This is the code for ACL2021 paper A Unified Generative Framework for Aspect-Based Sentiment Analysis

作者您好，我想要将ATE任务的数据集换成一个新的数据集（ASTE-Data-V2），在我将数据转换为ATE任务要求的格式后（与wang的数据格式一致），代码跑不通了。

bug存在于BartDecoder中，具体如下：

我输出了一下，这里input_ids并不是具体的tensor值，变成了数据的地址信息（比如 <torch.Tensor object at 0x7ff5ce4b2280>），positions也变成了类似情况。

请问你们在数据预处理阶段有对数据做什么特殊处理吗？或者ATE任务对数据有什么特殊要求吗？

Hi, I am reproducing you work, but I found that after running with the default settings on the pengb/14lab, I got the triplet metrics as follows: triple_f=57.73, triple_rec=56.81, triple_pre=58.68, while in the paper, the results should be: triple_f=58.69, triple_rec=56.19, triple_pre=61.41. Is there any suggestion?

您好，在我按照readme和issue#1安装好相关库后。 我在训练时遇到一个错误，不知道怎么回事。

Traceback (most recent call last):
  File "train.py", line 24, in <module>
    fitlog.set_log_dir('logs')
  File "miniconda3/envs/bart-absa/lib/python3.7/site-packages/fitlog/__init__.py", line 122, in set_log_dir
    _logger.set_log_dir(log_dir, new_log)
  File "miniconda3/envs/bart-absa/lib/python3.7/site-packages/fitlog/fastlog/logger.py", line 32, in wrapper
    return func(*args, **kwargs)
  File "miniconda3/envs/bart-absa/lib/python3.7/site-packages/fitlog/fastlog/logger.py", line 174, in set_log_dir
    raise NotADirectoryError("`{}` is not exist.".format(log_dir))
NotADirectoryError: `logs` is not exist.

无论设置的batch size是多少（4/8/32等都试过），都会显示一次如下信息。 input fields after batch(if batch size is 2): tgt_tokens: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2, 22]) src_tokens: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2, 41]) src_seq_len: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2]) tgt_seq_len: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2]) target fields after batch(if batch size is 2): tgt_tokens: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2, 22]) target_span: (1)type:numpy.ndarray (2)dtype:object, (3)shape:(2,) tgt_seq_len: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2]) 这一块输出没有看懂。请求作者帮忙解释一下，谢谢。

你好： 我参考你们的文章A Unified Generative Framework for Aspect-Based Sentiment，想用这个模型作中文的ABSA，于是我将原文的facebook/bart-base替换成fnlp/bart-base-chinese，但是我这里有以下几个问题：

1. 1：transformers在4.4.1版本加载模型时会报错：RuntimeError: Error(s) in loading state_dict for BartModel:

size mismatch for encoder.embed_positions.weight: copying a param with shape torch.Size([514, 768]) from checkpoint, the shape in current model is torch.Size([512, 768]). size mismatch for encoder.embed_positions.weight: copying a param with shape torch.Size([514, 768]) from checkpoint, the shape in current model is torch.Size([512, 768]). 这主要是在这里：model = BartSeq2SeqModel.build_model(bart_name, tokenizer, label_ids=label_ids, decoder_type=decoder_type,copy_gate=False, use_encoder_mlp=use_encoder_mlp, use_recur_pos=False)

1. 2：facebook提供的batr-base中有一些文件是merges.txt和json形式的vocab，这与您在huggingface上提供的不一致。我将您在

huggingface上提供的有关bart-base-chinese提供的文件用tokenizer.from_pretrained("bart-base-chinese")使用时，pytorch报错： OSError: Can't load tokenizer for 'bart-base-chinese'. Make sure that: 'bart-base-chinese' is a correct model identifier listed on 'https://huggingface.co/models' or 'bart-base-chinese' is the correct path to a directory containing relevant tokenizer files 请问这个该怎么解决？

python train.py --dataset pengb/14lap 你好，我尝试运行你的代码然后遇到了这个问题，但是本地能正常运行，在服务器上就有这个问题，我以为是python版本太低，换了python3.8还是出现了这个问题。 OSError: libstdc++.so.6: cannot open shared object file: No such file or directory

I'm following your readme, while using train.py in the dataset pengb I had this error:

` python train.py --dataset pengb/14lap
Read cache from caches/data_facebook/bart-base_pengb/14lap_False.pt.
The number of tokens in tokenizer  50265
50268 50273
input fields after batch(if batch size is 2):
        tgt_tokens: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2, 22])
        src_tokens: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2, 41])
        src_seq_len: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2])
        tgt_seq_len: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2])
target fields after batch(if batch size is 2):
        tgt_tokens: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2, 22])
        target_span: (1)type:numpy.ndarray (2)dtype:object, (3)shape:(2,)
        tgt_seq_len: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2])

training epochs started 2021-12-10-15-44-42-060416
Epoch 1/50:   0%|                                                          | 0/2850 [00:00<?, ?it/s, loss:{0:<6.5f}]Read cache from caches/data_facebook/bart-base_pengb/14lap_False.pt.
The number of tokens in tokenizer  50265
50268 50273
input fields after batch(if batch size is 2):
        tgt_tokens: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2, 22])
        src_tokens: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2, 41])
        src_seq_len: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2])
        tgt_seq_len: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2])
target fields after batch(if batch size is 2):
        tgt_tokens: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2, 22])
        target_span: (1)type:numpy.ndarray (2)dtype:object, (3)shape:(2,)
        tgt_seq_len: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2])

training epochs started 2021-12-10-15-44-52-779581
Traceback (most recent call last):
  File "<string>", line 1, in <module>
  File "C:\ProgramData\Anaconda3\lib\multiprocessing\spawn.py", line 116, in spawn_main
    exitcode = _main(fd, parent_sentinel)
  File "C:\ProgramData\Anaconda3\lib\multiprocessing\spawn.py", line 125, in _main
    prepare(preparation_data)
  File "C:\ProgramData\Anaconda3\lib\multiprocessing\spawn.py", line 236, in prepare
    _fixup_main_from_path(data['init_main_from_path'])
  File "C:\ProgramData\Anaconda3\lib\multiprocessing\spawn.py", line 287, in _fixup_main_from_path
    main_content = runpy.run_path(main_path,
  File "C:\ProgramData\Anaconda3\lib\runpy.py", line 265, in run_path
    return _run_module_code(code, init_globals, run_name,
  File "C:\ProgramData\Anaconda3\lib\runpy.py", line 97, in _run_module_code
    _run_code(code, mod_globals, init_globals,
  File "C:\ProgramData\Anaconda3\lib\runpy.py", line 87, in _run_code
    exec(code, run_globals)
  File "C:\Users\sp\.conda\envs\BARTABSA\project\BARTABSA-main\peng\train.py", line 155, in <module>
    trainer.train(load_best_model=False)
  File "C:\ProgramData\Anaconda3\lib\site-packages\fastNLP\core\trainer.py", line 667, in train
    raise e
  File "C:\ProgramData\Anaconda3\lib\site-packages\fastNLP\core\trainer.py", line 658, in train
    self._train()
  File "C:\ProgramData\Anaconda3\lib\site-packages\fastNLP\core\trainer.py", line 712, in _train
    for batch_x, batch_y in self.data_iterator:
  File "C:\ProgramData\Anaconda3\lib\site-packages\fastNLP\core\batch.py", line 266, in __iter__
    for indices, batch_x, batch_y in self.dataiter:
  File "C:\ProgramData\Anaconda3\lib\site-packages\torch\utils\data\dataloader.py", line 359, in __iter__
    return self._get_iterator()
  File "C:\ProgramData\Anaconda3\lib\site-packages\torch\utils\data\dataloader.py", line 305, in _get_iterator
    return _MultiProcessingDataLoaderIter(self)
  File "C:\ProgramData\Anaconda3\lib\site-packages\torch\utils\data\dataloader.py", line 918, in __init__
    w.start()
  File "C:\ProgramData\Anaconda3\lib\multiprocessing\process.py", line 121, in start
    self._popen = self._Popen(self)
  File "C:\ProgramData\Anaconda3\lib\multiprocessing\context.py", line 224, in _Popen
    return _default_context.get_context().Process._Popen(process_obj)
  File "C:\ProgramData\Anaconda3\lib\multiprocessing\context.py", line 327, in _Popen
    return Popen(process_obj)
  File "C:\ProgramData\Anaconda3\lib\multiprocessing\popen_spawn_win32.py", line 45, in __init__
    prep_data = spawn.get_preparation_data(process_obj._name)
  File "C:\ProgramData\Anaconda3\lib\multiprocessing\spawn.py", line 154, in get_preparation_data
    _check_not_importing_main()
  File "C:\ProgramData\Anaconda3\lib\multiprocessing\spawn.py", line 134, in _check_not_importing_main
    raise RuntimeError('''
RuntimeError:
        An attempt has been made to start a new process before the
        current process has finished its bootstrapping phase.

        This probably means that you are not using fork to start your
        child processes and you have forgotten to use the proper idiom
        in the main module:

            if __name__ == '__main__':
                freeze_support()
                ...`

Reading around it seems that : multiprocessing usually doesn't work in a console in Windows. When using a spawning system instead of fork, python must import modules and create state in the child process to get things to work. The outer script must be protected with a if name=="main": clause. I don't know where to change the code however. Any hints?