skipgram/train.py
def init_training(run_id, vocab_file, data_file, neg_samples, window,
    n_dims, batch_size=32, n_epochs=5, lr=.002):
  """ Configure logging, log the parameters of this training procedure and
  initialize training. """
  logging.info('Training embeddings with the following parameters:')
  logging.info(f'Vocab file: {vocab_file}')
  logging.info(f'Data file: {data_file}')
  logging.info(f'No. of negative samples: {neg_samples}')
  logging.info(f'No. of context words at each side: {window}')
  logging.info(f'Batch size: {batch_size}')
  logging.info(f'No. of dimensions of the embeddings: {n_dims}')
  dataset = Dataset( vocab_file, data_file, k=neg_samples, w=window)
  logging.info(f'Dataset has {dataset.vocab.n_words} words\n\n')
  model = Skipgram(dataset.vocab.n_words, n_dims)
  trainer = ModelTrainer(run_id, model, dataset)
  trainer.train(batch_size, n_epochs, lr)
 Configure logging, log the parameters of this training procedure and
  initialize training. 
Configure logging, log the parameters of this training procedure and
initialize training.
Configure logging log the parameters of this training procedure and initialize training .
def init_training(run_id, vocab_file, data_file, neg_samples, window,
    n_dims, batch_size=32, n_epochs=5, lr=.002):
  logging.info('Training embeddings with the following parameters:')
  logging.info(f'Vocab file: {vocab_file}')
  logging.info(f'Data file: {data_file}')
  logging.info(f'No. of negative samples: {neg_samples}')
  logging.info(f'No. of context words at each side: {window}')
  logging.info(f'Batch size: {batch_size}')
  logging.info(f'No. of dimensions of the embeddings: {n_dims}')
  dataset = Dataset( vocab_file, data_file, k=neg_samples, w=window)
  logging.info(f'Dataset has {dataset.vocab.n_words} words\n\n')
  model = Skipgram(dataset.vocab.n_words, n_dims)
  trainer = ModelTrainer(run_id, model, dataset)
  trainer.train(batch_size, n_epochs, lr)
def init_training ( run_id , vocab_file , data_file , neg_samples , window , n_dims , batch_size = 32 , n_epochs = 5 , lr = .002 ) : logging . info ( 'Training embeddings with the following parameters:' ) logging . info ( f'Vocab file: {vocab_file}' ) logging . info ( f'Data file: {data_file}' ) logging . info ( f'No. of negative samples: {neg_samples}' ) logging . info ( f'No. of context words at each side: {window}' ) logging . info ( f'Batch size: {batch_size}' ) logging . info ( f'No. of dimensions of the embeddings: {n_dims}' ) dataset = Dataset ( vocab_file , data_file , k = neg_samples , w = window ) logging . info ( f'Dataset has {dataset.vocab.n_words} words\n\n' ) model = Skipgram ( dataset . vocab . n_words , n_dims ) trainer = ModelTrainer ( run_id , model , dataset ) trainer . train ( batch_size , n_epochs , lr )
Configure logging, log the parameters of this training procedure and
initialize training.
Configure logging log the parameters of this training procedure and initialize training .
""" Configure logging, log the parameters of this training procedure and
  initialize training. """
run_id
vocab_file
data_file
neg_samples
window
n_dims
batch_size
n_epochs
lr