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joeynmt-en-kin / en_kin_args.yaml
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name: "en_kin_transformer"
data:
src:
lang: "en"
level: "bpe"
lowercase: False
tokenizer_type: "subword-nmt"
num_merges: 4000
tokenizer_cfg:
num_merges: 4000
codes: "models/en_kin_transformer/bpe.codes.4000"
pretokenizer: "none"
trg:
lang: "kin"
level: "bpe"
lowercase: False
tokenizer_type: "subword-nmt"
num_merges: 4000
tokenizer_cfg:
num_merges: 4000
codes: "models/en_kin_transformer/bpe.codes.4000"
pretokenizer: "none"
train: "data/train/en_kin_train"
dev: "data/val/en_kin_val"
test: "data/test/en_kin_test"
level: "bpe"
# lowercase: False
max_sent_length: 100
# src_vocab: "vocab.txt"
# trg_vocab: "vocab.txt"
dataset_type: "tsv"
testing:
beam_size: 15
beam_alpha: 1.0
eval_metrics: ["bleu"]
batch_type: "sentence"
sacrebleu_cfg: # sacrebleu options
remove_whitespace: True # `remove_whitespace` option in sacrebleu.corpus_chrf() function (defalut: True)
tokenize: "none" # `tokenize` option in sacrebleu.corpus_bleu() function (options include: "none" (use for already tokenized test data), "13a" (default minimal tokenizer), "intl" which mostly does punctuation and unicode, etc)
training:
#load_model: "{ models/{name}_transformer/1.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: 256
batch_type: "token"
eval_batch_size: 3600
eval_batch_type: "sentence"
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/en_kin_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_normal"
bias_initializer: "zeros"
init_gain: 1.0
embed_initializer: "xavier_normal"
embed_init_gain: 1.0
tied_embeddings: False
tied_softmax: True
encoder:
type: "transformer"
num_layers: 6
num_heads: 8
embeddings:
embedding_dim: 256
scale: True
dropout: 0.
# typically ff_size = 4 x hidden_size
hidden_size: 256
ff_size: 1024
dropout: 0.1
layer_norm: "post"
decoder:
type: "transformer"
num_layers: 6
num_heads: 8
embeddings:
embedding_dim: 256
scale: True
dropout: 0.
# typically ff_size = 4 x hidden_size
hidden_size: 256
ff_size: 1024
dropout: 0.1
layer_norm: "post"