def load_config(model_name):
    if model_name == "bert-large":
        config = {
            'architectures': ['BertForPreTraining'],
            'attention_probs_dropout_prob': 0.1,
            'gradient_checkpointing': False,
            'hidden_act': 'gelu',
            'hidden_dropout_prob': 0.1,
            'hidden_size': 1024,
            'initializer_range': 0.02,
            'intermediate_size': 4096,
            'layer_norm_eps': 1e-12,
            'max_position_embeddings': 512,
            'model_type': 'bert',
            'num_attention_heads': 16,
            'num_hidden_layers': 24,
            'pad_token_id': 0,
            'position_embedding_type': 'absolute',
            'type_vocab_size': 2,
            'use_cache': True,
            'return_dict': False,
            'vocab_size': 30522,
            }
    else:
        raise ValueError(f"No {model_name} config")

    return config
