runxinxu / git Goto Github PK

您好，在尝试用您的模型跑出结果后，得到如：./Exps/try/Output/dee_eval.test.pred_span.GIT.5.pkl的结果文件，其中一条数据是：
'''
(0, [0, 0, 1, 1, 0], [None, None, [[None, None, None, None, None, None]], [[(3330, 1290), None, None, None, None, None]], None], DocSpanInfo(span_token_tup_list=[(121, 121, 121, 127, 121, 126), (3943, 3862, 5500, 819), (4507,), (3330, 1290)], span_dranges_list=[[(0, 5, 11)], [(0, 16, 20)], [(9, 0, 1)], [(12, 10, 12), (13, 8, 10)]], span_mention_range_list=[(0, 1), (1, 2), (2, 3), (3, 5)], mention_drange_list=[(0, 5, 11), (0, 16, 20), (9, 0, 1), (12, 10, 12), (13, 8, 10)], mention_type_list=[1, 3, 5, 7, 7], event_dag_info=[None, None, None, None, [{(): {None}}, {(None,): {None}}, {(None, None): {None}}, {(None, None, None): {None}}, {(None, None, None, None): {None}}, {(None, None, None, None, None): {None}}, {(None, None, None, None, None, None): {None}}, {(None, None, None, None, None, None, None): {None}}, {(None, None, None, None, None, None, None, None): {None}}]], missed_sent_idx_list=[1, 4, 7, 8, 9, 10, 12, 13, 14, 16, 19]), [None, None, [(None, None, None, None, None, None)], [(3, None, None, None, None, None)], None])
'''
请问如何对上述数据进行解析，得到数据集中
'''
"recguid_eventname_eventdict_list": [
[
0,
"EquityPledge",
{
"Pledger": "李华青",
"PledgedShares": "1188600股",
"Pledgee": "海通证券股份有限公司",
"TotalHoldingShares": "22619999股",
"TotalHoldingRatio": "6.41%",
"TotalPledgedShares": "18200000股",
"StartDate": "2018年9月6日",
"EndDate": null,
"ReleasedDate": null
}
],
[
1,
"EquityPledge",
{
"Pledger": "李华青",
"PledgedShares": "12151000股",
"Pledgee": "海通证券股份有限公司",
"TotalHoldingShares": null,
"TotalHoldingRatio": "6.41%",
"TotalPledgedShares": "12151000股",
"StartDate": "2017年12月7日",
"EndDate": null,
"ReleasedDate": null
}
]
]
'''
的结果？
是否有结果解析的相关代码方便提供？
谢谢！

您好，我正在阅读您的代码和文章，有一些看不懂，希望您帮我解惑

请问这个公式怎么理解呢，
还有

是把L层拼接起来计算这个节点的最后一层embedding嘛

subprocess.CalledProcessError: Command '['/opt/anaconda3/envs/Doc2edag/bin/python', '-u', 'run_dee_task.py', '--local_rank=1', '--resume_latest_cpt', 'True', '--save_cpt_flag', 'True', '--data_dir', './Data', '--exp_dir', './Exps', '--task_name', 'try', '--num_train_epochs', '50', '--train_batch_size', '64', '--gradient_accumulation_steps', '8', '--eval_batch_size', '2', '--cpt_file_name', 'GIT']' returned non-zero exit status 1.

May I ask why this problem is? In addition, I have successfully reproduced Doc2EDAG, but the extraction results of event parameters are all numbers and cannot be visualized. How do you solve this problem

请问怎么使用单GPU运行

Hi, when I read the paper, I notice the words "role embedding" as follows. I read your codes carefully, but I still can't understand how to get it. Is it embedded by role_idx mapping? Moreover, I'd appreciate it if you could tell me where it is implemented in the code. Thanks!

We find the released code fail to achieve the performance in paper. We observe that the max_sent_num in your code is 32 while it's 64 in previous Doc2EDAG. We wonder if it's the reason?
我们发现开源代码无法复现出论文的指标，我们观察到GIT最大句数的设置是32而Doc2EDAG是64。不知道是否是这个原因呢？

dee_model.py 构建图时，第三种边，第720行到721行，len函数是不是应该不需要呢

Thanks for the excellent paper. But I have a question for the experiment setting:
As a sequence labeling task, we always use transformer encoder(like BERT) to solve the NER problem, for example, the author in Doc2EDAG use BERT as the first step backbone. However, in the paper, it is said that the vanilla transformer(encoder-decoder structure) is used in NER module, which confuse me a lot. I am wondering wher the decoder part of transformer is used for? Thanks.