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| session_name: base-baseline-encoder | |
| data_directory: "data" | |
| data_type: "CA_MSA" | |
| log_directory: "log_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: 2_000_000 | |
| learning_rate: 0.001 | |
| batch_size: 16 | |
| adam_beta1: 0.9 | |
| adam_beta2: 0.999 | |
| use_decay: true | |
| weight_decay: 0.0 | |
| encoder_embedding_dim: 256 | |
| decoder_embedding_dim: 256 | |
| encoder_dim: 512 # used by the decoder | |
| encoder_units: [256, 256, 256] | |
| use_batch_norm: true | |
| decoder_units: 256 | |
| decoder_layers: 2 | |
| attention_units: 256 | |
| use_decoder_prenet: true | |
| teacher_forcing_probability: 0.0 | |
| decoder_prenet_depth: [256, 128] | |
| is_attention_accumulative: true | |
| attention_type: LocationSensitive | |
| use_mixed_precision: false | |
| optimizer_type: Adam | |
| text_encoder: ArabicEncoderWithStartSymbol | |
| text_cleaner: null | |
| device: cuda | |
| # LOGGING | |
| evaluate_frequency: 5000 | |
| evaluate_with_error_rates_frequency: 5000 | |
| n_predicted_text_tensorboard: 10 # To be written to the tensorboard | |
| model_save_frequency: 5000 | |
| train_plotting_frequency: 1000 | |
| 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: null # load last saved model | |