Fixed major errors

README.md

@@ -20,7 +20,7 @@ To get started, cd into the directory examples/ and run demo.sh. It downloads th
 * sim_sif.py and sim_tfidf.py are for the textual similarity tasks in the paper,
 * supervised_sif_proj.sh is for the supervised tasks in the paper.
 
-Check these files to see the options.
+Check these files to see the options. Make sure you create "log" folder in "example" directory before running above code.
 
 ## Source code
 The code is separated into the following parts:

examples/sif_embedding.py

@@ -15,7 +15,7 @@
 word2weight = data_io.getWordWeight(weightfile, weightpara) # word2weight['str'] is the weight for the word 'str'
 weight4ind = data_io.getWeight(words, word2weight) # weight4ind[i] is the weight for the i-th word
 # load sentences
-x, m, _ = data_io.sentences2idx(sentences, words) # x is the array of word indices, m is the binary mask indicating whether there is a word in that location
+x, m = data_io.sentences2idx(sentences, words) # x is the array of word indices, m is the binary mask indicating whether there is a word in that location
 w = data_io.seq2weight(x, m, weight4ind) # get word weights
 
 # set parameters

examples/train.sh

@@ -1,2 +1,2 @@
 cd ../src
-THEANO_FLAGS=mode=FAST_RUN,device=cpu,floatX=float32 python train.py $@
+THEANO_FLAGS=mode=FAST_RUN,device=cpu,floatX=float32 python2.7 train.py $@

src/data_io.py

@@ -196,6 +196,7 @@ def sentences2idx(sentences, words):
     :param words: a dictionary, words['str'] is the indices of the word 'str'
     :return: x1, m1. x1[i, :] is the word indices in sentence i, m1[i,:] is the mask for sentence i (0 means no word at the location)
     """
+    seq1 = []
     for i in sentences:
         seq1.append(getSeq(i,words))
     x1,m1 = prepare_data(seq1)