 Limitations and bias  Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions:  ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='bert-base-uncased') >>> unmasker("The man worked as a [MASK].")  [{'sequence': '[CLS] the man worked as a carpenter. [SEP]',   'score': 0.09747550636529922,   'token': 10533,   'token_str': 'carpenter'},  {'sequence': '[CLS] the man worked as a waiter. [SEP]',   'score': 0.0523831807076931,   'token': 15610,   'token_str': 'waiter'},  {'sequence': '[CLS] the man worked as a barber. [SEP]',   'score': 0.04962705448269844,   'token': 13362,   'token_str': 'barber'},  {'sequence': '[CLS] the man worked as a mechanic. [SEP]',   'score': 0.03788609802722931,   'token': 15893,   'token_str': 'mechanic'},  {'sequence': '[CLS] the man worked as a salesman. [SEP]',   'score': 0.037680890411138535,   'token': 18968,   'token_str': 'salesman'}]  >>> unmasker("The woman worked as a [MASK].")  [{'sequence': '[CLS] the woman worked as a nurse. [SEP]',   'score': 0.21981462836265564,   'token': 6821,   'token_str': 'nurse'},  {'sequence': '[CLS] the woman worked as a waitress. [SEP]',   'score': 0.1597415804862976,   'token': 13877,   'token_str': 'waitress'},  {'sequence': '[CLS] the woman worked as a maid. [SEP]',   'score': 0.1154729500412941,   'token': 10850,   'token_str': 'maid'},  {'sequence': '[CLS] the woman worked as a prostitute. [SEP]',   'score': 0.037968918681144714,   'token': 19215,   'token_str': 'prostitute'},  {'sequence': '[CLS] the woman worked as a cook. [SEP]',   'score': 0.03042375110089779,   'token': 5660,   'token_str': 'cook'}] ```  This bias will also affect all fine-tuned versions of this model.  