datafile = "./model/data_fix=" + str(withFix) + "_pos=" + str(
withPos) + ".pkl"
modelfile = "./model/" + modelname + "__" + "data_fix=" + str(withFix) + "_pos=" + str(withPos) +\
"__Type_pos" + str(Poswidth) + "_1.h5"
batch_size = 32
retrain = False
Test = True
if not os.path.exists(datafile):
print("Precess data....")
get_data(trainfile,
devfile,
testfile,
w2v_file,
datafile,
w2v_k=100,
char_emd_dim=25,
withFix=withFix,
maxlen=maxlen,
Poswidth=Poswidth)
if not os.path.exists(modelfile):
print("Lstm data has extisted: " + datafile)
print("Training EE model....")
print(modelfile)
train_e2e_model(modelname,
datafile,
modelfile,
resultdir,
npochos=100,
hidden_dim=200,
batch_size=batch_size,
testfile = "./data/test.txt"
char2v_file = "./data/CCKS18CNER_Char2Vec.txt"
word2v_file = "./data/CCKS18CNER_Word2Vec.txt"
datafile = "./data/model/data5.pkl"
modelfile = "./data/model/model_char_word_CRF51.h5"
resultdir = "./data/result/"
batch_size = 32
retrain = True
Test = True
valid = False
Label = True
if not os.path.exists(datafile):
print("Precess data....")
get_data(trainfile=trainfile, testfile=testfile, w2v_file=word2v_file, char2v_file=char2v_file, datafile=datafile, w2v_k=100, char_emd_dim=100, maxlen=50)
if not os.path.exists(modelfile):
print("Lstm data has extisted: " + datafile)
print("Training EE model....")
print(modelfile)
train_e2e_model(modelname, datafile, modelfile, resultdir,
npochos=100, hidden_dim=200, batch_size=batch_size, retrain=False)
else:
if retrain:
print("ReTraining EE model....")
train_e2e_model(modelname, datafile, modelfile, resultdir,
npochos=100, hidden_dim=200, batch_size=batch_size, retrain=retrain)
if Test:
print("test EE model....")
user_datafile = "./model/" + dataname + ".pkl"
batch_size = 8
data_split = 1
retrain = False
Test = True
valid = False
Label = True
if not os.path.exists(user_datafile):
print("Process data....")
get_data(trainfile=trainfile,
testfile=testfile,
w2v_file=word2v_file,
c2v_file=char2v_file,
base_datafile=base_datafile,
user_datafile=user_datafile,
w2v_k=300,
c2v_k=100,
data_split=data_split,
maxlen=50)
print("data has extisted: " + user_datafile)
print('loading base data ...')
char_vob, target_vob, \
idex_2char, idex_2target, \
char_W, \
char_k, \
max_s = pickle.load(open(base_datafile, 'rb'))
print('loading user data ...')
train, train_label,\
test, test_label = pickle.load(open(user_datafile, 'rb'))
resultdir = "./data/result/Model1_model_TransE_---"
batch_size = 64
retrain = False
Test = True
valid = False
Label = False
if not os.path.exists(datafile):
print("Precess data....")
get_data(entity2idfile=entity2idfile,
relation2idfile=relation2idfile,
entity2vecfile=entity2vecfile,
relation2vecfile=relation2vecfile,
w2v_k=100,
trainfile=trainfile,
testfile=testfile,
testfile_KGC_h_t=testfile_KGC_h_t,
testfile_KGC_hr_=testfile_KGC_hr_,
testfile_KGC__rt=testfile_KGC__rt,
path_file=path_file,
max_p=3,
entityRank=entityRank,
datafile=datafile)
if not os.path.exists(modelfile):
print("data has extisted: " + datafile)
print("Training model....")
print(modelfile)
train_model(modelname,
datafile,
modelfile,
resultdir,
npochos=200,