We introduce random-roberta-mini, which is a unpretrained version of a mini RoBERTa model(4 layer and 256 heads). The weight of random-roberta-mini 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-mini 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-mini from Huggingface is to avoid using random seed in order to obtain the same randomness at each time.
The code to obtain such random model:
from transformers import RobertaConfig, RobertaModel def get_custom_blank_roberta(h=768, l=12): # Initializing a RoBERTa configuration configuration = RobertaConfig(num_attention_heads=h, num_hidden_layers=l) # Initializing a model from the configuration model = RobertaModel(configuration) return model rank="mini" h=256 l=4 model_type = "roberta" tokenizer = AutoTokenizer.from_pretrained("roberta-base") model_name ="random-"+model_type+"-"+rank model = get_custom_blank_roberta(h, l)
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