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* Add docstring explaining script a bit, and add handling of word vectors
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@ -1,4 +1,20 @@
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"""Set up a model dir, given the (committed) lang_data."""
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"""Set up a model directory.
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Requires:
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lang_data --- Rules for the tokenizer
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* prefix.txt
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* suffix.txt
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* infix.txt
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* morphs.json
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* specials.json
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corpora --- Data files
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* WordNet
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* words.sgt.prob --- Smoothed unigram probabilities
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* clusters.txt --- Output of hierarchical clustering, e.g. Brown clusters
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* vectors.tgz --- output of something like word2vec
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"""
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import plac
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from pathlib import Path
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@ -7,6 +23,7 @@ import codecs
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from spacy.en import get_lex_props
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from spacy.vocab import Vocab
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from spacy.vocab import write_binary_vectors
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def setup_tokenizer(lang_data_dir, tok_dir):
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@ -41,10 +58,13 @@ def _read_probs(loc):
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return probs
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def setup_vocab(src_dir, dst_dir):
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if not dst_dir.exists():
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dst_dir.mkdir()
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vectors_src = src_dir / 'vectors.tgz'
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if vectors_src.exists():
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write_binary_vectors(str(vectors_src), str(dst_dir / 'vec.bin'))
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vocab = Vocab(data_dir=None, get_lex_props=get_lex_props)
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clusters = _read_clusters(src_dir / 'clusters.txt')
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probs = _read_probs(src_dir / 'words.sgt.prob')
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@ -62,15 +82,21 @@ def setup_vocab(src_dir, dst_dir):
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vocab.strings.dump(str(dst_dir / 'strings.txt'))
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def main(lang_data_dir, model_dir):
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def main(lang_data_dir, corpora_dir, model_dir):
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model_dir = Path(model_dir)
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lang_data_dir = Path(lang_data_dir)
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corpora_dir = Path(corpora_dir)
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assert corpora_dir.exists()
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assert lang_data_dir.exists()
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if not model_dir.exists():
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model_dir.mkdir()
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setup_tokenizer(lang_data_dir, model_dir / 'tokenizer')
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setup_vocab(lang_data_dir, model_dir / 'vocab')
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setup_vocab(corpora_dir, model_dir / 'vocab')
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if not (model_dir / 'wordnet').exists():
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copytree(str(corpora_dir / 'wordnet'), str(model_dir / 'wordnet'))
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if __name__ == '__main__':
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