Python EntityDetection Examples

Example #1

print("Embedding match number {} out of {}".format(match_embedding, len(TEXT.vocab)))

train_iter = data.Iterator(train, batch_size=args.batch_size, device=args.gpu, train=True, repeat=False,
                                   sort=False, shuffle=True)
dev_iter = data.Iterator(dev, batch_size=args.batch_size, device=args.gpu, train=False, repeat=False,
                                   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 == 'EntityDetection':
    config.label = len(ED.vocab)
    model = EntityDetection(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("Entity Type", len(ED.vocab))

Example #2

                         sort_within_batch=False)
test_iter = data.Iterator(test,
                          batch_size=args.batch_size,
                          device="cuda",
                          train=False,
                          repeat=False,
                          sort=False,
                          shuffle=False,
                          sort_within_batch=False)

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

if args.dataset == 'EntityDetection':
    config.label = len(ED.vocab)
    model = EntityDetection(config)
else:
    print("Error Dataset")
    exit()

model.embed.weight.data.copy_(TEXT.vocab.vectors)
if args.cuda:
    model = model.to(torch.device("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("Entity Type", len(ED.vocab))

Example #3

                          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 == 'EntityDetection':
    config.label = len(ED.vocab)
    if config.entity_detection_mode.lower() == 'transformer':
        model = TransformerModel(config)
    else:
        model = EntityDetection(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("Entity Type", len(ED.vocab))

Example #4

File: train.py Project: tahoora/BuboQA

print("Embedding match number {} out of {}".format(match_embedding, len(TEXT.vocab)))

train_iter = data.Iterator(train, batch_size=args.batch_size, device=args.gpu, train=True, repeat=False,
                                   sort=False, shuffle=True, sort_within_batch=False)
dev_iter = data.Iterator(dev, batch_size=args.batch_size, device=args.gpu, train=False, repeat=False,
                                   sort=False, shuffle=False, 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 == 'EntityDetection':
    config.label = len(ED.vocab)
    model = EntityDetection(config)
else:
    print("Error Dataset")
    exit()

model.embed.weight.data.copy_(TEXT.vocab.vectors)
if args.cuda:
    print(args.gpu)
    modle = 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))