Datasets:
File size: 2,858 Bytes
cfe406c | 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 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 | """Sample docs per source and score corpus quality with cheap heuristics.
ponytail: heuristic eyeball score, not an LLM judge. Add LLM-judge pass if these
metrics look ambiguous and you need semantic quality, not just surface garble.
"""
import sys, re, random
import pyarrow.parquet as pq
SOURCES = ["eurlex", "parliamentary", "wikisource", "wikipedia",
"dziennik_ustaw", "wolne_lektury"]
N = int(sys.argv[1]) if len(sys.argv) > 1 else 200 # docs sampled per source
SHOW = 2 # raw samples printed per source
PL_DIAC = set("ąćęłńóśźżĄĆĘŁŃÓŚŹŻ")
LETTER = re.compile(r"[^\W\d_]", re.UNICODE)
def sample_rows(path, n):
"""Grab n docs spread across scattered row groups (cheap pseudo-random)."""
pf = pq.ParquetFile(path)
ng = pf.num_row_groups
groups = sorted(random.sample(range(ng), min(ng, 8)))
out = []
per = max(1, n // len(groups))
for g in groups:
tbl = pf.read_row_group(g, columns=["text"])
texts = tbl.column("text").to_pylist()
out += random.sample(texts, min(per, len(texts)))
if len(out) >= n:
break
return out[:n]
def score(text):
t = text or ""
n = len(t)
if n == 0:
return None
letters = sum(1 for c in t if LETTER.match(c))
diac = sum(1 for c in t if c in PL_DIAC)
repl = t.count("�") # OCR/decode garbage
digits = sum(c.isdigit() for c in t)
space = sum(c.isspace() for c in t)
words = t.split()
uniq = len(set(words)) / len(words) if words else 0
return dict(
chars=n,
letter_ratio=letters / n,
diac_per_kchar=1000 * diac / n,
repl_per_kchar=1000 * repl / n,
digit_ratio=digits / n,
space_ratio=space / n,
word_uniq=uniq,
)
def avg(rows, k):
v = [r[k] for r in rows if r]
return sum(v) / len(v) if v else 0
print(f"sampling {N} docs/source\n")
print(f"{'source':<16} {'chars':>8} {'letter%':>8} {'diac/k':>7} "
f"{'repl/k':>7} {'digit%':>7} {'uniq':>6} {'<200ch':>7}")
for s in SOURCES:
path = f"data/{s}/{s}.parquet"
docs = sample_rows(path, N)
scored = [score(d) for d in docs]
scored = [x for x in scored if x]
short = sum(1 for x in scored if x["chars"] < 200) / len(scored)
print(f"{s:<16} {avg(scored,'chars'):>8.0f} "
f"{100*avg(scored,'letter_ratio'):>7.1f}% "
f"{avg(scored,'diac_per_kchar'):>7.1f} "
f"{avg(scored,'repl_per_kchar'):>7.2f} "
f"{100*avg(scored,'digit_ratio'):>6.1f}% "
f"{avg(scored,'word_uniq'):>6.2f} "
f"{100*short:>6.1f}%")
print("\n=== raw samples ===")
for s in SOURCES:
docs = sample_rows(f"data/{s}/{s}.parquet", SHOW)
print(f"\n--- {s} ---")
for d in docs:
snippet = re.sub(r"\s+", " ", d)[:300]
print(f" [{len(d)} ch] {snippet}")
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