Requirement already satisfied: Keras>=2.2.4 in /usr/local/lib/python3.6/dist-packages (from seqeval) (2.3.1) Requirement already satisfied: numpy>=1.14.0 in /usr/local/lib/python3.6/dist-packages (from seqeval) (1.18.3) We can also use pre-trained ELMo from tensorflow hub repository.
word_index ), input_size = word_max_length, embedding_size = 32 ) embedding = TimeDistributed ( CNN_Embeddings )( Input_elmo ) x = Bidirectional ( LSTM ( units = 128, return_sequences = True, recurrent_dropout = 0.2, dropout = 0.2 ))( embedding ) x_rnn = Bidirectional ( LSTM ( units = 128, return_sequences = True, recurrent_dropout = 0.2, dropout = 0.2 ))( x ) x_add = add () # residual connection to the first biLSTM #-ELMo ends here - #- x_dense = TimeDistributed ( Dense ( 16, activation = "relu" ))( x_add ) out_1 = TimeDistributed ( Dense ( 1, activation = "sigmoid" ))( x_dense ) model2 = Model ( inputs =, outputs = ) shape ,), name = 'input_s' ) CNN_Embeddings = Char_CNN ( len ( tk. #Define model on top of Char_CNN: sentiment as input Input_elmo = Input ( shape = ( input_text_charact_padded.
source.ĭef Char_CNN ( vocab_size, input_size = 120, embedding_size = 32 ): # parameter conv_layers =, ,, #] ] fully_connected_layers = dropout_p = 0.5 # Embedding layer Initialization embedding_layer = Embedding ( vocab_size + 1, embedding_size, input_length = input_size, mask_zero = True )) # Model Construction # Input inputs = Input ( shape = ( input_size ,), name = 'input_c', dtype = 'int64' ) # shape=(?, 1014) # Embedding x = embedding_layer ( inputs ) # Conv for filter_num, filter_size, pooling_size in conv_layers : x = Conv1D ( filter_num, filter_size )( x ) x = Activation ( 'relu' )( x ) if pooling_size != - 1 : x = MaxPooling1D ( pool_size = pooling_size )( x ) # Final shape=(None, 34, 256) x = Flatten ()( x ) # (None, 8704) # Fully connected layers for dense_size in fully_connected_layers : x = Dense ( dense_size, activation = 'relu' )( x ) # dense_size = 1024 x = Dropout ( dropout_p )( x ) Char_CNN_Embeddings = Model ( inputs = inputs, outputs = x ) return Char_CNN_Embeddings However, in the case of ELMo and BERT (we will see it in a forthcoming lecture), since they are context dependent, we need the model that was used to train the vectors even after training, since the models generate the vectors for a word based on context. There is no need for the model itself that was used to train these vectors. All we need is the vectors for the words. This formulation has been addressed in the state of the art using many different approach, and more recently including some approximation based on Bidirectional Recurrent Networks.ĮLMo is inspired in the Language Modelling problem, which has the advantage of being a self-supervised task.Ī practical implication of this difference is that we can use word2vec and Glove vectors trained on a large corpus directly for downstream tasks. Given \(T\) tokens \((x_1,x_2,\cdots,x_T)\), a forward language model computes the probability of the sequence by modeling the probability of token \(x_k\) given the history \((x_1,\cdots, x_)\). Therefore, the same word can have different word vectors under different contexts. They are computed on top of two-layer Bidirectional Language Models (biLMs) with character convolutions, as a linear function of the internal network states. Unlike most widely used word embeddings ELMo word representations are functions of the entire input sentence, instead of the single word. So even if we had a sentence like “He went to the prison cell with his cell phone to extract blood cell samples from inmates”, where the word cell has different meanings based on the sentence context, these models just collapse them all into one vector for cell in their output source. Eventhough they provided a great improvement to many NLP task, such “constant” meaning was a major drawback of this word embeddings as the meaning of words changes based on context, and thus this wasn’t the best option for Language Modelling.įor instance, after we train word2vec/Glove on a corpus we get as output one vector representation for, say the word cell. Word embeddings such as word2vec or GloVe provides an exact meaning to words.