ai-content-maker/.venv/Lib/site-packages/benchmarks/bigtext_speed_benchmark.py

76 lines
2.1 KiB
Python

import blingfire
import nltk
import pysbd
import spacy
import stanza
from syntok.tokenizer import Tokenizer
import syntok.segmenter as syntok_segmenter
pysbd_segmenter = pysbd.Segmenter(language="en", clean=False, char_span=False)
nlp = spacy.blank('en')
nlp.add_pipe(nlp.create_pipe("sentencizer"))
nlp_dep = spacy.load('en_core_web_sm', disable=["ner"])
#stanza.download('en')
stanza_nlp = stanza.Pipeline(lang='en', processors='tokenize')
syntok_tokenizer = Tokenizer()
def blingfire_tokenize(text):
return blingfire.text_to_sentences(text).split('\n')
def nltk_tokenize(text):
return nltk.sent_tokenize(text)
def pysbd_tokenize(text):
segments = pysbd_segmenter.segment(text)
segments = [s.strip() for s in segments]
return segments
def spacy_tokenize(text):
return [sent.text.strip("\n") for sent in nlp(text).sents]
def spacy_dep_tokenize(text):
return [sent.text.strip("\n") for sent in nlp_dep(text).sents]
def stanza_tokenize(text):
return [e.text for e in stanza_nlp(text).sentences]
def make_sentences(segmented_tokens):
for sentence in segmented_tokens:
yield "".join(str(token) for token in sentence).strip()
def syntok_tokenize(text):
tokens = syntok_tokenizer.split(text)
result = syntok_segmenter.split(iter(tokens))
segments = [sent for sent in make_sentences(result)]
return segments
def speed_benchmark(big_text, tokenize_func):
segments = tokenize_func(big_text)
return segments
if __name__ == "__main__":
import time
libraries = (
blingfire_tokenize,
nltk_tokenize,
pysbd_tokenize,
spacy_tokenize,
spacy_dep_tokenize,
stanza_tokenize,
syntok_tokenize)
for tokenize_func in libraries:
t = time.time()
# wget http://www.gutenberg.org/files/1661/1661-0.txt -P benchmarks/
with open('benchmarks/1661-0.txt') as bigfile:
big_text = bigfile.read()
sentences = speed_benchmark(big_text, tokenize_func)
time_taken = time.time() - t
print()
print(tokenize_func.__name__)
print('Speed : {:>20.2f} ms'.format(time_taken * 1000))