readme: # random-roberta-base
  
  We introduce random-roberta-base, which is a unpretrained version of RoBERTa model. The weight of random-roberta-base is randomly initiated and this can be particularly useful when we aim to train a language model from scratch or benchmark the effect of pretraining.
  
  It's important to note that tokenizer of random-roberta-base is the same as roberta-base because it's not a trivial task to get a random tokenizer and it's less meaningful compared to the random weight.
  
  A debatable advantage of pulling random-roberta-base from Huggingface is to avoid using random seed in order to obtain the same randomness at each time.
  
  The code to obtain such a random model:
  
  ```python
  
  from transformers import AutoTokenizer, AutoModelForSequenceClassification
  
  def get_blank_model_from_hf(model_name="bert-base-cased"):
      model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=5)
      tokenizer = AutoTokenizer.from_pretrained(model_name)
      model.base_model.init_weights()
      model_name = "random-" + model_name
      base_model= model.base_model
      return base_model, tokenizer, model_name
  ```
  