Skip to content

Commit 891d84c

Browse files
author
tmikolov
committed
update to 0.1c version
1 parent 5815e5d commit 891d84c

8 files changed

+115
-93
lines changed

‎demo-analogy.sh

+4-4
Original file line numberDiff line numberDiff line change
@@ -3,9 +3,9 @@ if [ ! -e text8 ]; then
33
wget http://mattmahoney.net/dc/text8.zip -O text8.gz
44
gzip -d text8.gz -f
55
fi
6-
echo -----------------------------------------------------------------------------------------------------
7-
echo Note that for the word analogy to perform well, the models should be trained on much larger data sets
6+
echo ---------------------------------------------------------------------------------------------------
7+
echo Note that for the word analogy to perform well, the model should be trained on much larger data set
88
echo Example input: paris france berlin
9-
echo -----------------------------------------------------------------------------------------------------
10-
time ./word2vec -train text8 -output vectors.bin -cbow 0 -size 200 -window 5 -negative 0 -hs 1 -sample 1e-3 -threads 12 -binary 1
9+
echo ---------------------------------------------------------------------------------------------------
10+
time ./word2vec -train text8 -output vectors.bin -cbow 1 -size 200 -window 8 -negative 25 -hs 0 -sample 1e-4 -threads 20 -binary 1 -iter 15
1111
./word-analogy vectors.bin

‎demo-classes.sh

+1-1
Original file line numberDiff line numberDiff line change
@@ -3,6 +3,6 @@ if [ ! -e text8 ]; then
33
wget http://mattmahoney.net/dc/text8.zip -O text8.gz
44
gzip -d text8.gz -f
55
fi
6-
time ./word2vec -train text8 -output classes.txt -cbow 0 -size 200 -window 5 -negative 0 -hs 1 -sample 1e-3 -threads 12 -classes 500
6+
time ./word2vec -train text8 -output classes.txt -cbow 1 -size 200 -window 8 -negative 25 -hs 0 -sample 1e-4 -threads 20 -iter 15 -classes 500
77
sort classes.txt -k 2 -n > classes.sorted.txt
88
echo The word classes were saved to file classes.sorted.txt

‎demo-phrase-accuracy.sh

+9-10
Original file line numberDiff line numberDiff line change
@@ -1,12 +1,11 @@
11
make
2-
if [ ! -e text8 ]; then
3-
wget http://mattmahoney.net/dc/text8.zip -O text8.gz
4-
gzip -d text8.gz -f
2+
if [ ! -e news.2012.en.shuffled ]; then
3+
wget http://www.statmt.org/wmt14/training-monolingual-news-crawl/news.2012.en.shuffled.gz
4+
gzip -d news.2012.en.shuffled.gz -f
55
fi
6-
echo ----------------------------------------------------------------------------------------------------------------
7-
echo Note that the accuracy and coverage of the test set questions is going to be low with this small training corpus
8-
echo To achieve better accuracy, larger training set is needed
9-
echo ----------------------------------------------------------------------------------------------------------------
10-
time ./word2phrase -train text8 -output text8-phrase -threshold 500 -debug 2 -min-count 3
11-
time ./word2vec -train text8-phrase -output vectors-phrase.bin -cbow 0 -size 300 -window 10 -negative 0 -hs 1 -sample 1e-3 -threads 12 -binary 1 -min-count 3
12-
./compute-accuracy vectors-phrase.bin <questions-phrases.txt
6+
sed -e "s/’/'/g" -e "s/′/'/g" -e "s/''/ /g" < news.2012.en.shuffled | tr -c "A-Za-z'_ \n" " " > news.2012.en.shuffled-norm0
7+
time ./word2phrase -train news.2012.en.shuffled-norm0 -output news.2012.en.shuffled-norm0-phrase0 -threshold 200 -debug 2
8+
time ./word2phrase -train news.2012.en.shuffled-norm0-phrase0 -output news.2012.en.shuffled-norm0-phrase1 -threshold 100 -debug 2
9+
tr A-Z a-z < news.2012.en.shuffled-norm0-phrase1 > news.2012.en.shuffled-norm1-phrase1
10+
time ./word2vec -train news.2012.en.shuffled-norm1-phrase1 -output vectors-phrase.bin -cbow 1 -size 200 -window 10 -negative 25 -hs 0 -sample 1e-5 -threads 20 -binary 1 -iter 15
11+
./compute-accuracy vectors-phrase.bin < questions-phrases.txt

‎demo-phrases.sh

+9-6
Original file line numberDiff line numberDiff line change
@@ -1,8 +1,11 @@
11
make
2-
if [ ! -e text8 ]; then
3-
wget http://mattmahoney.net/dc/text8.zip -O text8.gz
4-
gzip -d text8.gz -f
2+
if [ ! -e news.2012.en.shuffled ]; then
3+
wget http://www.statmt.org/wmt14/training-monolingual-news-crawl/news.2012.en.shuffled.gz
4+
gzip -d news.2012.en.shuffled.gz -f
55
fi
6-
time ./word2phrase -train text8 -output text8-phrase -threshold 500 -debug 2
7-
time ./word2vec -train text8-phrase -output vectors-phrase.bin -cbow 0 -size 300 -window 10 -negative 0 -hs 1 -sample 1e-3 -threads 12 -binary 1
8-
./distance vectors-phrase.bin
6+
sed -e "s/’/'/g" -e "s/′/'/g" -e "s/''/ /g" < news.2012.en.shuffled | tr -c "A-Za-z'_ \n" " " > news.2012.en.shuffled-norm0
7+
time ./word2phrase -train news.2012.en.shuffled-norm0 -output news.2012.en.shuffled-norm0-phrase0 -threshold 200 -debug 2
8+
time ./word2phrase -train news.2012.en.shuffled-norm0-phrase0 -output news.2012.en.shuffled-norm0-phrase1 -threshold 100 -debug 2
9+
tr A-Z a-z < news.2012.en.shuffled-norm0-phrase1 > news.2012.en.shuffled-norm1-phrase1
10+
time ./word2vec -train news.2012.en.shuffled-norm1-phrase1 -output vectors-phrase.bin -cbow 1 -size 200 -window 10 -negative 25 -hs 0 -sample 1e-5 -threads 20 -binary 1 -iter 15
11+
./distance vectors-phrase.bin

‎demo-word-accuracy.sh

+1-1
Original file line numberDiff line numberDiff line change
@@ -3,6 +3,6 @@ if [ ! -e text8 ]; then
33
wget http://mattmahoney.net/dc/text8.zip -O text8.gz
44
gzip -d text8.gz -f
55
fi
6-
time ./word2vec -train text8 -output vectors.bin -cbow 0 -size 200 -window 5 -negative 0 -hs 1 -sample 1e-3 -threads 12 -binary 1
6+
time ./word2vec -train text8 -output vectors.bin -cbow 1 -size 200 -window 8 -negative 25 -hs 0 -sample 1e-4 -threads 20 -binary 1 -iter 15
77
./compute-accuracy vectors.bin 30000 < questions-words.txt
88
# to compute accuracy with the full vocabulary, use: ./compute-accuracy vectors.bin < questions-words.txt

‎demo-word.sh

+2-2
Original file line numberDiff line numberDiff line change
@@ -3,5 +3,5 @@ if [ ! -e text8 ]; then
33
wget http://mattmahoney.net/dc/text8.zip -O text8.gz
44
gzip -d text8.gz -f
55
fi
6-
time ./word2vec -train text8 -output vectors.bin -cbow 0 -size 200 -window 5 -negative 0 -hs 1 -sample 1e-3 -threads 12 -binary 1
7-
./distance vectors.bin
6+
time ./word2vec -train text8 -output vectors.bin -cbow 1 -size 200 -window 8 -negative 25 -hs 0 -sample 1e-4 -threads 20 -binary 1 -iter 15
7+
./distance vectors.bin

‎makefile

+2-2
Original file line numberDiff line numberDiff line change
@@ -1,6 +1,6 @@
11
CC = gcc
2-
#The -Ofast might not work with older versions of gcc; in that case, use -O2
3-
CFLAGS = -lm -pthread -Ofast -march=native -Wall -funroll-loops -Wno-unused-result
2+
#Using -Ofast instead of -O3 might result in faster code, but is supported only by newer GCC versions
3+
CFLAGS = -lm -pthread -O3 -march=native -Wall -funroll-loops -Wno-unused-result
44

55
all: word2vec word2phrase distance word-analogy compute-accuracy
66

‎word2vec.c

+87-67
Original file line numberDiff line numberDiff line change
@@ -37,15 +37,15 @@ struct vocab_word {
3737
char train_file[MAX_STRING], output_file[MAX_STRING];
3838
char save_vocab_file[MAX_STRING], read_vocab_file[MAX_STRING];
3939
struct vocab_word *vocab;
40-
int binary = 0, cbow = 0, debug_mode = 2, window = 5, min_count = 5, num_threads = 1, min_reduce = 1;
40+
int binary = 0, cbow = 1, debug_mode = 2, window = 5, min_count = 5, num_threads = 12, min_reduce = 1;
4141
int *vocab_hash;
4242
long long vocab_max_size = 1000, vocab_size = 0, layer1_size = 100;
43-
long long train_words = 0, word_count_actual = 0, file_size = 0, classes = 0;
44-
real alpha = 0.025, starting_alpha, sample = 0;
43+
long long train_words = 0, word_count_actual = 0, iter = 5, file_size = 0, classes = 0;
44+
real alpha = 0.025, starting_alpha, sample = 1e-3;
4545
real *syn0, *syn1, *syn1neg, *expTable;
4646
clock_t start;
4747

48-
int hs = 1, negative = 0;
48+
int hs = 0, negative = 5;
4949
const int table_size = 1e8;
5050
int *table;
5151

@@ -337,29 +337,32 @@ void ReadVocab() {
337337

338338
void InitNet() {
339339
long long a, b;
340+
unsigned long long next_random = 1;
340341
a = posix_memalign((void **)&syn0, 128, (long long)vocab_size * layer1_size * sizeof(real));
341342
if (syn0 == NULL) {printf("Memory allocation failed\n"); exit(1);}
342343
if (hs) {
343344
a = posix_memalign((void **)&syn1, 128, (long long)vocab_size * layer1_size * sizeof(real));
344345
if (syn1 == NULL) {printf("Memory allocation failed\n"); exit(1);}
345-
for (b = 0; b < layer1_size; b++) for (a = 0; a < vocab_size; a++)
346+
for (a = 0; a < vocab_size; a++) for (b = 0; b < layer1_size; b++)
346347
syn1[a * layer1_size + b] = 0;
347348
}
348349
if (negative>0) {
349350
a = posix_memalign((void **)&syn1neg, 128, (long long)vocab_size * layer1_size * sizeof(real));
350351
if (syn1neg == NULL) {printf("Memory allocation failed\n"); exit(1);}
351-
for (b = 0; b < layer1_size; b++) for (a = 0; a < vocab_size; a++)
352+
for (a = 0; a < vocab_size; a++) for (b = 0; b < layer1_size; b++)
352353
syn1neg[a * layer1_size + b] = 0;
353354
}
354-
for (b = 0; b < layer1_size; b++) for (a = 0; a < vocab_size; a++)
355-
syn0[a * layer1_size + b] = (rand() / (real)RAND_MAX - 0.5) / layer1_size;
355+
for (a = 0; a < vocab_size; a++) for (b = 0; b < layer1_size; b++) {
356+
next_random = next_random * (unsigned long long)25214903917 + 11;
357+
syn0[a * layer1_size + b] = (((next_random & 0xFFFF) / (real)65536) - 0.5) / layer1_size;
358+
}
356359
CreateBinaryTree();
357360
}
358361

359362
void *TrainModelThread(void *id) {
360-
long long a, b, d, word, last_word, sentence_length = 0, sentence_position = 0;
363+
long long a, b, d, cw, word, last_word, sentence_length = 0, sentence_position = 0;
361364
long long word_count = 0, last_word_count = 0, sen[MAX_SENTENCE_LENGTH + 1];
362-
long long l1, l2, c, target, label;
365+
long long l1, l2, c, target, label, local_iter = iter;
363366
unsigned long long next_random = (long long)id;
364367
real f, g;
365368
clock_t now;
@@ -374,11 +377,11 @@ void *TrainModelThread(void *id) {
374377
if ((debug_mode > 1)) {
375378
now=clock();
376379
printf("%cAlpha: %f Progress: %.2f%% Words/thread/sec: %.2fk ", 13, alpha,
377-
word_count_actual / (real)(train_words + 1) * 100,
380+
word_count_actual / (real)(iter * train_words + 1) * 100,
378381
word_count_actual / ((real)(now - start + 1) / (real)CLOCKS_PER_SEC * 1000));
379382
fflush(stdout);
380383
}
381-
alpha = starting_alpha * (1 - word_count_actual / (real)(train_words + 1));
384+
alpha = starting_alpha * (1 - word_count_actual / (real)(iter * train_words + 1));
382385
if (alpha < starting_alpha * 0.0001) alpha = starting_alpha * 0.0001;
383386
}
384387
if (sentence_length == 0) {
@@ -400,8 +403,16 @@ void *TrainModelThread(void *id) {
400403
}
401404
sentence_position = 0;
402405
}
403-
if (feof(fi)) break;
404-
if (word_count > train_words / num_threads) break;
406+
if (feof(fi) || (word_count > train_words / num_threads)) {
407+
word_count_actual += word_count - last_word_count;
408+
local_iter--;
409+
if (local_iter == 0) break;
410+
word_count = 0;
411+
last_word_count = 0;
412+
sentence_length = 0;
413+
fseek(fi, file_size / (long long)num_threads * (long long)id, SEEK_SET);
414+
continue;
415+
}
405416
word = sen[sentence_position];
406417
if (word == -1) continue;
407418
for (c = 0; c < layer1_size; c++) neu1[c] = 0;
@@ -410,58 +421,63 @@ void *TrainModelThread(void *id) {
410421
b = next_random % window;
411422
if (cbow) { //train the cbow architecture
412423
// in -> hidden
424+
cw = 0;
413425
for (a = b; a < window * 2 + 1 - b; a++) if (a != window) {
414426
c = sentence_position - window + a;
415427
if (c < 0) continue;
416428
if (c >= sentence_length) continue;
417429
last_word = sen[c];
418430
if (last_word == -1) continue;
419431
for (c = 0; c < layer1_size; c++) neu1[c] += syn0[c + last_word * layer1_size];
432+
cw++;
420433
}
421-
if (hs) for (d = 0; d < vocab[word].codelen; d++) {
422-
f = 0;
423-
l2 = vocab[word].point[d] * layer1_size;
424-
// Propagate hidden -> output
425-
for (c = 0; c < layer1_size; c++) f += neu1[c] * syn1[c + l2];
426-
if (f <= -MAX_EXP) continue;
427-
else if (f >= MAX_EXP) continue;
428-
else f = expTable[(int)((f + MAX_EXP) * (EXP_TABLE_SIZE / MAX_EXP / 2))];
429-
// 'g' is the gradient multiplied by the learning rate
430-
g = (1 - vocab[word].code[d] - f) * alpha;
431-
// Propagate errors output -> hidden
432-
for (c = 0; c < layer1_size; c++) neu1e[c] += g * syn1[c + l2];
433-
// Learn weights hidden -> output
434-
for (c = 0; c < layer1_size; c++) syn1[c + l2] += g * neu1[c];
435-
}
436-
// NEGATIVE SAMPLING
437-
if (negative > 0) for (d = 0; d < negative + 1; d++) {
438-
if (d == 0) {
439-
target = word;
440-
label = 1;
441-
} else {
442-
next_random = next_random * (unsigned long long)25214903917 + 11;
443-
target = table[(next_random >> 16) % table_size];
444-
if (target == 0) target = next_random % (vocab_size - 1) + 1;
445-
if (target == word) continue;
446-
label = 0;
434+
if (cw) {
435+
for (c = 0; c < layer1_size; c++) neu1[c] /= cw;
436+
if (hs) for (d = 0; d < vocab[word].codelen; d++) {
437+
f = 0;
438+
l2 = vocab[word].point[d] * layer1_size;
439+
// Propagate hidden -> output
440+
for (c = 0; c < layer1_size; c++) f += neu1[c] * syn1[c + l2];
441+
if (f <= -MAX_EXP) continue;
442+
else if (f >= MAX_EXP) continue;
443+
else f = expTable[(int)((f + MAX_EXP) * (EXP_TABLE_SIZE / MAX_EXP / 2))];
444+
// 'g' is the gradient multiplied by the learning rate
445+
g = (1 - vocab[word].code[d] - f) * alpha;
446+
// Propagate errors output -> hidden
447+
for (c = 0; c < layer1_size; c++) neu1e[c] += g * syn1[c + l2];
448+
// Learn weights hidden -> output
449+
for (c = 0; c < layer1_size; c++) syn1[c + l2] += g * neu1[c];
450+
}
451+
// NEGATIVE SAMPLING
452+
if (negative > 0) for (d = 0; d < negative + 1; d++) {
453+
if (d == 0) {
454+
target = word;
455+
label = 1;
456+
} else {
457+
next_random = next_random * (unsigned long long)25214903917 + 11;
458+
target = table[(next_random >> 16) % table_size];
459+
if (target == 0) target = next_random % (vocab_size - 1) + 1;
460+
if (target == word) continue;
461+
label = 0;
462+
}
463+
l2 = target * layer1_size;
464+
f = 0;
465+
for (c = 0; c < layer1_size; c++) f += neu1[c] * syn1neg[c + l2];
466+
if (f > MAX_EXP) g = (label - 1) * alpha;
467+
else if (f < -MAX_EXP) g = (label - 0) * alpha;
468+
else g = (label - expTable[(int)((f + MAX_EXP) * (EXP_TABLE_SIZE / MAX_EXP / 2))]) * alpha;
469+
for (c = 0; c < layer1_size; c++) neu1e[c] += g * syn1neg[c + l2];
470+
for (c = 0; c < layer1_size; c++) syn1neg[c + l2] += g * neu1[c];
471+
}
472+
// hidden -> in
473+
for (a = b; a < window * 2 + 1 - b; a++) if (a != window) {
474+
c = sentence_position - window + a;
475+
if (c < 0) continue;
476+
if (c >= sentence_length) continue;
477+
last_word = sen[c];
478+
if (last_word == -1) continue;
479+
for (c = 0; c < layer1_size; c++) syn0[c + last_word * layer1_size] += neu1e[c];
447480
}
448-
l2 = target * layer1_size;
449-
f = 0;
450-
for (c = 0; c < layer1_size; c++) f += neu1[c] * syn1neg[c + l2];
451-
if (f > MAX_EXP) g = (label - 1) * alpha;
452-
else if (f < -MAX_EXP) g = (label - 0) * alpha;
453-
else g = (label - expTable[(int)((f + MAX_EXP) * (EXP_TABLE_SIZE / MAX_EXP / 2))]) * alpha;
454-
for (c = 0; c < layer1_size; c++) neu1e[c] += g * syn1neg[c + l2];
455-
for (c = 0; c < layer1_size; c++) syn1neg[c + l2] += g * neu1[c];
456-
}
457-
// hidden -> in
458-
for (a = b; a < window * 2 + 1 - b; a++) if (a != window) {
459-
c = sentence_position - window + a;
460-
if (c < 0) continue;
461-
if (c >= sentence_length) continue;
462-
last_word = sen[c];
463-
if (last_word == -1) continue;
464-
for (c = 0; c < layer1_size; c++) syn0[c + last_word * layer1_size] += neu1e[c];
465481
}
466482
} else { //train skip-gram
467483
for (a = b; a < window * 2 + 1 - b; a++) if (a != window) {
@@ -611,7 +627,7 @@ int ArgPos(char *str, int argc, char **argv) {
611627
int main(int argc, char **argv) {
612628
int i;
613629
if (argc == 1) {
614-
printf("WORD VECTOR estimation toolkit v 0.1b\n\n");
630+
printf("WORD VECTOR estimation toolkit v 0.1c\n\n");
615631
printf("Options:\n");
616632
printf("Parameters for training:\n");
617633
printf("\t-train <file>\n");
@@ -623,18 +639,20 @@ int main(int argc, char **argv) {
623639
printf("\t-window <int>\n");
624640
printf("\t\tSet max skip length between words; default is 5\n");
625641
printf("\t-sample <float>\n");
626-
printf("\t\tSet threshold for occurrence of words. Those that appear with higher frequency");
627-
printf(" in the training data will be randomly down-sampled; default is 0 (off), useful value is 1e-5\n");
642+
printf("\t\tSet threshold for occurrence of words. Those that appear with higher frequency in the training data\n");
643+
printf("\t\twill be randomly down-sampled; default is 1e-3, useful range is (0, 1e-5)\n");
628644
printf("\t-hs <int>\n");
629-
printf("\t\tUse Hierarchical Softmax; default is 1 (0 = not used)\n");
645+
printf("\t\tUse Hierarchical Softmax; default is 0 (not used)\n");
630646
printf("\t-negative <int>\n");
631-
printf("\t\tNumber of negative examples; default is 0, common values are 5 - 10 (0 = not used)\n");
647+
printf("\t\tNumber of negative examples; default is 5, common values are 3 - 10 (0 = not used)\n");
632648
printf("\t-threads <int>\n");
633-
printf("\t\tUse <int> threads (default 1)\n");
649+
printf("\t\tUse <int> threads (default 12)\n");
650+
printf("\t-iter <int>\n");
651+
printf("\t\tRun more training iterations (default 5)\n");
634652
printf("\t-min-count <int>\n");
635653
printf("\t\tThis will discard words that appear less than <int> times; default is 5\n");
636654
printf("\t-alpha <float>\n");
637-
printf("\t\tSet the starting learning rate; default is 0.025\n");
655+
printf("\t\tSet the starting learning rate; default is 0.025 for skip-gram and 0.05 for CBOW\n");
638656
printf("\t-classes <int>\n");
639657
printf("\t\tOutput word classes rather than word vectors; default number of classes is 0 (vectors are written)\n");
640658
printf("\t-debug <int>\n");
@@ -646,9 +664,9 @@ int main(int argc, char **argv) {
646664
printf("\t-read-vocab <file>\n");
647665
printf("\t\tThe vocabulary will be read from <file>, not constructed from the training data\n");
648666
printf("\t-cbow <int>\n");
649-
printf("\t\tUse the continuous bag of words model; default is 0 (skip-gram model)\n");
667+
printf("\t\tUse the continuous bag of words model; default is 1 (use 0 for skip-gram model)\n");
650668
printf("\nExamples:\n");
651-
printf("./word2vec -train data.txt -output vec.txt -debug 2 -size 200 -window 5 -sample 1e-4 -negative 5 -hs 0 -binary 0 -cbow 1\n\n");
669+
printf("./word2vec -train data.txt -output vec.txt -size 200 -window 5 -sample 1e-4 -negative 5 -hs 0 -binary 0 -cbow 1 -iter 3\n\n");
652670
return 0;
653671
}
654672
output_file[0] = 0;
@@ -661,13 +679,15 @@ int main(int argc, char **argv) {
661679
if ((i = ArgPos((char *)"-debug", argc, argv)) > 0) debug_mode = atoi(argv[i + 1]);
662680
if ((i = ArgPos((char *)"-binary", argc, argv)) > 0) binary = atoi(argv[i + 1]);
663681
if ((i = ArgPos((char *)"-cbow", argc, argv)) > 0) cbow = atoi(argv[i + 1]);
682+
if (cbow) alpha = 0.05;
664683
if ((i = ArgPos((char *)"-alpha", argc, argv)) > 0) alpha = atof(argv[i + 1]);
665684
if ((i = ArgPos((char *)"-output", argc, argv)) > 0) strcpy(output_file, argv[i + 1]);
666685
if ((i = ArgPos((char *)"-window", argc, argv)) > 0) window = atoi(argv[i + 1]);
667686
if ((i = ArgPos((char *)"-sample", argc, argv)) > 0) sample = atof(argv[i + 1]);
668687
if ((i = ArgPos((char *)"-hs", argc, argv)) > 0) hs = atoi(argv[i + 1]);
669688
if ((i = ArgPos((char *)"-negative", argc, argv)) > 0) negative = atoi(argv[i + 1]);
670689
if ((i = ArgPos((char *)"-threads", argc, argv)) > 0) num_threads = atoi(argv[i + 1]);
690+
if ((i = ArgPos((char *)"-iter", argc, argv)) > 0) iter = atoi(argv[i + 1]);
671691
if ((i = ArgPos((char *)"-min-count", argc, argv)) > 0) min_count = atoi(argv[i + 1]);
672692
if ((i = ArgPos((char *)"-classes", argc, argv)) > 0) classes = atoi(argv[i + 1]);
673693
vocab = (struct vocab_word *)calloc(vocab_max_size, sizeof(struct vocab_word));
@@ -679,4 +699,4 @@ int main(int argc, char **argv) {
679699
}
680700
TrainModel();
681701
return 0;
682-
}
702+
}

0 commit comments

Comments
 (0)