translation/OpenNMT-py/examples/wiki_103.yaml

seed: 42
share_vocab: true
save_data: data/wikitext-103-raw/run/example
## Where the vocab(s) will be written
src_vocab: data/wikitext-103-raw/run/example.vocab.src
src_vocab_size: 60000
tgt_vocab_size: 60000
src_subword_type: bpe
src_subword_model: data/wikitext-103-raw/subwords.bpe
src_onmttok_kwargs: '{"mode": "aggressive", "joiner_annotate": True, "preserve_placeholders":
  True, "case_markup": True, "soft_case_regions": True, "preserve_segmented_tokens":
  True}'
transforms: [onmt_tokenize, filtertoolong]
src_seq_length: 512
tgt_seq_length: 512

# Prevent overwriting existing files in the folder
overwrite: True

# Corpus opts:
data:
    corpus_1:
        path_src: data/wikitext-103-raw/wiki.train.raw
    valid:
        path_src: data/wikitext-103-raw/wiki.valid.raw


# Vocabulary files that were just created
src_vocab: data/wikitext-103-raw/run/example.vocab.src

# Train on a single GPU
world_size: 1
gpu_ranks: [0]

# Where to save the checkpoints
save_model: data/wikitext-103-raw/run/model-lm
save_checkpoint_steps: 50000
train_steps: 1000000
valid_steps: 500
report_every: 100
tensorboard: true
tensorboard_log_dir: data/wikitext-103-raw/run/tensorboard

# Model
model_task: lm
encoder_type: transformer_lm
decoder_type: transformer_lm
position_encoding: true
dec_layers: 6
heads: 8
rnn_size: 512
word_vec_size: 512
transformer_ff: 2048
dropout_steps: [0]
dropout: [0.1]
attention_dropout: [0.1]
batch_size: 2048
batch_type: tokens

model_dtype: "fp32"
optim: "adam"
learning_rate: 2
warmup_steps: 8000
decay_method: "noam"
adam_beta2: 0.998
max_grad_norm: 0
label_smoothing: 0.1
param_init: 0
param_init_glorot: true
normalization: "tokens"