k = k.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)
        v = v.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)
        y = F.scaled_dot_product_attention(
            q, k, v, dropout_p=self.dropout if self.training else 0.0, is_causal=True
        )
        y = y.transpose(1, 2).contiguous().view(B, T, C)
        return self.c_proj(y)


class MLP(nn.Module):
    def __init__(self, cfg: GPTConfig):
        super().__init__()
        self.c_fc = nn.Linear(cfg.n_embd, 4 * cfg.n_embd, bias=cfg.bias)
        self.c_proj = nn.Linear(4 * cfg.n_embd, cfg.n_embd, bias=cfg.bias)
        self.dropout = nn.Dropout(cfg.dropout)

    def forward(self, x):
        return self.dropout(self.c_proj(F.gelu(self.c_fc(x))))


class Block(nn.Module):
    def __init__(self, cfg: GPTConfig):
        super().__init__()
        self.ln_1 = LayerNorm(cfg.n_embd, cfg.bias)
        self.attn = CausalSelfAttention(cfg)
        self.ln_2 = LayerNorm(cfg.n_embd, cfg.bias)
        self.mlp = MLP(cfg)

    def forward(self, x):
        x = x + self.attn(self.ln_1(x))
        x = x + self.mlp(self.ln_2(x))
        return x


class GPT(nn.Module):
    def __init__(self, cfg: GPTConfig):
        super().__init__()
        self.cfg = cfg
        self.transformer = nn.ModuleDict(dict(
            wte=nn.Embedding(cfg.vocab_size, cfg.n_embd),
            wpe=nn.Embedding(cfg.block_size, cfg.n_embd),
            drop=nn.Dropout(cfg.dropout),
            h=nn.ModuleList([Block(cfg) for _ in range(cfg.n_layer)]),
            ln_f=LayerNorm(cfg.n_embd, cfg.bias),
        ))
        self.lm_head = nn.Linear(cfg.n_embd, cfg.vocab_size, bias=False)
        self.transformer.wte.weight = self.lm_head.weight  # weight tying

        self.apply(self._init_weights)
        # GPT-2 style scaled init on residual projections
        for pn, p in self.named_parameters():
            if pn.endswith('c_proj.weight'):
                nn.init.normal_(p, mean=0.0, std=0.02 / math.sqrt(2 * cfg.n_layer))

    def _init_weights(self, module):
        if isinstance(module, nn.Linear):
            nn.init.normal_(module.weight, mean=0.0, std=0.02)
            if module.bias is not None:
                nn.init.zeros_(module.bias)
        elif isinstance(module, nn.Embedding):
            nn.init.normal_(module.weight, mean=0.0, std=0.02)

    def forward(self, idx, targets=None):
        """Return (logits, ce_loss). logits: [B, T, vocab]. ce_loss is the plain
        next-token cross-entropy if `targets` given, else None. The distillation
        loss is computed separately in kd_trainer.kd_loss."""
        B, T = idx.size()
        pos = torch.arange(0, T, dtype=torch.long, device=idx.device)
        x = self.transformer.drop(self.transformer.wte(idx) + self.transformer.wpe(pos))
        for block in self.transformer.h:
            x = block(x)
        x = self.transformer.ln_f(x)
        logits = self.lm_head(x)
        ce_loss = None
        if targets is not None:
            ce_loss = F.cross_entropy(
                logits.view(-1, logits.size(-1)), targets.reshape(-1), ignore_index=-1
            )
        return logits, ce_loss

    def configure_optimizers(self, weight_decay, lr, betas, device_type):
        decay, no_decay = [], []
        for n, p in self.named_parameters():
            if not p.requires_grad:
                continue
            (decay if p.dim() >= 2 else no_decay).append(p)
        groups = [
            {'params': decay, 'weight_decay': weight_decay},
            {'params': no_decay, 'weight_decay': 0.0},
        ]
        fused = device_type == 'cuda'
        return torch.optim.AdamW(groups, lr=lr, betas=betas, fused=fused)

    @torch.no_grad()
    def estimate_ppl(self, get_eval_batch, n_batches):
        """Mean per-token NLL over `n_batches` eval batches -> perplexity."""
        was_training = self.training
        self.eval()
        losses = torch.zeros(n_batches)
        for i in range(n_batches):
            x, y = get_eval_batch()
            _, loss = self(x, y)
            losses[i] = loss.item()
        if was_training:
            self.train()
        return math.exp(losses.mean().item())
[metadata] {"noOutputExpected": false}

{"id": 0, "text": "|Viewing Single Post From: Spoilers for the Week of February 11th|\n|Lil||Feb 1 2013, 09:58 AM|\nDon'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.\nSTEFANO!! STEFANO, STEFANO, STEFANO!!!! :cheer:\n|Spoilers for the Week of February 11th \u00b7 DAYS: News, Spoilers & Discussion|"}
{"id": 1, "text": "*sigh* Fundamentalist community, let me pass on some advice to you I learned from the atheistic community:\nIf you have set yourself on fire, do not run.\nOkay? Okay?? Please?\nLook, 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.\"\nI sat here, for two months, waiting for that to happen, anything to happen, and it didnt. Two months, on your own terms, you could have written a similar post to yesterdays. I would have given you the benefit of the doubt-- maybe you didnt know the credits werent visible to the audience, and I wouldnt have said a word beyond this, as its Harvards problem, not mine. This would have been a funny joke to those of us involved in dealing with you people, but it would have been a PR non-issue for you.\nBut after you set yourself on fire, you didnt douse it out with a bucket of ice cold reality and accountability. You ran. And youre still running.\nWhy not just state \"I screwed up. Sorry everyone.\" and move on? Why the excuses? Why the denial? Why the passive language? Why the vague words and cryptic capitalizations? Why the writes and rewrites of your 'press release'? We know it wasnt written of your own volition, or it would have been done *before* Harvard had to take action. And, your behavior before this, regarding this issue, is not indicative of someone who made an innocent mistake. Its weird.\nSo what with this frantic running? Is the inability to say \"I was wrong\" a pathological feature of Creationists? Or are you hiding something? Or is it both? Or is it more?\nAnd now we get Casey weighing in on the issue, according to cre8id at AboveTopSecret.com-- PBS/NOVA online - Intelligent Design on trial:\n...to my knowledge, Discovery Institute has neither authorized nor received nor is making use of any presentation that used that animation. We have had nothing to do with creating or selling a DVD of that animation, nor do we have anything to do with placing that presentation on Google Video.I dont know what he is talking about with that last part, but the first part sounds similar to DIs claims post-Dover (\"WE HAD NOTHING TO DO WITH DOVER!\"). Maybe Luskin is telling the truth. Maybe this was a magic non-science Creation-friendly narration with convenient edits that AiG or ICR would have killed for... but only Dembski could find it... but he cant tell us where... and he didnt share it with anyone... and its subsequently disappeared from the Internet...\nBut that simply isnt what Ive been told. Maybe this was all a silly Dembski mistake, blown out of proportion due to his decision to remain silent... But what if we find more videos of more DI fellows, presenting this animation?\n*shrug* If youve set yourself on fire, do not run, DI. If youve done it, better admit it to Harvard now, apologize, and move on."}
{"id": 2, "text": "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\nPeople 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\nFor Immediate Release May 14, 2013 UCSF Launches So
=== LINE COUNT (fast) ===
182016 data/pool.jsonl

[stdout]
{"id": 0, "text": "|Viewing Single Post From: Spoilers for the Week of February 11th|\n|Lil||Feb 1 2013, 09:58 AM|\nDon'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.\nSTEFANO!! STEFANO, STEFANO, STEFANO!!!! :cheer:\n|Spoilers for the Week of February 11th \u00b7 DAYS: News, Spoilers & Discussion|"}
{"id": 1, "text": "*sigh* Fundamentalist community, let me pass on some advice to you I learned from the atheistic community:\nIf you have set yourself on fire, do not run.\nOkay? Okay?? Please?\nLook, 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.\"\nI sat here, for two months, waiting for that to happen, anything to happen, and it didnt. Two months, on your own terms, you could have written a similar post to yesterdays. I would have given you the benefit of the doubt-- maybe you didnt know the credits werent visible to the audience, and I wouldnt have said a word beyond this, as its Harvards problem, not mine. This would have been a funny joke to those of us involved in dealing with you people, but it would have been a PR non-issue for you.\nBut after you set yourself on fire, you didnt douse it out with a bucket of ice cold reality and accountability. You ran. And youre still running.\nWhy not just state \"I screwed up. Sorry everyone.\" and move on? Why the excuses? Why the denial? Why the passive language? Why the vague words and cryptic capitalizations? Why the writes and rewrites of your 'press release'? We know it wasnt written of your own volition, or it would have been done *before* Harvard had to take action. And, your behavior before this, regarding this issue, is not indicative of someone who made an innocent mistake. Its weird.\nSo what with this frantic running? Is the inability to say \"I was wrong\" a pathological feature of Creationists? Or are you hiding something? Or is it both? Or is it more?\nAnd now we get Casey weighing in on the issue, according to cre8id at AboveTopSecret.com-- PBS/NOVA online - Intelligent Design on trial:\n...to my knowledge, Discovery Institute has neither authorized nor received nor is making use of any presentation that used that animation. We have had nothing to do with creating or selling a DVD of that animation, nor do we have anything to do with placing that presentation on Google Video.I dont know what he is talking about with that last part, but the first part sounds similar to DIs claims post-Dover (\"WE HAD NOTHING TO DO WITH DOVER!\"). Maybe Luskin is telling the truth. Maybe this was a magic non-science Creation-friendly narration with convenient edits that AiG or ICR would have killed for... but only Dembski could find it... but he cant tell us where... and he didnt share it with anyone... and its subsequently disappeared from the Internet...\nBut that simply isnt what Ive been told. Maybe this was all a silly Dembski mistake, blown out of proportion due to his decision to remain silent... But what if we find more videos of more DI fellows, presenting this animation?\n*shrug* If youve set yourself on fire, do not run, DI. If youve done it, better admit it to Harvard now, apologize, and move on."}
{"id": 2, "text": "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\nPeople 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\nFor Immediate Release May 14, 2013 UCSF Launches So
=== LINE COUNT (fast) ===
182016 data/pool.jsonl
[metadata] {"noOutputExpected": false}

dtype uint16 len 1000000
EOS count 2704
========================= AT 0 =========================
" 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 ) . \n<|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 
========================= AT 200000 =========================
' it followed the southern end of the Lodge Freeway . By the middle of 1961 , the Watervliet – Paw Paw and Jackson – Ann Arbor freeway gaps were completed , and the freeway was extended westward to Stevensville ; By the end of the year , I @-@ 94 / US 12 extended all the way to New Buffalo . In January 1962 , the state made the biggest rerouting change of all to US 12 : the designation was removed from the I @-@ 94 freeway from New Buffalo to Detroit and shifted to completely replace US 112 . \n<|endoftext|> In 1925 , US 112 was originally proposed to run from Oshkosh to Fremont , Wisconsin , on what later became U.S. Route 110 . When it was initially designated in November 1926 , US 112 made a sharp turn to the southwest to connect to US 20 in Elkhart , Indiana . In 1931 , a new trunkline highway was designated between M @-@ 60 at Niles and US 112 at Union . This highway was numbered M @-@ 151 . In 1933 , the section of US 112 from Union to Elkhart was renumbered US 112S . M @-@ 151 and US 112S each lasted until 1935 when US 112 was extended to replace M @-@ 151 . US 112 was also extended to run concurrently with M @-@ 60 to New Buffalo , and US 112S was renumbered M @-@ 205 . \n<|endoftext|> In 1936 , the section of US 112 along Michigan Avenue east of Ypsilanti was expanded into a " super highway " . In 1955 , a realignment of US 127 in southern Jackson County removed a shor
========================= AT 400000 =========================
' bite out of Walker\'s counting stats. He retired with 2,160 hits, 383 home runs and 230 stolen bases and that\'s very impressive, but those totals don\'t make you think "Hall of Famer" at first glance. Had he stayed healthy, Walker might have been able to eclipse 2,500 hits, 450 homers and 300 steals. Now those numbers grab your attention. The injuries hurt Walker\'s bulk production. No doubt about it.\n\nCoors Field: Walker played most of his career with the Rockies, which means he benefited from hitter friendly Coors Field. He was a career .381/.462/.710 hitter at Coors Field (!) and a career .282/.375/.501 hitter away from Coors Field. That\'s still really good! But clearly Walker\'s offensive stats were inflated by the thin mountain air.\n\nIt\'s important to keep in mind only 2,501 of Walker\'s 8,030 career plate appearances came at Coors Field, or 31.1 percent. Nearly 70 percent of his career plate appearances came elsewhere, so it\'s not like his career numbers are solely the product of that ballpark. He wasn\'t Ted Williams at Coors Field and Neifi Perez elsewhere, you know? Playing at Coors Field undeniably boosted Walker\'s stats. The man was great everywhere he played though.\n\nWill he make it?\n\nThis is Walker\'s seventh year on the Hall of Fame ballot and he topped out at 22.9 percent of the vote back in 2009. According to Ryan Thibodaux\'s tracker, Walker has 
========================= AT 600000 =========================
' flexibility to employees and saves seating space for the employer, amongst many other benefits Working from Home entails. However, many employees often get caught up with the comfort a WFH option provides and resultantly deliver poor productivity. If you too are availing the Work from Home option or work from a Home-Office then here are 6 proven ways to optimize your productivity:1. Designate Time SlotsRemember Work from Home doesn’t shorten your work or work hours. Designate time slot(s) in the morning, afternoon or evening and stick to them if you really want to be productive. Likewise, schedule breaks in between your work hours to unwind.2. Assign a Tidy CornerAssign a tidy corner for working every day. Invest in an ergonomic chair and table to work for long hours in the right posture. Sitting on a sofa or bed all day will harm your back besides making you slow. Also, ensure this corner is not the place you’ll have breakfast, lunch or dinner. You need some change even if it means changing places within your boundary wall.3. CommunicateKeep communicating with your team members over the phone or messenger or emails to keep yourself engaged and focused at work. Similarly, limit personal chats or talks during your designated work time.4. Dress UpTake a bath and dress up in the morning to get into the professional mindset. Working in your PJs may appear cool but it hampers your
========================= AT 800000 =========================
" true\n        };\n        client.Send(&quot;MyEmailAddress@gmail.com&quot;, &quot;some.email@some.com&quot;, &quot;test&quot;, &quot;testbody&quot;); \n    }\n</code></pre>\n<p>Any ideas?</p>\n<p><strong>UPDATE</strong></p>\n<p>More details.</p>\n<p>Maybe I should say what other attempts I made that gave me the same error:\n(Note when i didn't specify a port it tryed port 25)</p>\n<pre><code>    public static void Attempt2()\n    {\n        var fromAddress = new MailAddress(&quot;MyEmailAddy@gmail.com&quot;, &quot;From Name&quot;);\n        var toAddress = new MailAddress(&quot;MyEmailAddy@dfdf.com&quot;, &quot;To Name&quot;);\n        const string fromPassword = &quot;pass&quot;;\n        const string subject = &quot;Subject&quot;;\n        const string body = &quot;Body&quot;;\n        var smtp = new SmtpClient\n        {\n            Host = &quot;smtp.gmail.com&quot;,\n            Port = 587,\n            EnableSsl = true,\n            DeliveryMethod = SmtpDeliveryMethod.Network,\n            Use"

[stdout]
dtype uint16 len 1000000
EOS count 2704
========================= AT 0 =========================
" 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 ) . \n<|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 
========================= AT 200000 =========================
' it followed the southern end of the Lodge Freeway . By the middle of 1961 , the Watervliet – Paw Paw and Jackson – Ann Arbor freeway gaps were completed , and the freeway was extended westward to Stevensville ; By the end of the year , I @-@ 94 / US 12 extended all the way to New Buffalo . In January 1962 , the state made the biggest rerouting change of all to US 12 : the designation was removed from the I @-@ 94 freeway from New Buffalo to Detroit and shifted to completely replace US 112 . \n<|endoftext|> In 1925 , US 112 was originally proposed to run from Oshkosh to Fremont , Wisconsin , on what later became U.S. Route 110 . When it was initially designated in November 1926 , US 112 made a sharp turn to the southwest to connect to US 20 in Elkhart , Indiana . In 1931 , a new trunkline highway was designated between M @-@ 60 at Niles and US 112 at Union . This highway was numbered M @-@ 151 . In 1933 , the section of US 112 from Union to Elkhart was renumbered US 112S . M @-@ 151 and US 112S each lasted until 1935 when US 112 was extended to replace M @-@ 151 . US 112 was also extended to run concurrently with M @-@ 60 to New Buffalo , and US 112S was renumbered M @-@ 205 . \n<|endoftext|> In 1936 , the section of US 112 along Michigan Avenue east of Ypsilanti was expanded into a " super highway " . In 1955 , a realignment of US 127 in southern Jackson County removed a shor
========================= AT 400000 =========================
' bite out of Walker\'s counting stats. He retired with 2,160 hits, 383 home runs and 230 stolen bases and that\'s very impressive, but those totals don\'t make you think "Hall of Famer" at first glance. Had he stayed healthy, Walker might have been able to eclipse 2,500 hits, 450 homers and 300 steals. Now those numbers grab your attention. The injuries hurt Walker\'s bulk production. No doubt about it.\n\nCoors Field: Walker played most of his career with the Rockies, which means he benefited from hitter friendly Coors Field. He was a career .381/.462/.710 hitter at Coors Field (!) and a career .282/.375/.501 hitter away from Coors Field. That\'s still really good! But clearly Walker\'s offensive stats were inflated by the thin mountain air.\n\nIt\'s important to keep in mind only 2,501 of Walker\'s 8,030 career plate appearances came at Coors Field, or 31.1 percent. Nearly 70 percent of his career plate appearances came elsewhere, so it\'s not like his career numbers are solely the product of that ballpark. He wasn\'t Ted Williams at Coors Field and Neifi Perez elsewhere, you know? Playing at Coors Field undeniably boosted Walker\'s stats. The man was great everywhere he played though.\n\nWill he make it?\n\nThis is Walker\'s seventh year on the Hall of Fame ballot and he topped out at 22.9 percent of the vote back in 2009. According to Ryan Thibodaux\'s tracker, Walker has 
========================= AT 600000 =========================
' flexibility to employees and saves seating space for the employer, amongst many other benefits Working from Home entails. However, many employees often get caught up with the comfort a WFH option provides and resultantly deliver poor productivity. If you too are availing the Work from Home option or work from a Home-Office then here are 6 proven ways to optimize your productivity:1. Designate Time SlotsRemember Work from Home doesn’t shorten your work or work hours. Designate time slot(s) in the morning, afternoon or evening and stick to them if you really want to be productive. Likewise, schedule breaks in between your work hours to unwind.2. Assign a Tidy CornerAssign a tidy corner for working every day. Invest in an ergonomic chair and table to work for long hours in the right posture. Sitting on a sofa or bed all day will harm your back besides making you slow. Also, ensure this corner is not the place you’ll have breakfast, lunch or dinner. You need some change even if it means changing places within your boundary wall.3. CommunicateKeep communicating with your team members over the phone or messenger or emails to keep yourself engaged and focused at work. Similarly, limit personal chats or talks during your designated work time.4. Dress UpTake a bath and dress up in the morning to get into the professional mindset. Working in your PJs may appear cool but it hampers your
========================= AT 800000 =========================
" true\n        };\n        client.Send(&quot;MyEmailAddress@gmail.com&quot;, &quot;some.email@some.com&quot;, &quot;test&quot;, &quot;testbody&quot;); \n    }\n</code></pre>\n<p>Any ideas?</p>\n<p><strong>UPDATE</strong></p>\n<p>More details.</p>\n<p>Maybe I should say what other attempts I made that gave me the same error:\n(Note when i didn't specify a port it tryed port 25)</p>\n<pre><code>    public static void Attempt2()\n    {\n        var fromAddress = new MailAddress(&quot;MyEmailAddy@gmail.com&quot;, &quot;From Name&quot;);\n        var toAddress = new MailAddress(&quot;MyEmailAddy@dfdf.com&quot;, &quot;To Name&quot;);\n        const string fromPassword = &quot;pass&quot;;\n        const string subject = &quot;Subject&quot;;\n        const string body = &quot;Body&quot;;\n        var smtp = new SmtpClient\n        {\n            Host = &quot;smtp.gmail.com&quot;,\n            Port = 587,\n            EnableSsl = true,\n            DeliveryMethod = SmtpDeliveryMethod.Network,\n            Use"
[metadata] {"noOutputExpected": false}

NO orjson ModuleNotFoundError
NO sklearn ModuleNotFoundError
NO datasketch ModuleNotFoundError
NO scipy ModuleNotFoundError
OK numpy
OK regex
NO fasttext ModuleNotFoundError
/workspace/.live:
status.json

/workspace/.metrics:
sampler.pid
sampler.sh
system.jsonl
5330

[stdout]
NO orjson ModuleNotFoundError
NO sklearn ModuleNotFoundError
NO datasketch ModuleNotFoundError
NO scipy ModuleNotFoundError
OK numpy
OK regex
NO fasttext ModuleNotFoundError
/workspace/.live:
status.json

/workspace/.metrics:
sampler.pid
sampler.sh
system.jsonl
5330
[metadata] {"noOutputExpected": false}

0 WIKITEXT(w=7) ' Beardmore was sufficiently impressed with Shackleton to offer financial support , but other donatio'
20000 WIKITEXT(w=32) ' Ministers — the cabinet being its executive committee — headed by the prime minister . Any minister'
40000 WIKITEXT(w=12) ' " , such as Joe DiMaggio ( Italian @-@ American ) and Arthur Miller ( Jewish ) . According to Banne'
60000 WIKITEXT(w=3) " 's final performance was in Stockholm , Sweden , at the Solnahallen Arena on September 26 , 1986 , "
80000 WIKITEXT(w=17) ' behalf of South Korea . The United States , a member of the UN , simultaneously committed ground fo'
100000 WIKITEXT(w=11) ' Picross . Another new item , the GB Sounds , changes the background music to the original 8 @-@ bit'
120000 WIKITEXT(w=35) ' , so named because it was once a rail line into downtown Negaunee . The trunkline turns east on Jac'
140000 WIKITEXT(w=33) ' would again compare the East and the West , writing that " while the East sank deeper into darkness'
160000 WIKITEXT(w=15) " ) . \n<|endoftext|> The December 2000 nor 'easter was a significant winter storm that impacted the M"
180000 WIKITEXT(w=13) ' The video shows Stan playing tennis against herself in a futuristic setting . The track was success'
200000 WIKITEXT(w=20) ' it followed the southern end of the Lodge Freeway . By the middle of 1961 , the Watervliet – Paw Pa'
220000 WIKITEXT(w=33) ' high approval ratings steadily declined , while his disapproval numbers increased significantly . I'
240000 WIKITEXT(w=31) 'aics differ across countries , including Australia , China , Germany , Israel , Japan , and the Unit'
260000 PROSE ' tonnes shipped in 2015, further exacerbating a tightening rice market as drought has also hit top r'
280000 PROSE '�t even know how to be there.\n\nThey don’t know how to promote themselves. They couldn’t even find th'
300000 PROSE ' the hiring manager. Here again, the hiring manager typically reviews resumes for the desired conten'
320000 PROSE ". EDD is calculated using Parikh's formula by adding nine months to the start of the last menstrual "
340000 PROSE ' it. Note that using MonoGame will not save you from that license cost, as much as they hate talking'
360000 PROSE ' Godfrey and Seth Scarborough\n\nShare this post<|endoftext|>The latest trend in Japan? PICKING YOUR N'
380000 PROSE ' upon them. There was little or any attempt at including Russia in a company of the nations of equal'
400000 PROSE " bite out of Walker's counting stats. He retired with 2,160 hits, 383 home runs and 230 stolen bases"
420000 PROSE ' can get back into football shape after the long layoff.<|endoftext|>5 APRIL, 2017 – Fortec Motorspo'
440000 PROSE ' how new motherhood really can be. To let her in on all the real secrets of being a mother.\n\nI wante'
460000 PROSE 'Comments (none posted)\n\nthe most popular open source ERP system\n\nOdoo is, according to Wikipedia , "'
480000 PROSE 'ness, fairness and accuracy.”\n\nSome frustrations with Baker’s stewardship recently spilled outside t'
500000 PROSE 'I fucking hate when he does this shit pic.twitter.com/kpmcHnW4Cz — Miley Ray Cyrus (@MileyCyrus) Apr'
520000 PROSE ' in Etah and Jaithra town Yadav alleged that BJP has "copied" his party\'s poll\'s manifesto and asked'
540000 PROSE ' rebounded from six-week lows as investors pared back holdings of low-risk government debt.The yield'
560000 PROSE ' next round of examination. It also contained the names of five male applicants which shocked other '
580000 PROSE '.Amritsar — less than 30 km from the new border — endured horrific rioting, with areas like Katra Ja'
600000 PROSE ' flexibility to employees and saves seating space for the employer, amongst many other benefits Work'
620000 PROSE ' weightage, etc for MHT CET 2018 have been set by Maharashtra State Board of Secondary and Higher Se'
640000 PROSE ' to the Lieutenant Governor”, “forwarded to the Lieutenant Governor”, “submitted to the Lieutenant G'
660000 PROSE ' four decades.<|endoftext|>He started his music career on Youtube and went on to give fans the party'
680000 PROSE ' Elgar, Shorter in length around middle, Elgar ducks under it. 235/0\n51.5 M Rahman to Elgar, Angles '
700000 PROSE 'But what we do know and understand perhaps is that we’re at a loss - a loss of a consolidated identi'
720000 PROSE ' media to report cases of sexual offences against child victims, section 228-A of the IPC deals with'
740000 PROSE ' the world."Her show Superstore follows the lives of different individuals working in the store. The'
760000 STACKEX(se=97) ' echo "Line 0: \'${LINES[0]}\'"\n    echo "Line 1: \'${LINES[1]}\'"\n    # Line 0: \'Hello\'\n    # Line 1: \''
780000 STACKEX(se=112) '     $("&lt;input type=\'hidden\' name=\'body\'&gt;").attr("value",body).appendTo($form);\n\n        $("&l'
800000 STACKEX(se=88) ' true\n        };\n        client.Send(&quot;MyEmailAddress@gmail.com&quot;, &quot;some.email@some.com'
820000 STACKEX(se=88) "/doc/refman/5.1/en/create-table.html</a></p><|endoftext|><p>I'm working on a new WPF application and"
840000 STACKEX(se=71) ' recreated\n  from other resources. It is\n  commonplace to exclude derived\n  resources from version a'
860000 STACKEX(se=45) 'html#rails_env" rel="nofollow noreferrer">the docs</a> you can set RailEnv. By default Passenger run'
880000 STACKEX(se=10) ' at my code and suggest how can I resolve this error.</p>\n\n<pre><code>&lt;head&gt;\n&lt;script type="'
900000 STACKEX(se=31) ' application (not an applet) that needs to access a web service. Proxies for the web service have be'
920000 STACKEX(se=92) ">. And it's not documented there, so it probably doesn't exist. (If it does exist, it's probably und"
940000 STACKEX(se=87) 'View.Items[i]);\n    // OR\n    // ListView1.Items.RemoveAt(i);\n  }\n}\n</code></pre>\n\n<p>EDIT:<br>\nThis'
960000 STACKEX(se=77) '-php/1301012#1301012">Quick question about sessions in PHP</a> which explains how to stay signed in.'
980000 STACKEX(se=78) 'http://www.google.co.uk/search?q=css+named+colors</a></p>\n\n<p>w3c html/css standards only specify 16'

[stdout]
0 WIKITEXT(w=7) ' Beardmore was sufficiently impressed with Shackleton to offer financial support , but other donatio'
20000 WIKITEXT(w=32) ' Ministers — the cabinet being its executive committee — headed by the prime minister . Any minister'
40000 WIKITEXT(w=12) ' " , such as Joe DiMaggio ( Italian @-@ American ) and Arthur Miller ( Jewish ) . According to Banne'
60000 WIKITEXT(w=3) " 's final performance was in Stockholm , Sweden , at the Solnahallen Arena on September 26 , 1986 , "
80000 WIKITEXT(w=17) ' behalf of South Korea . The United States , a member of the UN , simultaneously committed ground fo'
100000 WIKITEXT(w=11) ' Picross . Another new item , the GB Sounds , changes the background music to the original 8 @-@ bit'
120000 WIKITEXT(w=35) ' , so named because it was once a rail line into downtown Negaunee . The trunkline turns east on Jac'
140000 WIKITEXT(w=33) ' would again compare the East and the West , writing that " while the East sank deeper into darkness'
160000 WIKITEXT(w=15) " ) . \n<|endoftext|> The December 2000 nor 'easter was a significant winter storm that impacted the M"
180000 WIKITEXT(w=13) ' The video shows Stan playing tennis against herself in a futuristic setting . The track was success'
200000 WIKITEXT(w=20) ' it followed the southern end of the Lodge Freeway . By the middle of 1961 , the Watervliet – Paw Pa'
220000 WIKITEXT(w=33) ' high approval ratings steadily declined , while his disapproval numbers increased significantly . I'
240000 WIKITEXT(w=31) 'aics differ across countries , including Australia , China , Germany , Israel , Japan , and the Unit'
260000 PROSE ' tonnes shipped in 2015, further exacerbating a tightening rice market as drought has also hit top r'
280000 PROSE '�t even know how to be there.\n\nThey don’t know how to promote themselves. They couldn’t even find th'
300000 PROSE ' the hiring manager. Here again, the hiring manager typically reviews resumes for the desired conten'
320000 PROSE ". EDD is calculated using Parikh's formula by adding nine months to the start of the last menstrual "
340000 PROSE ' it. Note that using MonoGame will not save you from that license cost, as much as they hate talking'
360000 PROSE ' Godfrey and Seth Scarborough\n\nShare this post<|endoftext|>The latest trend in Japan? PICKING YOUR N'
380000 PROSE ' upon them. There was little or any attempt at including Russia in a company of the nations of equal'
400000 PROSE " bite out of Walker's counting stats. He retired with 2,160 hits, 383 home runs and 230 stolen bases"
420000 PROSE ' can get back into football shape after the long layoff.<|endoftext|>5 APRIL, 2017 – Fortec Motorspo'
440000 PROSE ' how new motherhood really can be. To let her in on all the real secrets of being a mother.\n\nI wante'
460000 PROSE 'Comments (none posted)\n\nthe most popular open source ERP system\n\nOdoo is, according to Wikipedia , "'
480000 PROSE 'ness, fairness and accuracy.”\n\nSome frustrations with Baker’s stewardship recently spilled outside t'
500000 PROSE 'I fucking hate when he does this shit pic.twitter.com/kpmcHnW4Cz — Miley Ray Cyrus (@MileyCyrus) Apr'
520000 PROSE ' in Etah and Jaithra town Yadav alleged that BJP has "copied" his party\'s poll\'s manifesto and asked'
540000 PROSE ' rebounded from six-week lows as investors pared back holdings of low-risk government debt.The yield'
560000 PROSE ' next round of examination. It also contained the names of five male applicants which shocked other '
580000 PROSE '.Amritsar — less than 30 km from the new border — endured horrific rioting, with areas like Katra Ja'
600000 PROSE ' flexibility to employees and saves seating space for the employer, amongst many other benefits Work'
620000 PROSE ' weightage, etc for MHT CET 2018 have been set by Maharashtra State Board of Secondary and Higher Se'
640000 PROSE ' to the Lieutenant Governor”, “forwarded to the Lieutenant Governor”, “submitted to the Lieutenant G'
660000 PROSE ' four decades.<|endoftext|>He started his music career on Youtube and went on to give fans the party'
680000 PROSE ' Elgar, Shorter in length around middle, Elgar ducks under it. 235/0\n51.5 M Rahman to Elgar, Angles '
700000 PROSE 'But what we do know and understand perhaps is that we’re at a loss - a loss of a consolidated identi'
720000 PROSE ' media to report cases of sexual offences against child victims, section 228-A of the IPC deals with'
740000 PROSE ' the world."Her show Superstore follows the lives of different individuals working in the store. The'
760000 STACKEX(se=97) ' echo "Line 0: \'${LINES[0]}\'"\n    echo "Line 1: \'${LINES[1]}\'"\n    # Line 0: \'Hello\'\n    # Line 1: \''
780000 STACKEX(se=112) '     $("&lt;input type=\'hidden\' name=\'body\'&gt;").attr("value",body).appendTo($form);\n\n        $("&l'
800000 STACKEX(se=88) ' true\n        };\n        client.Send(&quot;MyEmailAddress@gmail.com&quot;, &quot;some.email@some.com'
820000 STACKEX(se=88) "/doc/refman/5.1/en/create-table.html</a></p><|endoftext|><p>I'm working on a new WPF application and"
840000 STACKEX(se=71) ' recreated\n  from other resources. It is\n  commonplace to exclude derived\n  resources from version a'
860000 STACKEX(se=45) 'html#rails_env" rel="nofollow noreferrer">the docs</a> you can set RailEnv. By default Passenger run'
880000 STACKEX(se=10) ' at my code and suggest how can I resolve this error.</p>\n\n<pre><code>&lt;head&gt;\n&lt;script type="'
900000 STACKEX(se=31) ' application (not an applet) that needs to access a web service. Proxies for the web service have be'
920000 STACKEX(se=92) ">. And it's not documented there, so it probably doesn't exist. (If it does exist, it's probably und"
940000 STACKEX(se=87) 'View.Items[i]);\n    // OR\n    // ListView1.Items.RemoveAt(i);\n  }\n}\n</code></pre>\n\n<p>EDIT:<br>\nThis'
960000 STACKEX(se=77) '-php/1301012#1301012">Quick question about sessions in PHP</a> which explains how to stay signed in.'
980000 STACKEX(se=78) 'http://www.google.co.uk/search?q=css+named+colors</a></p>\n\n<p>w3c html/css standards only specify 16'
[metadata] {"noOutputExpected": false}

sampled 26003 wikitext-style 0 stackex-style 9
---SE--- '<|endoftext|>Please note that you should never self-prescribe TCM ingredients. A TCM ingredient is almost never eaten on its own but as part of a formula containing several ingredients that act together. Please consult a professional TCM practitioner, they will be best able to guide you.\nPreparation'
---SE--- ' media is an increasing part of everyday life for many of us. I know that I use it both for work and personally.\nSuccess in of chemistry is usually underpinned by a sound knowledge of key concepts, such as a good working knowledge of atoms and bonding.\nTAPS aims to develop support for a valid, relia'
---SE--- ' })();<|endoftext|>Hampton Bay Ceiling Fan | The Home Depot Community\nHome\nThe Home Depot Community\nDiscussion\nSign In to join the community | Help\nWelcome, DEMO | Sign Out | Help\nAppliances\nAppliances Bath & Faucets Build & Remodel Building Materials Customer Care Decor Doors & Windows Electrical F'
---SE--- ' Sciences Community & Beyond<|endoftext|>Wicket 1.4.10 released | Apache Wicket\nQuick Start\nDownload\nDocumentation\nSupport\nContribute\nCommunity\nApache\nWicket 1.4.10 released\n11 Aug 2010\nThis is the tenth maintenance release of the 1.4.x series and brings over thirty bug fixes and improvements.\nAs we'
---SE--- 'ديوانية الاسبانية\nتسجيل الدخول\nسجل\nENGLISH\nالصفحة الرئيسية\nأخبار\nمعدل التحويل\nأسعار الذهب\nعروض\nالمدونات\nهدف\nالإعلانات المبوبة\nالمنتديات\nأكثر من\nأشرطة فيديو حملات\nENGLISH\nالبحث المتقدم\nبحث\nالبحث المتقدم\nجدولة حدث\nملاحظة !!! عزيزي المستخدم، جميع النصوص العربية قد تمت ترجمتها من نصوص الانجليزية باستخدا'
---SE--- " Baby Pink\nhttps://www.agolde.com/balloon-sleeve-cropped-in-baby-pink?___store=agolde 25549 Balloon Sleeve Cropped in Baby Pink https://www.agolde.com/media/catalog/product/b/a/balloon_pink.jpg 158.00 USD OutOfStock /Shop /Shop/Women's /Shop/Women's/Tops /Shop/Women's/Spring Summer Collection /Shop/"

real	0m1.802s
user	0m1.410s
sys	0m0.080s

[stdout]
sampled 26003 wikitext-style 0 stackex-style 9
---SE--- '<|endoftext|>Please note that you should never self-prescribe TCM ingredients. A TCM ingredient is almost never eaten on its own but as part of a formula containing several ingredients that act together. Please consult a professional TCM practitioner, they will be best able to guide you.\nPreparation'
---SE--- ' media is an increasing part of everyday life for many of us. I know that I use it both for work and personally.\nSuccess in of chemistry is usually underpinned by a sound knowledge of key concepts, such as a good working knowledge of atoms and bonding.\nTAPS aims to develop support for a valid, relia'
---SE--- ' })();<|endoftext|>Hampton Bay Ceiling Fan | The Home Depot Community\nHome\nThe Home Depot Community\nDiscussion\nSign In to join the community | Help\nWelcome, DEMO | Sign Out | Help\nAppliances\nAppliances Bath & Faucets Build & Remodel Building Materials Customer Care Decor Doors & Windows Electrical F'
---SE--- ' Sciences Community & Beyond<|endoftext|>Wicket 1.4.10 released | Apache Wicket\nQuick Start\nDownload\nDocumentation\nSupport\nContribute\nCommunity\nApache\nWicket 1.4.10 released\n11 Aug 2010\nThis is the tenth maintenance release of the 1.4.x series and brings over thirty bug fixes and improvements.\nAs we'
---SE--- 'ديوانية الاسبانية\nتسجيل الدخول\nسجل\nENGLISH\nالصفحة الرئيسية\nأخبار\nمعدل التحويل\nأسعار الذهب\nعروض\nالمدونات\nهدف\nالإعلانات المبوبة\nالمنتديات\nأكثر من\nأشرطة فيديو حملات\nENGLISH\nالبحث المتقدم\nبحث\nالبحث المتقدم\nجدولة حدث\nملاحظة !!! عزيزي المستخدم، جميع النصوص العربية قد تمت ترجمتها من نصوص الانجليزية باستخدا'
---SE--- " Baby Pink\nhttps://www.agolde.com/balloon-sleeve-cropped-in-baby-pink?___store=agolde 25549 Balloon Sleeve Cropped in Baby Pink https://www.agolde.com/media/catalog/product/b/a/balloon_pink.jpg 158.00 USD OutOfStock /Shop /Shop/Women's /Shop/Women's/Tops /Shop/Women's/Spring Summer Collection /Shop/"

real	0m1.802s
user	0m1.410s
sys	0m0.080s
[metadata] {"noOutputExpected": false}

n 26003 {'code_brace': 1018, 'func_call': 1905, 'import': 62, 'html_ent': 28, 'shell': 146, 'md_code': 0}
codeish(>=3 markers) 36 0.1%
--- 'If 2 boys consume 2x calories every y/2 days. 8 girls consume z/2 calories every y days. How many calories will 12 girls & 12 boys consume in 12 days ?\nAdjacent sides of the rectangular plot are 20meter and 12meter respectively. A dishonest builder in an attempt to make more profit has encroached 2meter of land on all four sides and then by doing the fencing of the entire plot, he sold it to a cus'
--- ' is the language used to manipulate relational databases. In this tutorial, we are laying down the foundations of working with SQL using one of its implementations MYSQL. MYSQL is an open source database system used by millions of systems out there. While working with your database, you usually want to do basic stuff like creating or dropping(deleting) databases, adding tables to the database, dro'
--- " numeric value.<|endoftext|>Script to launch Amazon EC2 Spot instances | peteris.rocks\npeteris.rocks\nBlog\nAbout\nNow\nScript to launch Amazon EC2 Spot instances\nQuickly launch and provision an Amazon EC2 spot instance with a simple Python script\nLast updated on October 10, 2016\nThis script will\ncreate a new key pair if one doesn't exist\ncreate a security group if one doesn't exist\nrequest a new spot"

[stdout]
n 26003 {'code_brace': 1018, 'func_call': 1905, 'import': 62, 'html_ent': 28, 'shell': 146, 'md_code': 0}
codeish(>=3 markers) 36 0.1%
--- 'If 2 boys consume 2x calories every y/2 days. 8 girls consume z/2 calories every y days. How many calories will 12 girls & 12 boys consume in 12 days ?\nAdjacent sides of the rectangular plot are 20meter and 12meter respectively. A dishonest builder in an attempt to make more profit has encroached 2meter of land on all four sides and then by doing the fencing of the entire plot, he sold it to a cus'
--- ' is the language used to manipulate relational databases. In this tutorial, we are laying down the foundations of working with SQL using one of its implementations MYSQL. MYSQL is an open source database system used by millions of systems out there. While working with your database, you usually want to do basic stuff like creating or dropping(deleting) databases, adding tables to the database, dro'
--- " numeric value.<|endoftext|>Script to launch Amazon EC2 Spot instances | peteris.rocks\npeteris.rocks\nBlog\nAbout\nNow\nScript to launch Amazon EC2 Spot instances\nQuickly launch and provision an Amazon EC2 spot instance with a simple Python script\nLast updated on October 10, 2016\nThis script will\ncreate a new key pair if one doesn't exist\ncreate a security group if one doesn't exist\nrequest a new spot"
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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": "#!/usr/bin/env python3\n\"\"\"\nCurate a 12M-token pretraining selection from a raw web pool for a BROAD\nmulti-domain high-quality English target (encyclopedic / HQ web prose / news /\ntechnical Q&A).\n\nSTATED CRITERION\n----------------\nA document is selected iff it (1) passes a fixed set of mechanical junk filters\n(length, English-ness, boilerplate, line-repetition, symbol/digit/caps ratios)\nand (2) ranks highly under a *register-conditional Moore-Lewis cross-entropy\ndifference*:\n\n        s_r(d) = H_general(d) - H_r(d)\n\nwhere H_r is the per-word cross-entropy of document d under an interpolated\nunigram+bigram model of target register r, and H_general is the same quantity\nunder a background model estimated from the pool itself.  s_r(d) is large when d\nlooks much more like register r than like generic web crawl.\n\nThe four registers are read off the DISCLOSED dev target (`multi_dev.npy`), which\nis four contiguous equal blocks (wiki / web prose / news / technical Q&A).  The\nfinal selection fills the 12M-token budget with an EQUAL token quota per register\n(3M each), each quota taken in descending s_r order, because the evaluation\naverages loss uniformly over the four registers.  Near-duplicates are dropped.\n\nStages are cached in /tmp so the script is cheap to re-run.\nUsage:  python3 curate.py [--out selection.json]\n\"\"\"\nimport argparse, hashlib, html, json, math, os, re, sys\nimport multiprocessing as mp\nimport numpy as np\n\nPOOL = \"/workspace/data/pool.jsonl\"\nDEV = \"/workspace/data/multi_dev.npy\"\nCACHE = \"/tmp/curate_cache\"\nBUDGET = 12_000_000\nN_REG = 4\nREG_NAMES = [\"wiki\", \"webprose\", \"news\", \"techqa\"]\n# dev target = 4 contiguous blocks of equal length (verified by inspection)\nos.makedirs(CACHE, exist_ok=True)\n\nWORD_RE = re.compile(r\"[a-z]+|[0-9]+|[^\\sa-z0-9]\")\nV_UNI = 1 << 19          # hashed unigram table\nV_BI = 1 << 21           # hashed bigram table\nMAXW = 6000              # words scored per doc\nLAMBDA_BI = 0.65         # bigram/unigram interpolation\n\n\n# ----------------------------------------------------------------- hashing\ndef hash_words(words):\n    \"\"\"map word strings -> uint64 ids via stable md5 (independent of PYTHONHASHSEED)\"\"\"\n    return np.array([int.from_bytes(hashlib.md5(w.encode()).digest()[:8], \"little\")\n                     for w in words], dtype=np.uint64)\n\n\ndef feat_ids(words):\n    \"\"\"(unigram slots, bigram slots) for a word list.\"\"\"\n    h = hash_words(words)\n    u = (h % V_UNI).astype(np.int64)\n    if len(h) < 2:\n        return u, np.zeros(0, dtype=np.int64)\n    b = ((h[:-1] * np.uint64(1000003) + h[1:]) % V_BI).astype(np.int64)\n    return u, b\n\n\n# ----------------------------------------------------------------- references\ndef normalize_ref(text, kind):\n    \"\"\"Strip surface artifacts so the reference measures register/content, not markup.\"\"\"\n    if kind == \"wiki\":                      # WikiText-103 detokenization artifacts\n        text = text.replace(\" @-@ \", \"-\").replace(\" @,@ \", \",\").replace(\" @.@ \", \".\")\n        text = re.sub(r\" ([,.;:!?\\)\\]])\", r\"\\1\", text)\n        text = re.sub(r\"([\\(\\[]) \", r\"\\1\", text)\n    if kind == \"techqa\":                    # StackExchange HTML wrapper\n        text = re.sub(r\"<[^>\\n]{1,40}>\", \" \", text)\n        text = html.unescape(text)\n    return text\n\n\ndef build_references():\n    from transformers import AutoTokenizer\n    tok = AutoTokenizer.from_pretrained(\"gpt2\")\n    d = np.load(DEV)\n    n = len(d) // N_REG\n    refs = []\n    for r in range(N_REG):\n        txt = tok.decode(d[r * n:(r + 1) * n])\n        txt = txt.replace(\"<|endoftext|>\", \"\\n\")\n        refs.append(normalize_ref(txt, REG_NAMES[r] if REG_NAMES[r] in (\"wiki\", \"techqa\") else \"x\"))\n    return refs\n\n\ndef count_model(texts):\n    \"\"\"interpolated unigram+bigram log-prob tables from a list of texts\"\"\"\n    uc = np.zeros(V_UNI, dtype=np.float64)\n    bc = np.zeros(V_BI, dtype=np.float64)\n    for t in texts:\n        w = WORD_RE.findall(t.lower())\n        for i in range(0, len(w), 50000):\n            u, b = feat_ids(w[i:i + 50000])\n            np.add.at(uc, u, 1.0)\n            np.add.at(bc, b, 1.0)\n    return uc, bc\n\n\ndef logprob_tables(uc, bc):\n    \"\"\"P(w|prev) = L*P_bi + (1-L)*P_uni ; bigram context total approximated by unigram count.\"\"\"\n    Nu = uc.sum()\n    p_uni = (uc + 0.5) / (Nu + 0.5 * V_UNI)\n    return p_uni, bc, Nu\n\n\n# ----------------------------------------------------------------- features\nSTOP = set(\"the and of to in a is that for it was as on with be by are this at from or an \"\n           \"have has not but they he she we you\".split())\nBOILER = [\n    \"sign in\", \"log in\", \"cookie\", \"privacy policy\", \"all rights reserved\", \"click here\",\n    \"javascript\", \"terms of service\", \"subscribe\", \"read more\", \"share this\", \"posted by\",\n    \"add to cart\", \"©\", \"follow us\", \"newsletter\", \"advertisement\", \"skip to content\",\n    \"leave a reply\", \"your email address\", \"no comments\", \"related posts\", \"tags:\",\n]\nNAUGHTY = [\"porn\", \"xxx\", \"sex chat\", \"viagra\", \"casino\", \"escort\", \"nude\", \"milf\",\n           \"camgirl\", \"betting odds\", \"payday loan\", \"cialis\", \"hentai\", \"fuck\", \"slut\"]\nCODE_PATS = [r\"[{};]\\s*\\n\", r\"\\b\\w+\\([\\w\\$\\\"']\", r\"^\\s*(?:import |from \\w+ import|#include|\"\n             r\"using \\w|require\\(|def |class |function |public |private |var |const |let |\\$)\",\n             r\"&(?:lt|gt|quot|amp);\", r\"(?:sudo |npm |pip install|apt-get|git clone|SELECT .* FROM|\"\n             r\"</?\\w+>|==|!=|=>|->|\\[\\]|::)\"]\nCODE_RE = [re.compile(p, re.M | re.I) for p in CODE_PATS]\nTERM_PUNCT = ('.', '!', '?', '\"', \"'\", ')', ':', ';', '”', '’')\nNF = 18  # number of scalar features\n\n\ndef doc_features(text, models):\n    n = len(text)\n    low = text.lower()\n    words = WORD_RE.findall(low)\n    alpha = [w for w in words if w[0].isalpha()]\n    nw = max(1, len(alpha))\n    lines = [l for l in text.split(\"\\n\") if l.strip()]\n    nl = max(1, len(lines))\n    letters = sum(c.isalpha() for c in text)\n    f = np.zeros(NF, dtype=np.float32)\n    f[0] = n\n    f[1] = len(alpha)\n    f[2] = sum(1 for w in alpha if w in STOP) / nw\n    f[3] = sum(1 for c in text if ord(c) > 127) / max(1, n)\n    f[4] = sum(len(w) for w in alpha) / nw\n    f[5] = sum(1 for l in lines if l.rstrip().endswith(TERM_PUNCT)) / nl\n    f[6] = 1.0 - len(set(lines)) / nl\n    f[7] = sum(1 for c in text if c.isupper()) / max(1, letters)\n    f[8] = sum(1 for c in text if c.isdigit()) / max(1, n)\n    f[9] = sum(low.count(b) for b in BOILER) / (len(alpha) / 100.0 + 1)\n    f[10] = sum(low.count(b) for b in NAUGHTY)\n    f[11] = sum(1 for r in CODE_RE if r.search(text))\n    f[12] = sum(len(r.findall(text)) for r in CODE_RE[:2]) / (len(alpha) / 100.0 + 1)\n    f[13] = np.mean([len(l) for l in lines]) if lines else 0\n    # ---- Moore-Lewis cross-entropy differences (features 14..17)\n    w = words[:MAXW]\n    if len(w) >= 30:\n        u, b = feat_ids(w)\n        ce = np.zeros(N_REG + 1, dtype=np.float64)\n        for m, (p_uni, bc, Nu) in enumerate(models):\n            pu = p_uni[u]\n            pb = (bc[b] + 0.1) / (uc_ctx(bc, u, Nu) + 0.1 * 200.0)\n            lp = np.log(LAMBDA_BI * pb + (1 - LAMBDA_BI) * pu[1:])\n            ce[m] = -(lp.mean() * 0.9 + np.log(pu).mean() * 0.1)\n        for r in range(N_REG):\n            f[14 + r] = ce[N_REG] - ce[r]     # H_general - H_register\n    else:\n        f[14:18] = -10.0\n    return f\n\n\ndef uc_ctx(bc, u, Nu):\n    \"\"\"context normalizer for the hashed bigram: use a constant scaled by corpus size.\n    (hashed bigrams lose exact context totals; a constant keeps the score monotone\n    in bigram frequency, which is all the ranking needs)\"\"\"\n    return Nu / 40.0\n\n\n# ----------------------------------------------------------------- parallel pass\n_G = {}\n\n\ndef _init(models):\n    _G[\"models\"] = models\n\n\ndef _work(args):\n    start, end = args\n    out_f, out_id = [], []\n    with open(POOL, \"rb\") as fh:\n        if start:\n            fh.seek(start - 1)\n            fh.readline()\n        while True:\n            pos = fh.tell()\n            if pos >= end:\n                break\n            line = fh.readline()\n            if not line:\n                break\n            try:\n                r = json.loads(line)\n            except Exception:\n                continue\n            out_id.append(r[\"id\"])\n            out_f.append(doc_features(r[\"text\"], _G[\"models\"]))\n    return np.array(out_id, dtype=np.int64), np.array(out_f, dtype=np.float32)\n\n\ndef score_pool(nproc=16):\n    cf = os.path.join(CACHE, \"scores.npz\")\n    if os.path.exists(cf):\n        z = np.load(cf)\n        return z[\"ids\"], z[\"feats\"]\n    print(\"[1/3] building reference + background language models ...\", flush=True)\n    refs = build_references()\n    models = []\n    for r in range(N_REG):\n        uc, bc = count_model([refs[r]])\n        models.append(logprob_tables(uc, bc))\n    # background model: every 13th pool doc\n    bg = []\n    with open(POOL) as fh:\n        for i, line in enumerate(fh):\n            if i % 13 == 0:\n                bg.append(json.loads(line)[\"text\"])\n    print(f\"      background sample: {len(bg)} docs\", flush=True)\n    uc, bc = count_model(bg)\n    models.append(logprob_tables(uc, bc))\n    del bg\n\n    sz = os.path.getsize(POOL)\n    bounds = [(sz * k // nproc, sz * (k + 1) // nproc) for k in range(nproc)]\n    print(f\"[2/3] scoring 182k docs on {nproc} procs ...\", flush=True)\n    with mp.Pool(nproc, initializer=_init, initargs=(models,)) as p:\n        res = p.map(_work, bounds)\n    ids = np.concatenate([r[0] for r in res])\n    feats = np.concatenate([r[1] for r in res])\n    o = np.argsort(ids)\n    ids, feats = ids[o], feats[o]\n    np.savez(cf, ids=ids, feats=feats)\n    print(f\"      scored {len(ids)} docs -> {cf}\", flush=True)\n    return ids, feats\n\n\n# ----------------------------------------------------------------- token cache\ndef _tok_work(chunk):\n    from transformers import AutoTokenizer\n    tok = _G.get(\"tok\")\n    if tok is None:\n        tok = AutoTokenizer.from_pretrained(\"gpt2\")\n        _G[\"tok\"] = tok\n    ids, texts = chunk\n    enc = tok(texts, add_special_tokens=False)[\"input_ids\"]\n    return ids, [np.array(e, dtype=np.uint16) for e in enc]\n\n\ndef tokenize_subset(cand_ids, tag, nproc=16):\n    \"\"\"exact GPT-2 token ids for a candidate subset -> {id: np.uint16 array}\"\"\"\n    cf = os.path.join(CACHE, f\"tok_{tag}.npz\")\n    if os.path.exists(cf):\n        z = np.load(cf, allow_pickle=True)\n        return dict(zip(z[\"ids\"].tolist(), z[\"arrs\"]))\n    want = set(int(i) for i in cand_ids)\n    texts, tids = [], []\n    with open(POOL) as fh:\n        for line in fh:\n            r = json.loads(line)\n            if r[\"id\"] in want:\n                tids.append(r[\"id\"])\n                texts.append(r[\"text\"])\n    chunks = []\n    step = max(1, len(tids) // (nproc * 4))\n    for i in range(0, len(tids), step):\n        chunks.append((tids[i:i + step], texts[i:i + step]))\n    os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n    with mp.Pool(nproc, initializer=_init, initargs=(None,)) as p:\n        res = p.map(_tok_work, chunks)\n    out = {}\n    for ii, aa in res:\n        for i, a in zip(ii, aa):\n            out[i] = a\n    np.savez(cf, ids=np.array(list(out.keys())), arrs=np.array(list(out.values()), dtype=object))\n    return out\n\n\n# ----------------------------------------------------------------- selection\ndef keep_mask(F, tech=False):\n    \"\"\"mechanical junk filters (Gopher/C4-flavoured). `tech` relaxes prose-shape rules.\"\"\"\n    nb, nalpha, stop, nonascii, awl, termp, duplines, upper, digit, boiler, naughty = (\n        F[:, 0], F[:, 1], F[:, 2], F[:, 3], F[:, 4], F[:, 5], F[:, 6], F[:, 7], F[:, 8],\n        F[:, 9], F[:, 10])\n    m = (nb > 700) & (nb < 500_000) & (nalpha > 110)\n    m &= stop > (0.045 if tech else 0.075)\n    m &= nonascii < 0.06\n    m &= (awl > 3.0) & (awl < (11.0 if tech else 8.0))\n    m &= termp > (0.15 if tech else 0.40)\n    m &= duplines < 0.30\n    m &= upper < 0.16\n    m &= digit < (0.14 if tech else 0.09)\n    m &= boiler < (5.0 if tech else 2.2)\n    m &= naughty <= 1\n    return m\n\n\ndef build_selection(ids, F, per_reg_tokens=BUDGET // N_REG, overshoot=2.6, verbose=True):\n    n = len(ids)\n    base = keep_mask(F, tech=False)\n    techm = keep_mask(F, tech=True) & (F[:, 11] >= 3)      # technical docs: relaxed shape\n    ok = [base, base, base, base]\n    ok[3] = base | techm\n    if verbose:\n        print(f\"[3/3] filters keep {base.sum()}/{n} prose-clean, \"\n              f\"{techm.sum()} technical\", flush=True)\n\n    # rank candidates per register by Moore-Lewis score, take a generous candidate pool\n    cand, order = set(), []\n    for r in range(N_REG):\n        s = np.where(ok[r], F[:, 14 + r], -1e9)\n        k = int(per_reg_tokens * overshoot / 900)          # ~900 tok/doc heuristic\n        idx = np.argsort(-s)[:max(k, 4000)]\n        order.append(idx)\n        cand.update(ids[idx].tolist())\n\n    toks = tokenize_subset(sorted(cand), \"cand\")\n\n    # fill an equal token quota per register, interleaved so priority order is balanced\n    picked, seen, dup, per_list = set(), set(), 0, [[] for _ in range(N_REG)]\n    used = [0] * N_REG\n    for r in range(N_REG):\n        for i in order[r]:\n            did = int(ids[i])\n            if used[r] >= per_reg_tokens:\n                break\n            if did in picked:\n                continue\n            a = toks.get(did)\n            if a is None or len(a) < 64:\n                continue\n            key = fingerprint(a)\n            if key in seen:\n                dup += 1\n                continue\n            seen.add(key)\n            picked.add(did)\n            per_list[r].append(did)\n            used[r] += len(a) + 1\n    if verbose:\n        for r in range(N_REG):\n            print(f\"      {REG_NAMES[r]:9s}: {len(per_list[r]):6d} docs  {used[r]/1e6:.2f}M tok\",\n                  flush=True)\n        print(f\"      near-dups dropped: {dup}\", flush=True)\n\n    # round-robin interleave so that any prefix of the list is register-balanced\n    sel = []\n    for j in range(max(len(x) for x in per_list)):\n        for r in range(N_REG):\n            if j < len(per_list[r]):\n                sel.append(per_list[r][j])\n    return sel, toks\n\n\ndef fingerprint(arr):\n    \"\"\"cheap near-dup key: sorted min-8 of hashed 12-token shingles\"\"\"\n    a = arr[:512].astype(np.int64)\n    if len(a) < 16:\n        return bytes(a.tobytes())\n    sh = np.lib.stride_tricks.sliding_window_view(a, 12)\n    h = (sh * np.arange(1, 13, dtype=np.int64) * 2654435761).sum(1) & 0x7FFFFFFF\n    return tuple(np.sort(h)[:8].tolist())\n\n\ndef main():\n    ap = argparse.ArgumentParser()\n    ap.add_argument(\"--out\", default=\"/workspace/submission/selection.json\")\n    ap.add_argument(\"--overshoot\", type=float, default=2.6)\n    a = ap.parse_args()\n    ids, F = score_pool()\n    sel, toks = build_selection(ids, F, overshoot=a.overshoot)\n    tot = sum(len(toks[i]) + 1 for i in sel)\n    json.dump(sel, open(a.out, \"w\"))\n    print(f\"wrote {len(sel)} ids ({tot/1e6:.2f}M tokens, budget {BUDGET/1e6:.0f}M) -> {a.out}\")\n\n\nif __name__ == \"__main__\":\n    main()\n", "structuredPatch": [], "originalFile": null, "userModified": false}

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[metadata] {"noOutputExpected": false}

refs [1133933, 1102916, 1095026, 699536] 3.6s
WIKI-REF 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 Geograph'
TECH-REF sample: " 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. \n So, the que"
reg models 4.0s
bg model 1877 10.3s
scored 300 0.36s -> per-doc 1.21ms
mean feats [2921.74   494.67     0.304    0.003    4.711    0.734    0.001    0.051    0.011    0.057    0.157    0.113    0.022  283.005   -0.495   -0.154   -0.338   -0.622]
==== TOP for wiki [0.305 0.232]
    'Private Beleza rests in a home fit for heroes\nThis article was published in the Cape Argus on 17th July 2014 under the title “Private Beleza rests in a home fit for heroes” and is reprinted here with the kind permission of the Cap'
    "The 'Gossip Girl' star will make her Broadway debut on the production based on John Steinbeck's 1937 novella of the same title.\nLeighton Meester is set to make her Broadway debut in the revival of John Steinbeck's 1937 classic dra"
==== TOP for webprose [0.224 0.196]
    'Texans players to watch\nBrian Cushing, inside linebacker\nHe’s off to a terrific start, leading the team with 24 tackles — 16 unassisted, according to the coaches’ review. He has two sacks, four tackles for loss and an interception'
    'I rotate my newspaper reading where I can, and I was surprised to see that the Telegraph has one of the most inciteful explanations of how we have got to where we are on devolution.\nI have long thought that Scottish Labour is larg'
==== TOP for news [0.202 0.189]
    'Polish-born Chicago boxer Andrew Golota will plead not guilty at a court date July 19 to charges of unlawful possession of 12 guns, his lawyer said Friday. "There are two sides to every story, and we look forward to a full explora'
    "The 'Gossip Girl' star will make her Broadway debut on the production based on John Steinbeck's 1937 novella of the same title.\nLeighton Meester is set to make her Broadway debut in the revival of John Steinbeck's 1937 classic dra"
==== TOP for techqa [0.222 0.087]
    'Nicely application-level wrapper for anti-virus software.\nRuby gem to interact with ClamAV daemon(Clamd)\nFancy messaging library for Ruby\nClamp provides an object-model for command-line utilities. It handles parsing of comman...\nR'
    "Hey PD Team,\nI've created forum account recently and it was created based on Linkedin secondary language instead of primary language english.\nEven I updated my profile second language Firstname and Lastname to english on Linkedin,"

[stdout]
refs [1133933, 1102916, 1095026, 699536] 3.6s
WIKI-REF 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 Geograph'
TECH-REF sample: " 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. \n So, the que"
reg models 4.0s
bg model 1877 10.3s
scored 300 0.36s -> per-doc 1.21ms
mean feats [2921.74   494.67     0.304    0.003    4.711    0.734    0.001    0.051    0.011    0.057    0.157    0.113    0.022  283.005   -0.495   -0.154   -0.338   -0.622]
==== TOP for wiki [0.305 0.232]
    'Private Beleza rests in a home fit for heroes\nThis article was published in the Cape Argus on 17th July 2014 under the title “Private Beleza rests in a home fit for heroes” and is reprinted here with the kind permission of the Cap'
    "The 'Gossip Girl' star will make her Broadway debut on the production based on John Steinbeck's 1937 novella of the same title.\nLeighton Meester is set to make her Broadway debut in the revival of John Steinbeck's 1937 classic dra"
==== TOP for webprose [0.224 0.196]
    'Texans players to watch\nBrian Cushing, inside linebacker\nHe’s off to a terrific start, leading the team with 24 tackles — 16 unassisted, according to the coaches’ review. He has two sacks, four tackles for loss and an interception'
    'I rotate my newspaper reading where I can, and I was surprised to see that the Telegraph has one of the most inciteful explanations of how we have got to where we are on devolution.\nI have long thought that Scottish Labour is larg'
==== TOP for news [0.202 0.189]
    'Polish-born Chicago boxer Andrew Golota will plead not guilty at a court date July 19 to charges of unlawful possession of 12 guns, his lawyer said Friday. "There are two sides to every story, and we look forward to a full explora'
    "The 'Gossip Girl' star will make her Broadway debut on the production based on John Steinbeck's 1937 novella of the same title.\nLeighton Meester is set to make her Broadway debut in the revival of John Steinbeck's 1937 classic dra"
==== TOP for techqa [0.222 0.087]
    'Nicely application-level wrapper for anti-virus software.\nRuby gem to interact with ClamAV daemon(Clamd)\nFancy messaging library for Ruby\nClamp provides an object-model for command-line utilities. It handles parsing of comman...\nR'
    "Hey PD Team,\nI've created forum account recently and it was created based on Linkedin secondary language instead of primary language english.\nEven I updated my profile second language Firstname and Lastname to english on Linkedin,"
[metadata] {"noOutputExpected": false}

patched
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4960

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patched
syntax OK
4960
[metadata] {"noOutputExpected": false}

600 docs 1.30 ms/doc
vs-bg  mean [-1.743 -1.186 -1.583 -2.393]  std [0.648 0.562 0.604 0.69 ]
contr  mean [-1.589 -1.003 -1.441 -2.302]  std [0.643 0.552 0.59  0.742]
corr(vs-bg across regs):
 [[1.    0.733 0.812 0.265]
 [0.733 1.    0.849 0.573]
 [0.812 0.849 1.    0.402]
 [0.265 0.573 0.402 1.   ]]
== wiki vsBG [ 0.285 -0.257]
     'Washington (D.C.) – Public Schools;\nWashington Normal School;\nWilson, James Ormond;\nWilson Normal School;\nWilson Teachers College\nThe college was named in honor of James '
     'Private Beleza rests in a home fit for heroes\nThis article was published in the Cape Argus on 17th July 2014 under the title “Private Beleza rests in a home fit for heroe'
== wiki CONTR [ 0.774 -0.03 ]
     'Washington (D.C.) – Public Schools;\nWashington Normal School;\nWilson, James Ormond;\nWilson Normal School;\nWilson Teachers College\nThe college was named in honor of James '
     "2 Min Read\nLOS ANGELES (Reuters) - Singer-songwriter Taylor Swift was named Billboard's woman of the year on Tuesday, becoming the youngest artist ever to receive the hon"
== webprose vsBG [-0.083 -0.298]
     'I rotate my newspaper reading where I can, and I was surprised to see that the Telegraph has one of the most inciteful explanations of how we have got to where we are on '
     'Spain: Casillas (Víctor Valdés 46’); Azpilicueta, Javi Martínez, Sergio Ramos (Raúl Albiol 65‘), Jordi Alba; Busquets (Xabi Alonso 46’), Thiago; Pedro (Cazorla 82’), Fàbr'
== webprose CONTR [0.184 0.115]
     "St. patrick's day is quickly approaching! help mr. mcmahan come up with an addition or subtraction story problem about this fun day! for example, thomas fitzpatrick had 1"
     'I had him i drop him, even say he did get traded it take while to learn plays plus he be a back up... He has no value this year...\nMJD, Chris Johnson, Morris, Mendy, Dwje'
== news vsBG [ 0.11  -0.234]
     'Agartala, Sep 15 (IANS) The BJP on Tuesday said it would soon launch a stir if Tripura’s Left Front government evicted tribals to set up a firing range for a paramilitary'
     "Simona Halep's remarkable run continued as she crushed Wimbledon specialist Sabine Lisicki to reach her first semi-final at the All England Club.\nThis time last year, Hal"
== news CONTR [0.201 0.024]
     'Agartala, Sep 15 (IANS) The BJP on Tuesday said it would soon launch a stir if Tripura’s Left Front government evicted tribals to set up a firing range for a paramilitary'
     'January is National Mentoring Month! This month is nationally dedicated to mentoring as well as honoring our mentors.\nMentoring plays a critical role in the lives of many'
== techqa vsBG [-0.544 -0.597]
     'Sorry you are unauthorized to download this file.\nIf you have an account with this site, you can try the following links to download the file:\nThis link is a direct downl'
     'I’m a big lover of using base machine for SharePoint development work instead of using different VMs. I love that approach,\n- SharePoint runs much faster as it has more c'
== techqa CONTR [-0.224 -0.365]
     'Sorry you are unauthorized to download this file.\nIf you have an account with this site, you can try the following links to download the file:\nThis link is a direct downl'
     'I’m a big lover of using base machine for SharePoint development work instead of using different VMs. I love that approach,\n- SharePoint runs much faster as it has more c'

[stdout]
600 docs 1.30 ms/doc
vs-bg  mean [-1.743 -1.186 -1.583 -2.393]  std [0.648 0.562 0.604 0.69 ]
contr  mean [-1.589 -1.003 -1.441 -2.302]  std [0.643 0.552 0.59  0.742]
corr(vs-bg across regs):
 [[1.    0.733 0.812 0.265]
 [0.733 1.    0.849 0.573]
 [0.812 0.849 1.    0.402]
 [0.265 0.573 0.402 1.   ]]
== wiki vsBG [ 0.285 -0.257]
     'Washington (D.C.) – Public Schools;\nWashington Normal School;\nWilson, James Ormond;\nWilson Normal School;\nWilson Teachers College\nThe college was named in honor of James '
     'Private Beleza rests in a home fit for heroes\nThis article was published in the Cape Argus on 17th July 2014 under the title “Private Beleza rests in a home fit for heroe'
== wiki CONTR [ 0.774 -0.03 ]
     'Washington (D.C.) – Public Schools;\nWashington Normal School;\nWilson, James Ormond;\nWilson Normal School;\nWilson Teachers College\nThe college was named in honor of James '
     "2 Min Read\nLOS ANGELES (Reuters) - Singer-songwriter Taylor Swift was named Billboard's woman of the year on Tuesday, becoming the youngest artist ever to receive the hon"
== webprose vsBG [-0.083 -0.298]
     'I rotate my newspaper reading where I can, and I was surprised to see that the Telegraph has one of the most inciteful explanations of how we have got to where we are on '
     'Spain: Casillas (Víctor Valdés 46’); Azpilicueta, Javi Martínez, Sergio Ramos (Raúl Albiol 65‘), Jordi Alba; Busquets (Xabi Alonso 46’), Thiago; Pedro (Cazorla 82’), Fàbr'
== webprose CONTR [0.184 0.115]
     "St. patrick's day is quickly approaching! help mr. mcmahan come up with an addition or subtraction story problem about this fun day! for example, thomas fitzpatrick had 1"
     'I had him i drop him, even say he did get traded it take while to learn plays plus he be a back up... He has no value this year...\nMJD, Chris Johnson, Morris, Mendy, Dwje'
== news vsBG [ 0.11  -0.234]
     'Agartala, Sep 15 (IANS) The BJP on Tuesday said it would soon launch a stir if Tripura’s Left Front government evicted tribals to set up a firing range for a paramilitary'
     "Simona Halep's remarkable run continued as she crushed Wimbledon specialist Sabine Lisicki to reach her first semi-final at the All England Club.\nThis time last year, Hal"
== news CONTR [0.201 0.024]
     'Agartala, Sep 15 (IANS) The BJP on Tuesday said it would soon launch a stir if Tripura’s Left Front government evicted tribals to set up a firing range for a paramilitary'
     'January is National Mentoring Month! This month is nationally dedicated to mentoring as well as honoring our mentors.\nMentoring plays a critical role in the lives of many'
== techqa vsBG [-0.544 -0.597]
     'Sorry you are unauthorized to download this file.\nIf you have an account with this site, you can try the following links to download the file:\nThis link is a direct downl'
     'I’m a big lover of using base machine for SharePoint development work instead of using different VMs. I love that approach,\n- SharePoint runs much faster as it has more c'
== techqa CONTR [-0.224 -0.365]
     'Sorry you are unauthorized to download this file.\nIf you have an account with this site, you can try the following links to download the file:\nThis link is a direct downl'
     'I’m a big lover of using base machine for SharePoint development work instead of using different VMs. I love that approach,\n- SharePoint runs much faster as it has more c'
[metadata] {"noOutputExpected": false}

launched pid 909
[1/3] building reference + background language models ...

[stdout]
launched pid 909
[1/3] building reference + background language models ...
[metadata] {"noOutputExpected": false}

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

[metadata] {"type": "create", "filePath": "/workspace/fast_pack.py", "content": "#!/usr/bin/env python3\n\"\"\"Diagnostic packer: same semantics as pack_selection.py (tokenize in priority order,\nEOS after each doc, truncate at 12M) but reuses the cached tokenization so repeated\ndev evals are cheap. Verified against pack_selection.py output.\"\"\"\nimport json, os, sys, glob\nimport multiprocessing as mp\nimport numpy as np\n\nsys.path.insert(0, \"/workspace/submission\")\nimport curate as C\n\nBUDGET = 12_000_000\nSEL = sys.argv[1] if len(sys.argv) > 1 else \"/workspace/submission/selection.json\"\nOUT = sys.argv[2] if len(sys.argv) > 2 else \"/tmp/dev_train.npy\"\n\nsel = json.load(open(SEL))\ntoks = {}\nfor f in glob.glob(os.path.join(C.CACHE, \"tok_*.npz\")):\n    z = np.load(f)\n    flat, off, tid = z[\"flat\"], z[\"off\"], z[\"ids\"]\n    for k in range(len(tid)):\n        toks[int(tid[k])] = flat[off[k]:off[k + 1]]\n\n# tokenize anything not cached yet (bounded: only what the budget can consume)\nneed, run = [], 0\nfor i in sel:\n    if run >= BUDGET:\n        break\n    a = toks.get(int(i))\n    if a is None:\n        need.append(int(i))\n        run += 1200          # optimistic estimate while probing\n    else:\n        run += len(a) + 1\nif need:\n    print(f\"tokenizing {len(need)} uncached ids ...\", flush=True)\n    tag = \"extra_\" + str(abs(hash(tuple(sorted(need)[:50]))) % 10 ** 8)\n    toks.update(C.tokenize_subset(need, tag))\n\nparts, tot = [], 0\nused = 0\nfor i in sel:\n    a = toks[int(i)]\n    parts.append(a)\n    parts.append(np.array([50256], dtype=np.uint16))\n    tot += len(a) + 1\n    used += 1\n    if tot >= BUDGET:\n        break\narr = np.concatenate(parts)[:BUDGET].astype(np.uint16)\nnp.save(OUT, arr)\nprint(f\"packed {len(arr)} tokens from {used}/{len(sel)} ids -> {OUT}\")\n", "structuredPatch": [], "originalFile": null, "userModified": false}

wrote eval_blocks.py
[1/3] building reference + background language models ...
      background sample: 14002 docs
4890

[stdout]
wrote eval_blocks.py
[1/3] building reference + background language models ...
      background sample: 14002 docs
4890
[metadata] {"noOutputExpected": false}

random baseline selection: 30000 ids
[2/3] scoring 182k docs on 15 procs ...
      scored 182016 docs -> /tmp/curate_cache/scores.npz
DONE (182016,) (182016, 22)
4820

[stdout]
random baseline selection: 30000 ids
[2/3] scoring 182k docs on 15 procs ...
      scored 182016 docs -> /tmp/curate_cache/scores.npz
DONE (182016,) (182016, 22)
4820
[metadata] {"noOutputExpected": false}

docs 182016 prose-clean 81548 tech 599 tech&base 156
  nbytes    p10=561.000 med=2246.000 p90=8458.000
  nalpha    p10=91.000 med=359.000 p90=1344.000
  stop      p10=0.107 med=0.278 p90=0.347
  nonascii  p10=0.000 med=0.002 p90=0.008
  awl       p10=4.210 med=4.867 p90=5.644
  termp     p10=0.048 med=0.500 p90=0.941
  duplines  p10=0.000 med=0.000 p90=0.226
  upper     p10=0.025 med=0.060 p90=0.157
  digit     p10=0.000 med=0.011 p90=0.051
  boiler    p10=0.000 med=0.000 p90=0.942
  naughty   p10=0.000 med=0.000 p90=0.000
score percentiles vs-bg per reg:
  wiki [-3.461 -2.605 -2.133 -1.771] max -0.689
  webprose [-2.922 -2.008 -1.531 -1.212] max -0.359
  news [-3.314 -2.408 -1.92  -1.529] max -0.599
  techqa [-4.107 -3.046 -2.069 -1.355] max -0.265
==================== wiki
  [s=-0.69 code=0 nb=1876] 'Consanguinity alliance by blood, as affinity (q.v.) is alliance by marriage. Certain degrees of consanguinity are among the impediments to marriage, both by the law of nature and by the reve'
  [s=-0.92 code=0 nb=3641] 'Renewable electricity production, from sources such as wind power and solar power, is sometimes criticized for being variable or intermittent, but is not true for concentrated solar, geother'
  [s=-1.03 code=0 nb=808] 'AceShowbiz - "The Celebrity Apprentice" will return a week longer than it was originally planned. The fifth season of the reality series, which was initially set to debut on February 12, is '
  [s=-1.14 code=0 nb=24054] 'Hill 303 massacre\n|Hill 303 massacre|\nBodies of massacre victims gathered near Waegwan, South Korea, many with their hands still bound\n|Location||Hill 303, Waegwan, South Korea|\n|Date||Augus'
==================== webprose
  [s=-0.93 code=0 nb=864] 'Cowboys owner Jerry Jones said the offensive line is an area that can, and will, improve.\nHe is putting faith in offensive line coach Bill Callahan.\nAsked what can be done to help the offens'
  [s=-0.96 code=0 nb=3058] "Ben Mendelsohn is riding a late-career wave of international success, and it's built on being menacing.\nAfter gaining international attention for his role as Pope — the most violent and terr"
  [s=-0.97 code=0 nb=710] "President Bill Clinton sent this message to supporters today:\nI know how President Obama feels right now because I've been there.\nThese final 20 days will be the toughest of the whole campai"
  [s=-0.97 code=0 nb=2283] 'Today, as he was getting his guitar out of the case and while I was making him a cup of tea, he asked me out of the blue, "So how\'s Barack Obama doing?"\nMe: Oh... Pretty well I think. I mean'
==================== news
  [s=-0.82 code=0 nb=857] 'It said the exclusive courts for child victims have to be set up in every district having over 100 cases under the POCSO Act.\nThe bench said the Centre would have to appoint trained, sensiti'
  [s=-0.89 code=0 nb=1237] '<|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, Navin Sin'
  [s=-0.98 code=0 nb=1750] 'pur: Prime Minister Manmohan Singh on Friday kicked off the Congress campaign from Kanpur on Friday. Addressing the crowds, he said, "We have sent funds under various national schemes to Utt'
  [s=-0.98 code=0 nb=823] ' redistributed.)<|endoftext|>Accompanied by a high-power delegation of military leadership, US Defence Secretary Chu-ck Hagel’s three-day visit to the capital is aimed at “nurturing” ties wi'
==================== techqa
  [s=-0.26 code=1 nb=746] "Hi I need to show the Image as well as some data related to that Image in JSP.Image and their related data's are stored in database. Because both the content type are different also i dont k"
  [s=-0.42 code=2 nb=3373] 'XForms/Read and write with get and put\nSometimes all you need to do is to put a nice user friendly form that edits a single static XML file. In this case a static file is any file where you '
  [s=-0.49 code=2 nb=1605] "I'm not sure if I worded my topic title properly, which is probably why I haven't been able to search for the answer to my problem just yet (oh, have I tried, just can't find the right keywo"
  [s=-0.78 code=3 nb=1695] '<|endoftext|>I\'m trying to set up my RoR 3 application to receive emails and then process those emails and insert them into the database.\nIn my application, I have a "jobs" controller. Users'

[stdout]
docs 182016 prose-clean 81548 tech 599 tech&base 156
  nbytes    p10=561.000 med=2246.000 p90=8458.000
  nalpha    p10=91.000 med=359.000 p90=1344.000
  stop      p10=0.107 med=0.278 p90=0.347
  nonascii  p10=0.000 med=0.002 p90=0.008
  awl       p10=4.210 med=4.867 p90=5.644
  termp     p10=0.048 med=0.500 p90=0.941
  duplines  p10=0.000 med=0.000 p90=0.226
  upper     p10=0.025 med=0.060 p90=0.157
  digit     p10=0.000 med=0.011 p90=0.051
  boiler    p10=0.000 med=0.000 p90=0.942
  naughty   p10=0.000 med=0.000 p90=0.000
score percentiles vs-bg per reg:
  wiki [-3.461 -2.605 -2.133 -1.771] max -0.689
  webprose [-2.922 -2.008 -1.531 -1.212] max -0.359
  news [-3.314 -2.408 -1.92  -1.529] max -0.599
  techqa [-4.107 -3.046 -2.069 -1.355] max -0.265
==================== wiki
  [s=-0.69 code=0 nb=1876] 'Consanguinity alliance by blood, as affinity (q.v.) is alliance by marriage. Certain degrees of consanguinity are among the impediments to marriage, both by the law of nature and by the reve'
  [s=-0.92 code=0 nb=3641] 'Renewable electricity production, from sources such as wind power and solar power, is sometimes criticized for being variable or intermittent, but is not true for concentrated solar, geother'
  [s=-1.03 code=0 nb=808] 'AceShowbiz - "The Celebrity Apprentice" will return a week longer than it was originally planned. The fifth season of the reality series, which was initially set to debut on February 12, is '
  [s=-1.14 code=0 nb=24054] 'Hill 303 massacre\n|Hill 303 massacre|\nBodies of massacre victims gathered near Waegwan, South Korea, many with their hands still bound\n|Location||Hill 303, Waegwan, South Korea|\n|Date||Augus'
==================== webprose
  [s=-0.93 code=0 nb=864] 'Cowboys owner Jerry Jones said the offensive line is an area that can, and will, improve.\nHe is putting faith in offensive line coach Bill Callahan.\nAsked what can be done to help the offens'
  [s=-0.96 code=0 nb=3058] "Ben Mendelsohn is riding a late-career wave of international success, and it's built on being menacing.\nAfter gaining international attention for his role as Pope — the most violent and terr"
  [s=-0.97 code=0 nb=710] "President Bill Clinton sent this message to supporters today:\nI know how President Obama feels right now because I've been there.\nThese final 20 days will be the toughest of the whole campai"
  [s=-0.97 code=0 nb=2283] 'Today, as he was getting his guitar out of the case and while I was making him a cup of tea, he asked me out of the blue, "So how\'s Barack Obama doing?"\nMe: Oh... Pretty well I think. I mean'
==================== news
  [s=-0.82 code=0 nb=857] 'It said the exclusive courts for child victims have to be set up in every district having over 100 cases under the POCSO Act.\nThe bench said the Centre would have to appoint trained, sensiti'
  [s=-0.89 code=0 nb=1237] '<|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, Navin Sin'
  [s=-0.98 code=0 nb=1750] 'pur: Prime Minister Manmohan Singh on Friday kicked off the Congress campaign from Kanpur on Friday. Addressing the crowds, he said, "We have sent funds under various national schemes to Utt'
  [s=-0.98 code=0 nb=823] ' redistributed.)<|endoftext|>Accompanied by a high-power delegation of military leadership, US Defence Secretary Chu-ck Hagel’s three-day visit to the capital is aimed at “nurturing” ties wi'
==================== techqa
  [s=-0.26 code=1 nb=746] "Hi I need to show the Image as well as some data related to that Image in JSP.Image and their related data's are stored in database. Because both the content type are different also i dont k"
  [s=-0.42 code=2 nb=3373] 'XForms/Read and write with get and put\nSometimes all you need to do is to put a nice user friendly form that edits a single static XML file. In this case a static file is any file where you '
  [s=-0.49 code=2 nb=1605] "I'm not sure if I worded my topic title properly, which is probably why I haven't been able to search for the answer to my problem just yet (oh, have I tried, just can't find the right keywo"
  [s=-0.78 code=3 nb=1695] '<|endoftext|>I\'m trying to set up my RoR 3 application to receive emails and then process those emails and insert them into the database.\nIn my application, I have a "jobs" controller. Users'
[metadata] {"noOutputExpected": false}

Token indices sequence length is longer than the specified maximum sequence length for this model (1071 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (1114 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (2192 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (1602 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (1433 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (3048 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (1062 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (2316 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (1086 > 1024). Running this sequence through the model will result in indexing errors
      wiki     :   3503 docs  3.00M tok
      webprose :   3269 docs  3.00M tok
      news     :   3711 docs  3.00M tok
      techqa   :   2594 docs  3.00M tok
      near-dups dropped: 127
wrote 13077 ids (12.00M tokens, budget 12M) -> /workspace/submission/selection.json

real	0m17.744s
user	1m48.170s
sys	0m46.120s

[stdout]
Token indices sequence length is longer than the specified maximum sequence length for this model (1071 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (1114 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (2192 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (1602 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (1433 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (3048 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (1062 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (2316 > 1024). Running this sequence through the model will result in indexing errors
Token indices sequence length is longer than the specified maximum sequence length for this model (1086 > 1024). Running this sequence through the model will result in indexing errors
      wiki     :   3503 docs  3.00M tok
      webprose :   3269 docs  3.00M tok
      news     :   3711 docs  3.00M tok
      techqa   :   2594 docs  3.00M tok
      near-dups dropped: 127
wrote 13077 ids (12.00M tokens, budget 12M) -> /workspace/submission/selection.json

real	0m17.744s
user	1m48.170s
sys	0m46.120s
[metadata] {"noOutputExpected": false}

a = toks[int(i)]
KeyError: 84270
packed 12000000 tokens from 13070/13077 ids -> /tmp/tr_bal.npy
4750

[stdout]
    a = toks[int(i)]
KeyError: 84270
packed 12000000 tokens from 13070/13077 ids -> /tmp/tr_bal.npy
4750
[metadata] {"noOutputExpected": false}

patched
tokenizing 1242 uncached ids ...
packed 12000000 tokens from 10934/30000 ids -> /tmp/tr_random.npy

real	0m11.793s
user	1m22.320s
sys	0m46.620s

[stdout]
patched
tokenizing 1242 uncached ids ...
packed 12000000 tokens from 10934/30000 ids -> /tmp/tr_random.npy

real	0m11.793s
user	1m22.320s
sys	0m46.620s
[metadata] {"noOutputExpected": false}

[BALANCED] BLOCK_PPL {'wiki': 930.74, 'webprose': 281.42, 'news': 350.5, 'techqa': 227.48}
[BALANCED] HELDOUT_PPL 380.516  loss 5.9415  train_s 109  /tmp/tr_bal.npy
[RANDOM] BLOCK_PPL {'wiki': 1021.66, 'webprose': 335.04, 'news': 446.65, 'techqa': 363.65}
[RANDOM] HELDOUT_PPL 485.457  loss 6.1851  train_s 109  /tmp/tr_random.npy
[1]-  Done                    ( python3 eval_blocks.py --train_npy /tmp/tr_random.npy --heldout_npy data/multi_dev.npy --out_json /tmp/e_random.json 2>&1 | grep -E "BLOCK_PPL|HELDOUT" | sed 's/^/[RANDOM] /' )
[2]+  Done                    ( python3 eval_blocks.py --train_npy /tmp/tr_bal.npy --heldout_npy data/multi_dev.npy --out_json /tmp/e_bal.json 2>&1 | grep -E "BLOCK_PPL|HELDOUT" | sed 's/^/[BALANCED] /' )
4589

[stdout]
[BALANCED] BLOCK_PPL {'wiki': 930.74, 'webprose': 281.42, 'news': 350.5, 'techqa': 227.48}
[BALANCED] HELDOUT_PPL 380.516  loss 5.9415  train_s 109  /tmp/tr_bal.npy
[RANDOM] BLOCK_PPL {'wiki': 1021.66, 'webprose': 335.04, 'news': 446.65, 'techqa': 363.65}
[RANDOM] HELDOUT_PPL 485.457  loss 6.1851  train_s 109  /tmp/tr_random.npy
[1]-  Done                    ( python3 eval_blocks.py --train_npy /tmp/tr_random.npy --heldout_npy data/multi_dev.npy --out_json /tmp/e_random.json 2>&1 | grep -E "BLOCK_PPL|HELDOUT" | sed 's/^/[RANDOM] /' )
[2]+  Done                    ( python3 eval_blocks.py --train_npy /tmp/tr_bal.npy --heldout_npy data/multi_dev.npy --out_json /tmp/e_bal.json 2>&1 | grep -E "BLOCK_PPL|HELDOUT" | sed 's/^/[BALANCED] /' )
4589
[metadata] {"noOutputExpected": false}

wiki-marker hist [175182   6635    145     35     16      3]
qa-marker  hist [177587   4145    266     15      3]
wiki>=2 & clean: 45 docs, est tokens 0.25M
wiki>=3 & clean: 7 docs, est tokens 0.05M
wiki>=4 & clean: 2 docs, est tokens 0.01M
  WIKI-ISH: 'Francis with the Toronto Blue Jays in 2015\nJanuary 8, 1981 |\nVancouver, British Columbia\n|August 25, 2004, for the Colorado Rockies|\n|October 4, 2015, for the Toronto Blue Jays|\n|Earned run average||4'
  WIKI-ISH: 'Roderick Luke Miller|\n|Spouse(s)||(1) Clemencia "Clemy" Clotworthy Miller (died)|\n(2) Jane Brinkhaus Gaiennie Miller (surviving spouse)\nRoderick Luke Miller, known as Rod Miller (October 20, 1924 -- J'
  WIKI-ISH: 'ancouver Expedition - WikiVividly\nWikiVividly\nWiki as never seen before with photo\ngalleries, discover something new today\nTOP LISTS / STORIES\nclick links in text for more info\nTest\nVancouver Expediti'

[stdout]
wiki-marker hist [175182   6635    145     35     16      3]
qa-marker  hist [177587   4145    266     15      3]
wiki>=2 & clean: 45 docs, est tokens 0.25M
wiki>=3 & clean: 7 docs, est tokens 0.05M
wiki>=4 & clean: 2 docs, est tokens 0.01M
  WIKI-ISH: 'Francis with the Toronto Blue Jays in 2015\nJanuary 8, 1981 |\nVancouver, British Columbia\n|August 25, 2004, for the Colorado Rockies|\n|October 4, 2015, for the Toronto Blue Jays|\n|Earned run average||4'
  WIKI-ISH: 'Roderick Luke Miller|\n|Spouse(s)||(1) Clemencia "Clemy" Clotworthy Miller (died)|\n(2) Jane Brinkhaus Gaiennie Miller (surviving spouse)\nRoderick Luke Miller, known as Rod Miller (October 20, 1924 -- J'
  WIKI-ISH: 'ancouver Expedition - WikiVividly\nWikiVividly\nWiki as never seen before with photo\ngalleries, discover something new today\nTOP LISTS / STORIES\nclick links in text for more info\nTest\nVancouver Expediti'
[metadata] {"noOutputExpected": false}

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

[metadata] {"type": "create", "filePath": "/workspace/variants.py", "content": "#!/usr/bin/env python3\n\"\"\"Build selection variants from the cached scores (all packing served from one\npre-tokenized candidate superset, so each variant costs only a training run).\"\"\"\nimport json, os, sys\nimport numpy as np\nsys.path.insert(0, \"/workspace/submission\")\nimport curate as C\n\nz = np.load(\"/tmp/curate_cache/scores.npz\")\nIDS, F = z[\"ids\"], z[\"feats\"]\nMK = np.load(\"/tmp/curate_cache/markers.npz\")\nWIKI_MK, QA_MK = MK[\"wiki\"].astype(np.float32), MK[\"qa\"].astype(np.float32)\nBASE = C.keep_mask(F, False)\nTECH = C.keep_mask(F, True) & (F[:, 11] >= 3)\nBUDGET = 12_000_000\n\n\ndef superset(k_per_reg=20000):\n    \"\"\"union of the deepest candidate lists any variant might use\"\"\"\n    s = set()\n    for col in (14, 18):\n        for r in range(4):\n            ok = BASE | TECH if r == 3 else BASE\n            sc = np.where(ok, F[:, col + r], -1e9)\n            s.update(IDS[np.argsort(-sc)[:k_per_reg]].tolist())\n    s.update(IDS[(WIKI_MK >= 1) & BASE].tolist())\n    return sorted(s)\n\n\ndef score_cols(col, wiki_boost=0.0, qa_boost=0.0, len_pref=0.0):\n    S = F[:, col:col + 4].copy()\n    S[:, 0] += wiki_boost * np.minimum(WIKI_MK, 3.0)\n    S[:, 3] += qa_boost * np.minimum(QA_MK, 3.0)\n    if len_pref:\n        S += len_pref * np.log1p(F[:, [0]] / 2000.0)\n    return S\n\n\ndef build(toks, col=14, mix=(0.25, 0.25, 0.25, 0.25), wiki_boost=0.0, qa_boost=0.0,\n          len_pref=0.0, tail=6000, verbose=True):\n    S = score_cols(col, wiki_boost, qa_boost, len_pref)\n    quota = [m * BUDGET for m in mix]\n    picked, seen, per = set(), set(), [[] for _ in range(4)]\n    used = [0.0] * 4\n    orders = []\n    for r in range(4):\n        ok = BASE | TECH if r == 3 else BASE\n        orders.append(np.argsort(-np.where(ok, S[:, r], -1e9)))\n    for r in range(4):\n        for i in orders[r]:\n            if used[r] >= quota[r]:\n                break\n            did = int(IDS[i])\n            if did in picked:\n                continue\n            a = toks.get(did)\n            if a is None or len(a) < 64:\n                continue\n            k = C.fingerprint(a)\n            if k in seen:\n                continue\n            seen.add(k); picked.add(did); per[r].append(did); used[r] += len(a) + 1\n    sel = []\n    for j in range(max(len(x) for x in per)):\n        for r in range(4):\n            if j < len(per[r]):\n                sel.append(per[r][j])\n    # tail buffer: extra ids past the budget (unused by the packer, guards the 12M floor)\n    extra = []\n    for r in range(4):\n        for i in orders[r]:\n            did = int(IDS[i])\n            if did not in picked and toks.get(did) is not None and len(extra) < tail:\n                picked.add(did); extra.append(did)\n    if verbose:\n        print(\"   \", [f\"{C.REG_NAMES[r]}:{len(per[r])}d/{used[r]/1e6:.2f}M\" for r in range(4)],\n              f\"+{len(extra)} tail\")\n    return sel + extra\n\n\ndef flat_quality(toks, tail=6000):\n    \"\"\"ablation: no register balance - rank by mean vs-background score over all registers\"\"\"\n    s = np.where(BASE, F[:, 14:18].mean(1), -1e9)\n    order = np.argsort(-s)\n    sel, seen, tot = [], set(), 0\n    for i in order:\n        did = int(IDS[i]); a = toks.get(did)\n        if a is None or len(a) < 64:\n            continue\n        k = C.fingerprint(a)\n        if k in seen:\n            continue\n        seen.add(k); sel.append(did); tot += len(a) + 1\n        if tot >= BUDGET * 1.5:\n            break\n    return sel\n\n\nif __name__ == \"__main__\":\n    cand = superset()\n    print(f\"superset {len(cand)} docs -> tokenizing (cached)\", flush=True)\n    toks = C.tokenize_subset(cand, \"super\")\n    print(f\"cached {len(toks)} docs\", flush=True)\n    specs = {\n        \"v2_contrast\":  dict(col=18),\n        \"v3_tilt\":      dict(col=14, mix=(0.20, 0.235, 0.235, 0.33)),\n        \"v4_boost\":     dict(col=14, wiki_boost=0.35, qa_boost=0.20),\n    }\n    for name, kw in specs.items():\n        print(name, flush=True)\n        sel = build(toks, **kw)\n        json.dump(sel, open(f\"/tmp/sel_{name}.json\", \"w\"))\n    json.dump(flat_quality(toks), open(\"/tmp/sel_v5_flat.json\", \"w\"))\n    print(\"v5_flat (no register balance) written\")\n", "structuredPatch": [], "originalFile": null, "userModified": false}

superset 44856 docs -> tokenizing (cached)
cached 44856 docs
v2_contrast
    ['wiki:3370d/3.00M', 'webprose:3237d/3.00M', 'news:3886d/3.00M', 'techqa:2649d/3.00M'] +6000 tail
v3_tilt
    ['wiki:2886d/2.40M', 'webprose:3053d/2.82M', 'news:3568d/2.82M', 'techqa:3664d/3.96M'] +6000 tail
v4_boost
    ['wiki:3257d/3.00M', 'webprose:3256d/3.00M', 'news:3727d/3.00M', 'techqa:2528d/3.00M'] +6000 tail
v5_flat (no register balance) written

real	0m27.392s
user	2m29.310s
sys	0m55.530s
4359

[stdout]
superset 44856 docs -> tokenizing (cached)
cached 44856 docs
v2_contrast
    ['wiki:3370d/3.00M', 'webprose:3237d/3.00M', 'news:3886d/3.00M', 'techqa:2649d/3.00M'] +6000 tail
v3_tilt
    ['wiki:2886d/2.40M', 'webprose:3053d/2.82M', 'news:3568d/2.82M', 'techqa:3664d/3.96M'] +6000 tail
v4_boost
    ['wiki:3257d/3.00M', 'webprose:3256d/3.00M', 'news:3727d/3.00M', 'techqa:2528d/3.00M'] +6000 tail
v5_flat (no register balance) written

real	0m27.392s
user	2m29.310s
sys	0m55.530s
4359
[metadata] {"noOutputExpected": false}

packed 12000000 tokens from 13135/19142 ids -> /tmp/tr_v2_contrast.npy
packed 12000000 tokens from 13171/19171 ids -> /tmp/tr_v3_tilt.npy
packed 12000000 tokens from 12764/18768 ids -> /tmp/tr_v4_boost.npy
packed 12000000 tokens from 12502/18512 ids -> /tmp/tr_v5_flat.npy
4359

[stdout]
packed 12000000 tokens from 13135/19142 ids -> /tmp/tr_v2_contrast.npy
packed 12000000 tokens from 13171/19171 ids -> /tmp/tr_v3_tilt.npy
packed 12000000 tokens from 12764/18768 ids -> /tmp/tr_v4_boost.npy
packed 12000000 tokens from 12502/18512 ids -> /tmp/tr_v5_flat.npy
4359
[metadata] {"noOutputExpected": false}

[1]   Done                    ( python3 eval_blocks.py --train_npy /tmp/tr_$v.npy --heldout_npy data/multi_dev.npy --out_json /tmp/e_$v.json > /tmp/log_$v.txt 2>&1 )
[2]   Done                    ( python3 eval_blocks.py --train_npy /tmp/tr_$v.npy --heldout_npy data/multi_dev.npy --out_json /tmp/e_$v.json > /tmp/log_$v.txt 2>&1 )
[3]-  Done                    ( python3 eval_blocks.py --train_npy /tmp/tr_$v.npy --heldout_npy data/multi_dev.npy --out_json /tmp/e_$v.json > /tmp/log_$v.txt 2>&1 )
[4]+  Done                    ( python3 eval_blocks.py --train_npy /tmp/tr_$v.npy --heldout_npy data/multi_dev.npy --out_json /tmp/e_$v.json > /tmp/log_$v.txt 2>&1 )
BLOCK_PPL {'wiki': 940.24, 'webprose': 276.45, 'news': 353.04, 'techqa': 225.55}
HELDOUT_PPL 379.526  loss 5.9389  train_s 216  v4_boost.npy
BLOCK_PPL {'wiki': 898.27, 'webprose': 288.76, 'news': 356.43, 'techqa': 222.56}
HELDOUT_PPL 378.941  loss 5.9374  train_s 216  v2_contrast.npy
BLOCK_PPL {'wiki': 1094.12, 'webprose': 284.91, 'news': 427.95, 'techqa': 830.05}
HELDOUT_PPL 577.282  loss 6.3583  train_s 215  v5_flat.npy
BLOCK_PPL {'wiki': 960.4, 'webprose': 289.43, 'news': 361.34, 'techqa': 211.85}
HELDOUT_PPL 382.215  loss 5.9460  train_s 216  v3_tilt.npy
3999

[stdout]
[1]   Done                    ( python3 eval_blocks.py --train_npy /tmp/tr_$v.npy --heldout_npy data/multi_dev.npy --out_json /tmp/e_$v.json > /tmp/log_$v.txt 2>&1 )
[2]   Done                    ( python3 eval_blocks.py --train_npy /tmp/tr_$v.npy --heldout_npy data/multi_dev.npy --out_json /tmp/e_$v.json > /tmp/log_$v.txt 2>&1 )
[3]-  Done                    ( python3 eval_blocks.py --train_npy /tmp/tr_$v.npy --heldout_npy data/multi_dev.npy --out_json /tmp/e_$v.json > /tmp/log_$v.txt 2>&1 )
[4]+  Done                    ( python3 eval_blocks.py --train_npy /tmp/tr_$v.npy --heldout_npy data/multi_dev.npy --out_json /tmp/e_$v.json > /tmp/log_$v.txt 2>&1 )
BLOCK_PPL {'wiki': 940.24, 'webprose': 276.45, 'news': 353.04, 'techqa': 225.55}
HELDOUT_PPL 379.526  loss 5.9389  train_s 216  v4_boost.npy
BLOCK_PPL {'wiki': 898.27, 'webprose': 288.76, 'news': 356.43, 'techqa': 222.56}
HELDOUT_PPL 378.941  loss 5.9374  train_s 216  v2_contrast.npy
BLOCK_PPL {'wiki': 1094.12, 'webprose': 284.91, 'news': 427.95, 'techqa': 830.05}
HELDOUT_PPL 577.282  loss 6.3583  train_s 215  v5_flat.npy
BLOCK_PPL {'wiki': 960.4, 'webprose': 289.43, 'news': 361.34, 'techqa': 211.85}
HELDOUT_PPL 382.215  loss 5.9460  train_s 216  v3_tilt.npy
3999
[metadata] {"noOutputExpected": false}