session_name: base data_directory: "ashaar" data_type: "CA_MSA" log_directory: "/content/drive/MyDrive/Research/Barmajan/Diacritization/log_ashaar_dir" load_training_data: true load_test_data: false load_validation_data: true n_training_examples: null # null load all training examples, good for fast loading n_test_examples: null # null load all test examples n_validation_examples: null # null load all validation examples test_file_name: "test.csv" is_data_preprocessed: false # The data file is organized as (original text | text | diacritics) data_separator: '|' # Required if the data already processed diacritics_separator: '*' # Required if the data already processed text_encoder: ArabicEncoderWithStartSymbol text_cleaner: valid_arabic_cleaners # a white list that uses only Arabic letters, punctuations, and a space max_len: 600 # sentences larger than this size will not be used max_steps: 25_000 learning_rate: 0.001 batch_size: 32 adam_beta1: 0.9 adam_beta2: 0.999 use_decay: true weight_decay: 0.0 embedding_dim: 256 use_prenet: false prenet_sizes: [512, 256] cbhg_projections: [128, 256] cbhg_filters: 16 cbhg_gru_units: 256 post_cbhg_layers_units: [256, 256] post_cbhg_use_batch_norm: true use_mixed_precision: false optimizer_type: Adam device: cuda # LOGGING evaluate_frequency: 1000 evaluate_with_error_rates_frequency: 1000 n_predicted_text_tensorboard: 10 # To be written to the tensorboard model_save_frequency: 1000 train_plotting_frequency: 50000000 # No plotting for this model n_steps_avg_losses: [100, 500, 1_000, 5_000] # command line display of average loss values for the last n steps error_rates_n_batches: 10000 # if calculating error rate is slow, then you can specify the number of batches to be calculated test_model_path: null # load the last saved model train_resume_model_path: "/content/drive/MyDrive/Research/Barmajan/Diacritization/log_cleaned_dir/CA_MSA.base.cbhg/models/20000-snapshot.pt" # load last saved model