用于问答的管道加载模型和标记器

Question

您好，我正在尝试使用“fmikaelian/flaubert-base-uncased-squad”来回答问题。我知道我应该加载模型和标记器。我不知道我应该怎么做。

我的代码基本就远了

from transformers import pipeline, BertTokenizer

nlp = pipeline('question-answering', \
model='fmikaelian/flaubert-base-uncased-squad', \
tokenizer='fmikaelian/flaubert-base-uncased-squad')

这很可能可以用两个班轮解决。

非常感谢

编辑

我也尝试过使用自动模型，但似乎没有：

OSError: Model name 'flaubert-base-uncased-squad' was not found in model name list (bert-base-uncased, bert-large-uncased, bert-base-cased, bert-large-cased, bert-base-multilingual-uncased, bert-base-multilingual-cased, bert-base-chinese, bert-base-german-cased, bert-large-uncased-whole-word-masking, bert-large-cased-whole-word-masking, bert-large-uncased-whole-word-masking-finetuned-squad, bert-large-cased-whole-word-masking-finetuned-squad, bert-base-cased-finetuned-mrpc, bert-base-german-dbmdz-cased, bert-base-german-dbmdz-uncased). We assumed 'flaubert-base-uncased-squad' was a path or url to a configuration file named config.json or a directory containing such a file but couldn't find any such file at this path or url.

编辑 II 我尝试按照以下代码建议的方法来加载从 S3 保存的模型：

tokenizer_ = FlaubertTokenizer.from_pretrained(MODELS)
model_ = FlaubertModel.from_pretrained(MODELS)


p = transformers.QuestionAnsweringPipeline(
    model=transformers.AutoModel.from_pretrained(MODELS), 
    tokenizer=transformers.AutoTokenizer.from_pretrained(MODELS)
)

question_="Quel est le montant de la garantie?"
language_="French"
context_="le montant de la garantie est € 1000"

output=p({'question':question_, 'context': context_})
print(output)

不幸的是，我收到了以下错误：

Traceback (most recent call last):
  File "<string>", line 1, in <module>
  File "C:\Users\... ...\AppData\Local\Continuum\Anaconda3\envs\nlp_nlp\lib\multiprocessing\spawn.py", line 105, in spawn_main
    exitcode = _main(fd)
Traceback (most recent call last):
  File "C:\Users\... ...\AppData\Local\Continuum\Anaconda3\envs\nlp_nlp\lib\multiprocessing\spawn.py", line 114, in _main
  File "question_extraction.py", line 61, in <module>
        prepare(preparation_data)
output=p({'question':question_, 'context': context_})  File "C:\Users\... ...\AppData\Local\Continuum\Anaconda3\envs\nlp_nlp\lib\multiprocessing\spawn.py", line 225, in prepare

      File "C:\Users\... ...\AppData\Local\Continuum\Anaconda3\envs\nlp_nlp\lib\site-packages\transformers\pipelines.py", line 802, in __call__
_fixup_main_from_path(data['init_main_from_path'])
  File "C:\Users\... ...\AppData\Local\Continuum\Anaconda3\envs\nlp_nlp\lib\multiprocessing\spawn.py", line 277, in _fixup_main_from_path
    run_name="__mp_main__")
  File "C:\Users\... ...\AppData\Local\Continuum\Anaconda3\envs\nlp_nlp\lib\runpy.py", line 263, in run_path
    pkg_name=pkg_name, script_name=fname)
  File "C:\Users\... ...\AppData\Local\Continuum\Anaconda3\envs\nlp_nlp\lib\runpy.py", line 96, in _run_module_code
    mod_name, mod_spec, pkg_name, script_name)
  File "C:\Users\... ...\AppData\Local\Continuum\Anaconda3\envs\nlp_nlp\lib\runpy.py", line 85, in _run_code
    exec(code, run_globals)
  File "C:\Users\... ...\Box Sync\nlp - 2...\NLP\src\question_extraction.py", line 61, in <module>
    output=p({'question':question_, 'context': context_})
  File "C:\Users\... ...\AppData\Local\Continuum\Anaconda3\envs\nlp_nlp\lib\site-packages\transformers\pipelines.py", line 802, in __call__
    for example in examples
      File "C:\Users\... ...\AppData\Local\Continuum\Anaconda3\envs\nlp_nlp\lib\site-packages\transformers\pipelines.py", line 802, in <listcomp>
for example in examples
for example in examples  File "C:\Users\... ...\AppData\Local\Continuum\Anaconda3\envs\nlp_nlp\lib\site-packages\transformers\pipelines.py", line 802, in <listcomp>

      File "C:\Users\... ...\AppData\Local\Continuum\Anaconda3\envs\nlp_nlp\lib\site-packages\transformers\data\processors\squad.py", line 304, in squad_convert_examples_to_features
for example in examples
  File "C:\Users\... ...\AppData\Local\Continuum\Anaconda3\envs\nlp_nlp\lib\site-packages\transformers\data\processors\squad.py", line 304, in squad_convert_examples_to_features
        with Pool(threads, initializer=squad_convert_example_to_features_init, initargs=(tokenizer,)) as p:with Pool(threads, initializer=squad_convert_example_to_features_init, initargs=(tokenizer,)) as p:

  File "C:\Users\... ...\AppData\Local\Continuum\Anaconda3\envs\nlp_nlp\lib\multiprocessing\context.py", line 119, in Pool
  File "C:\Users\... ...\AppData\Local\Continuum\Anaconda3\envs\nlp_nlp\lib\multiprocessing\context.py", line 119, in Pool
        context=self.get_context())context=self.get_context())

  File "C:\Users\... ...\AppData\Local\Continuum\Anaconda3\envs\nlp_nlp\lib\multiprocessing\pool.py", line 174, in __init__
  File "C:\Users\... ...\AppData\Local\Continuum\Anaconda3\envs\nlp_nlp\lib\multiprocessing\pool.py", line 174, in __init__
        self._repopulate_pool()self._repopulate_pool()

  File "C:\Users\... ...\AppData\Local\Continuum\Anaconda3\envs\nlp_nlp\lib\multiprocessing\pool.py", line 239, in _repopulate_pool
  File "C:\Users\... ...\AppData\Local\Continuum\Anaconda3\envs\nlp_nlp\lib\multiprocessing\pool.py", line 239, in _repopulate_pool
    w.start()
    w.start()
  File "C:\Users\... ...\AppData\Local\Continuum\Anaconda3\envs\nlp_nlp\lib\multiprocessing\process.py", line 105, in start
  File "C:\Users\... ...\AppData\Local\Continuum\Anaconda3\envs\nlp_nlp\lib\multiprocessing\process.py", line 105, in start
    self._popen = self._Popen(self)
      File "C:\Users\... ...\AppData\Local\Continuum\Anaconda3\envs\nlp_nlp\lib\multiprocessing\context.py", line 322, in _Popen
self._popen = self._Popen(self)
      File "C:\Users\... ...\AppData\Local\Continuum\Anaconda3\envs\nlp_nlp\lib\multiprocessing\context.py", line 322, in _Popen
return Popen(process_obj)
return Popen(process_obj)  File "C:\Users\... ...\AppData\Local\Continuum\Anaconda3\envs\nlp_nlp\lib\multiprocessing\popen_spawn_win32.py", line 65, in __init__

  File "C:\Users\... ...\AppData\Local\Continuum\Anaconda3\envs\nlp_nlp\lib\multiprocessing\popen_spawn_win32.py", line 33, in __init__
        prep_data = spawn.get_preparation_data(process_obj._name)reduction.dump(process_obj, to_child)

  File "C:\Users\... ...\AppData\Local\Continuum\Anaconda3\envs\nlp_nlp\lib\multiprocessing\spawn.py", line 143, in get_preparation_data
  File "C:\Users\... ...\AppData\Local\Continuum\Anaconda3\envs\nlp_nlp\lib\multiprocessing\reduction.py", line 60, in dump
    _check_not_importing_main()
  File "C:\Users\... ...\AppData\Local\Continuum\Anaconda3\envs\nlp_nlp\lib\multiprocessing\spawn.py", line 136, in _check_not_importing_main
    is not going to be frozen to produce an executable.''')
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()
                ...

        The "freeze_support()" line can be omitted if the program
        is not going to be frozen to produce an executable.
    ForkingPickler(file, protocol).dump(obj)
BrokenPipeError: [Errno 32] Broken pipe

*EDIT IV *

我通过将函数放在“ma​​in”中解决了之前的 EDIT 错误。 不幸的是，当我运行以下代码时：

tokenizer_ = FlaubertTokenizer.from_pretrained(MODELS)
model_ = FlaubertModel.from_pretrained(MODELS)

def question_extraction(text, question, model, tokenizer, language="French", verbose=False):

    if language=="French":
        nlp = pipeline('question-answering', \
        model=model, \
        tokenizer=tokenizer)
    else:
        nlp=pipeline('question-answering')

    output=nlp({'question':question, 'context': text})

    answer, score = output.answer, output.score 

    if verbose==True:
        print("Q: ", question ,"\n",\
              "A:", answer,"\n", \
              "Confidence (%):", "{0:.2f}".format(str(score*100) )
              )

    return answer, score

if __name__=="__main__":
    question_="Quel est le montant de la garantie?"
    language_="French"
    text="le montant de la garantie est € 1000"

    answer, score=question_extraction(text, question_, model_, tokenizer_, language_, verbose= True)

我收到以下错误：

C:\...\NLP\src>python question_extraction.py
OK
OK
convert squad examples to features: 100%|████████████████████████████████████████████████| 1/1 [00:00<00:00,  4.66it/s]
add example index and unique id: 100%|███████████████████████████████████████████████████████████| 1/1 [00:00<?, ?it/s]
Traceback (most recent call last):
  File "question_extraction.py", line 77, in <module>
    answer, score=question_extraction(text, question_, model_, tokenizer_, language_, verbose= True)
  File "question_extraction.py", line 60, in question_extraction
    output=nlp({'question':question, 'context': text})
  File "C:\...\transformers\pipelines.py", line 818, in __call__
    start, end = self.model(**fw_args)
ValueError: not enough values to unpack (expected 2, got 1)

Answer 1

如the source 中所述，有一个特定的QuestionAnsweringPipeline。下面的示例是我用来成功加载 Flaubert 模型的示例。

import transformers as trf
p = trf.QuestionAnsweringPipeline(model=trf.AutoModel.from_pretrained("fmikaelian/flaubert-base-uncased-squad"), tokenizer=trf.AutoTokenizer.from_pretrained("fmikaelian/flaubert-base-uncased-squad"))

当然，也可以使用预训练模型FlaubertForQuestionAnswering，因为pipelines 刚刚发布了最新版本，可能会有所变化。