Pipeline Loading Models and Tokenizers for Q&A - huggingface-transformers

Question

Hi I'm trying to use 'fmikaelian/flaubert-base-uncased-squad' for question answering. I understand that I should load the model and the tokenizers. I'm not sure how should I do this.
My code is basically far
from transformers import pipeline, BertTokenizer
nlp = pipeline('question-answering', \
model='fmikaelian/flaubert-base-uncased-squad', \
tokenizer='fmikaelian/flaubert-base-uncased-squad')
Most probably this can be solve with a two liner.
Many thanks
EDIT
I have also tried to use automodels but it seems those are not there:
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.
EDIT II
I tried following the approach suggested with the following code that loads models that have been saved from S3:
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)
Unfortunately I have been getting the following error:
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 *
I solved the previous EDIT error by placing the functions inside the "main".
Unfortunately when I run the following code:
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)
I'm getting the following error:
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

As stated in the source, there is a specific QuestionAnsweringPipeline. Below example is what I used to successfully load the Flaubert model.
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"))
Of course, there is also the alternative to use the pre-trained model FlaubertForQuestionAnswering, since pipelines just got released with the latest release and might be subject to change.