-h,--help                             Print this help message and exit
--version                             Print the version number and exit
-c,--config VECTOR ...                Configuration file(s). If multiple, later overrides earlier
-w,--workspace UINT=2048              Preallocate  arg  MB of work space
--log TEXT                            Log training process information to file given by  arg
--log-level TEXT=info                 Set verbosity level of logging: trace, debug, info, warn,
                                      err(or), critical, off
--log-time-zone TEXT                  Set time zone for the date shown on logging
--quiet                               Suppress all logging to stderr. Logging to files still works
--quiet-translation                   Suppress logging for translation
--seed UINT                           Seed for all random number generators. 0 means initialize
                                      randomly
--clip-gemm FLOAT                     If not 0 clip GEMM input values to +/- arg
--interpolate-env-vars                allow the use of environment variables in paths, of the form
                                      ${VAR_NAME}
--relative-paths                      All paths are relative to the config file location
--dump-config TEXT                    Dump current (modified) configuration to stdout and exit.
                                      Possible values: full, minimal

-m,--model TEXT=model.npz             Path prefix for model to be saved/resumed. Supported file
                                      extensions: .npz, .bin
--pretrained-model TEXT               Path prefix for pre-trained model to initialize model weights
--ignore-model-config                 Ignore the model configuration saved in npz file
--type TEXT=amun                      Model type: amun, nematus, s2s, multi-s2s, transformer
--dim-vocabs VECTOR=0,0 ...           Maximum items in vocabulary ordered by rank, 0 uses all
                                      items in the provided/created vocabulary file
--dim-emb INT=512                     Size of embedding vector
--dim-rnn INT=1024                    Size of rnn hidden state
--enc-type TEXT=bidirectional         Type of encoder RNN : bidirectional, bi-unidirectional,
                                      alternating (s2s)
--enc-cell TEXT=gru                   Type of RNN cell: gru, lstm, tanh (s2s)
--enc-cell-depth INT=1                Number of transitional cells in encoder layers (s2s)
--enc-depth INT=1                     Number of encoder layers (s2s)
--dec-cell TEXT=gru                   Type of RNN cell: gru, lstm, tanh (s2s)
--dec-cell-base-depth INT=2           Number of transitional cells in first decoder layer (s2s)
--dec-cell-high-depth INT=1           Number of transitional cells in next decoder layers (s2s)
--dec-depth INT=1                     Number of decoder layers (s2s)
--skip                                Use skip connections (s2s)
--layer-normalization                 Enable layer normalization
--right-left                          Train right-to-left model
--best-deep                           Use Edinburgh deep RNN configuration (s2s)
--special-vocab VECTOR ...            Model-specific special vocabulary ids
--tied-embeddings                     Tie target embeddings and output embeddings in output layer
--tied-embeddings-src                 Tie source and target embeddings
--tied-embeddings-all                 Tie all embedding layers and output layer
--transformer-heads INT=8             Number of heads in multi-head attention (transformer)
--transformer-no-projection           Omit linear projection after multi-head attention
                                      (transformer)
--transformer-dim-ffn INT=2048        Size of position-wise feed-forward network (transformer)
--transformer-ffn-depth INT=2         Depth of filters (transformer)
--transformer-ffn-activation TEXT=swish
                                      Activation between filters: swish or relu (transformer)
--transformer-dim-aan INT=2048        Size of position-wise feed-forward network in AAN
                                      (transformer)
--transformer-aan-depth INT=2         Depth of filter for AAN (transformer)
--transformer-aan-activation TEXT=swish
                                      Activation between filters in AAN: swish or relu (transformer)
--transformer-aan-nogate              Omit gate in AAN (transformer)
--transformer-decoder-autoreg TEXT=self-attention
                                      Type of autoregressive layer in transformer decoder:
                                      self-attention, average-attention (transformer)
--transformer-tied-layers VECTOR ...  List of tied decoder layers (transformer)
--transformer-guided-alignment-layer TEXT=last
                                      Last or number of layer to use for guided alignment training
                                      in transformer
--transformer-preprocess TEXT         Operation before each transformer layer: d = dropout, a =
                                      add, n = normalize
--transformer-postprocess-emb TEXT=d  Operation after transformer embedding layer: d = dropout, a
                                      = add, n = normalize
--transformer-postprocess TEXT=dan    Operation after each transformer layer: d = dropout, a =
                                      add, n = normalize
--dropout-rnn FLOAT                   Scaling dropout along rnn layers and time (0 = no dropout)
--dropout-src FLOAT                   Dropout source words (0 = no dropout)
--dropout-trg FLOAT                   Dropout target words (0 = no dropout)
--grad-dropping-rate FLOAT            Gradient Dropping rate (0 = no gradient Dropping)
--grad-dropping-momentum FLOAT        Gradient Dropping momentum decay rate (0.0 to 1.0)
--grad-dropping-warmup UINT=100       Do not apply gradient dropping for the first arg steps
--transformer-dropout FLOAT           Dropout between transformer layers (0 = no dropout)
--transformer-dropout-attention FLOAT Dropout for transformer attention (0 = no dropout)
--transformer-dropout-ffn FLOAT       Dropout for transformer filter (0 = no dropout)

--cost-type TEXT=ce-mean              Optimization criterion: ce-mean, ce-mean-words, ce-sum,
                                      perplexity
--overwrite                           Do not create model checkpoints, only overwrite main model
                                      file with last checkpoint. Reduces disk usage
--no-reload                           Do not load existing model specified in --model arg
-t,--train-sets VECTOR ...            Paths to training corpora: source target
-v,--vocabs VECTOR ...                Paths to vocabulary files have to correspond to
                                      --train-sets. If this parameter is not supplied we look for
                                      vocabulary files source.{yml,json} and target.{yml,json}.
                                      If these files do not exist they are created
-e,--after-epochs UINT                Finish after this many epochs, 0 is infinity
--after-batches UINT                  Finish after this many batch updates, 0 is infinity
--disp-freq TEXT=1000u                Display information every  arg  updates (append 't' for
                                      every  arg  target labels)
--disp-first UINT                     Display nformation for the first  arg  updates
--disp-label-counts                   Display label counts when logging loss progress
--save-freq TEXT=10000u               Save model file every  arg  updates (append 't' for every
                                      arg  target labels)
--max-length UINT=50                  Maximum length of a sentence in a training sentence pair
--max-length-crop                     Crop a sentence to max-length instead of ommitting it if
                                      longer than max-length
--no-shuffle                          Skip shuffling of training data before each epoch
--no-restore-corpus                   Skip restoring corpus state after training is restarted
-T,--tempdir TEXT=/tmp                Directory for temporary (shuffled) files and database
--sqlite TEXT                         Use disk-based sqlite3 database for training corpus storage,
                                      default is temporary with path creates persistent storage
--sqlite-drop                         Drop existing tables in sqlite3 database
-d,--devices VECTOR=0 ...             Specifies GPU ID(s) to use for training. Defaults to
                                      0..num-devices-1
--num-devices UINT                    Number of GPUs to use for this process. Defaults to
                                      length(devices) or 1
--cpu-threads UINT=0                  Use CPU-based computation with this many independent
                                      threads, 0 means GPU-based computation
--mini-batch INT=64                   Size of mini-batch used during update
--mini-batch-words INT                Set mini-batch size based on words instead of sentences
--mini-batch-fit                      Determine mini-batch size automatically based on
                                      sentence-length to fit reserved memory
--mini-batch-fit-step UINT=10         Step size for mini-batch-fit statistics
--maxi-batch INT=100                  Number of batches to preload for length-based sorting
--maxi-batch-sort TEXT=trg            Sorting strategy for maxi-batch: none, src, trg (not
                                      available for decoder)
--shuffle-in-ram                      Keep shuffled corpus in RAM, do not write to temp file
-o,--optimizer TEXT=adam              Optimization algorithm: sgd, adagrad, adam
--optimizer-params VECTOR ...         Parameters for optimization algorithm, e.g. betas for adam
--optimizer-delay UINT=1              SGD update delay, 1 = no delay
--sync-sgd                            Use synchronous SGD instead of asynchronous for multi-gpu
                                      training
-l,--learn-rate FLOAT=0.0001          Learning rate
--lr-report                           Report learning rate for each update
--lr-decay FLOAT                      Per-update decay factor for learning rate: lr <- lr * arg (0
                                      to disable)
--lr-decay-strategy TEXT=epoch+stalled
                                      Strategy for learning rate decaying: epoch, batches,
                                      stalled, epoch+batches, epoch+stalled
--lr-decay-start VECTOR=10,1 ...      The first number of (epoch, batches, stalled) validations to
                                      start learning rate decaying (tuple)
--lr-decay-freq UINT=50000            Learning rate decaying frequency for batches, requires
                                      --lr-decay-strategy to be batches
--lr-decay-reset-optimizer            Reset running statistics of optimizer whenever learning rate
                                      decays
--lr-decay-repeat-warmup              Repeat learning rate warmup when learning rate is decayed
--lr-decay-inv-sqrt TEXT=0            Decrease learning rate at arg / sqrt(no. batches) starting
                                      at arg  (append 't' or 'e' for sqrt(target labels or
                                      epochs))
--lr-warmup TEXT=0                    Increase learning rate linearly for  arg  first batches
                                      (append 't' for  arg  first target labels)
--lr-warmup-start-rate FLOAT          Start value for learning rate warmup
--lr-warmup-cycle                     Apply cyclic warmup
--lr-warmup-at-reload                 Repeat warmup after interrupted training
--label-smoothing FLOAT               Epsilon for label smoothing (0 to disable)
--clip-norm FLOAT=1                   Clip gradient norm to  argcli.add<int>(0 to disable)
--exponential-smoothing FLOAT=0       Maintain smoothed version of parameters for validation and
                                      saving with smoothing factor. 0 to disable
--guided-alignment TEXT=none          Path to a file with word alignments. Use guided alignment to
                                      guide attention or 'none'
--guided-alignment-cost TEXT=mse      Cost type for guided alignment: ce (cross-entropy), mse
                                      (mean square error), mult (multiplication)
--guided-alignment-weight FLOAT=0.1   Weight for guided alignment cost
--data-weighting TEXT                 Path to a file with sentence or word weights
--data-weighting-type TEXT=sentence   Processing level for data weighting: sentence, word
--embedding-vectors VECTOR ...        Paths to files with custom source and target embedding vectors
--embedding-normalization             Normalize values from custom embedding vectors to [-1, 1]
--embedding-fix-src                   Fix source embeddings. Affects all encoders
--embedding-fix-trg                   Fix target embeddings. Affects all decoders
--multi-node                          Enable asynchronous multi-node training through MPI (and
                                      legacy sync if combined with --sync-sgd)
--multi-node-overlap=true             Overlap model computations with MPI communication
--ulr=false                           Enable ULR (Universal Language Representation)
--ulr-query-vectors TEXT              Path to file with universal sources embeddings from
                                      projection into universal space
--ulr-keys-vectors TEXT               Path to file with universal sources embeddings of traget
                                      keys from projection into universal space
--ulr-trainable-transformation=false  Make Query Transformation Matrix A trainable
--ulr-dim-emb INT                     ULR monolingual embeddings dimension
--ulr-dropout FLOAT=0                 ULR dropout on embeddings attentions. Default is no dropout
--ulr-softmax-temperature FLOAT=1     ULR softmax temperature to control randomness of
                                      predictions. Deafult is 1.0: no temperature

--valid-sets VECTOR ...               Paths to validation corpora: source target
--valid-freq TEXT=10000u              Validate model every  arg  updates (append 't' for every
                                      arg  target labels)
--valid-metrics VECTOR=cross-entropy ...
                                      Metric to use during validation: cross-entropy,
                                      ce-mean-words, perplexity, valid-script,  translation,
                                      bleu, bleu-detok. Multiple metrics can be specified
--early-stopping UINT=10              Stop if the first validation metric does not improve for
                                      arg  consecutive validation steps
-b,--beam-size UINT=12                Beam size used during search with validating translator
-n,--normalize FLOAT=0                Divide translation score by pow(translation length, arg)
--max-length-factor FLOAT=3           Maximum target length as source length times factor
--word-penalty FLOAT                  Subtract (arg * translation length) from translation score
--allow-unk                           Allow unknown words to appear in output
--n-best                              Generate n-best list
--valid-mini-batch INT=32             Size of mini-batch used during validation
--valid-max-length UINT=1000          Maximum length of a sentence in a validating sentence pair
--valid-script-path TEXT              Path to external validation script. It should print a single
                                      score to stdout. If the option is used with validating
                                      translation, the output translation file will be passed as
                                      a first argument
--valid-translation-output TEXT       Path to store the translation
--keep-best                           Keep best model for each validation metric
--valid-log TEXT                      Log validation scores to file given by  arg