def __init__(self, packer, l_map, SCRF_l_map):
self.packer = packer
self.l_map = l_map
self.SCRF_l_map = SCRF_l_map
self.r_l_map = utils.revlut(l_map)
self.SCRF_r_l_map = utils.revlut(SCRF_l_map)
def __init__(self, c_map, l_map, seg_l_map, ent_l_map, w_map, win_size, gpu=0):
self.l_map = l_map
self.seg_l_map = seg_l_map
self.ent_l_map = ent_l_map
self.w_map = w_map
self.c_map = c_map
self.r_w_map = utils.revlut(w_map)
self.r_l_map = utils.revlut(l_map)
self.gpu = gpu
self.win_size = win_size
def __init__(self, packer, c_map, l_map, removed_label):
self.removed_label = removed_label
self.packer = packer
self.l_map = l_map
self.r_l_map = utils.revlut(l_map)
self.c_map = c_map
self.r_c_map = utils.revlut(c_map)
self.totalp_counts = {}
self.truep_counts = {}
self.fn_counts = {}
self.fp_counts = {}
self.f1 = {}
def __init__(self, packer, l_map):
self.packer = packer
self.l_map = l_map
self.r_l_map = utils.revlut(l_map)
self.totalp_counts={}
self.truep_counts={}
self.fn_counts={}
self.fp_counts={}
self.f1={}
def __init__(self, packer, l_map):
self.packer = packer
self.l_map = l_map
self.r_l_map = utils.revlut(l_map)
best_f1 = float('-inf')
best_acc = float('-inf')
track_list = list()
start_time = time.time()
epoch_list = range(args.start_epoch, args.start_epoch + args.epoch)
patience_count = 0
evaluator = eval_wc(packer, c_map, l_map)
evaluator_filter = eval_wc(packer, c_map, l_map, ['B-site', 'I-site', 'O'])
print("start training...")
loss_list=[]
crf_loss_list=[]
lm_loss_list=[]
f1_test_list=[]
r_c_map = utils.revlut(c_map)
for epoch_idx, args.start_epoch in enumerate(epoch_list):
epoch_loss = 0
crf_loss=0
lm_loss=0
ner_model.train()
args.lambda0=max(1-args.lambda0_dr*args.start_epoch,args.lambda0_min_value)
data={"train":dataset_loader,"co":co_dataset_loader}
for i in data:
for f_f, f_p, b_f, b_p, w_f, tg_v, mask_v, len_v in tqdm(
itertools.chain.from_iterable(data[i]), mininterval=2,
desc=' - Tot it %d (epoch %d)' % (tot_length, args.start_epoch), leave=False, file=sys.stdout):
mask_v=mask_v.bool()
f_f, f_p, b_f, b_p, w_f, tg_v, mask_v = packer.repack_vb(f_f, f_p, b_f, b_p, w_f, tg_v, mask_v, len_v)