def run(args):
logger.info('load data from disk')
processing = Preprocessor(min_len=2,
stopwords_path=config['stopword_path'])
examples = []
with open(config['data_path'], 'r') as fr:
for i, line in enumerate(fr):
# 数据首行为列名
if i == 0 and False:
continue
line = line.strip("\n")
line = processing(line)
if line:
examples.append(line.split())
logger.info("initializing emnedding model")
word2vec_model = gensim_word2vec.Word2Vec(
sg=1,
iter=10,
size=args.embedd_dim,
window=args.window_size,
min_count=args.min_freq,
save_path=config['gensim_embedding_path'],
num_workers=args.num_workers,
seed=args.seed)
word2vec_model.train_w2v([[word for word in document]
for document in examples])
def main():
print('模型存放在' + str(config['pytorch_embedding_path']))
parser = argparse.ArgumentParser(
description='PyTorch Word2Vec model training')
parser.add_argument("--model", type=str, default='skip_gram')
parser.add_argument("--task", type=str, default='training word vector')
parser.add_argument('--seed',
default=2018,
type=int,
help='Seed for initializing training.')
parser.add_argument('--resume',
default=False,
type=bool,
help='Choose whether resume checkpoint model')
parser.add_argument('--embedd_dim', default=300, type=int)
parser.add_argument('--epochs', default=6, type=int)
parser.add_argument('--window_size', default=5, type=int)
parser.add_argument('--n_gpus', default='0', type=str)
parser.add_argument('--min_freq', default=20, type=int)
parser.add_argument('--sample', default=1e-3, type=float)
parser.add_argument('--negative_sample_num', default=5, type=int)
parser.add_argument('--learning_rate', default=0.025, type=float)
parser.add_argument('--weight_decay', default=5e-4, type=float)
parser.add_argument('--vocab_size', default=30000000, type=int)
args = parser.parse_args()
init_logger(log_file=config['log_dir'] / (args.model + ".log"))
logger.info(f"seed is {args.seed}")
seed_everything(seed=args.seed)
run(args)
def main():
parser = argparse.ArgumentParser(
description='Gensim Word2Vec model training')
parser.add_argument("--model", type=str, default='gensim_word2vec')
parser.add_argument("--task", type=str, default='training word vector')
parser.add_argument('--seed',
default=2018,
type=int,
help='Seed for initializing training.')
parser.add_argument('--resume',
default=False,
type=bool,
help='Choose whether resume checkpoint model')
parser.add_argument('--embedd_dim', default=300, type=int)
parser.add_argument('--spochs', default=6, type=int)
parser.add_argument('--window_size', default=5, str=int)
parser.add_argument('--n_gpu', default='0', type=str)
parser.add_argument('--min_freq', default=5, type=int)
parser.add_argument('--sample', default=1e-3, type=float)
parser.add_argument('--negative_sample_num', default=5, type=int)
parser.add_argument('--learning_rate', default=0.025, type=float)
parser.add_argument('--weight_decay', default=5e-4, type=float)
parser.add_argument('--vocab_size', default=30000000, type=int)
parser.add_argument('--num_workers', default=10)
args = parser.parse_args()
init_logger(log_file=config['log_dir'] / (args.model + ".log"))
logger.info("seed is %d" % args['seed'])
seed_everything(seed=args['seed'])
run(args)
def run(args):
# **************************** 加载数据集 ****************************
logger.info('starting load train data from disk')
train_dataset = DataLoader(skip_header=False,
negative_num=args.negative_sample_num,
window_size=args.window_size,
data_path=config['data_path'],
vocab_path=config['vocab_path'],
vocab_size=args.vocab_size,
min_freq=args.min_freq,
shuffle=True,
seed=args.seed,
sample=args.sample)
# **************************** 模型和优化器 ***********************
logger.info("initializing model")
model = SkipGram(embedding_dim=args.embedd_dim,
vocab_size=len(train_dataset.vocab))
optimizer = optim.SGD(params=model.parameters(), lr=args.learning_rate)
# **************************** callbacks ***********************
logger.info("initializing callbacks")
train_monitor = TrainingMonitor(file_dir=config['figure_dir'],
arch=args.model)
lr_scheduler = StepLR(optimizer=optimizer,
lr=args.learning_rate,
epochs=args.epochs)
# **************************** training model ***********************
logger.info('training model....')
trainer = Trainer(model=model,
vocab=train_dataset.vocab,
optimizer=optimizer,
epochs=args.epochs,
logger=logger,
training_monitor=train_monitor,
lr_scheduler=lr_scheduler,
n_gpu=args.n_gpus,
model_save_path=config['model_save_path'],
vector_save_path=config['pytorch_embedding_path'])
trainer.train(train_data=train_dataset)