def train_supervised(<EOL>input,<EOL>lr=<NUM_LIT:0.1>,<EOL>dim=<NUM_LIT:100>,<EOL>ws=<NUM_LIT:5>,<EOL>epoch=<NUM_LIT:5>,<EOL>minCount=<NUM_LIT:1>,<EOL>minCountLabel=<NUM_LIT:0>,<EOL>minn=<NUM_LIT:0>,<EOL>maxn=<NUM_LIT:0>,<EOL>neg=<NUM_LIT:5>,<EOL>wordNgrams=<NUM_LIT:1>,<EOL>loss="<STR_LIT>",<EOL>bucket=<NUM_LIT>,<EOL>thread=multiprocessing.cpu_count() - <NUM_LIT:1>,<EOL>lrUpdateRate=<NUM_LIT:100>,<EOL>t=<NUM_LIT>,<EOL>label="<STR_LIT>",<EOL>verbose=<NUM_LIT:2>,<EOL>pretrainedVectors="<STR_LIT>",<EOL>):
model = "<STR_LIT>"<EOL>a = _build_args(locals())<EOL>ft = _FastText()<EOL>fasttext.train(ft.f, a)<EOL>return ft<EOL>
Train a supervised model and return a model object.

input must be a filepath. The input text does not need to be tokenized
as per the tokenize function, but it must be preprocessed and encoded
as UTF-8. You might want to consult standard preprocessing scripts such
as tokenizer.perl mentioned here: http://www.statmt.org/wmt07/baseline.html

The input file must must contain at least one label per line. For an
example consult the example datasets which are part of the fastText
repository such as the dataset pulled by classification-example.sh.