Datasets:
File size: 2,627 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 | #!/usr/bin/env python3
"""Train a Polish byte-level BPE (32k) on a domain-balanced sample of the corpus,
then report fertility vs GPT-2 BPE. Runs where the parquet shards live (slayer)."""
import glob, sys, time
import pyarrow.parquet as pq
from tokenizers import Tokenizer, models, trainers, pre_tokenizers, decoders
DATA = "/home/ubuntu/dynaword/data"
OUT = "/home/ubuntu/dynaword/polish_bpe_32k.json"
CAP = 220 * 1024 * 1024 # ~220 MB text per source -> balances away the 71% legal skew
VOCAB = 32768
def files():
return sorted(glob.glob(f"{DATA}/*/*.parquet"))
def balanced_texts():
for f in files():
src = f.split("/")[-2]; got = 0; done = False
for batch in pq.ParquetFile(f).iter_batches(columns=["text"], batch_size=1000):
for x in batch.column("text"):
s = x.as_py()
if not s:
continue
yield s
got += len(s)
if got >= CAP:
done = True; break
if done:
break
print(f" sampled {src}: ~{got/1e6:.0f} MB", file=sys.stderr, flush=True)
t0 = time.time()
tok = Tokenizer(models.BPE(unk_token=None))
tok.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=False)
tok.decoder = decoders.ByteLevel()
trainer = trainers.BpeTrainer(
vocab_size=VOCAB, min_frequency=2,
special_tokens=["<|endoftext|>"],
initial_alphabet=pre_tokenizers.ByteLevel.alphabet())
print("training BPE...", file=sys.stderr, flush=True)
tok.train_from_iterator(balanced_texts(), trainer=trainer)
tok.save(OUT)
print(f"trained in {time.time()-t0:.0f}s | vocab={tok.get_vocab_size()} | saved {OUT}")
# fertility vs GPT-2 on a held-out-ish sample (later docs of wikipedia, general domain)
import tiktoken
gpt2 = tiktoken.get_encoding("gpt2")
sample = []
for b in pq.ParquetFile(f"{DATA}/wikipedia/wikipedia.parquet").iter_batches(columns=["text"], batch_size=1000):
for x in b.column("text"):
sample.append(x.as_py())
if len(sample) >= 6000:
break
sample = sample[4000:6000] # avoid the head used in training
words = sum(len(s.split()) for s in sample)
chars = sum(len(s) for s in sample)
ours = sum(len(e.ids) for e in tok.encode_batch(sample))
g2 = sum(len(x) for x in gpt2.encode_ordinary_batch(sample))
print(f"\nFertility on {len(sample)} held-out PL docs ({words:,} words):")
print(f" polish-32k : {ours/words:.3f} tok/word | {ours/chars:.3f} tok/char")
print(f" gpt2-50k : {g2/words:.3f} tok/word | {g2/chars:.3f} tok/char")
print(f" -> {g2/ours:.2f}x fewer tokens with the Polish BPE")
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