configuration_sip_finetune.py

from transformers import T5Config

class SIPFinetuningModelConfig(T5Config):
    model_type = "sip_finetune"
    
    def __init__(self,
                 num_examples: int = 32,
                 prefix_length: int = 50,
                 random_selection: bool = True,
                 # don't change these unless you change what the prefix of the model is initialized with:
                 prefix_max_init_length: int = 70,
                 num_precomputed_examples: int = 400,
                 **kwargs):
        # These are all about the initialization of the prefix.
        self.num_examples = num_examples
        self.prefix_length = prefix_length
        self.random_selection = random_selection
        self.prefix_max_init_length = prefix_max_init_length
        self.num_precomputed_examples = num_precomputed_examples
        super().__init__(**kwargs)