config.py

"""An example config dictionary for the whole summarization pipeline"""

    config = {
    'dataset_path' : 'autotransformer/data/electronics_dataset_1.pkl',
    'dataset' : None,

    'extractive' : False,
    'device' : None,

    'src_vocab_path' : 'autotransformer/models/electronics/src_vocab.pt',
    'src_vocab' : None,
    'trg_vocab_path' : 'autotransformer/models/electronics/trg_vocab.pt',
    'autoencoder_path': 'autotransformer/models/electronics/electronics_autoencoder_epoch7_weights.pt',
    'autoencoder' : None,
    'ae_batchsize': 5000,

    'density_parameter' : .04,
    'minimum_samples': 4,
    'min_clusters': 5,
    'max_acceptable_clusters':30,
    'min_num_candidates': 100,

    'BERT_finetune_path' : 'bert_finetune/models/finetune_electronics_mae1.pt',
    'BERT_config_path' : 'bert_finetune/models/finetune_electronics_mae1config.json',
    'BERT_finetune_model' : None,
    'BERT_batchsize': 100,

    'opt_function' : GeneticBertOptimizer().optimize,

    'opt_dict' : {
        'optimize_population': 96,#for optimization methods with a population at optimization estimates,
        #this is the number of optimization estimates used by the algorithm
        'n_elite': 5,
        'length_range': (5,20),
        'p_replace': .33,
        'p_remove': .33,
        'p_add': .33,
        'max_iter': 100,
        'print_iters': 10
        }
    }