working with text data figure training and validat ion accuracy when using pret rained word embeddings the model quickly starts overfittingwhich is unsurprising given the small number of training samples validation accuracy has high variance for the same reasonbut it seems to reach the high note that your mileage may varybecause you have so few training samplesperformance is heavily dependent on exactly which samples you choose--and you're choosing them at random if this works poorly for youtry choosing different random set of samplesfor the sake of the exercise (in real lifeyou don' get to choose your training datayou can also train the same model without loading the pretrained word embeddings and without freezing the embedding layer in that caseyou'll learn taskspecific embedding of the input tokenswhich is generally more powerful than pretrained word embeddings when lots of data is available but in this caseyou have only training samples let' try it (see figures and listing training he same model wit hout pret rained word embeddings from keras models import sequential from keras layers import embeddingflattendense model sequential(model add(embedding(max_wordsembedding_diminput_length=maxlen)model add(flatten()model add(dense( activation='relu')model add(dense( activation='sigmoid')model summary(model compile(optimizer='rmsprop'loss='binary_crossentropy'metrics=['acc']history model fit(x_trainy_trainepochs= batch_size= validation_data=(x_valy_val)licensed to