You can initialize a model without pre-trained weights using
from transformers import BertConfig, BertForSequenceClassification

# either load pre-trained config
config = BertConfig.from_pretrained("bert-base-cased")
#  or instantiate yourself
config = BertConfig(
    vocab_size=2048,
    max_position_embeddings=768,
    intermediate_size=2048,
    hidden_size=512,
    num_attention_heads=8,
    num_hidden_layers=6,
    type_vocab_size=5,
    hidden_dropout_prob=0.1,
    attention_probs_dropout_prob=0.1,
    num_labels=3,
)

# pass the config to model constructor instead of from_pretrained
# this creates the model as per the params in config 
# but with weights randomly initialized
model = BertForSequenceClassification(config)

and as it’s a ForSequenceClassification model, the existing run_glue.py script can be used to train this model, just initialize model using config instead of .from_pretrained