File size: 1,656 Bytes
15ddd55 | 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 | import os
import tiktoken
import numpy as np
from datasets import load_dataset
from tqdm import tqdm
DATASET_NAME = "HuggingFaceFW/fineweb-edu"
SAMPLE_NAME = "sample-10BT"
TARGET_TOKENS = 300_000_000
SKIP_TOKENS = 100_002_100
enc = tiktoken.get_encoding("gpt2")
def process(example):
ids = enc.encode_ordinary(example['text'])
ids.append(enc.eot_token)
return {'ids': ids, 'len': len(ids)}
if __name__ == "__main__":
print(f"Loading streaming dataset {DATASET_NAME}...")
dataset = load_dataset(DATASET_NAME, name=SAMPLE_NAME, split='train', streaming=True)
all_tokens = []
total_tokens = 0
skipped = 0
pbar_skip = tqdm(total=SKIP_TOKENS, desc="Skipping already-used tokens")
for example in dataset:
tokens = process(example)['ids']
skipped += len(tokens)
pbar_skip.update(len(tokens))
if skipped >= SKIP_TOKENS:
break
pbar_skip.close()
pbar = tqdm(total=TARGET_TOKENS, desc="Collecting NEW tokens")
for example in dataset:
tokens = process(example)['ids']
all_tokens.extend(tokens)
total_tokens += len(tokens)
pbar.update(len(tokens))
if total_tokens >= TARGET_TOKENS:
break
pbar.close()
n = len(all_tokens)
train_data = all_tokens[:int(n*0.95)]
val_data = all_tokens[int(n*0.95):]
for name, d in [('train', train_data), ('val', val_data)]:
arr = np.array(d, dtype=np.uint16)
filename = f"{name}.bin"
arr.tofile(filename)
print(f"Saved {filename} with {len(d):,} tokens.")
print("\nDone!") |