File size: 2,895 Bytes
ccbd209 9bf4115 ccbd209 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 | """Data curator: pull web data at scale, judge it against the rubric, keep only the
high-quality, COMPRESS it into compact training items, and discard the raw.
"Compressed" here = the judge distills each accepted doc into a tight
(instruction, ideal_response) pair -- far smaller than the raw page, and directly
trainable. Raw pages are never persisted (the 'weighted not stored' principle).
"""
import json
from pathlib import Path
ROOT = Path(__file__).resolve().parent.parent
def web_pull(queries, per_query=10):
"""Stream real, pre-filtered web data at scale from a HF corpus (FineWeb-Edu by
default) and yield (source, text). This IS the 'pull internet data at insane
quantity' step -- the judge then re-filters it for our rubric and discards the rest."""
from datasets import load_dataset
from config import CORPUS_DATASET, CORPUS_CONFIG
n = max(1, per_query) * max(1, len(queries))
ds = load_dataset(CORPUS_DATASET, name=CORPUS_CONFIG, split="train", streaming=True)
count = 0
for ex in ds:
text = ex.get("text") or ex.get("content") or ""
if len(text) < 200:
continue
yield (CORPUS_DATASET, text)
count += 1
if count >= n:
break
def curate(docs, judge, accept_threshold, max_keep):
"""docs: iterable of (source, text). Returns compact accepted items; raw discarded."""
kept = []
for source, text in docs:
if len(kept) >= max_keep:
break
# judge distills + rates the doc into a trainable pair
item = _distill_doc(judge, source, text)
if item and item["score"] >= accept_threshold:
kept.append({"instruction": item["instruction"],
"response": item["response"], "score": item["score"]})
# raw `text` goes out of scope here -> never written to disk
return kept
def _distill_doc(judge, source, text):
"""Use the judge model to compress a raw doc into one (instruction, ideal answer)
pair and score its quality. Compression = the compact pair, not the raw page."""
sys = ("Extract ONE high-value instruction a user might ask, and the ideal concise "
"answer, strictly grounded in the SOURCE. Then rate the answer 0..1. "
"Return ONLY JSON: {\"instruction\":str,\"response\":str,\"score\":float}.")
msg = [{"role": "system", "content": sys},
{"role": "user", "content": f"SOURCE ({source}):\n{text[:6000]}"}]
import re
from core.genutil import chat_generate
t = chat_generate(judge.model, judge.tok, msg, max_new_tokens=512, do_sample=False)
m = re.search(r"\{.*\}", t, re.DOTALL)
try:
d = json.loads(m.group(0))
return {"instruction": d["instruction"], "response": d["response"],
"score": float(max(0.0, min(1.0, d.get("score", 0.0))))}
except Exception:
return None
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