jiahaozhenbang/scope

Source code for the paper "Improving Chinese Spelling Check by Character Pronunciation Prediction: The Effects of Adaptivity and Granularity" in EMNLP 2022

scope's Issues

下面这个报错是怎么回事？

About pretraining data

Thanks for your great work!
Could you please provide the parallel csc data which generate by using confusion set and wiki2019zh ? ❤

AttributeError: Can't pickle local object 'get_linear_schedule_with_warmup.<locals>.lr_lambda'

The following error occurred when I used 2 GeForce RTX 3090 with 24G memory on the server. Please kindly answer it.
Traceback (most recent call last):
File "/root/autodl-tmp/SCOPE/finetune/train.py", line 433, in
main()
File "/root/autodl-tmp/SCOPE/finetune/train.py", line 426, in main
trainer.fit(model)
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 735, in fit
self._call_and_handle_interrupt(
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 682, in _call_and_handle_interrupt
return trainer_fn(*args, **kwargs)
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 770, in _fit_impl
self._run(model, ckpt_path=ckpt_path)
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1193, in _run
self._dispatch()
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1272, in _dispatch
self.training_type_plugin.start_training(self)
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/plugins/training_type/ddp_spawn.py", line 173, in start_training
self.spawn(self.new_process, trainer, self.mp_queue, return_result=False)
File "/root/miniconda3/lib/python3.8/site-packages/pytorch_lightning/plugins/training_type/ddp_spawn.py", line 201, in spawn
mp.spawn(self._wrapped_function, args=(function, args, kwargs, return_queue), nprocs=self.num_processes)
File "/root/miniconda3/lib/python3.8/site-packages/torch/multiprocessing/spawn.py", line 239, in spawn
return start_processes(fn, args, nprocs, join, daemon, start_method='spawn')
File "/root/miniconda3/lib/python3.8/site-packages/torch/multiprocessing/spawn.py", line 188, in start_processes
process.start()
File "/root/miniconda3/lib/python3.8/multiprocessing/process.py", line 121, in start
self._popen = self._Popen(self)
File "/root/miniconda3/lib/python3.8/multiprocessing/context.py", line 284, in _Popen
return Popen(process_obj)
File "/root/miniconda3/lib/python3.8/multiprocessing/popen_spawn_posix.py", line 32, in init
super().init(process_obj)
File "/root/miniconda3/lib/python3.8/multiprocessing/popen_fork.py", line 19, in init
self._launch(process_obj)
File "/root/miniconda3/lib/python3.8/multiprocessing/popen_spawn_posix.py", line 47, in _launch
reduction.dump(process_obj, fp)
File "/root/miniconda3/lib/python3.8/multiprocessing/reduction.py", line 60, in dump
ForkingPickler(file, protocol).dump(obj)
AttributeError: Can't pickle local object 'get_linear_schedule_with_warmup..lr_lambda'

Excuse me, have you provide the code for the pre-training section?

Excuse me, have you provide the code for the pre-training section?

On training with 2 gpu

Hello, excuse me, I want to know how you use two cpus for training, I wonder if it is convenient for you to tell me, looking forward to your reply! Thanks!

'FPT\\vocab.txt'文件在哪呢？

request for the final model ckpt

Thanks for your great work!
Could you please provide the final finetuned model? ❤

Can't reproduce the results.

I try to reproduce the result in the paper. I completely follow the environment in the README.md file.

I have run 30 epochs by 'train.sh' script, and the final checkpoints are the following:

epoch=23-df=79.3537-cf=78.0969.ckpt 
epoch=25-df=80.1070-cf=78.1445.ckpt 
epoch=26-df=80.1810-cf=78.5520.ckpt 
epoch=28-df=80.1802-cf=78.7387.ckpt  
epoch=29-df=80.2158-cf=78.5971.ckpt

I used the final checkpoint to evaluate the model by sighan 2015 and I got the results is following:

# without CIC
'sent-detect-acc': 85.36363636363636, 'sent-detect-p': 78.10858143607706, 'sent-detect-r': 82.43992606284658, 'sent-detect-f1': 80.21582733812951, 'sent-correct-acc': 84.54545454545455, 'sent-correct-p': 76.5323992994746, 'sent-correct-r': 80.77634011090574, 'sent-correct-f1': 78.59712230215827, 'char-detect-f1': 86.45614035087719, 'char-correct-f1': 91.12964366944655

# with CIC
{'sent-detect-acc': 86.27272727272727, 'sent-detect-p': 79.75133214920072, 'sent-detect-r': 82.99445471349352, 'sent-detect-f1': 81.34057971014492, 'sent-correct-acc': 85.45454545454545, 'sent-correct-p': 78.15275310834814, 'sent-correct-r': 81.33086876155268, 'sent-correct-f1': 79.71014492753623, 'char-detect-f1': 87.1578947368421, 'char-correct-f1': 91.38972809667673}

The sentence-level correction results of the without CIC is the same as the Ablation Study. But the other results are totally different compared to the results in the paper.

I want to know what I did wrong and how to get the results in the paper. Looking forward to your reply.