List of options

Invoke a command without arguments to list available arguments and their default values:

$ ./fasttext supervised
Empty input or output path.

The following arguments are mandatory:
  -input              training file path
  -output             output file path

  The following arguments are optional:
  -verbose            verbosity level [2]

  The following arguments for the dictionary are optional:
  -minCount           minimal number of word occurrences [1]
  -minCountLabel      minimal number of label occurrences [0]
  -wordNgrams         max length of word ngram [1]
  -bucket             number of buckets [2000000]
  -minn               min length of char ngram [0]
  -maxn               max length of char ngram [0]
  -t                  sampling threshold [0.0001]
  -label              labels prefix [__label__]

  The following arguments for training are optional:
  -lr                 learning rate [0.1]
  -lrUpdateRate       change the rate of updates for the learning rate [100]
  -dim                size of word vectors [100]
  -ws                 size of the context window [5]
  -epoch              number of epochs [5]
  -neg                number of negatives sampled [5]
  -loss               loss function {ns, hs, softmax} [softmax]
  -thread             number of threads [12]
  -pretrainedVectors  pretrained word vectors for supervised learning []
  -saveOutput         whether output params should be saved [0]

  The following arguments for quantization are optional:
  -cutoff             number of words and ngrams to retain [0]
  -retrain            finetune embeddings if a cutoff is applied [0]
  -qnorm              quantizing the norm separately [0]
  -qout               quantizing the classifier [0]
  -dsub               size of each sub-vector [2]

Defaults may vary by mode. (Word-representation modes skipgram and cbow use a default -minCount of 5.)

Hyperparameter optimization (autotune) is activated when you provide a validation file with -autotune-validation argument.

The following arguments are for autotune:
  -autotune-validation            validation file to be used for evaluation
  -autotune-metric                metric objective {f1, f1:labelname} [f1]
  -autotune-predictions           number of predictions used for evaluation  [1]
  -autotune-duration              maximum duration in seconds [300]
  -autotune-modelsize             constraint model file size [] (empty = do not quantize)