| name: "enbin_transformer" | |
| data: | |
| src: "en" | |
| trg: "bin" | |
| train: "/content/drive/My Drive/masakhane/en-bin-baseline/train.bpe" | |
| dev: "/content/drive/My Drive/masakhane/en-bin-baseline/dev.bpe" | |
| test: "/content/drive/My Drive/masakhane/en-bin-baseline/test.bpe" | |
| level: "bpe" | |
| lowercase: False | |
| max_sent_length: 100 | |
| src_vocab: "/content/drive/My Drive/masakhane/en-bin-baseline/vocab.txt" | |
| trg_vocab: "/content/drive/My Drive/masakhane/en-bin-baseline/vocab.txt" | |
| testing: | |
| beam_size: 5 | |
| alpha: 1.0 | |
| training: | |
| # load_model: "/content/drive/My Drive/masakhane/en-bin-baseline/models/enbin_transformer_orig/142000.ckpt" # if uncommented, load a pre-trained model from this checkpoint | |
| random_seed: 42 | |
| optimizer: "adam" | |
| normalization: "tokens" | |
| adam_betas: [0.9, 0.999] | |
| scheduling: "plateau" # TODO: try switching from plateau to Noam scheduling | |
| patience: 5 # For plateau: decrease learning rate by decrease_factor if validation score has not improved for this many validation rounds. | |
| learning_rate_factor: 0.5 # factor for Noam scheduler (used with Transformer) | |
| learning_rate_warmup: 1000 # warmup steps for Noam scheduler (used with Transformer) | |
| decrease_factor: 0.7 | |
| loss: "crossentropy" | |
| learning_rate: 0.0003 | |
| learning_rate_min: 0.00000001 | |
| weight_decay: 0.0 | |
| label_smoothing: 0.1 | |
| batch_size: 4096 | |
| batch_type: "token" | |
| eval_batch_size: 3600 | |
| eval_batch_type: "token" | |
| batch_multiplier: 1 | |
| early_stopping_metric: "ppl" | |
| epochs: 400 # TODO: Decrease for when playing around and checking of working. Around 30 is sufficient to check if its working at all | |
| validation_freq: 100 # TODO: Set to at least once per epoch. | |
| logging_freq: 100 | |
| eval_metric: "bleu" | |
| model_dir: "" | |
| overwrite: True # TODO: Set to True if you want to overwrite possibly existing models. | |
| shuffle: True | |
| use_cuda: True | |
| max_output_length: 100 | |
| print_valid_sents: [0, 1, 2, 3] | |
| keep_last_ckpts: 3 | |
| model: | |
| initializer: "xavier" | |
| bias_initializer: "zeros" | |
| init_gain: 1.0 | |
| embed_initializer: "xavier" | |
| embed_init_gain: 1.0 | |
| tied_embeddings: True | |
| tied_softmax: True | |
| encoder: | |
| type: "transformer" | |
| num_layers: 6 | |
| num_heads: 4 # TODO: Increase to 8 for larger data. | |
| embeddings: | |
| embedding_dim: 256 # TODO: Increase to 512 for larger data. | |
| scale: True | |
| dropout: 0.2 | |
| # typically ff_size = 4 x hidden_size | |
| hidden_size: 256 # TODO: Increase to 512 for larger data. | |
| ff_size: 1024 # TODO: Increase to 2048 for larger data. | |
| dropout: 0.3 | |
| decoder: | |
| type: "transformer" | |
| num_layers: 6 | |
| num_heads: 4 # TODO: Increase to 8 for larger data. | |
| embeddings: | |
| embedding_dim: 256 # TODO: Increase to 512 for larger data. | |
| scale: True | |
| dropout: 0.2 | |
| # typically ff_size = 4 x hidden_size | |
| hidden_size: 256 # TODO: Increase to 512 for larger data. | |
| ff_size: 1024 # TODO: Increase to 2048 for larger data. | |
| dropout: 0.3 | |