mirror of https://github.com/explosion/spaCy.git
110 lines
3.1 KiB
Python
110 lines
3.1 KiB
Python
"""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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from shutil import copyfile
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from shutil import copytree
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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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if not tok_dir.exists():
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tok_dir.mkdir()
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for filename in ('infix.txt', 'morphs.json', 'prefix.txt', 'specials.json',
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'suffix.txt'):
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src = lang_data_dir / filename
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dst = tok_dir / filename
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if not dst.exists():
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copyfile(str(src), str(dst))
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def _read_clusters(loc):
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clusters = {}
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for line in codecs.open(str(loc), 'r', 'utf8'):
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try:
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cluster, word, freq = line.split()
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except ValueError:
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continue
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# If the clusterer has only seen the word a few times, its cluster is
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# unreliable.
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if int(freq) >= 3:
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clusters[word] = cluster
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else:
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clusters[word] = '0'
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return clusters
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def _read_probs(loc):
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probs = {}
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for i, line in enumerate(codecs.open(str(loc), 'r', 'utf8')):
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prob, word = line.split()
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prob = float(prob)
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probs[word] = prob
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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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lexicon = []
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for word, prob in reversed(sorted(probs.items(), key=lambda item: item[1])):
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entry = get_lex_props(word)
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if word in clusters or float(prob) >= -17:
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entry['prob'] = float(prob)
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cluster = clusters.get(word, '0')
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# Decode as a little-endian string, so that we can do & 15 to get
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# the first 4 bits. See _parse_features.pyx
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entry['cluster'] = int(cluster[::-1], 2)
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vocab[word] = entry
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vocab.dump(str(dst_dir / 'lexemes.bin'))
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vocab.strings.dump(str(dst_dir / 'strings.txt'))
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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(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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plac.call(main)
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