monologg / r-bert Goto Github PK

Hi,

In the paper, FClayers for two entities share the same parameters. However, in model.py file, there are two FClayers with different parameters. I want to ask about the performance of these two settings. Thanks.

This is a good job. Thank you.
And
I remember the f1-score > 88 some days ago, but the f1-score < 88 I ran last night.
Because the entities fully-connected layer use the same weight after the update?

Could you share how to adjust parameters such as the learning rate to get more results?
Because I want to use R-BERT in my dataset, but the result is not very well.
Thanks.

When I run the training command this error shows up, do you have any idea what could cause it?

06/21/2020 10:13:15 - INFO - transformers.modeling_utils - loading weights file ./model\pytorch_model.bin
Traceback (most recent call last):
File "D:\WorkSpace\Pythonspace\R-BERT-master\trainer.py", line 200, in load_model
self.model = self.model_class.from_pretrained(self.args.model_dir)
File "D:\Programming\Anaconda3\lib\site-packages\transformers\modeling_utils.py", line 512, in from_pretrained
model = cls(config, *model_args, **model_kwargs)
TypeError: init() missing 1 required positional argument: 'args'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "D:/WorkSpace/Pythonspace/R-BERT-master/main.py", line 63, in
main(args)
File "D:/WorkSpace/Pythonspace/R-BERT-master/main.py", line 21, in main
trainer.load_model()
File "D:\WorkSpace\Pythonspace\R-BERT-master\trainer.py", line 204, in load_model
raise Exception("Some model files might be missing...")
Exception: Some model files might be missing...

https://github.com/DongPoLI/Mul-BERT

Hi,
I'm trying to train R-BERT on my dataset with different relations using your repo. I reformatted my dataset to be in your format for semeval, but when I run the train eval command I get an error because the evaluator tries to read from the result file and finds it empty.

When I looked at the eval directory before training, I found two files, I understand that the answer_keys file in used during evaluating model performance, I constructed my own answer_keys file.

About the second file, I changed the if part that contains relations for semeval to my relations. I still got the same error:
Evaluating: 100% 1178/1178 [02:04<00:00, 9.48it/s]
Bad file format on line 37675: '155333 date of birth(e1, e2)'
Iteration: 3% 249/7354 [03:24<1:37:08, 1.22it/s]
Epoch: 0% 0/10 [03:24<?, ?it/s]
Traceback (most recent call last):
File "main.py", line 117, in
main(args)
File "main.py", line 19, in main
trainer.train()
File "/content/drive/MyDrive/R-BERT/trainer.py", line 125, in train
self.evaluate("test") # There is no dev set for semeval task
File "/content/drive/MyDrive/R-BERT/trainer.py", line 192, in evaluate
result = compute_metrics(preds, out_label_ids)
File "/content/drive/MyDrive/R-BERT/utils.py", line 54, in compute_metrics
return acc_and_f1(preds, labels)
File "/content/drive/MyDrive/R-BERT/utils.py", line 65, in acc_and_f1
"f1": official_f1(),
File "/content/drive/MyDrive/R-BERT/official_eval.py", line 17, in official_f1
macro_result = list(f)[-1]
IndexError: list index out of range

Hello, thanks for your works.
I got the final F1 result 82.0% after 5 epoch training while 89.25% in the paper. What about you?

When trying to run

python main.py --do_train --do_eval

I obtain a CUDA out of memory error. Is there any way to fix this?

I am a student experimenting with Relation Extraction models and do not have extra memory at my disposal.

Full error trace:
File "main.py", line 65, in
main(args)
File "main.py", line 18, in main
trainer.train()
File "C:\Users\Famke\R-BERT-master\trainer.py", line 84, in train
outputs = self.model(**inputs)
File "C:\Users\Famke\Anaconda3\lib\site-packages\torch\nn\modules\module.py", line 532, in call
result = self.forward(*input, **kwargs)
File "C:\Users\Famke\R-BERT-master\model.py", line 58, in forward
token_type_ids=token_type_ids) # sequence_output, pooled_output, (hidden_states), (attentions)
File "C:\Users\Famke\Anaconda3\lib\site-packages\torch\nn\modules\module.py", line 532, in call
result = self.forward(*input, **kwargs)
File "C:\Users\Famke\Anaconda3\lib\site-packages\transformers\modeling_bert.py", line 790, in forward
encoder_attention_mask=encoder_extended_attention_mask,
File "C:\Users\Famke\Anaconda3\lib\site-packages\torch\nn\modules\module.py", line 532, in call
result = self.forward(*input, **kwargs)
File "C:\Users\Famke\Anaconda3\lib\site-packages\transformers\modeling_bert.py", line 407, in forward
hidden_states, attention_mask, head_mask[i], encoder_hidden_states, encoder_attention_mask
File "C:\Users\Famke\Anaconda3\lib\site-packages\torch\nn\modules\module.py", line 532, in call
result = self.forward(*input, **kwargs)
File "C:\Users\Famke\Anaconda3\lib\site-packages\transformers\modeling_bert.py", line 368, in forward
self_attention_outputs = self.attention(hidden_states, attention_mask, head_mask)
File "C:\Users\Famke\Anaconda3\lib\site-packages\torch\nn\modules\module.py", line 532, in call
result = self.forward(*input, **kwargs)
File "C:\Users\Famke\Anaconda3\lib\site-packages\transformers\modeling_bert.py", line 314, in forward
hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask
File "C:\Users\Famke\Anaconda3\lib\site-packages\torch\nn\modules\module.py", line 532, in call
result = self.forward(*input, **kwargs)
File "C:\Users\Famke\Anaconda3\lib\site-packages\transformers\modeling_bert.py", line 234, in forward
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
RuntimeError: CUDA out of memory. Tried to allocate 18.00 MiB (GPU 0; 4.00 GiB total capacity; 3.06 GiB already allocated; 2.57 MiB free; 3.11 GiB reserved in total by PyTorch)
Epoch: 0%| | 0/5 [00:01<?, ?it/s]
Iteration: 0%| | 0/500 [00:01<?, ?it/s]

As above
Thanks Park.

Hello author, I have reproduced your project. When using my own dataset (with consistent format), there are only 9 types of labels. When training (GPU), an error message
RuntimeError: CUDA error: device side asset triggered will be generated.
When using CPU, the error message is
IndexError: Target 11 is out of bounds
, and I am unable to train. Do you know the reason?

07/01/2020 19:31:38 - INFO - data_loader - Loading features from cached file ./data/cached_train_semeval_bert-base-uncased_384
Segmentation fault (core dumped)

I run this model without any changes.

你好：
请教一下，为什么执行了python3 main.py --do_train --do_eval之后，result.txt 文件是空的，请问这是什么原因呢？谢谢

Excuse me, when I train the R-bert in chinese dataset, it shows the error-->'list index out of range',because the result.txt in eval is empty, so i want to know if the official_eval.py can test the chinese data?
if not, could u give me some advices?
Thanks a lot!!