translation/OpenNMT-py/examples/cnndm.yaml

## Where the vocab(s) will be written
save_data: cnndm/run/example
# Prevent overwriting existing files in the folder
overwrite: False

# filter long examples
src_seq_length: 10000
tgt_seq_length: 10000
src_seq_length_trunc: 400
tgt_seq_length_trunc: 100

# common vocabulary for source and target
share_vocab: True

# Corpus opts:
data:
    cnndm:
        path_src: cnndm/train.txt.src
        path_tgt: cnndm/train.txt.tgt.tagged
        transforms: []
        weight: 1
    valid:
        path_src: cnndm/val.txt.src
        path_tgt: cnndm/val.txt.tgt.tagged
        transforms: []

src_vocab_size: 50000
tgt_vocab_size: 50000

src_vocab: cnndm/run/example.vocab.src
tgt_vocab: cnndm/run/example.vocab.tgt

save_model: cnndm/run/model
copy_attn: true
global_attention: mlp
word_vec_size: 128
rnn_size: 512
layers: 1
encoder_type: brnn
train_steps: 200000
max_grad_norm: 2
dropout: 0
batch_size: 16
valid_batch_size: 16
optim: adagrad
learning_rate: 0.15
adagrad_accumulator_init: 0.1
reuse_copy_attn: true
copy_loss_by_seqlength: true
bridge: true
seed: 777
world_size: 2
gpu_ranks: [0, 1]