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word2vec
Commit 891d84c6 authored by tmikolov's avatar tmikolov
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update to 0.1c version

parent 5815e5d0
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demo-analogy.sh
View file @ 891d84c6
......@@ -3,9 +3,9 @@ if [ ! -e text8 ]; then
wget http://mattmahoney.net/dc/text8.zip -O text8.gz
gzip -d text8.gz -f
fi
echo -----------------------------------------------------------------------------------------------------
echo Note that for the word analogy to perform well, the models should be trained on much larger data sets
echo ---------------------------------------------------------------------------------------------------
echo Note that for the word analogy to perform well, the model should be trained on much larger data set
echo Example input: paris france berlin
echo -----------------------------------------------------------------------------------------------------
time ./word2vec -train text8 -output vectors.bin -cbow 0 -size 200 -window 5 -negative 0 -hs 1 -sample 1e-3 -threads 12 -binary 1
echo ---------------------------------------------------------------------------------------------------
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
./word-analogy vectors.bin
demo-classes.sh
View file @ 891d84c6
......@@ -3,6 +3,6 @@ if [ ! -e text8 ]; then
wget http://mattmahoney.net/dc/text8.zip -O text8.gz
gzip -d text8.gz -f
fi
time ./word2vec -train text8 -output classes.txt -cbow 0 -size 200 -window 5 -negative 0 -hs 1 -sample 1e-3 -threads 12 -classes 500
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
sort classes.txt -k 2 -n > classes.sorted.txt
echo The word classes were saved to file classes.sorted.txt
demo-phrase-accuracy.sh
View file @ 891d84c6
make
if [ ! -e text8 ]; then
wget http://mattmahoney.net/dc/text8.zip -O text8.gz
gzip -d text8.gz -f
if [ ! -e news.2012.en.shuffled ]; then
wget http://www.statmt.org/wmt14/training-monolingual-news-crawl/news.2012.en.shuffled.gz
gzip -d news.2012.en.shuffled.gz -f
fi
echo ----------------------------------------------------------------------------------------------------------------
echo Note that the accuracy and coverage of the test set questions is going to be low with this small training corpus
echo To achieve better accuracy, larger training set is needed
echo ----------------------------------------------------------------------------------------------------------------
time ./word2phrase -train text8 -output text8-phrase -threshold 500 -debug 2 -min-count 3
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
./compute-accuracy vectors-phrase.bin <questions-phrases.txt
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
time ./word2phrase -train news.2012.en.shuffled-norm0 -output news.2012.en.shuffled-norm0-phrase0 -threshold 200 -debug 2
time ./word2phrase -train news.2012.en.shuffled-norm0-phrase0 -output news.2012.en.shuffled-norm0-phrase1 -threshold 100 -debug 2
tr A-Z a-z < news.2012.en.shuffled-norm0-phrase1 > news.2012.en.shuffled-norm1-phrase1
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
./compute-accuracy vectors-phrase.bin < questions-phrases.txt
demo-phrases.sh
View file @ 891d84c6
make
if [ ! -e text8 ]; then
wget http://mattmahoney.net/dc/text8.zip -O text8.gz
gzip -d text8.gz -f
if [ ! -e news.2012.en.shuffled ]; then
wget http://www.statmt.org/wmt14/training-monolingual-news-crawl/news.2012.en.shuffled.gz
gzip -d news.2012.en.shuffled.gz -f
fi
time ./word2phrase -train text8 -output text8-phrase -threshold 500 -debug 2
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
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
time ./word2phrase -train news.2012.en.shuffled-norm0 -output news.2012.en.shuffled-norm0-phrase0 -threshold 200 -debug 2
time ./word2phrase -train news.2012.en.shuffled-norm0-phrase0 -output news.2012.en.shuffled-norm0-phrase1 -threshold 100 -debug 2
tr A-Z a-z < news.2012.en.shuffled-norm0-phrase1 > news.2012.en.shuffled-norm1-phrase1
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
./distance vectors-phrase.bin
demo-word-accuracy.sh
View file @ 891d84c6
......@@ -3,6 +3,6 @@ if [ ! -e text8 ]; then
wget http://mattmahoney.net/dc/text8.zip -O text8.gz
gzip -d text8.gz -f
fi
time ./word2vec -train text8 -output vectors.bin -cbow 0 -size 200 -window 5 -negative 0 -hs 1 -sample 1e-3 -threads 12 -binary 1
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
./compute-accuracy vectors.bin 30000 < questions-words.txt
# to compute accuracy with the full vocabulary, use: ./compute-accuracy vectors.bin < questions-words.txt
demo-word.sh
View file @ 891d84c6
......@@ -3,5 +3,5 @@ if [ ! -e text8 ]; then
wget http://mattmahoney.net/dc/text8.zip -O text8.gz
gzip -d text8.gz -f
fi
time ./word2vec -train text8 -output vectors.bin -cbow 0 -size 200 -window 5 -negative 0 -hs 1 -sample 1e-3 -threads 12 -binary 1
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
./distance vectors.bin
makefile
View file @ 891d84c6
CC = gcc
#The -Ofast might not work with older versions of gcc; in that case, use -O2
CFLAGS = -lm -pthread -Ofast -march=native -Wall -funroll-loops -Wno-unused-result
#Using -Ofast instead of -O3 might result in faster code, but is supported only by newer GCC versions
CFLAGS = -lm -pthread -O3 -march=native -Wall -funroll-loops -Wno-unused-result
all: word2vec word2phrase distance word-analogy compute-accuracy
......
word2vec.c
View file @ 891d84c6
......@@ -37,15 +37,15 @@ struct vocab_word {
char train_file[MAX_STRING], output_file[MAX_STRING];
char save_vocab_file[MAX_STRING], read_vocab_file[MAX_STRING];
struct vocab_word *vocab;
int binary = 0, cbow = 0, debug_mode = 2, window = 5, min_count = 5, num_threads = 1, min_reduce = 1;
int binary = 0, cbow = 1, debug_mode = 2, window = 5, min_count = 5, num_threads = 12, min_reduce = 1;
int *vocab_hash;
long long vocab_max_size = 1000, vocab_size = 0, layer1_size = 100;
long long train_words = 0, word_count_actual = 0, file_size = 0, classes = 0;
real alpha = 0.025, starting_alpha, sample = 0;
long long train_words = 0, word_count_actual = 0, iter = 5, file_size = 0, classes = 0;
real alpha = 0.025, starting_alpha, sample = 1e-3;
real *syn0, *syn1, *syn1neg, *expTable;
clock_t start;
int hs = 1, negative = 0;
int hs = 0, negative = 5;
const int table_size = 1e8;
int *table;
......@@ -337,29 +337,32 @@ void ReadVocab() {
void InitNet() {
long long a, b;
unsigned long long next_random = 1;
a = posix_memalign((void **)&syn0, 128, (long long)vocab_size * layer1_size * sizeof(real));
if (syn0 == NULL) {printf("Memory allocation failed\n"); exit(1);}
if (hs) {
a = posix_memalign((void **)&syn1, 128, (long long)vocab_size * layer1_size * sizeof(real));
if (syn1 == NULL) {printf("Memory allocation failed\n"); exit(1);}
for (b = 0; b < layer1_size; b++) for (a = 0; a < vocab_size; a++)
for (a = 0; a < vocab_size; a++) for (b = 0; b < layer1_size; b++)
syn1[a * layer1_size + b] = 0;
}
if (negative>0) {
a = posix_memalign((void **)&syn1neg, 128, (long long)vocab_size * layer1_size * sizeof(real));
if (syn1neg == NULL) {printf("Memory allocation failed\n"); exit(1);}
for (b = 0; b < layer1_size; b++) for (a = 0; a < vocab_size; a++)
for (a = 0; a < vocab_size; a++) for (b = 0; b < layer1_size; b++)
syn1neg[a * layer1_size + b] = 0;
}
for (b = 0; b < layer1_size; b++) for (a = 0; a < vocab_size; a++)
syn0[a * layer1_size + b] = (rand() / (real)RAND_MAX - 0.5) / layer1_size;
for (a = 0; a < vocab_size; a++) for (b = 0; b < layer1_size; b++) {
next_random = next_random * (unsigned long long)25214903917 + 11;
syn0[a * layer1_size + b] = (((next_random & 0xFFFF) / (real)65536) - 0.5) / layer1_size;
}
CreateBinaryTree();
}
void *TrainModelThread(void *id) {
long long a, b, d, word, last_word, sentence_length = 0, sentence_position = 0;
long long a, b, d, cw, word, last_word, sentence_length = 0, sentence_position = 0;
long long word_count = 0, last_word_count = 0, sen[MAX_SENTENCE_LENGTH + 1];
long long l1, l2, c, target, label;
long long l1, l2, c, target, label, local_iter = iter;
unsigned long long next_random = (long long)id;
real f, g;
clock_t now;
......@@ -374,11 +377,11 @@ void *TrainModelThread(void *id) {
if ((debug_mode > 1)) {
now=clock();
printf("%cAlpha: %f Progress: %.2f%% Words/thread/sec: %.2fk ", 13, alpha,
word_count_actual / (real)(train_words + 1) * 100,
word_count_actual / (real)(iter * train_words + 1) * 100,
word_count_actual / ((real)(now - start + 1) / (real)CLOCKS_PER_SEC * 1000));
fflush(stdout);
}
alpha = starting_alpha * (1 - word_count_actual / (real)(train_words + 1));
alpha = starting_alpha * (1 - word_count_actual / (real)(iter * train_words + 1));
if (alpha < starting_alpha * 0.0001) alpha = starting_alpha * 0.0001;
}
if (sentence_length == 0) {
......@@ -400,8 +403,16 @@ void *TrainModelThread(void *id) {
}
sentence_position = 0;
}
if (feof(fi)) break;
if (word_count > train_words / num_threads) break;
if (feof(fi) || (word_count > train_words / num_threads)) {
word_count_actual += word_count - last_word_count;
local_iter--;
if (local_iter == 0) break;
word_count = 0;
last_word_count = 0;
sentence_length = 0;
fseek(fi, file_size / (long long)num_threads * (long long)id, SEEK_SET);
continue;
}
word = sen[sentence_position];
if (word == -1) continue;
for (c = 0; c < layer1_size; c++) neu1[c] = 0;
......@@ -410,6 +421,7 @@ void *TrainModelThread(void *id) {
b = next_random % window;
if (cbow) { //train the cbow architecture
// in -> hidden
cw = 0;
for (a = b; a < window * 2 + 1 - b; a++) if (a != window) {
c = sentence_position - window + a;
if (c < 0) continue;
......@@ -417,7 +429,10 @@ void *TrainModelThread(void *id) {
last_word = sen[c];
if (last_word == -1) continue;
for (c = 0; c < layer1_size; c++) neu1[c] += syn0[c + last_word * layer1_size];
cw++;
}
if (cw) {
for (c = 0; c < layer1_size; c++) neu1[c] /= cw;
if (hs) for (d = 0; d < vocab[word].codelen; d++) {
f = 0;
l2 = vocab[word].point[d] * layer1_size;
......@@ -463,6 +478,7 @@ void *TrainModelThread(void *id) {
if (last_word == -1) continue;
for (c = 0; c < layer1_size; c++) syn0[c + last_word * layer1_size] += neu1e[c];
}
}
} else { //train skip-gram
for (a = b; a < window * 2 + 1 - b; a++) if (a != window) {
c = sentence_position - window + a;
......@@ -611,7 +627,7 @@ int ArgPos(char *str, int argc, char **argv) {
int main(int argc, char **argv) {
int i;
if (argc == 1) {
printf("WORD VECTOR estimation toolkit v 0.1b\n\n");
printf("WORD VECTOR estimation toolkit v 0.1c\n\n");
printf("Options:\n");
printf("Parameters for training:\n");
printf("\t-train <file>\n");
......@@ -623,18 +639,20 @@ int main(int argc, char **argv) {
printf("\t-window <int>\n");
printf("\t\tSet max skip length between words; default is 5\n");
printf("\t-sample <float>\n");
printf("\t\tSet threshold for occurrence of words. Those that appear with higher frequency");
printf(" in the training data will be randomly down-sampled; default is 0 (off), useful value is 1e-5\n");
printf("\t\tSet threshold for occurrence of words. Those that appear with higher frequency in the training data\n");
printf("\t\twill be randomly down-sampled; default is 1e-3, useful range is (0, 1e-5)\n");
printf("\t-hs <int>\n");
printf("\t\tUse Hierarchical Softmax; default is 1 (0 = not used)\n");
printf("\t\tUse Hierarchical Softmax; default is 0 (not used)\n");
printf("\t-negative <int>\n");
printf("\t\tNumber of negative examples; default is 0, common values are 5 - 10 (0 = not used)\n");
printf("\t\tNumber of negative examples; default is 5, common values are 3 - 10 (0 = not used)\n");
printf("\t-threads <int>\n");
printf("\t\tUse <int> threads (default 1)\n");
printf("\t\tUse <int> threads (default 12)\n");
printf("\t-iter <int>\n");
printf("\t\tRun more training iterations (default 5)\n");
printf("\t-min-count <int>\n");
printf("\t\tThis will discard words that appear less than <int> times; default is 5\n");
printf("\t-alpha <float>\n");
printf("\t\tSet the starting learning rate; default is 0.025\n");
printf("\t\tSet the starting learning rate; default is 0.025 for skip-gram and 0.05 for CBOW\n");
printf("\t-classes <int>\n");
printf("\t\tOutput word classes rather than word vectors; default number of classes is 0 (vectors are written)\n");
printf("\t-debug <int>\n");
......@@ -646,9 +664,9 @@ int main(int argc, char **argv) {
printf("\t-read-vocab <file>\n");
printf("\t\tThe vocabulary will be read from <file>, not constructed from the training data\n");
printf("\t-cbow <int>\n");
printf("\t\tUse the continuous bag of words model; default is 0 (skip-gram model)\n");
printf("\t\tUse the continuous bag of words model; default is 1 (use 0 for skip-gram model)\n");
printf("\nExamples:\n");
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");
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");
return 0;
}
output_file[0] = 0;
......@@ -661,6 +679,7 @@ int main(int argc, char **argv) {
if ((i = ArgPos((char *)"-debug", argc, argv)) > 0) debug_mode = atoi(argv[i + 1]);
if ((i = ArgPos((char *)"-binary", argc, argv)) > 0) binary = atoi(argv[i + 1]);
if ((i = ArgPos((char *)"-cbow", argc, argv)) > 0) cbow = atoi(argv[i + 1]);
if (cbow) alpha = 0.05;
if ((i = ArgPos((char *)"-alpha", argc, argv)) > 0) alpha = atof(argv[i + 1]);
if ((i = ArgPos((char *)"-output", argc, argv)) > 0) strcpy(output_file, argv[i + 1]);
if ((i = ArgPos((char *)"-window", argc, argv)) > 0) window = atoi(argv[i + 1]);
......@@ -668,6 +687,7 @@ int main(int argc, char **argv) {
if ((i = ArgPos((char *)"-hs", argc, argv)) > 0) hs = atoi(argv[i + 1]);
if ((i = ArgPos((char *)"-negative", argc, argv)) > 0) negative = atoi(argv[i + 1]);
if ((i = ArgPos((char *)"-threads", argc, argv)) > 0) num_threads = atoi(argv[i + 1]);
if ((i = ArgPos((char *)"-iter", argc, argv)) > 0) iter = atoi(argv[i + 1]);
if ((i = ArgPos((char *)"-min-count", argc, argv)) > 0) min_count = atoi(argv[i + 1]);
if ((i = ArgPos((char *)"-classes", argc, argv)) > 0) classes = atoi(argv[i + 1]);
vocab = (struct vocab_word *)calloc(vocab_max_size, sizeof(struct vocab_word));
......
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