HuggingFace / PipelineException:没有在输入中找到mask_token (<mask>)

Question

目标:通过多个for-loop通过models，print()经过的时间。

处理一个模型工作得很好：

i=0
start = time.time()
unmasker = pipeline('fill-mask', model=models[i])
unmasker("Hello I'm a [MASK] model.", top_k=1)
end = time.time() 
df = df.append({'Model': models[i], 'Time': end-start}, ignore_index=True)

Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForMaskedLM: ['cls.seq_relationship.bias', 'cls.seq_relationship.weight']
- This IS expected if you are initializing BertForMaskedLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing BertForMaskedLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).

但是，迭代许多模型名称会导致标题错误。

代码：

from transformers import pipeline
import time

models = ['bert-base-uncased', 'roberta-base', 'distilbert-base-uncased', 'bert-base-cased', 'albert-base-v2', 'roberta-large', 'bert-large-uncased albert-large-v2', 'albert-base-v2', 'bert-large-cased', 'albert-base-v1', 'bert-large-cased-whole-word-masking', 'bert-large-uncased-whole-word-masking', 'albert-xxlarge-v2', 'google/bigbird-roberta-large', 'albert-xlarge-v2', 'albert-xxlarge-v1', 'facebook/muppet-roberta-large', 'facebook/muppet-roberta-base', 'albert-large-v1', 'albert-xlarge-v1']

for _model in models:
    start = time.time()
    unmasker = pipeline('fill-mask', model=_model)
    unmasker("Hello I'm a [MASK] model.", top_k=1)  # default: top_k=5
    end = time.time()

    print(end-start)

Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForMaskedLM: ['cls.seq_relationship.bias', 'cls.seq_relationship.weight']
- This IS expected if you are initializing BertForMaskedLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing BertForMaskedLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
---------------------------------------------------------------------------
PipelineException                         Traceback (most recent call last)
<ipython-input-19-13b5f651657e> in <module>
      3     start = time.time()
      4     unmasker = pipeline('fill-mask', model=_model)
----> 5     unmasker("Hello I'm a [MASK] model.", top_k=1)  # default: top_k=5
      6     end = time.time()
      7 

~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/transformers/pipelines/fill_mask.py in __call__(self, inputs, *args, **kwargs)
    224             - **token** (`str`) -- The predicted token (to replace the masked one).
    225         """
--> 226         outputs = super().__call__(inputs, **kwargs)
    227         if isinstance(inputs, list) and len(inputs) == 1:
    228             return outputs[0]

~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/transformers/pipelines/base.py in __call__(self, inputs, num_workers, batch_size, *args, **kwargs)
   1099                 return self.iterate(inputs, preprocess_params, forward_params, postprocess_params)
   1100         else:
-> 1101             return self.run_single(inputs, preprocess_params, forward_params, postprocess_params)
   1102 
   1103     def run_multi(self, inputs, preprocess_params, forward_params, postprocess_params):

~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/transformers/pipelines/base.py in run_single(self, inputs, preprocess_params, forward_params, postprocess_params)
   1105 
   1106     def run_single(self, inputs, preprocess_params, forward_params, postprocess_params):
-> 1107         model_inputs = self.preprocess(inputs, **preprocess_params)
   1108         model_outputs = self.forward(model_inputs, **forward_params)
   1109         outputs = self.postprocess(model_outputs, **postprocess_params)

~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/transformers/pipelines/fill_mask.py in preprocess(self, inputs, return_tensors, **preprocess_parameters)
     82             return_tensors = self.framework
     83         model_inputs = self.tokenizer(inputs, return_tensors=return_tensors)
---> 84         self.ensure_exactly_one_mask_token(model_inputs)
     85         return model_inputs
     86 

~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/transformers/pipelines/fill_mask.py in ensure_exactly_one_mask_token(self, model_inputs)
     76         else:
     77             for input_ids in model_inputs["input_ids"]:
---> 78                 self._ensure_exactly_one_mask_token(input_ids)
     79 
     80     def preprocess(self, inputs, return_tensors=None, **preprocess_parameters) -> Dict[str, GenericTensor]:

~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/transformers/pipelines/fill_mask.py in _ensure_exactly_one_mask_token(self, input_ids)
     67                 "fill-mask",
     68                 self.model.base_model_prefix,
---> 69                 f"No mask_token ({self.tokenizer.mask_token}) found on the input",
     70             )
     71 

PipelineException: No mask_token (<mask>) found on the input

请让我知道，如果还有什么我可以补充的帖子，以澄清。

Answer 1

只有某些模型会抛出这个错误。

由于我正在试验任何模型的运行时，下面的内容就足够了。我成功地管理了大多数模特。

我应用了try except逻辑。注意，处理异常而不具体在except语句中命名错误被认为是错误的做法。

for _model in models:
    for i in range(10):
        start = time.time()
        try:
            unmasker = pipeline('fill-mask', model=_model)
            unmasker("Hello I'm a [MASK] model.", top_k=1)  # default: top_k=5
            print(_model)
        except: continue
        end = time.time()

        df = df.append({'Model': _model, 'Time': end-start}, ignore_index=True)
        print(df)
        df.to_csv('model_performance.csv', index=False)