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masakhane_benchmarks

african-languages

machine-translation

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masakhane_benchmarks / en-am /jw300-amharic-baseline /config.yaml

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	name: "enam_transformer"

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

	testing:
	beam_size: 5
	alpha: 1.0

	training:
	#load_model: "models/enam_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/enam_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