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name: "enti_transformer"
data:
src: "en"
trg: "ti"
train: "data/enti/train.bpe"
dev: "data/enti/dev.bpe"
test: "data/enti/test.bpe"
level: "bpe"
lowercase: False
max_sent_length: 100
src_vocab: "data/enti/vocab.txt"
trg_vocab: "data/enti/vocab.txt"
testing:
beam_size: 5
alpha: 1.0
training:
#load_model: "models/enti_transformer/12000.ckpt" # if given, load a pre-trained model from this checkpoint
random_seed: 42
optimizer: "adam"
normalization: "tokens"
adam_betas: [0.9, 0.999]
scheduling: "noam" # Try switching from plateau to Noam scheduling
learning_rate_factor: 0.5 # factor for Noam scheduler (used with Transformer)
learning_rate_warmup: 1000 # warmup steps for Noam scheduler (used with Transformer)
patience: 8
decrease_factor: 0.7
loss: "crossentropy"
learning_rate: 0.0002
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: 14 # TODO: Decrease for when playing around and checking of working. Around 30 is sufficient to check if its working at all
validation_freq: 400 # Decrease this for testing
logging_freq: 100
eval_metric: "bleu"
model_dir: "models/enti_transformer"
overwrite: True
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: 8
embeddings:
embedding_dim: 512
scale: True
dropout: 0.
# typically ff_size = 4 x hidden_size
hidden_size: 512
ff_size: 2048
dropout: 0.3
decoder:
type: "transformer"
num_layers: 6
num_heads: 8
embeddings:
embedding_dim: 512
scale: True
dropout: 0.
# typically ff_size = 4 x hidden_size
hidden_size: 512
ff_size: 2048
dropout: 0.3