예제 #1
0
                         sort=False,
                         shuffle=False)
test_iter = data.Iterator(test,
                          batch_size=args.batch_size,
                          device=args.gpu,
                          train=False,
                          repeat=False,
                          sort=False,
                          shuffle=False)

config = args
config.words_num = len(TEXT.vocab)

if args.dataset == 'RelationPrediction':
    config.rel_label = len(RELATION.vocab)
    model = RelationPrediction(config)
else:
    print("Error Dataset")
    exit()

model.embed.weight.data.copy_(TEXT.vocab.vectors)
if args.cuda:
    model.cuda()
    print("Shift model to GPU")

print(config)
print("VOCAB num", len(TEXT.vocab))
print("Train instance", len(train))
print("Dev instance", len(dev))
print("Test instance", len(test))
print("Relation Type", len(RELATION.vocab))
예제 #2
0
                         sort_within_batch=False)
test_iter = data.Iterator(test,
                          batch_size=args.batch_size,
                          device=args.gpu,
                          train=False,
                          repeat=False,
                          sort=False,
                          shuffle=False,
                          sort_within_batch=False)

config = args
config.words_num = len(TEXT.vocab)

if args.dataset == 'RelationPrediction':
    config.rel_label = len(RELATION.vocab)
    model = RelationPrediction(config)
else:
    print("Error Dataset")
    exit()

model.embed.weight.data.copy_(TEXT.vocab.vectors)
if args.cuda:
    model = model.to(torch.device("cuda:{}".format(args.gpu)))
    print("Shift model to GPU")

print(config)
print("VOCAB num", len(TEXT.vocab))
print("Train instance", len(train))
print("Dev instance", len(dev))
print("Test instance", len(test))
print("Relation Type", len(RELATION.vocab))
예제 #3
0
                          device=device,
                          train=False,
                          repeat=False,
                          sort=False,
                          shuffle=False,
                          sort_within_batch=False)

config = args
config.words_num = len(TEXT.vocab)

if args.dataset == 'RelationPrediction':
    config.rel_label = len(RELATION.vocab)
    if config.relation_prediction_mode.lower() == 'transformer':
        model = TransformerModel(config)
    else:
        model = RelationPrediction(config)
else:
    print("Error Dataset")
    exit()

model.embed.weight.data.copy_(TEXT.vocab.vectors)
if args.cuda:
    model = model.to(torch.device("cuda:{}".format(args.gpu)))
    print("Shift model to GPU")

print(config)
print("VOCAB num", len(TEXT.vocab))
print("Train instance", len(train))
print("Dev instance", len(dev))
print("Test instance", len(test))
print("Relation Type", len(RELATION.vocab))