def main():
# create instance of config，这里的config实现了load data的作用
#拥有词表、glove训练好的embeddings矩阵、str->id的function
config = Config()
# build model
model = NERModel(config)
model.build("train")
# model.restore_session("results/crf/model.weights/") # optional, restore weights
# model.reinitialize_weights("proj")
# create datasets [(char_ids), word_id]
# processing_word = get_processing_word(lowercase=True)
dev = CoNLLDataset(config.filename_dev)
train = CoNLLDataset(config.filename_train)
test = CoNLLDataset(config.filename_test)
train4cl = CoNLLdata4classifier(train,
processing_word=config.processing_word,
processing_tag=config.processing_tag)
dev4cl = CoNLLdata4classifier(dev,
processing_word=config.processing_word,
processing_tag=config.processing_tag)
test4cl = CoNLLdata4classifier(test,
processing_word=config.processing_word,
processing_tag=config.processing_tag)
# train model
model.train(train4cl, dev4cl, test4cl)
def main():
# create instance of config
config = Config()
# build model
model = NERModel(config)
model.build("train")
model.restore_session(config.dir_model)
# create dataset
processing_word = get_processing_word(lowercase=True)
if len(sys.argv) == 2:
if sys.argv[1] == 'test':
test = CoNLLDataset(config.filename_test, processing_word)
elif sys.argv[1] == 'dev':
test = CoNLLDataset(config.filename_dev, processing_word)
else:
assert len(sys.argv) == 1
test = CoNLLDataset(config.filename_test, processing_word)
test4cl = CoNLLdata4classifier(test, processing_word=config.processing_word,
processing_tag=config.processing_tag)
# evaluate and interact
model.evaluate(test4cl)
def main():
# create instance of config，这里的config实现了load data的作用
#拥有词表、glove训练好的embeddings矩阵、str->id的function
config = Config()
config.nepochs = 200
config.dropout = 0.5
config.batch_size = 60
config.lr_method = "adam"
config.lr = 0.0005
config.lr_decay = 1.0
config.clip = -2.0 # if negative, no clipping
config.nepoch_no_imprv = 8
config.dir_model = config.dir_output + "model.finetuning.weights/"
# build model
model = NERModel(config)
model.build("fine_tuning")
model.restore_session("results/test/model.weights/",
indicate="fine_tuning")
# model.restore_session("results/crf/model.weights/") # optional, restore weights
# model.reinitialize_weights("proj")
# create datasets [(char_ids), word_id]
# processing_word = get_processing_word(lowercase=True)
dev = CoNLLDataset(config.filename_dev)
train = CoNLLDataset(config.filename_train)
test = CoNLLDataset(config.filename_test)
# train model
train4cl = CoNLLdata4classifier(train,
processing_word=config.processing_word,
processing_tag=config.processing_tag,
context_length=config.context_length)
dev4cl = CoNLLdata4classifier(dev,
processing_word=config.processing_word,
processing_tag=config.processing_tag,
context_length=config.context_length)
test4cl = CoNLLdata4classifier(test,
processing_word=config.processing_word,
processing_tag=config.processing_tag,
context_length=config.context_length)
model.train(train4cl, dev4cl, test4cl)