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import multiprocessing as mp
import numpy as np
from datasets import load_dataset
import tiktoken
def num_tokens_from_string(string: str):
"""Returns the number of tokens in a text string."""
num_tokens = len(encoding.encode(string))
return num_tokens
def cnt_token_in_hf_wiki_dset(data):
data["token_cnt"] = num_tokens_from_string(data["text"])
return data
if __name__ == "__main__":
dataset = load_dataset("sabilmakbar/sea_wiki")
encoding = tiktoken.encoding_for_model('gpt-4')
stat_dict = {}
for split, dset in dataset.items():
dset_text = dset.select_columns(['text'])
print(f"Counting total token in split lang: {split}")
dset_text = dset_text.map(cnt_token_in_hf_wiki_dset, num_proc=max(mp.cpu_count()-2,1))
token_data = list(dset_text["token_cnt"])
total_token = sum(token_data)
avg_token = sum(token_data)/len(token_data)
min_token = min(token_data)
max_token = max(token_data)
deciles = np.percentile(token_data, np.arange(10, 100, 10)).tolist()
stat_dict[split] = {"total": total_token, "avg": avg_token, "min": min_token, "max": max_token, "deciles": deciles}
# for markdown table format
print("| Lang Code | Total Token | Avg Token per Article | Min Token | Max Token | Token Deciles List |")
print("| :---: | ---: | ---: | ---: | ---: | :--- |")
for key, data in stat_dict.items():
print(f"| {key} | {data['total']:,} | {data['avg']:,} | {data['min']:,} | {data['max']:,} | {[round(num,2) for num in data['deciles']]} |")
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