52 lines
1.6 KiB
Python
52 lines
1.6 KiB
Python
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# Natural Language Toolkit: Language Model Unit Tests
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#
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# Copyright (C) 2001-2023 NLTK Project
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# Author: Ilia Kurenkov <ilia.kurenkov@gmail.com>
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# URL: <https://www.nltk.org/>
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# For license information, see LICENSE.TXT
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from functools import partial
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from itertools import chain
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from nltk.util import everygrams, pad_sequence
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flatten = chain.from_iterable
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pad_both_ends = partial(
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pad_sequence,
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pad_left=True,
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left_pad_symbol="<s>",
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pad_right=True,
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right_pad_symbol="</s>",
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)
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pad_both_ends.__doc__ = """Pads both ends of a sentence to length specified by ngram order.
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Following convention <s> pads the start of sentence </s> pads its end.
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"""
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def padded_everygrams(order, sentence):
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"""Helper with some useful defaults.
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Applies pad_both_ends to sentence and follows it up with everygrams.
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"""
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return everygrams(list(pad_both_ends(sentence, n=order)), max_len=order)
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def padded_everygram_pipeline(order, text):
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"""Default preprocessing for a sequence of sentences.
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Creates two iterators:
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- sentences padded and turned into sequences of `nltk.util.everygrams`
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- sentences padded as above and chained together for a flat stream of words
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:param order: Largest ngram length produced by `everygrams`.
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:param text: Text to iterate over. Expected to be an iterable of sentences.
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:type text: Iterable[Iterable[str]]
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:return: iterator over text as ngrams, iterator over text as vocabulary data
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"""
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padding_fn = partial(pad_both_ends, n=order)
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return (
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(everygrams(list(padding_fn(sent)), max_len=order) for sent in text),
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flatten(map(padding_fn, text)),
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)
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