02/15/2020 11:53:51 - INFO - farm.infer -   Could not find `ahotrod/xlnet_large_squad2_512` locally. Try to download from model hub ...
02/15/2020 11:53:55 - WARNING - farm.modeling.language_model -   Could not automatically detect from language model name what language it is. 
	 We guess it's an *ENGLISH* model ... 
	 If not: Init the language model by supplying the 'language' param.
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-7-6229fadcc5e8> in <module>
     11 # reader = TransformersReader(model="distilbert-base-uncased-distilled-squad", tokenizer="distilbert-base-uncased", use_gpu=0) # use_gpu=-1)
     12 
---> 13 reader = FARMReader(model_name_or_path="ahotrod/xlnet_large_squad2_512", use_gpu=True)

/media/dn/dssd/nlp/haystack/haystack/reader/farm.py in __init__(self, model_name_or_path, context_window_size, no_ans_threshold, batch_size, use_gpu, n_candidates_per_passage)
     51 
     52 
---> 53         self.inferencer = Inferencer.load(model_name_or_path, batch_size=batch_size, gpu=use_gpu, task_type="question_answering")
     54         self.inferencer.model.prediction_heads[0].context_window_size = context_window_size
     55         self.inferencer.model.prediction_heads[0].no_ans_threshold = no_ans_threshold

~/anaconda3/envs/nlpu/lib/python3.7/site-packages/farm/infer.py in load(cls, model_name_or_path, batch_size, gpu, task_type, return_class_probs, strict, max_seq_len)
    145                                  "'question_answering', 'embeddings', 'text_classification'")
    146 
--> 147             model = AdaptiveModel.convert_from_transformers(model_name_or_path, device, task_type)
    148             config = AutoConfig.from_pretrained(model_name_or_path)
    149             tokenizer = Tokenizer.load(model_name_or_path)

~/anaconda3/envs/nlpu/lib/python3.7/site-packages/farm/modeling/adaptive_model.py in convert_from_transformers(cls, model_name_or_path, device, task_type)
    438 
    439         if task_type == "question_answering":
--> 440             ph = QuestionAnsweringHead.load(model_name_or_path)
    441             adaptive_model = cls(language_model=lm, prediction_heads=[ph], embeds_dropout_prob=0.1,
    442                                lm_output_types="per_token", device=device)

~/anaconda3/envs/nlpu/lib/python3.7/site-packages/farm/modeling/prediction_head.py in load(cls, pretrained_model_name_or_path)
    917             head = cls(layer_dims=[full_qa_model.config.hidden_size, 2], loss_ignore_index=-1, task_name="question_answering")
    918             # transfer weights for head from full model
--> 919             head.feed_forward.feed_forward[0].load_state_dict(full_qa_model.qa_outputs.state_dict())
    920             del full_qa_model
    921 

~/anaconda3/envs/nlpu/lib/python3.7/site-packages/torch/nn/modules/module.py in __getattr__(self, name)
    574                 return modules[name]
    575         raise AttributeError("'{}' object has no attribute '{}'".format(
--> 576             type(self).__name__, name))
    577 
    578     def __setattr__(self, name, value):

AttributeError: 'XLNetForQuestionAnswering' object has no attribute 'qa_outputs'