from tvecs.bilingual_generator import bilingual_generator as bg from tvecs.vector_space_mapper import vector_space_mapper as vm from gensim.models import KeyedVectors import os train_bilingual_corpus = 'data/bilingual_dictionary/en-fr.txt' test_bilingual_corpus = 'data/bilingual_dictionary/en-fr.txt' bilingual_dict = bg.load_bilingual_dictionary(train_bilingual_corpus) vector_space_mapper = vm.VectorSpaceMapper( model_1=KeyedVectors.load_word2vec_format( 'data/models/t-vex-english-fb-model'), model_2=KeyedVectors.load_word2vec_format( 'data/models/t-vex-french-model') #change , bilingual_dict=bilingual_dict) vector_space_mapper.map_vector_spaces() print("Training MSE: {} %".format( vector_space_mapper.obtain_mean_square_error_from_dataset( dataset_path=train_bilingual_corpus))) print("Testing MSE: {} %".format( vector_space_mapper.obtain_mean_square_error_from_dataset( dataset_path=test_bilingual_corpus))) word1 = raw_input("Please input the english word :") word2 = raw_input("Please input another english word :")
from tvecs.bilingual_generator import bilingual_generator as bg from tvecs.vector_space_mapper import vector_space_mapper as vm from gensim.models import Word2Vec import os from gensim.models import KeyedVectors # train_bilingual_corpus = 'data/bilingual_dictionary/en-fr.txt' # test_bilingual_corpus = 'data/bilingual_dictionary/en-fr.txt' # bilingual_dict = bg.load_bilingual_dictionary( 'data/bilingual_dictionary/en-bn.txt') vector_space_mapper = vm.VectorSpaceMapper( model_1=Word2Vec.load(os.path.join('data', 'models', 't-vex-english-model')), model_2=KeyedVectors.load_word2vec_format( 'data/models/t-vex-bengali-model'), bilingual_dict=bilingual_dict) vector_space_mapper.map_vector_spaces() # print("Training MSE: {} %".format(vector_space_mapper.obtain_mean_square_error_from_dataset( # dataset_path=train_bilingual_corpus # ))) # # print("Testing MSE: {} %".format(vector_space_mapper.obtain_mean_square_error_from_dataset( # dataset_path=test_bilingual_corpus # ))) word = raw_input("Please input the english word :")
from tvecs.bilingual_generator import bilingual_generator as bg from tvecs.vector_space_mapper import vector_space_mapper as vm from gensim.models import Word2Vec import os # train_bilingual_corpus = 'data/bilingual_dictionary/en-fr.txt' # test_bilingual_corpus = 'data/bilingual_dictionary/en-fr.txt' # bilingual_dict = bg.load_bilingual_dictionary( 'data/bilingual_dictionary/english_hindi_bd') vector_space_mapper = vm.VectorSpaceMapper( model_1=Word2Vec.load(os.path.join('data', 'models', 't-vex-english-model')), model_2=Word2Vec.load(os.path.join('data', 'models', 't-vex-hindi-model')), bilingual_dict=bilingual_dict) vector_space_mapper.map_vector_spaces() # print("Training MSE: {} %".format(vector_space_mapper.obtain_mean_square_error_from_dataset( # dataset_path=train_bilingual_corpus # ))) # # print("Testing MSE: {} %".format(vector_space_mapper.obtain_mean_square_error_from_dataset( # dataset_path=test_bilingual_corpus # ))) word = raw_input("Please input the english word :") while word != '///': print vector_space_mapper.get_recommendations_from_word(word.lower(),