你好，我在使用gp和bp方式进行英文实体识别，训练时p,r,f1都是0，训练数据是conllu2003，预训练模型是bert-base-uncased，训练数据的样式如图所示

def extract_entities(configs, tokenizer, text, model, device):
"""
从验证集中预测到相关实体
"""
predict_results = {}
encode_results = tokenizer(text, padding='max_length')
input_ids = encode_results.get('input_ids')
token = tokenizer.convert_ids_to_tokens(input_ids)
mapping = rematch(text, token)
token_ids = torch.unsqueeze(torch.LongTensor(input_ids), 0) .to(device)
attention_mask = torch.unsqueeze(torch.LongTensor(encode_results.get('attention_mask')), 0).to(device)
model_outputs = model(token_ids, attention_mask).detach().to('cpu')
decision_threshold = float(configs.decision_threshold)
for model_output in model_outputs:
start = np.where(model_output[:, :, 0] > decision_threshold)
end = np.where(model_output[:, :, 1] > decision_threshold)
for _start, predicate1 in zip(*start):
for _end, predicate2 in zip(*end):
if _start <= _end and predicate1 == predicate2:
if len(mapping[_start]) > 0 and len(mapping[_end]) > 0:
start_in_text = mapping[_start][0]
end_in_text = mapping[_end][-1]
entity_text = text[start_in_text: end_in_text + 1]
predict_results.setdefault(predicate1, set()).add(entity_text)
break
return predict_results

to('cpu')
验证时 RuntimeError: CUDA error: device-side assert triggered

你好，请问一下start/end这种标注方式，相对于传统的CRF有什么优点呢？

token_results = self.tokenizer(text)

in engines/data.py
while your "mode=train" in your system.config