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89 lines
2.4 KiB
Python
89 lines
2.4 KiB
Python
"""
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Cleaners are transformations that run over the input text at both training and eval time.
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Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners"
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hyperparameter. Some cleaners are English-specific. You"ll typically want to use:
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1. "english_cleaners" for English text
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2. "transliteration_cleaners" for non-English text that can be transliterated to ASCII using
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the Unidecode library (https://pypi.python.org/pypi/Unidecode)
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3. "basic_cleaners" if you do not want to transliterate (in this case, you should also update
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the symbols in symbols.py to match your data).
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"""
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import re
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from unidecode import unidecode
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from .numbers import normalize_numbers
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# Regular expression matching whitespace:
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_whitespace_re = re.compile(r"\s+")
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# List of (regular expression, replacement) pairs for abbreviations:
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_abbreviations = [(re.compile("\\b%s\\." % x[0], re.IGNORECASE), x[1]) for x in [
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("mrs", "misess"),
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("mr", "mister"),
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("dr", "doctor"),
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("st", "saint"),
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("co", "company"),
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("jr", "junior"),
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("maj", "major"),
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("gen", "general"),
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("drs", "doctors"),
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("rev", "reverend"),
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("lt", "lieutenant"),
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("hon", "honorable"),
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("sgt", "sergeant"),
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("capt", "captain"),
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("esq", "esquire"),
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("ltd", "limited"),
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("col", "colonel"),
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("ft", "fort"),
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]]
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def expand_abbreviations(text):
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for regex, replacement in _abbreviations:
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text = re.sub(regex, replacement, text)
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return text
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def expand_numbers(text):
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return normalize_numbers(text)
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def lowercase(text):
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"""lowercase input tokens."""
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return text.lower()
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def collapse_whitespace(text):
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return re.sub(_whitespace_re, " ", text)
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def convert_to_ascii(text):
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return unidecode(text)
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def basic_cleaners(text):
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"""Basic pipeline that lowercases and collapses whitespace without transliteration."""
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text = lowercase(text)
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text = collapse_whitespace(text)
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return text
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def transliteration_cleaners(text):
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"""Pipeline for non-English text that transliterates to ASCII."""
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text = convert_to_ascii(text)
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text = lowercase(text)
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text = collapse_whitespace(text)
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return text
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def english_cleaners(text):
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"""Pipeline for English text, including number and abbreviation expansion."""
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text = convert_to_ascii(text)
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text = lowercase(text)
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text = expand_numbers(text)
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text = expand_abbreviations(text)
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text = collapse_whitespace(text)
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return text
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