diff --git a/UPCOMING.md b/UPCOMING.md
index 2b28055f..9a132607 100644
--- a/UPCOMING.md
+++ b/UPCOMING.md
@@ -4,6 +4,8 @@ Upcoming Updates to `shorttext`
 Confirmed Updates
 -----------------
 
+* Removed most explicit user-specification of `vecsize` for given word-embedding models.
+
 Expected Updates
 ----------------
 
diff --git a/shorttext/classifiers/__init__.py b/shorttext/classifiers/__init__.py
index 653f8a05..00ba75be 100644
--- a/shorttext/classifiers/__init__.py
+++ b/shorttext/classifiers/__init__.py
@@ -4,10 +4,6 @@
 from .embed import frameworks
 from .embed.sumvec import frameworks as sumvecframeworks
 
-from .bow.topic.LatentTopicModeling import GensimTopicModeler, LDAModeler, LSIModeler, RPModeler
-from .bow.topic.LatentTopicModeling import AutoencodingTopicModeler, load_autoencoder_topic
-from .bow.topic.LatentTopicModeling import load_gensimtopicmodel
-
 from .bow.topic.TopicVectorDistanceClassification import TopicVecCosineDistanceClassifier as TopicVectorCosineDistanceClassifier
 from .bow.topic.TopicVectorDistanceClassification import train_autoencoder_cosineClassifier, train_gensimtopicvec_cosineClassifier
 from .bow.topic.TopicVectorDistanceClassification import load_autoencoder_cosineClassifier, load_gensimtopicvec_cosineClassifier
diff --git a/shorttext/classifiers/bow/topic/LatentTopicModeling.py b/shorttext/classifiers/bow/topic/LatentTopicModeling.py
deleted file mode 100644
index fc109bc5..00000000
--- a/shorttext/classifiers/bow/topic/LatentTopicModeling.py
+++ /dev/null
@@ -1,12 +0,0 @@
-
-# for backward compatibility
-
-from shorttext.generators.bow.GensimTopicModeling import gensim_topic_model_dict
-from shorttext.generators.bow.LatentTopicModeling import LatentTopicModeler
-from shorttext.generators.bow.GensimTopicModeling import GensimTopicModeler
-from shorttext.generators.bow.GensimTopicModeling import LDAModeler
-from shorttext.generators.bow.GensimTopicModeling import LSIModeler
-from shorttext.generators.bow.GensimTopicModeling import RPModeler
-from shorttext.generators.bow.AutoEncodingTopicModeling import AutoencodingTopicModeler, load_autoencoder_topicmodel
-from shorttext.generators import load_gensimtopicmodel
-from shorttext.generators import load_autoencoder_topicmodel as load_autoencoder_topic
diff --git a/shorttext/classifiers/bow/topic/SkLearnClassification.py b/shorttext/classifiers/bow/topic/SkLearnClassification.py
index 7b66c505..54dd6ced 100644
--- a/shorttext/classifiers/bow/topic/SkLearnClassification.py
+++ b/shorttext/classifiers/bow/topic/SkLearnClassification.py
@@ -3,9 +3,8 @@
 from sklearn.externals import joblib
 
 from shorttext.utils import textpreprocessing as textpreprocess
-from .LatentTopicModeling import AutoencodingTopicModeler, load_autoencoder_topicmodel
-from .LatentTopicModeling import LDAModeler, LSIModeler, RPModeler
-from .LatentTopicModeling import load_gensimtopicmodel
+from shorttext.generators import load_autoencoder_topicmodel, load_gensimtopicmodel
+from shorttext.generators import LDAModeler, LSIModeler, RPModeler, AutoencodingTopicModeler
 import shorttext.utils.classification_exceptions as e
 import shorttext.utils.compactmodel_io as cio
 
diff --git a/shorttext/classifiers/bow/topic/TopicVectorDistanceClassification.py b/shorttext/classifiers/bow/topic/TopicVectorDistanceClassification.py
index 3e47f838..a27b05a4 100644
--- a/shorttext/classifiers/bow/topic/TopicVectorDistanceClassification.py
+++ b/shorttext/classifiers/bow/topic/TopicVectorDistanceClassification.py
@@ -1,8 +1,7 @@
 
 from shorttext.utils import textpreprocessing as textpreprocess
-from .LatentTopicModeling import LatentTopicModeler, GensimTopicModeler
-from .LatentTopicModeling import AutoencodingTopicModeler, load_autoencoder_topicmodel
-from .LatentTopicModeling import load_gensimtopicmodel
+from shorttext.generators import LatentTopicModeler, GensimTopicModeler, AutoencodingTopicModeler
+from shorttext.generators import load_autoencoder_topicmodel, load_gensimtopicmodel
 
 
 class TopicVecCosineDistanceClassifier:
diff --git a/shorttext/classifiers/bow/topic/__init__.py b/shorttext/classifiers/bow/topic/__init__.py
index af52dc94..6467258f 100644
--- a/shorttext/classifiers/bow/topic/__init__.py
+++ b/shorttext/classifiers/bow/topic/__init__.py
@@ -1,3 +1,3 @@
-from . import LatentTopicModeling
+
 from . import TopicVectorDistanceClassification
 from . import SkLearnClassification
\ No newline at end of file
diff --git a/shorttext/classifiers/embed/nnlib/VarNNEmbedVecClassification.py b/shorttext/classifiers/embed/nnlib/VarNNEmbedVecClassification.py
index 35663099..eeb3766b 100644
--- a/shorttext/classifiers/embed/nnlib/VarNNEmbedVecClassification.py
+++ b/shorttext/classifiers/embed/nnlib/VarNNEmbedVecClassification.py
@@ -30,55 +30,19 @@ class VarNNEmbeddedVecClassifier:
 
     A pre-trained Google Word2Vec model can be downloaded `here
     <https://drive.google.com/file/d/0B7XkCwpI5KDYNlNUTTlSS21pQmM/edit>`_.
-
-        Examples
-
-    >>> import shorttext
-    >>> # load the Word2Vec model
-    >>> wvmodel = shorttext.utils.load_word2vec_model('GoogleNews-vectors-negative300.bin.gz', binary=True)
-    >>>
-    >>> # load the training data
-    >>> trainclassdict = shorttext.data.subjectkeywords()
-    >>>
-    >>> # initialize the classifier and train
-    >>> kmodel = shorttext.classifiers.frameworks.CNNWordEmbed(len(trainclassdict.keys()), vecsize=300)    # using convolutional neural network model
-    >>> classifier = shorttext.classifiers.VarNNEmbeddedVecClassifier(wvmodel, vecsize=300)
-    >>> classifier.train(trainclassdict, kmodel)
-    Epoch 1/10
-    45/45 [==============================] - 0s - loss: 1.0578
-    Epoch 2/10
-    45/45 [==============================] - 0s - loss: 0.5536
-    Epoch 3/10
-    45/45 [==============================] - 0s - loss: 0.3437
-    Epoch 4/10
-    45/45 [==============================] - 0s - loss: 0.2282
-    Epoch 5/10
-    45/45 [==============================] - 0s - loss: 0.1658
-    Epoch 6/10
-    45/45 [==============================] - 0s - loss: 0.1273
-    Epoch 7/10
-    45/45 [==============================] - 0s - loss: 0.1052
-    Epoch 8/10
-    45/45 [==============================] - 0s - loss: 0.0961
-    Epoch 9/10
-    45/45 [==============================] - 0s - loss: 0.0839
-    Epoch 10/10
-    45/45 [==============================] - 0s - loss: 0.0743
-    >>> classifier.score('artificial intelligence')
-    {'mathematics': 0.57749695, 'physics': 0.33749574, 'theology': 0.085007325}
     """
-    def __init__(self, wvmodel, vecsize=100, maxlen=15, with_gensim=False):
+    def __init__(self, wvmodel, vecsize=None, maxlen=15, with_gensim=False):
         """ Initialize the classifier.
 
         :param wvmodel: Word2Vec model
-        :param vecsize: length of the embedded vectors in the model (Default: 100)
+        :param vecsize: length of the embedded vectors in the model (Default: None, directly extracted from word-embedding model)
         :param maxlen: maximum number of words in a sentence (Default: 15)
         :type wvmodel: gensim.models.keyedvectors.KeyedVectors
         :type vecsize: int
         :type maxlen: int
         """
         self.wvmodel = wvmodel
-        self.vecsize = vecsize
+        self.vecsize = self.wvmodel.vector_size if vecsize == None else vecsize
         self.maxlen = maxlen
         self.with_gensim = with_gensim
         self.trained = False
@@ -289,13 +253,13 @@ def score(self, shorttext):
 
         return scoredict
 
-def load_varnnlibvec_classifier(wvmodel, name, compact=True, vecsize=100):
+def load_varnnlibvec_classifier(wvmodel, name, compact=True, vecsize=None):
     """ Load a :class:`shorttext.classifiers.VarNNEmbeddedVecClassifier` instance from file, given the pre-trained Word2Vec model.
 
     :param wvmodel: Word2Vec model
     :param name: name (if compact=True) or prefix (if compact=False) of the file path
     :param compact whether model file is compact (Default: True)
-    :param vecsize: length of embedded vectors in the model (Default: 100)
+    :param vecsize: length of embedded vectors in the model (Default: None, extracted directly from the word-embedding model)
     :return: the classifier
     :type wvmodel: gensim.models.keyedvectors.KeyedVectors
     :type name: str
diff --git a/shorttext/classifiers/embed/nnlib/frameworks.py b/shorttext/classifiers/embed/nnlib/frameworks.py
index a8325a2d..6608a355 100644
--- a/shorttext/classifiers/embed/nnlib/frameworks.py
+++ b/shorttext/classifiers/embed/nnlib/frameworks.py
@@ -54,6 +54,9 @@ def CNNWordEmbed(nb_labels,
     :type with_gensim: bool
     :rtype: keras.models.Sequential or keras.models.Model
     """
+    if wvmodel != None:
+        vecsize = wvmodel.vector_size
+
     if with_gensim == True:
         embedding_layer = wvmodel.get_keras_embedding()
         sequence_input = Input(shape=(maxlen,), dtype='int32')
@@ -144,6 +147,9 @@ def DoubleCNNWordEmbed(nb_labels,
     :type with_gensim: bool
     :rtype: keras.models.Sequential or keras.models.Model
     """
+    if wvmodel != None:
+        vecsize = wvmodel.vector_size
+
     if with_gensim == True:
         embedding_layer = wvmodel.get_keras_embedding()
         sequence_input = Input(shape=(maxlen,), dtype='int32')
@@ -250,6 +256,9 @@ def CLSTMWordEmbed(nb_labels,
     :type with_gensim: bool
     :rtype: keras.models.Sequential or keras.models.Model
     """
+    if wvmodel != None:
+        vecsize = wvmodel.vector_size
+
     if with_gensim == True:
         embedding_layer = wvmodel.get_keras_embedding()
         sequence_input = Input(shape=(maxlen,), dtype='int32')
diff --git a/shorttext/classifiers/embed/sumvec/SumEmbedVecClassification.py b/shorttext/classifiers/embed/sumvec/SumEmbedVecClassification.py
index d7866082..7683bf3d 100644
--- a/shorttext/classifiers/embed/sumvec/SumEmbedVecClassification.py
+++ b/shorttext/classifiers/embed/sumvec/SumEmbedVecClassification.py
@@ -24,18 +24,18 @@ class SumEmbeddedVecClassifier:
     <https://drive.google.com/file/d/0B7XkCwpI5KDYNlNUTTlSS21pQmM/edit>`_.
     """
 
-    def __init__(self, wvmodel, vecsize=100, simfcn=lambda u, v: 1-cosine(u, v)):
+    def __init__(self, wvmodel, vecsize=None, simfcn=lambda u, v: 1-cosine(u, v)):
         """ Initialize the classifier.
 
         :param wvmodel: Word2Vec model
-        :param vecsize: length of the embedded vectors in the model (Default: 100)
+        :param vecsize: length of the embedded vectors in the model (Default: None, directly extracted from word-embedding model)
         :param simfcn: similarity function (Default: cosine similarity)
         :type wvmodel: gensim.models.keyedvectors.KeyedVectors
         :type vecsize: int
         :type simfcn: function
         """
         self.wvmodel = wvmodel
-        self.vecsize = vecsize
+        self.vecsize = self.wvmodel.vector_size if vecsize == None else vecsize
         self.simfcn = simfcn
         self.trained = False
 
@@ -131,13 +131,13 @@ def score(self, shorttext):
                 scoredict[classtype] = np.nan
         return scoredict
 
-def load_sumword2vec_classifier(wvmodel, name, compact=True, vecsize=100):
+def load_sumword2vec_classifier(wvmodel, name, compact=True, vecsize=None):
     """ Load a :class:`shorttext.classifiers.SumEmbeddedVecClassifier` instance from file, given the pre-trained Word2Vec model.
 
     :param wvmodel: Word2Vec model
     :param name: name (if compact=True) or prefix (if compact=False) of the file path
     :param compact whether model file is compact (Default: True)
-    :param vecsize: length of embedded vectors in the model (Default: 100)
+    :param vecsize: length of embedded vectors in the model (Default: None, directly extracted from word-embedding model)
     :return: the classifier
     :type wvmodel: gensim.models.keyedvectors.KeyedVectors
     :type name: str
diff --git a/shorttext/classifiers/embed/sumvec/VarNNSumEmbedVecClassification.py b/shorttext/classifiers/embed/sumvec/VarNNSumEmbedVecClassification.py
index 695108ac..11ed6953 100644
--- a/shorttext/classifiers/embed/sumvec/VarNNSumEmbedVecClassification.py
+++ b/shorttext/classifiers/embed/sumvec/VarNNSumEmbedVecClassification.py
@@ -24,18 +24,18 @@ class VarNNSumEmbeddedVecClassifier:
     <https://drive.google.com/file/d/0B7XkCwpI5KDYNlNUTTlSS21pQmM/edit>`_.
 
     """
-    def __init__(self, wvmodel, vecsize=100, maxlen=15):
+    def __init__(self, wvmodel, vecsize=None, maxlen=15):
         """ Initialize the classifier.
 
         :param wvmodel: Word2Vec model
-        :param vecsize: length of the embedded vectors in the model (Default: 100)
+        :param vecsize: length of embedded vectors in the model (Default: None, extracted directly from the word-embedding model)
         :param maxlen: maximum number of words in a sentence (Default: 15)
         :type wvmodel: gensim.models.word2vec.Word2Vec
         :type vecsize: int
         :type maxlen: int
         """
         self.wvmodel = wvmodel
-        self.vecsize = vecsize
+        self.vecsize = self.wvmodel.vector_size if vecsize==None else vecsize
         self.maxlen = maxlen
         self.trained = False
 
diff --git a/shorttext/generators/__init__.py b/shorttext/generators/__init__.py
index 9cd8fbe8..bdfdc412 100644
--- a/shorttext/generators/__init__.py
+++ b/shorttext/generators/__init__.py
@@ -1,5 +1,5 @@
 from .bow.GensimTopicModeling import load_gensimtopicmodel
 from .bow.AutoEncodingTopicModeling import load_autoencoder_topicmodel
 
-from .bow.GensimTopicModeling import GensimTopicModeler, LDAModeler, LSIModeler, RPModeler
+from .bow.GensimTopicModeling import LatentTopicModeler, GensimTopicModeler, LDAModeler, LSIModeler, RPModeler
 from .bow.AutoEncodingTopicModeling import AutoencodingTopicModeler
\ No newline at end of file
diff --git a/shorttext/smartload.py b/shorttext/smartload.py
index 884a7799..d332d9ea 100644
--- a/shorttext/smartload.py
+++ b/shorttext/smartload.py
@@ -8,7 +8,7 @@
 from .classifiers import load_maxent_classifier
 
 
-def smartload_compact_model(filename, wvmodel, preprocessor=standard_text_preprocessor_1(), vecsize=100):
+def smartload_compact_model(filename, wvmodel, preprocessor=standard_text_preprocessor_1(), vecsize=None):
     """ Load appropriate classifier or model from the binary model.
 
     The second parameter, `wvmodel`, can be set to `None` if no Word2Vec model is needed.
@@ -16,11 +16,13 @@ def smartload_compact_model(filename, wvmodel, preprocessor=standard_text_prepro
     :param filename: path of the compact model file
     :param wvmodel: Word2Vec model
     :param preprocessor: text preprocessor (Default: `shorttext.utils.textpreprocess.standard_text_preprocessor_1`)
+    :param vecsize: length of embedded vectors in the model (Default: None, extracted directly from the word-embedding model)
     :return: appropriate classifier or model
     :raise: AlgorithmNotExistException
     :type filename: str
     :type wvmodel: gensim.models.keyedvectors.KeyedVectors
     :type preprocessor: function
+    :type vecsize: int
     """
     classifier_name = cio.get_model_classifier_name(filename)
     if classifier_name in ['ldatopic', 'lsitopic', 'rptopic']:
diff --git a/test/test_var_nn_embedded_vec_classifier.py b/test/test_var_nn_embedded_vec_classifier.py
index b4b85c93..17384bf9 100644
--- a/test/test_var_nn_embedded_vec_classifier.py
+++ b/test/test_var_nn_embedded_vec_classifier.py
@@ -18,10 +18,10 @@ def tearDown(self):
 
     def testCNNWordEmbedWithoutGensim(self):
   		# create keras model using `CNNWordEmbed` class
-  		keras_model = shorttext.classifiers.frameworks.CNNWordEmbed(wvmodel=self.w2v_model, nb_labels=len(self.trainclass_dict.keys()), vecsize=100, with_gensim=False)
+  		keras_model = shorttext.classifiers.frameworks.CNNWordEmbed(wvmodel=self.w2v_model, nb_labels=len(self.trainclass_dict.keys()), vecsize=None, with_gensim=False)
 
     	# create and train classifier using keras model constructed above
-		main_classifier = shorttext.classifiers.VarNNEmbeddedVecClassifier(self.w2v_model, with_gensim=False, vecsize=100)
+		main_classifier = shorttext.classifiers.VarNNEmbeddedVecClassifier(self.w2v_model, with_gensim=False, vecsize=None)
 		main_classifier.train(self.trainclass_dict, keras_model, nb_epoch=2)
 
 		# compute classification score
@@ -30,10 +30,10 @@ def testCNNWordEmbedWithoutGensim(self):
 
     def testCNNWordEmbedWithGensim(self):
 		# create keras model using `CNNWordEmbed` class
-		keras_model = shorttext.classifiers.frameworks.CNNWordEmbed(wvmodel=self.w2v_model, nb_labels=len(self.trainclass_dict.keys()), vecsize=100, with_gensim=True)
+		keras_model = shorttext.classifiers.frameworks.CNNWordEmbed(wvmodel=self.w2v_model, nb_labels=len(self.trainclass_dict.keys()), vecsize=None, with_gensim=True)
 
     	# create and train classifier using keras model constructed above
-		main_classifier = shorttext.classifiers.VarNNEmbeddedVecClassifier(self.w2v_model, with_gensim=True, vecsize=100)
+		main_classifier = shorttext.classifiers.VarNNEmbeddedVecClassifier(self.w2v_model, with_gensim=True, vecsize=None)
 		main_classifier.train(self.trainclass_dict, keras_model, nb_epoch=2)
 
 		# compute classification score
@@ -42,10 +42,10 @@ def testCNNWordEmbedWithGensim(self):
 
     def testDoubleCNNWordEmbedWithoutGensim(self):
   		# create keras model using `DoubleCNNWordEmbed` class
-  		keras_model = shorttext.classifiers.frameworks.DoubleCNNWordEmbed(wvmodel=self.w2v_model, nb_labels=len(self.trainclass_dict.keys()), vecsize=100, with_gensim=False)
+  		keras_model = shorttext.classifiers.frameworks.DoubleCNNWordEmbed(wvmodel=self.w2v_model, nb_labels=len(self.trainclass_dict.keys()), vecsize=None, with_gensim=False)
 
     	# create and train classifier using keras model constructed above
-		main_classifier = shorttext.classifiers.VarNNEmbeddedVecClassifier(self.w2v_model, with_gensim=False, vecsize=100)
+		main_classifier = shorttext.classifiers.VarNNEmbeddedVecClassifier(self.w2v_model, with_gensim=False, vecsize=None)
 		main_classifier.train(self.trainclass_dict, keras_model, nb_epoch=2)
 
 		# compute classification score
@@ -54,10 +54,10 @@ def testDoubleCNNWordEmbedWithoutGensim(self):
 
     def testDoubleCNNWordEmbedWithGensim(self):
 		# create keras model using `DoubleCNNWordEmbed` class
-		keras_model = shorttext.classifiers.frameworks.DoubleCNNWordEmbed(wvmodel=self.w2v_model, nb_labels=len(self.trainclass_dict.keys()), vecsize=100, with_gensim=True)
+		keras_model = shorttext.classifiers.frameworks.DoubleCNNWordEmbed(wvmodel=self.w2v_model, nb_labels=len(self.trainclass_dict.keys()), vecsize=None, with_gensim=True)
 
     	# create and train classifier using keras model constructed above
-		main_classifier = shorttext.classifiers.VarNNEmbeddedVecClassifier(self.w2v_model, with_gensim=True, vecsize=100)
+		main_classifier = shorttext.classifiers.VarNNEmbeddedVecClassifier(self.w2v_model, with_gensim=True, vecsize=None)
 		main_classifier.train(self.trainclass_dict, keras_model, nb_epoch=2)
 
 		# compute classification score
@@ -66,10 +66,10 @@ def testDoubleCNNWordEmbedWithGensim(self):
 
     def testCLSTMWordEmbedWithoutGensim(self):
   		# create keras model using `CLSTMWordEmbed` class
-  		keras_model = shorttext.classifiers.frameworks.CLSTMWordEmbed(wvmodel=self.w2v_model, nb_labels=len(self.trainclass_dict.keys()), vecsize=100, with_gensim=False)
+  		keras_model = shorttext.classifiers.frameworks.CLSTMWordEmbed(wvmodel=self.w2v_model, nb_labels=len(self.trainclass_dict.keys()), vecsize=None, with_gensim=False)
 
     	# create and train classifier using keras model constructed above
-		main_classifier = shorttext.classifiers.VarNNEmbeddedVecClassifier(self.w2v_model, with_gensim=False, vecsize=100)
+		main_classifier = shorttext.classifiers.VarNNEmbeddedVecClassifier(self.w2v_model, with_gensim=False, vecsize=None)
 		main_classifier.train(self.trainclass_dict, keras_model, nb_epoch=2)
 
 		# compute classification score
@@ -78,10 +78,10 @@ def testCLSTMWordEmbedWithoutGensim(self):
 
     def testCLSTMWordEmbedWithGensim(self):
   		# create keras model using `CLSTMWordEmbed` class
-  		keras_model = shorttext.classifiers.frameworks.CLSTMWordEmbed(wvmodel=self.w2v_model, nb_labels=len(self.trainclass_dict.keys()), vecsize=100, with_gensim=True)
+  		keras_model = shorttext.classifiers.frameworks.CLSTMWordEmbed(wvmodel=self.w2v_model, nb_labels=len(self.trainclass_dict.keys()), vecsize=None, with_gensim=True)
 
     	# create and train classifier using keras model constructed above
-		main_classifier = shorttext.classifiers.VarNNEmbeddedVecClassifier(self.w2v_model, with_gensim=True, vecsize=100)
+		main_classifier = shorttext.classifiers.VarNNEmbeddedVecClassifier(self.w2v_model, with_gensim=True, vecsize=None)
 		main_classifier.train(self.trainclass_dict, keras_model, nb_epoch=2)
 
 		# compute classification score