We introduce random-albert-base-v2, which is a unpretrained version of Albert model. The weight of random-albert-base-v2 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-albert-base-v2 is the same as albert-base-v2 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-albert-base-v2 from Huggingface is to avoid using random seed in order to obtain the same randomness at each time.
The code to obtain a such random model:
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
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