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