def trainPredModel(data): ''' Split dataset to training and evaluation (20% evaluation split) Fit model to training data, and generate a doc with evaluation as well as the summary of errors ''' process = ProcessData(sentdata) padded_sequence, tokenizer, labels = process.createTrainData() ft = createFTEmbedding() ft.processDict(tokenizer) x_train, x_val, y_train, y_val = train_test_split(padded_sequence, labels) model = compileModel(padded_sequence, labels, tokenizer, ft.embedding_matrix) model.fit(x_train, y_train, batch_size=20, epochs=10, validation_data=(x_val, y_val)) y_pred = model.predict(x_val) translateEvalData(y_pred, y_val, x_val, tokenizer) return model, x_train, x_val, y_train, y_val, y_pred