def main2():
for zzz in LANGUAGE_LIST:
lang = zzz[0]
# get original embed
system(
"wget -nc -O %s/wiki.%s.vec https://s3-us-west-1.amazonaws.com/fasttext-vectors/wiki.%s.vec"
% (OUT_DIR, lang, lang),
pp=True)
# project with LIB-matrix
lang_dict = FastVector(vector_file='%s/wiki.%s.vec' % (OUT_DIR, lang))
lang_dict.apply_transform("%s/alignment_matrices/%s.txt" %
(LIB_DIR, lang))
lang_dict.export("%s/wiki.multi.%s.vec" % (OUT_DIR, lang))
def main():
# first get the English one
lang = "en"
system(
"wget -nc -O %s/wiki.%s.vec https://s3-us-west-1.amazonaws.com/fasttext-vectors/wiki.%s.vec"
% (OUT_DIR, lang, lang),
pp=True)
# en_dict = FastVector(vector_file='%s/wiki.en.vec' % OUT_DIR)
for zzz in LANGUAGE_LIST:
lang, fnames = zzz[0], zzz[1]
printing("Dealing with lang %s." % lang)
for curf in ["train", "dev", "test"]:
out_fname = "%s/%s_%s.conllu" % (OUT_DIR, lang, curf)
fout = zopen(out_fname, "w")
for fname in fnames:
last_name = fname.split("-")[-1].lower()
path_name = "%s/%s/%s_%s-ud-%s.conllu" % (UD2_DIR, fname, lang,
last_name, curf)
if os.path.exists(path_name):
with zopen(path_name) as fin:
deal_conll_file(fin, fout)
fout.close()
# stat
system('cat %s | grep -E "^$" | wc' % out_fname, pp=True)
system('cat %s | grep -Ev "^$" | wc' % out_fname, pp=True)
system(
"cat %s | grep -Ev '^$' | cut -f 5 -d $'\t'| grep -Ev 'PUNCT|SYM' | wc"
% out_fname,
pp=True)
# get original embed
system(
"wget -nc -O %s/wiki.%s.vec https://s3-us-west-1.amazonaws.com/fasttext-vectors/wiki.%s.vec"
% (OUT_DIR, lang, lang),
pp=True)
# project with LIB-matrix
lang_dict = FastVector(vector_file='%s/wiki.%s.vec' % (OUT_DIR, lang))
lang_dict.apply_transform("%s/alignment_matrices/%s.txt" %
(LIB_DIR, lang))
lang_dict.export("%s/wiki.multi.%s.vec" % (OUT_DIR, lang))
# return orthogonal transformation which aligns source language to the target
return np.matmul(U, V)
lang1_dictionary = FastVector(vector_file=args.lang1)
lang2_dictionary = FastVector(vector_file=args.lang2)
bilingual_dictionary = []
file_object = open(args.dict, "r")
lines = file_object.readlines()
for line in lines:
line = re.sub(r'\n', '', line)
w_lang2, w_lang1 = line.split('\t')
if w_lang1 in lang1_dictionary.word2id.keys(
) and w_lang2 in lang2_dictionary.word2id.keys():
bilingual_dictionary.append(tuple((w_lang2, w_lang1)))
print("Dic Size: " + str(len(bilingual_dictionary)))
# form the training matrices# form
source_matrix, target_matrix = make_training_matrices(lang1_dictionary,
lang2_dictionary,
bilingual_dictionary)
# learn and apply the transformation
transform = learn_transformation(source_matrix, target_matrix)
lang1_dictionary.apply_transform(transform)
lang1_dictionary.export(args.out1)
lang2_dictionary.export(args.out2)
en_vector = en_dictionary["love"]
zh_vector = zh_dictionary["爱"]
# going to print 0.0004326613965749648
print(FastVector.cosine_similarity(en_vector, zh_vector))
zh_words = set(zh_dictionary.word2id.keys())
en_words = set(en_dictionary.word2id.keys())
overlap = list(zh_words & en_words)
bilingual_dictionary = [(entry, entry) for entry in overlap]
# form the training matrices
source_matrix, target_matrix = make_training_matrices(en_dictionary,
zh_dictionary,
bilingual_dictionary)
# learn and apply the transformation
transform = learn_transformation(source_matrix, target_matrix)
en_dictionary.apply_transform(transform)
en_vector = en_dictionary["love"]
zh_vector = zh_dictionary["爱"]
# going to print 0.18727020978991674
print(FastVector.cosine_similarity(en_vector, zh_vector))
en_dictionary.export("cc.en.aligned.to.zh.vec")
embedding = gluonnlp.embedding.FastText.from_file('cc.en.aligned.to.zh.vec')
embedding.serialize('cc.en.300.aligned.to.zh.vec.npz')
print "Readling Dictionary"
BI_DICT = codecs.open("o.s2t_f", "r").readlines()
BI_DICT = parse_BI(BI_DICT)
print "Readling Dictionary (END)"
# SRC_WORD = "昨天"
# TGT_WORD = "yesterday"
SRC_WORD = "钥匙"
TGT_WORD = "keys"
en_dictionary = FastVector(vector_file='en.emb.orig.vec')
other_dictionary = FastVector(vector_file='tizh.emb.orig.vec')
test_word(other_dictionary, en_dictionary, SRC_WORD, TGT_WORD)
# form the training matrices
print "Learning SVD"
source_matrix, target_matrix = make_training_matrices(other_dictionary,
en_dictionary, BI_DICT)
# learn and apply the transformation
transform = learn_transformation(source_matrix, target_matrix)
other_dictionary.apply_transform(transform)
# zh
test_word(other_dictionary, en_dictionary, SRC_WORD, TGT_WORD)
# ti
SRC_WORD = "กุญ"
test_word(other_dictionary, en_dictionary, SRC_WORD, TGT_WORD)
print "Writing transform Qe out"
other_dictionary.export("Qe")
#coding=utf-8
from fasttext import FastVector
fr_dictionary = FastVector(vector_file='wiki.en.vec')
fr_dictionary.export('fr.vec.txt')
