update to 0.1c version

demo-analogy.sh

@@ -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

@@ -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

@@ -1,12 +1,11 @@
 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

@@ -1,8 +1,11 @@
 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
-./distance vectors-phrase.bin
+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

@@ -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

@@ -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
-./distance vectors.bin
+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

@@ -1,6 +1,6 @@
 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

@@ -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;
@@ -410,58 +421,63 @@ 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;
         if (c >= sentence_length) continue;
         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 (hs) for (d = 0; d < vocab[word].codelen; d++) {
-        f = 0;
-        l2 = vocab[word].point[d] * layer1_size;
-        // Propagate hidden -> output
-        for (c = 0; c < layer1_size; c++) f += neu1[c] * syn1[c + l2];
-        if (f <= -MAX_EXP) continue;
-        else if (f >= MAX_EXP) continue;
-        else f = expTable[(int)((f + MAX_EXP) * (EXP_TABLE_SIZE / MAX_EXP / 2))];
-        // 'g' is the gradient multiplied by the learning rate
-        g = (1 - vocab[word].code[d] - f) * alpha;
-        // Propagate errors output -> hidden
-        for (c = 0; c < layer1_size; c++) neu1e[c] += g * syn1[c + l2];
-        // Learn weights hidden -> output
-        for (c = 0; c < layer1_size; c++) syn1[c + l2] += g * neu1[c];
-      }
-      // NEGATIVE SAMPLING
-      if (negative > 0) for (d = 0; d < negative + 1; d++) {
-        if (d == 0) {
-          target = word;
-          label = 1;
-        } else {
-          next_random = next_random * (unsigned long long)25214903917 + 11;
-          target = table[(next_random >> 16) % table_size];
-          if (target == 0) target = next_random % (vocab_size - 1) + 1;
-          if (target == word) continue;
-          label = 0;
+      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;
+          // Propagate hidden -> output
+          for (c = 0; c < layer1_size; c++) f += neu1[c] * syn1[c + l2];
+          if (f <= -MAX_EXP) continue;
+          else if (f >= MAX_EXP) continue;
+          else f = expTable[(int)((f + MAX_EXP) * (EXP_TABLE_SIZE / MAX_EXP / 2))];
+          // 'g' is the gradient multiplied by the learning rate
+          g = (1 - vocab[word].code[d] - f) * alpha;
+          // Propagate errors output -> hidden
+          for (c = 0; c < layer1_size; c++) neu1e[c] += g * syn1[c + l2];
+          // Learn weights hidden -> output
+          for (c = 0; c < layer1_size; c++) syn1[c + l2] += g * neu1[c];
+        }
+        // NEGATIVE SAMPLING
+        if (negative > 0) for (d = 0; d < negative + 1; d++) {
+          if (d == 0) {
+            target = word;
+            label = 1;
+          } else {
+            next_random = next_random * (unsigned long long)25214903917 + 11;
+            target = table[(next_random >> 16) % table_size];
+            if (target == 0) target = next_random % (vocab_size - 1) + 1;
+            if (target == word) continue;
+            label = 0;
+          }
+          l2 = target * layer1_size;
+          f = 0;
+          for (c = 0; c < layer1_size; c++) f += neu1[c] * syn1neg[c + l2];
+          if (f > MAX_EXP) g = (label - 1) * alpha;
+          else if (f < -MAX_EXP) g = (label - 0) * alpha;
+          else g = (label - expTable[(int)((f + MAX_EXP) * (EXP_TABLE_SIZE / MAX_EXP / 2))]) * alpha;
+          for (c = 0; c < layer1_size; c++) neu1e[c] += g * syn1neg[c + l2];
+          for (c = 0; c < layer1_size; c++) syn1neg[c + l2] += g * neu1[c];
+        }
+        // hidden -> in
+        for (a = b; a < window * 2 + 1 - b; a++) if (a != window) {
+          c = sentence_position - window + a;
+          if (c < 0) continue;
+          if (c >= sentence_length) continue;
+          last_word = sen[c];
+          if (last_word == -1) continue;
+          for (c = 0; c < layer1_size; c++) syn0[c + last_word * layer1_size] += neu1e[c];
         }
-        l2 = target * layer1_size;
-        f = 0;
-        for (c = 0; c < layer1_size; c++) f += neu1[c] * syn1neg[c + l2];
-        if (f > MAX_EXP) g = (label - 1) * alpha;
-        else if (f < -MAX_EXP) g = (label - 0) * alpha;
-        else g = (label - expTable[(int)((f + MAX_EXP) * (EXP_TABLE_SIZE / MAX_EXP / 2))]) * alpha;
-        for (c = 0; c < layer1_size; c++) neu1e[c] += g * syn1neg[c + l2];
-        for (c = 0; c < layer1_size; c++) syn1neg[c + l2] += g * neu1[c];
-      }
-      // hidden -> in
-      for (a = b; a < window * 2 + 1 - b; a++) if (a != window) {
-        c = sentence_position - window + a;
-        if (c < 0) continue;
-        if (c >= sentence_length) continue;
-        last_word = sen[c];
-        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) {
@@ -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;
@@ -661,13 +679,15 @@ 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]);
   if ((i = ArgPos((char *)"-sample", argc, argv)) > 0) sample = atof(argv[i + 1]);
   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));
@@ -679,4 +699,4 @@ int main(int argc, char **argv) {
   }
   TrainModel();
   return 0;
-}
+}