# TODO Replace customClassifier dengan main task + auxiliary task custom_classifier = {} custom_classifier[target_task] = [('LSTM', 100), 'CRF'] for task in aux_task: custom_classifier[task] = ['CRF'] params = { 'classifier': ['CRF'], 'LSTM-Size': [100], 'dropout': (0.25, 0.25), 'charEmbeddings': 'CNN', 'customClassifier': custom_classifier } model = BiLSTM(params) model.setMappings(mappings, embeddings) model.setDataset(datasets, data) model.storeResults("/".join( [args.root_dir_result, args.directory_name, "performance.out"])) # Path to store performance scores for dev / test model.predictionSavePath = "/".join([ args.root_dir_result, args.directory_name, "predictions", "[ModelName]_[Data].conll" ]) # Path to store predictions model.modelSavePath = "/".join( [args.root_dir_result, args.directory_name, "models/[ModelName].h5"]) # Path to store models model.fit(epochs=args.nb_epoch)
###################################################### #Load the embeddings and the dataset embeddings, mappings, data = loadDatasetPickle(pickleFile) # Some network hyperparameters params = { 'classifier': ['CRF'], 'LSTM-Size': [100], 'dropout': (0.25, 0.25), 'charEmbeddings': 'CNN' } model = BiLSTM(params) model.setMappings(mappings, embeddings) model.setDataset(datasets, data, mainModelName='MIT_Restaurant') # KHUSUS MULTITSAK model.storeResults("/".join( ["results", args.directory_name, "performance.out"])) #Path to store performance scores for dev / test model.predictionSavePath = "/".join([ "results", args.directory_name, "predictions", "[ModelName]_[Epoch]_[Data].conll" ]) #Path to store predictions model.modelSavePath = "/".join([ "results", args.directory_name, "models/model_[DevScore]_[TestScore]_[Epoch].h5" ]) #Path to store models model.fit(epochs=50)