As I know, BERT is limit the position embedding as 512. However, when I look at the code, I found position id, input id and etc. have 1024 size. I quite confusing about this concept. Could you explain about the difference above those?

Hello, I was curious that in the Quick Start section, what does this "--max_mention_ori_length: 8" mean? If I run the different dataset, should I change it based on my data size? Thanks.

In Modeling_bert.py BertForACEBothOneDropoutSub, why ner classifier doesn't concatenate m1_states while BrtForSpanMarkerNER concatenate them to make a feature vector? Could you explain in more detail about the e1,e2, and m1? As I see the code, I think train_re.sh can train ner and re together with options, is it possible? if it is possible what are the exact options that I need? Also, I want to know, Is a subject-oriented packaging strategy only used in evaluation?

Hi! Firstly, thanks for publishing your research and models! :)

I have trouble evaluating the NER model with the given command CUDA_VISIBLE_DEVICES=0 python3 run_acener.py --model_type bertspanmarker ... The output is a json-file with only one line: {"dev_best_f1": 0}

The last 3 lines of the log-output are:

02/04/2022 17:37:16 - INFO - __main__ -   Training/evaluation parameters Namespace(adam_epsilon=1e-08, alpha=1, cache_dir='', config_name='', data_dir='../scierc/raw_data', dev_file='dev.json', device=device(type='cuda'), do_eval=True, do_lower_case=True, do_test=False, do_train=False, eval_all_checkpoints=True, evaluate_during_training=True, fp16=True, fp16_opt_level='O1', gradient_accumulation_steps=1, group_axis=-1, group_edge=False, group_sort=False, learning_rate=2e-05, lminit=True, local_rank=-1, logging_steps=5, max_grad_norm=1.0, max_mention_ori_length=8, max_pair_length=256, max_seq_length=512, max_steps=-1, model_name_or_path='../bert_models/scibert_scivocab_uncased', model_type='bertspanmarker', n_gpu=1, no_cuda=False, no_test=False, norm_emb=False, num_train_epochs=50.0, onedropout=True, output_dir='../sciner_models/sciner-scibert', output_results=True, overwrite_cache=False, overwrite_output_dir=True, per_gpu_eval_batch_size=16, per_gpu_train_batch_size=8, save_steps=2000, save_total_limit=1, seed=42, server_ip='', server_port='', shuffle=False, test_file='test.json', tokenizer_name='', train_file='train.json', use_full_layer=-1, warmup_steps=-1, weight_decay=0.0)
02/04/2022 17:37:16 - INFO - __main__ -   Evaluate on test set
02/04/2022 17:37:16 - INFO - __main__ -   Evaluate the following checkpoints: []

As you can see in the first line, I changed the original command in the following way:

1. --model_name_or_path ../bert_models/scibert_scivocab_uncased I couldn't find a folder "scibert-uncased", so I downloaded the 4th model from huggingface as described in the "Training Script"-section (AllenAI) - is this maybe the wrong model?
2. --data_dir ../scierc/raw_data I downloaded the SciERC raw_data from their website to execute the evaluation on - is this the wrong dataset?

你好，在阅读代码时碰上几个疑问，可否解惑一下：

1. 下列代码中的[30002]、[30003]、[3]、[4]表示什么？有何作用？ https://github.com/thunlp/PL-Marker/blob/91b03f3ff58ad29fd7b9920a954a02d12756f05d/run_re.py#L373-L378

2. 下列代码为什么要加一个(10000, 10000, 'NIL')的命名实体信息？并且在实体两两组合成候选关系对时，sub可以是(10000, 10000, 'NIL')，obj又不能是(10000, 10000, 'NIL')，这又是为什么？ https://github.com/thunlp/PL-Marker/blob/91b03f3ff58ad29fd7b9920a954a02d12756f05d/run_re.py#L283-L284

Hi, Thanks for sharing this awesome work. I have a few doubts please help me to understand:

1. I have a set of text paragraphs and want to extract entities and relationships between the entities detected. How would I prepare my dataset for NER and Relation Extraction model on this paragraph? What formate should I follow?
2. If any tool you could recommend or any way to prepare tor annotate he data according to the desired format that the model is expecting, it would be a great help.

Thanks.

run_ner.py中第221行到238行主要是为了获取target_tokens，能不能麻烦解释一下其中的逻辑？为什么要这么处理？其中涉及到的half_context_length、left_context_length、right_context_length都表示什么意思？非常感谢。 https://github.com/thunlp/PL-Marker/blob/b4863d47e2197b8d410e3d693d684707df9df2a1/run_ner.py#L221-L238

您好，我在您代码基础上改了一版代码，想要实现全悬浮标记的方法。运行结果表示f1达到了预期效果，但是f1_with_ner,和ner_f1的结果特别差，并且ner_f1的结果随着训练变得越来越差。我找了很久没有找到问题，我似乎用的也是golden的dev文件做的训练呀，为什么ner的f1一直在下降，但是您的代码中对应的ner_f1一直是1.0呢。如果您能够帮我看看代码问题出在哪了就更好了，或者您告诉问题可能出现在哪里也非常感谢。下面是训练的部分截图，最后附上我修改后的代码和运行脚本，谢谢

run_train_re_approx.zip

首先感谢作者出色的工作,有个小小的疑问问您：

为什么在运行关系抽取任务中，在验证集中f1_with_ner的结果能够达到1.0呀， 难道运用的是对应的golden ner嘛，如果是的话能否指出对应代码在run_re.py中的位置，我看好像用的是模型预测的结果做的呀，但是按道理应该不可能到1.0.

谢谢

你好，请问，为神魔要将Conll03数据集处理为I-label的形式，这样的话， 数据集的labelmap= {'O':0,'I-label':num}了吗？就不存在‘B-label’了吧， 但是，代码中定义的label_map，包括了B-label的呀。 而且，在分类中，模型给出的target-label=9， 所以，数据集，为什么要把B-label替换为I-label呢？

您好，我们在ace05数据上训练ner_PLMarker模型时，如果使用bert-base-uncased + fp16参数，或者albert-xxlarge-v1 没有fp16参数时都可以正常训练，但使用albert-xxlarge-v1 + fp16时会出错，错误出现在AlbertAttention 的 mixed_query_layer = self.query(input_ids) 处，amp cached_cast 会报 IndexError: tuple index out of range的错误。不知道你们有没有遇到过这种问题。