# 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)
