masakhane_benchmarks / en-kam /tuned-jw300-baseline /transformer_enkam25000.yaml

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	name: "enkam25000_transformer"

	data:
	src: "en"
	trg: "kam"
	train: "data/enkam/25000/train.bpe"
	dev: "data/enkam/25000/dev.bpe"
	test: "data/enkam/25000/test.bpe"
	level: "bpe"
	lowercase: False
	max_sent_length: 100
	src_vocab: "data/enkam/25000/vocab.txt"
	trg_vocab: "data/enkam/25000/vocab.txt"

	testing:
	beam_size: 5
	alpha: 1.0

	training:
	# load_model: "models/enkam25000_transformer/103000.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: 30 # TODO: Decrease for when playing around and checking of working. Around 30 is sufficient to check if its working at all
	validation_freq: 1000 # TODO: Set to at least once per epoch.
	logging_freq: 100
	eval_metric: "bleu"
	model_dir: "models/enkam25000_transformer"
	overwrite: False # 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