src/build_embeddings.py
def build_merge_embeddings():
	"""
	DESCRIPTION: 
	    Loads the pretrained word embeddings from Stanford and builds
	    also the word embeddings matrix based on our training dataset by using the 
	    glove_python method. Then all the missing words from the pretrained word
	    embeddings are filled by the glove_python word embeddings.
	OUTPUT: 
	    glove_words: merged python dictionary of the form (word, [vector of embeddings])
	"""
	print('Build merged Embeddings')	
	os.system('join -i -a1 -a2 ' +PRETRAINED_EMBEDDINGS_FILE + ' ' + MY_GLOVE_PYTHON_EMBEDDINGS_TXT_FILE +' 2>/dev/null | cut -d \' \' -f1-'+str(algorithm['options']['WE']['we_features'])+" > "+ MERGED_EMBEDDINGS_FILE)
	glove_words = load_glove_embeddings_from_txt_file(MERGED_EMBEDDINGS_FILE)
	return glove_words

	DESCRIPTION: 
	    Loads the pretrained word embeddings from Stanford and builds
	    also the word embeddings matrix based on our training dataset by using the 
	    glove_python method. Then all the missing words from the pretrained word
	    embeddings are filled by the glove_python word embeddings.
	OUTPUT: 
	    glove_words: merged python dictionary of the form (word, [vector of embeddings])
	
Loads the pretrained word embeddings from Stanford and builds
also the word embeddings matrix based on our training dataset by using the
glove_python method. Then all the missing words from the pretrained word
embeddings are filled by the glove_python word embeddings.
OUTPUT:
glove_words: merged python dictionary of the form (word, [vector of embeddings])
Loads the pretrained word embeddings from Stanford and builds also the word embeddings matrix based on our training dataset by using the glove_python method . Then all the missing words from the pretrained word embeddings are filled by the glove_python word embeddings . OUTPUT : glove_words : merged python dictionary of the form ( word [ vector of embeddings ] )
def build_merge_embeddings():
	print('Build merged Embeddings')	
	os.system('join -i -a1 -a2 ' +PRETRAINED_EMBEDDINGS_FILE + ' ' + MY_GLOVE_PYTHON_EMBEDDINGS_TXT_FILE +' 2>/dev/null | cut -d \' \' -f1-'+str(algorithm['options']['WE']['we_features'])+" > "+ MERGED_EMBEDDINGS_FILE)
	glove_words = load_glove_embeddings_from_txt_file(MERGED_EMBEDDINGS_FILE)
	return glove_words
def build_merge_embeddings ( ) : print ( 'Build merged Embeddings' ) os . system ( 'join -i -a1 -a2 ' + PRETRAINED_EMBEDDINGS_FILE + ' ' + MY_GLOVE_PYTHON_EMBEDDINGS_TXT_FILE + ' 2>/dev/null | cut -d \' \' -f1-' + str ( algorithm [ 'options' ] [ 'WE' ] [ 'we_features' ] ) + " > " + MERGED_EMBEDDINGS_FILE ) glove_words = load_glove_embeddings_from_txt_file ( MERGED_EMBEDDINGS_FILE ) return glove_words
DESCRIPTION:
Loads the pretrained word embeddings from Stanford and builds
also the word embeddings matrix based on our training dataset by using the
glove_python method.
DESCRIPTION : Loads the pretrained word embeddings from Stanford and builds also the word embeddings matrix based on our training dataset by using the glove_python method .
"""
	DESCRIPTION: 
	    Loads the pretrained word embeddings from Stanford and builds
	    also the word embeddings matrix based on our training dataset by using the 
	    glove_python method. Then all the missing words from the pretrained word
	    embeddings are filled by the glove_python word embeddings.
	OUTPUT: 
	    glove_words: merged python dictionary of the form (word, [vector of embeddings])
	"""