Repository: piskvorky/gensim

Issue:
Gensim error while loading Hebrew
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#### Description
Gensim error while loading Hebrew

#### Steps/Code/Corpus to Reproduce
```
from gensim.models.wrappers import FastText
model = FastText.load_fasttext_format('wiki.he')
```
#### Expected Results

#### Actual Results
---------------------------------------------------------------------------
AssertionError                            Traceback (most recent call last)
<ipython-input-11-edebc82131f5> in <module>()
      2 
      3 #num_dims = 300
----> 4 model = FastText.load_fasttext_format('wiki.he')

/home/vmadmin/anaconda3/lib/python3.5/site-packages/gensim/models/wrappers/fasttext.py in load_fasttext_format(cls, model_file)
    236         model = cls()
    237         model.wv = cls.load_word2vec_format('%s.vec' % model_file)
--> 238         model.load_binary_data('%s.bin' % model_file)
    239         return model
    240 

/home/vmadmin/anaconda3/lib/python3.5/site-packages/gensim/models/wrappers/fasttext.py in load_binary_data(self, model_binary_file)
    253         with utils.smart_open(model_binary_file, 'rb') as f:
    254             self.load_model_params(f)
--> 255             self.load_dict(f)
    256             self.load_vectors(f)
    257 

/home/vmadmin/anaconda3/lib/python3.5/site-packages/gensim/models/wrappers/fasttext.py in load_dict(self, file_handle)
    274         (vocab_size, nwords, _) = self.struct_unpack(file_handle, '@3i')
    275         # Vocab stored by [Dictionary::save](https://github.com/facebookresearch/fastText/blob/master/src/dictionary.cc)
--> 276         assert len(self.wv.vocab) == nwords, 'mismatch between vocab sizes'
    277         assert len(self.wv.vocab) == vocab_size, 'mismatch between vocab sizes'
    278         ntokens, = self.struct_unpack(file_handle, '@q')

AssertionError: mismatch between vocab sizes

#### Versions
Linux-4.4.0-75-generic-x86_64-with-debian-stretch-sid
Python 3.5.2 |Anaconda custom (64-bit)| (default, Jul  2 2016, 17:53:06) 
[GCC 4.4.7 20120313 (Red Hat 4.4.7-1)]
NumPy 1.11.1
SciPy 0.18.1
gensim 1.0.1
FAST_VERSION 2




Discussion:
Looks a bit like https://github.com/RaRe-Technologies/gensim/issues/1236. The error message might seem to imply a problem with how fasttext produces the data. Whereas the description above used gensim 1.0.1, it also reproduces with gensym 2.0.0.

>  File "<stdin>", line 1, in <module>
>   File "/usr/local/lib/python2.7/dist-packages/gensim/models/wrappers/fasttext.py", line 239, in load_fasttext_format
>     model.load_binary_data('%s.bin' % model_file, encoding=encoding)
>   File "/usr/local/lib/python2.7/dist-packages/gensim/models/wrappers/fasttext.py", line 256, in load_binary_data
>     self.load_dict(f, encoding=encoding)
>   File "/usr/local/lib/python2.7/dist-packages/gensim/models/wrappers/fasttext.py", line 277, in load_dict
>     assert len(self.wv.vocab) == nwords, 'mismatch between vocab sizes'

Notably this doesn't happen with all pretrained (Hebrew) embeddings created by fasttext. 
Has this issue been resolved?
If yes, can you please share the reference?
Hi @kewlcoder This is not an issue with gensim wrapper but an issue with the trained FastText model as there is a mismatch in vocab between .bin and .vec files. @prakhar2b , could you please investigate and raise an issue in FastText repo?

Please use `FastText.load_word2vec_format('FILENAME.vec')` as a workaround for now.
@tmylk @jayantj The mismatch is not only for the pretrained models released by facebook, but we are getting this error for all the models trained by fasttext, which was not the case earlier. Something might have changed in the fastText, looking into the code to see if it's intentional, then I'll raise an issue in the fastText repo.
@prakhar2b Sounds good.
Also, the assertion to check for mismatch between vocab sizes between the `.vec` and `.bin` file was written as part of a defensive approach to make sure there weren't any "silent" bugs.
In case the mismatch doesn't make an actual difference, and it is possible to proceed with loading the model, changing the assert to a warning log would be a decent solution, IMO.
About 8 days ago, fastText added two additional int32_t's to the .bin model header. These are "magic" and "version." As of right now, fasttext.py does not account for these integers when reading in model parameters causing it to be off by 2 integers for the rest of the reads. Take a look at the "checkModel" function in https://github.com/facebookresearch/fastText/blob/master/src/fasttext.cc to see what I'm talking about.

EDIT:
Also important to note is the addition of dictionary pruning which adds an int64_t (pruneidx_size) following the previous int64_t (ntokens) when reading in the dictionary from the .bin file. This is not accounted for in the current version of fasttext.py.

I have made some quick edits to fasttext.py that make it compatible with the latest version of fastText models. All I did was add additional reads so a plain model without quantization/dictionary pruning can be read. Please note that I didn't do any extensive testing so use at your own risk.

[fasttext.zip](https://github.com/RaRe-Technologies/gensim/files/990431/fasttext.zip)

@dgg5503 @tmylk @jayantj  struct_unpack for models trained by fastText using text8 data -
```(self.wv.vocab from .vec file, nwords & vocab_size from .bin using struct.unpack)```

parameter  | fastText (old) | fastText(new)
-------------  | ----------------  | ----------------
len(self.wv.vocab) | 71290 | 71290
nwords | 71290 | 1058682594
vocab_size | 71290 | -350469331

I'm not sure why we have negative value for vocab_size. @jayantj (please comment on this). If this is undesirable, we should report it to fastText repo.

Also, if this is an intentional mismatch (which seems to be), then there is no point in making any assert or warning statement. I think we should use vec and bin file separately for different purposes assuming that facebook's fastText code is working fine. This was also discussed for [issue #1261](https://github.com/RaRe-Technologies/gensim/issues/1261) (improving fasttext loading time) while making comparision with salestock's fasttext loading mechanism which only used bin file for loading.

@prakhar2b Are you sure about this? Looking at those values, it seems very likely to me that we are reading the wrong bytes for the values of `nwords` and `vocab_size`.

Also, on a different note: the issue raised about the model trained on French wiki is quite old, before the FastText `magic` or `version` variables were added. I believe they are probably two different issues.
UPDATE : I've solved this. I'll submit a final PR asap. Thanks @dgg5503 for the suggestions.

Resolving patch:
diff --git a/gensim/models/wrappers/fasttext.py b/gensim/models/wrappers/fasttext.py
--- a/gensim/models/wrappers/fasttext.py
+++ b/gensim/models/wrappers/fasttext.py
@@ -42,6 +42,8 @@
 
 logger = logging.getLogger(__name__)
 
+FASTTEXT_FILEFORMAT_MAGIC = 793712314
+
 
 class FastTextKeyedVectors(KeyedVectors):
     """
@@ -257,7 +259,15 @@ def load_binary_data(self, model_binary_file, encoding='utf8'):
             self.load_vectors(f)
 
     def load_model_params(self, file_handle):
-        (dim, ws, epoch, minCount, neg, _, loss, model, bucket, minn, maxn, _, t) = self.struct_unpack(file_handle, '@12i1d')
+        magic, version = self.struct_unpack(file_handle, '@2i')
+        if magic == FASTTEXT_FILEFORMAT_MAGIC:  # newer format
+            self.new_format = True
+            dim, ws, epoch, minCount, neg, _, loss, model, bucket, minn, maxn, _, t = self.struct_unpack(file_handle, '@12i1d')
+        else:  # older format
+            self.new_format = False
+            dim = magic
+            ws = version
+            epoch, minCount, neg, _, loss, model, bucket, minn, maxn, _, t = self.struct_unpack(file_handle, '@10i1d')
         # Parameters stored by [Args::save](https://github.com/facebookresearch/fastText/blob/master/src/args.cc)
         self.vector_size = dim
         self.window = ws
@@ -272,11 +282,13 @@ def load_model_params(self, file_handle):
         self.sample = t
 
     def load_dict(self, file_handle, encoding='utf8'):
-        (vocab_size, nwords, _) = self.struct_unpack(file_handle, '@3i')
+        vocab_size, nwords, _ = self.struct_unpack(file_handle, '@3i')
         # Vocab stored by [Dictionary::save](https://github.com/facebookresearch/fastText/blob/master/src/dictionary.cc)
         assert len(self.wv.vocab) == nwords, 'mismatch between vocab sizes'
         assert len(self.wv.vocab) == vocab_size, 'mismatch between vocab sizes'
-        ntokens, = self.struct_unpack(file_handle, '@q')
+        self.struct_unpack(file_handle, '@1q')  # number of tokens
+        if self.new_format:
+            pruneidx_size, = self.struct_unpack(file_handle, '@q')
         for i in range(nwords):
             word_bytes = b''
             char_byte = file_handle.read(1)
@@ -285,12 +297,17 @@ def load_dict(self, file_handle, encoding='utf8'):
                 word_bytes += char_byte
                 char_byte = file_handle.read(1)
             word = word_bytes.decode(encoding)
-            count, _ = self.struct_unpack(file_handle, '@ib')
-            _ = self.struct_unpack(file_handle, '@i')
+            count, _ = self.struct_unpack(file_handle, '@qb')
             assert self.wv.vocab[word].index == i, 'mismatch between gensim word index and fastText word index'
             self.wv.vocab[word].count = count
 
+        if self.new_format:
+            for j in range(pruneidx_size):
+                self.struct_unpack(file_handle, '@2i')
+
     def load_vectors(self, file_handle):
+        if self.new_format:
+            self.struct_unpack(file_handle, '@?')  # bool quant_input in fasttext.cc
         num_vectors, dim = self.struct_unpack(file_handle, '@2q')
         # Vectors stored by [Matrix::save](https://github.com/facebookresearch/fastText/blob/master/src/matrix.cc)
         assert self.vector_size == dim, 'mismatch between model sizes'


Test patch:
diff --git a/gensim/test/test_data/lee_fasttext_new.bin b/gensim/test/test_data/lee_fasttext_new.bin
new file mode 100644
Binary files /dev/null and b/gensim/test/test_data/lee_fasttext_new.bin differ
diff --git a/gensim/test/test_data/lee_fasttext_new.vec b/gensim/test/test_data/lee_fasttext_new.vec
new file mode 100644
--- /dev/null
+++ b/gensim/test/test_data/lee_fasttext_new.vec
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+lack -0.28125 -0.049111 0.12849 -0.858 0.024126 -0.87174 0.43113 0.075511 -0.28163 0.027479 
+document -0.097176 -0.17444 0.14257 -0.85602 0.30242 -0.96306 0.56906 0.25605 -0.2711 -0.016344 
+explanation -0.38645 -0.18002 0.055084 -0.86557 0.18408 -0.97789 0.48657 0.18924 -0.18177 0.083669 
+Sultan -0.41645 -0.16765 0.21599 -1.0014 0.30501 -1.0955 0.46808 0.10096 -0.14567 -0.054145 
+reduced -0.21167 -0.080213 0.11708 -0.94597 -0.17172 -0.89563 0.39573 0.26074 -0.2101 0.15443 
+violent -0.11532 -0.13904 0.23495 -0.86285 0.3395 -1.0167 0.46774 0.32004 -0.18438 -0.10924 
+understanding -0.36627 -0.18224 0.14645 -0.93409 0.068406 -0.91532 0.43653 0.039594 -0.22393 -0.033249 
+farm -0.2685 -0.094387 0.36153 -1.0836 0.22668 -0.88753 0.28388 0.019861 -0.054735 -0.082909 
+Lord -0.44769 -0.071266 -0.13184 -0.99477 0.64386 -1.1481 0.62058 -0.35432 0.088442 -0.19185 
+nearby -0.21346 -0.21208 0.0129 -0.97925 0.22082 -0.93188 0.49707 0.01654 -0.27514 0.09273 
+Toowoomba -0.11086 -0.016608 0.12698 -0.98588 0.18283 -0.93644 0.51906 0.27584 -0.1859 0.068254 
+redundancy -0.37279 -0.2126 0.13548 -0.92321 0.20175 -0.91295 0.37839 0.091666 -0.15195 -0.016421 
+credit -0.04897 0.00094891 0.18637 -0.93684 0.2433 -1.0365 0.50859 0.28308 -0.33012 -0.1834 
+entitlements -0.22865 -0.17366 0.25398 -0.95994 0.25625 -0.97027 0.48133 0.29908 -0.17171 0.0098023 
+paying -0.27379 -0.21393 0.14191 -0.81481 -0.31424 -0.86041 0.34354 0.12124 -0.28744 0.086008 
+Stuart -0.32065 -0.045169 0.047934 -0.93749 -0.01232 -0.94264 0.55829 0.019187 -0.23201 0.012652 
+administrators -0.38915 -0.10678 0.10779 -0.90749 0.15211 -0.9458 0.49626 -0.0076005 -0.15467 0.13405 
+150 -0.10669 0.052089 -0.080921 -0.99111 0.28881 -0.88495 0.50056 0.040523 -0.24744 -0.046004 
+technology -0.3845 -0.1054 0.095591 -0.89516 -0.023254 -0.88988 0.44832 -0.036531 -0.17773 0.062035 
+holding -0.30153 -0.1088 0.12251 -0.9244 0.088088 -0.93542 0.44833 0.070584 -0.22506 -0.058827 
+normal -0.33125 -0.08666 0.21988 -0.91425 -0.26755 -0.74247 0.3954 0.059246 -0.2519 0.29009 
+Amin -0.67553 -0.21073 0.21461 -1.0165 -0.20907 -0.78149 0.34416 -0.081891 -0.18986 0.18107 
+Adam -0.44946 -0.048925 0.12921 -0.91341 -0.258 -0.85795 0.404 0.10336 -0.19082 0.18 
+crashed -0.28983 -0.19277 0.28008 -1.0251 0.36846 -1.0111 0.34961 0.023464 -0.10933 -0.062239 
+natural -0.07196 -0.27232 0.38912 -0.98727 -0.1257 -0.81443 0.29644 0.32534 -0.29026 0.20611 
+begin -0.67268 -0.064473 -0.036203 -1.0396 0.39318 -1.0634 0.52304 -0.39897 -0.10272 -0.20808 
+Up -0.12987 -0.31596 0.37794 -0.92846 0.34058 -0.87118 0.55491 0.18954 -0.065122 0.20761 
+celebrations -0.35697 -0.24729 0.14783 -0.91635 0.17408 -0.84252 0.47401 0.19342 -0.26351 0.14232 
+reject -0.36215 -0.083687 0.019168 -0.88026 -0.34821 -0.80624 0.35637 0.060272 -0.22834 0.17106 
+options -0.43006 -0.32979 0.099825 -0.8432 0.28397 -0.94099 0.45111 0.18693 -0.12492 0.077929 
+single -0.36768 -0.0077289 0.12405 -0.89668 -0.13589 -0.91868 0.43771 0.023178 -0.19012 0.020062 
+handling -0.19383 -0.086433 0.073078 -0.84088 -0.022429 -0.96951 0.52248 0.18913 -0.26988 -0.062818 
+match. -0.10931 0.18295 -0.0038214 -0.83137 -0.24983 -0.95995 0.53487 0.20493 -0.49541 -0.1036 
+summit -0.31419 0.0098797 0.035068 -0.96421 0.14451 -1.0258 0.50231 -0.018111 -0.23626 -0.029974 
+talks. -0.35229 -0.15515 0.07993 -0.99309 0.11575 -0.89788 0.43005 0.018662 -0.17911 0.030351 
+All -0.82992 -0.12558 0.23378 -0.8514 -0.078518 -0.93991 0.46519 -0.30953 0.013705 0.18509 
+settlement -0.34912 -0.2379 0.10761 -0.96062 0.31773 -1.0578 0.52877 0.033297 -0.049265 -0.010945 
+searching -0.24236 -0.17512 0.17781 -0.95078 -0.16358 -0.74868 0.38088 0.085915 -0.27398 0.19703 
+dollars -0.35512 -0.096205 0.13233 -1.0636 0.25495 -0.91864 0.53955 -0.11415 -0.17314 -0.015355 
+guess -0.27593 -0.11835 0.12093 -0.93565 -0.078513 -0.84341 0.43357 0.059761 -0.35287 0.10868 
+Kieren -0.28648 -0.052006 0.019997 -0.93716 0.048119 -1.0201 0.5268 -0.082165 -0.20706 0.12905 
+23 -0.21086 -0.057727 -0.088208 -0.96071 0.56566 -1.1161 0.58002 -0.23208 -0.13051 -0.046464 
+Bonn -0.63015 -0.090793 -0.010465 -0.89356 0.37004 -1.1065 0.49491 -0.13038 -0.12322 -0.18684 
+... -0.43566 -0.18061 0.06388 -0.86017 0.013587 -0.82546 0.47717 -0.017943 -0.095205 0.12177 
+prepare -0.27096 -0.23845 0.17099 -0.88899 -0.17308 -0.79854 0.33358 0.18857 -0.27852 0.22166 
+champion -0.37096 -0.075525 0.033513 -0.87721 0.032021 -1.0175 0.42515 0.086005 -0.22466 0.0095515 
+Pollock -0.17072 0.075854 0.034762 -0.99117 0.025959 -0.84027 0.51192 0.054958 -0.34076 0.13271 
+television. -0.44044 -0.21914 0.064641 -0.95829 0.15258 -0.9742 0.43971 -0.15748 -0.021531 0.11953 
+begun -0.44643 -0.063631 0.062254 -0.89464 -0.26594 -0.77289 0.43425 -0.022602 -0.29919 0.059812 
+coast -0.096154 -0.098287 0.25499 -0.86603 0.41534 -0.9861 0.60581 0.24712 -0.30546 -0.11746 
+leave -0.48798 -0.14912 0.15468 -1.0082 0.28873 -0.94628 0.46997 -0.14209 0.015677 0.16177 
+St -0.15052 -0.038151 0.074615 -0.94029 0.62285 -0.93937 0.64598 0.084345 -0.21029 -0.10022 
+Sydney. 0.26468 0.080405 0.32812 -1.0438 0.026161 -0.72776 0.57149 0.22449 -0.45732 0.20119 
+losing -0.30082 -0.14351 0.13768 -0.94164 -0.073193 -0.87297 0.40935 -0.035951 -0.20497 0.083264 
+work. -0.33936 -0.20084 0.24815 -1.0307 0.14337 -0.84725 0.4851 0.038437 -0.18419 0.15475 
+counts -0.18804 -0.080086 0.15436 -0.93251 0.17173 -0.92948 0.56095 0.42776 -0.34996 0.025217 
+26-year-old -0.23247 -0.16833 0.10058 -0.95923 0.12781 -0.89816 0.45605 0.021855 -0.15746 0.094273 
+suggested -0.22162 -0.0057453 0.067216 -1.0141 0.46976 -1.0677 0.51246 -0.14477 -0.10022 -0.052623 
+projects -0.38763 -0.26072 0.012426 -1.01 0.17994 -0.91495 0.52402 -0.090687 -0.25331 0.078616 
+understood -0.39868 -0.13931 0.061138 -0.91247 0.04974 -0.9366 0.44174 -0.038707 -0.21788 0.022325 
+various -0.42015 -0.30838 0.073304 -0.85863 0.37029 -1.0839 0.46362 0.028696 -0.00035373 -0.010771 
+debate -0.12791 -0.10166 0.26514 -0.92209 0.13826 -0.92997 0.45138 0.26024 -0.26033 0.064546 
+Bill -0.39838 -0.033529 0.094462 -0.87625 -0.2477 -0.78666 0.481 0.13671 -0.29902 0.21293 
+happens -0.21991 -0.11012 -0.0028922 -0.9438 0.12635 -0.90128 0.55503 0.0095414 -0.22928 0.17408 
+Commissioner -0.49284 -0.16213 0.12342 -0.90232 0.031972 -0.97126 0.46812 -0.01738 -0.030934 0.15521 
+Deputy -0.36846 -0.16084 0.078157 -0.82351 -0.050522 -0.92733 0.5193 0.07008 -0.23301 0.11732 
+civilians -0.26415 -0.15822 0.068405 -0.98221 0.50329 -1.0065 0.52038 -0.038207 -0.18997 -0.052481 
+threatening -0.25182 -0.045476 0.10861 -0.89748 0.2866 -1.0112 0.42588 -0.062959 -0.30542 -0.11057 
+women's -0.19161 -0.071387 0.19565 -1.0621 0.22142 -1.0313 0.47957 0.15791 -0.22421 0.099062 
+containment -0.24544 -0.31776 0.35774 -1.0603 0.27667 -0.94787 0.45011 0.25615 -0.13414 0.083895 
+stand -0.32437 -0.29159 0.29872 -0.96611 0.10026 -0.85177 0.31083 0.053036 -0.15696 0.15546 
+MacGill -0.31908 0.030595 0.093019 -0.94111 -0.093146 -0.86372 0.49992 0.057094 -0.24437 0.11818 
+putting -0.38698 -0.14535 0.03712 -0.90819 0.081707 -0.9084 0.42326 -0.057029 -0.26089 -0.021234 
+determine -0.4176 -0.097098 0.14469 -0.93155 0.11262 -1.0324 0.43527 0.012073 -0.099626 0.040556 
+Israel. -0.47031 -0.063841 -0.37841 -0.97351 1.1357 -1.3919 0.65755 -0.52246 0.1681 -0.27245 
+forecast -0.19474 -0.079622 0.15075 -1.103 0.64718 -1.1086 0.38859 0.13428 -0.16916 -0.21521 
+During -0.34549 -0.037444 0.073162 -0.8123 -0.10379 -0.97766 0.4863 0.040554 -0.3512 -0.056979 
+bureau -0.18654 -0.084556 0.22349 -0.94008 0.17144 -0.88225 0.50717 0.042983 -0.26211 0.16669 
+findings -0.15385 0.024256 0.097106 -0.90701 -0.0062973 -0.87097 0.45966 0.092639 -0.31016 0.048742 
+fear -0.13347 -0.033548 0.051886 -1.0621 0.69087 -1.067 0.49215 -0.082951 -0.19496 -0.24658 
+data -0.34699 -0.17287 -0.01897 -0.93141 0.24327 -0.91246 0.46874 -0.18289 -0.18046 0.081533 
+gone -0.26956 -0.29501 0.25143 -1.0867 0.19435 -0.93538 0.33428 0.0059832 -0.084104 0.060041 
+record -0.3523 0.061825 0.089374 -0.8624 0.10975 -0.93357 0.47165 0.088856 -0.19873 -0.037258 
+hoping -0.18615 -0.052057 0.080318 -0.91618 0.10148 -0.88347 0.48114 -0.0095034 -0.35875 -0.1065 
+Israelis. -0.23908 -0.12511 -0.23259 -1.0042 0.8512 -1.1663 0.54631 -0.34526 -0.007403 -0.12883 
+Hamid -0.30894 -0.25919 0.073896 -0.86696 -0.060983 -0.83277 0.46301 0.015398 -0.12849 0.15373 
+present -0.28584 -0.21305 0.1917 -0.82763 -0.2293 -0.86695 0.41269 0.28361 -0.18485 0.14715 
+live -0.36521 -0.27784 0.21715 -1.0707 0.52157 -1.1198 0.27499 -0.065779 0.10911 -0.14034 
+ahead. -0.18178 -0.16656 0.22029 -0.91903 0.26257 -0.96658 0.42367 0.15003 -0.19482 0.13553 
+warning -0.057134 -0.10892 0.21894 -0.93471 0.075207 -0.93845 0.32891 0.098712 -0.29838 -0.10296 
+trapped -0.39018 -0.17152 0.16364 -0.90232 0.39176 -1.0995 0.41738 -0.031247 -0.14006 -0.14978 
+markets -0.22304 0.049703 0.023357 -0.89652 0.028235 -0.93915 0.54958 0.10373 -0.31918 -0.015176 
+Sergeant -0.22138 -0.073868 0.18847 -0.89862 -0.12118 -0.88672 0.44595 0.059898 -0.20127 0.20083 
+Seven -0.15834 0.012167 0.075471 -0.9344 -0.0060505 -0.87636 0.57467 -0.091161 -0.18515 0.16282 
+firm -0.20924 0.13692 0.229 -1.0004 0.087825 -0.74557 0.46068 0.063433 -0.19736 -0.013484 
+welcomed -0.33084 -0.13075 -0.031394 -0.933 0.12591 -0.93544 0.43404 -0.0071708 -0.19188 0.02752 
+responding -0.27598 -0.17165 0.25794 -0.96352 -0.021708 -0.90139 0.36233 0.13162 -0.20719 0.013575 
+law -0.23204 0.098585 0.084543 -0.87951 0.045764 -1.0408 0.39644 0.083872 -0.19391 0.06397 
+deputy -0.35117 -0.094578 0.069262 -0.8022 0.21323 -1.0385 0.49627 0.14174 -0.18405 -0.036922 
+unidentified -0.21788 -0.19765 0.16739 -0.96515 0.26464 -1.0681 0.45954 0.19657 -0.10865 -0.040348 
+clashes -0.18525 -0.090192 0.13899 -0.92914 0.19958 -0.99479 0.48018 0.0076089 -0.22087 0.057853 
+ago, -0.12196 -0.25092 0.21986 -0.78537 -0.43743 -0.83485 0.35358 0.5775 -0.36967 0.27372 
+replied: -0.53654 -0.30794 0.0668 -0.95327 -0.32244 -0.86974 0.32233 0.0035099 -0.12836 0.27396 
+path -0.29637 -0.0050163 -0.09227 -0.91761 0.61522 -1.2084 0.51978 -0.070516 -0.022066 -0.26732 
+search -0.33437 -0.14509 0.16727 -0.8863 0.019863 -0.77068 0.47079 0.019677 -0.24613 0.18844 
+hundred -0.025627 -0.11448 0.18116 -0.96779 0.2532 -0.93224 0.3929 0.12679 -0.19685 -0.13179 
+state. -0.18528 -0.23642 0.26504 -1.0479 0.57506 -1.0035 0.41344 -0.17807 -0.09178 -0.023322 
+efforts -0.2879 -0.13298 0.14961 -0.93882 0.13162 -0.93926 0.49649 0.17908 -0.18039 0.022261 
+tree -0.13421 0.049938 0.039293 -0.85099 -0.054489 -0.95255 0.48989 0.20728 -0.25372 0.081726 
+telephone -0.31696 -0.22565 0.13317 -1.0219 0.2304 -1.0449 0.39416 -0.015374 -0.058222 0.064146 
+problem -0.23808 -0.055263 0.21232 -0.94406 -0.52183 -0.69284 0.40925 0.16283 -0.35028 0.39899 
+approached -0.3755 -0.24085 0.045111 -0.96186 0.31842 -0.96877 0.4262 -0.091481 -0.06434 0.076188 
+chairman -0.24927 -0.10477 0.17401 -0.90386 0.013637 -0.93125 0.41858 0.049651 -0.20749 0.15118 
+Afroz -0.3492 -0.11356 0.046444 -0.96179 0.49956 -1.1739 0.50381 -0.029897 -0.15488 -0.24651 
+Monday, -0.18335 0.075194 0.19071 -0.85914 -0.074304 -0.8325 0.45894 0.23434 -0.32902 0.11567 
+advance -0.35487 -0.13718 0.083065 -1.0344 0.076132 -0.86051 0.39686 0.031053 -0.1538 0.12614 
diff --git a/gensim/test/test_fasttext_wrapper.py b/gensim/test/test_fasttext_wrapper.py
--- a/gensim/test/test_fasttext_wrapper.py
+++ b/gensim/test/test_fasttext_wrapper.py
@@ -35,8 +35,10 @@ def setUp(self):
         self.ft_path = os.path.join(ft_home, 'fasttext') if ft_home else None
         self.corpus_file = datapath('lee_background.cor')
         self.test_model_file = datapath('lee_fasttext')
+        self.test_new_model_file = datapath('lee_fasttext_new')
         # Load pre-trained model to perform tests in case FastText binary isn't available in test environment
         self.test_model = fasttext.FastText.load_fasttext_format(self.test_model_file)
+        self.test_new_model = fasttext.FastText.load_fasttext_format(self.test_new_model_file)
 
     def model_sanity(self, model):
         """Even tiny models trained on any corpus should pass these sanity checks"""
@@ -139,6 +141,36 @@ def testLoadFastTextFormat(self):
         self.assertEquals(self.test_model.wv.min_n, 3)
         self.model_sanity(model)
 
+    def testLoadFastTextNewFormat(self):
+        """ Test model successfully loaded from fastText (new format) .vec and .bin files """
+        new_model = fasttext.FastText.load_fasttext_format(self.test_new_model_file)
+        vocab_size, model_size = 1763, 10
+        self.assertEqual(self.test_new_model.wv.syn0.shape, (vocab_size, model_size))
+        self.assertEqual(len(self.test_new_model.wv.vocab), vocab_size, model_size)
+        self.assertEqual(self.test_new_model.wv.syn0_all.shape, (self.test_new_model.num_ngram_vectors, model_size))
+
+        expected_vec_new = [-0.025627,
+                            -0.11448,
+                             0.18116,
+                            -0.96779,
+                             0.2532,
+                            -0.93224,
+                             0.3929,
+                             0.12679,
+                            -0.19685,
+                            -0.13179]  # obtained using ./fasttext print-word-vectors lee_fasttext_new.bin < queries.txt
+
+        self.assertTrue(numpy.allclose(self.test_new_model["hundred"], expected_vec_new, 0.001))
+        self.assertEquals(self.test_new_model.min_count, 5)
+        self.assertEquals(self.test_new_model.window, 5)
+        self.assertEquals(self.test_new_model.iter, 5)
+        self.assertEquals(self.test_new_model.negative, 5)
+        self.assertEquals(self.test_new_model.sample, 0.0001)
+        self.assertEquals(self.test_new_model.bucket, 1000)
+        self.assertEquals(self.test_new_model.wv.max_n, 6)
+        self.assertEquals(self.test_new_model.wv.min_n, 3)
+        self.model_sanity(new_model)
+
     def testLoadModelWithNonAsciiVocab(self):
         """Test loading model with non-ascii words in vocab"""
         model = fasttext.FastText.load_fasttext_format(datapath('non_ascii_fasttext'))