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Merge pull request #1279 from oroszgy/model_cli_v2
Added vector loading to model cli
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commit
876f38c548
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@ -1,33 +1,51 @@
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# coding: utf8
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from __future__ import unicode_literals
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import bz2
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import gzip
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import math
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from ast import literal_eval
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from pathlib import Path
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import numpy as np
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import spacy
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from preshed.counter import PreshCounter
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import spacy
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from ..compat import fix_text
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from .. import util
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from ..compat import fix_text
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def model(cmd, lang, model_dir, freqs_data, clusters_data, vectors_data):
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def model(cmd, lang, model_dir, freqs_data, clusters_data, vectors_data,
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min_doc_freq=5, min_word_freq=200):
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model_path = Path(model_dir)
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freqs_path = Path(freqs_data)
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clusters_path = Path(clusters_data) if clusters_data else None
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vectors_path = Path(vectors_data) if vectors_data else None
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check_dirs(freqs_path, clusters_path, vectors_path)
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# vocab = util.get_lang_class(lang).Defaults.create_vocab()
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vocab = util.get_lang_class(lang).Defaults.create_vocab()
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nlp = spacy.blank(lang)
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vocab = nlp.vocab
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probs, oov_prob = read_probs(freqs_path)
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probs, oov_prob = read_probs(
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freqs_path, min_doc_freq=int(min_doc_freq), min_freq=int(min_doc_freq))
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clusters = read_clusters(clusters_path) if clusters_path else {}
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populate_vocab(vocab, clusters, probs, oov_prob)
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add_vectors(vocab, vectors_path)
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create_model(model_path, nlp)
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def add_vectors(vocab, vectors_path):
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with bz2.BZ2File(vectors_path.as_posix()) as f:
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num_words, dim = next(f).split()
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vocab.clear_vectors(int(dim))
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for line in f:
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word_w_vector = line.decode("utf8").strip().split(" ")
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word = word_w_vector[0]
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vector = np.array([float(val) for val in word_w_vector[1:]])
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if word in vocab:
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vocab.set_vector(word, vector)
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def create_model(model_path, model):
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if not model_path.exists():
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model_path.mkdir()
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