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Downstream Model Design of Pre-trained Language Model for Relation Extraction Task
**自定义数据集**格式和semeval完全一样，只是关系的种类多一些。请指教是什么原因？ ValueError: Caught ValueError in DataLoader worker process 1. Original Traceback (most recent call last): File "/home/qwh/.local/lib/python3.6/site-packages/torch/utils/data/_utils/worker.py", line 178, in _worker_loop data = fetcher.fetch(index) File "/home/qwh/.local/lib/python3.6/site-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch...
The task offical score is macro f1, (9+1)-way evaluation with directionality taken into account, also used in most of the papers, but the result reported in paper is micro f1?...
When I am trying to load trained model with model.load_state_dict(torch.load("ckpt") I am getting this RuntimeError: storage has wrong size: expected -4883207186230854459 got 768 error. When I searched it says it...
I am getting a warning "Token indices sequence length is longer than the specified maximum sequence length for this model (730 > 512). Running this sequence through the model will...
``` @@@@@@@@@@@ args @@@@@@@@@@@ {'metric': 'micro_f1', 'cuda_device': 3, 'seed': 31415926535897932, 'opt': 'adam', 'use_cls': True, 'subject_1': False, 'large_bert': False, 'continue_train': False, 'eval': False, 'add_subject_loss': False, 'weight_decay': 1e-05, 'lr': 5e-05, 'max_epoch': 100,...
Where can I get the BERT pre-trained model that you used as you stated in your paper. I think you are referring to standard BERT trained by google research but...