def neural_assistant_tiny():
"""HParams for tiny neural_assistant model."""
hparams = transformer.transformer_tiny_tpu()
hparams.add_hparam("pos_weight", 1.0) # weight for positive triples
hparams.add_hparam("similarity_fuction",
"bilinear") # dot_product or bilinear
hparams.add_hparam("pool_technique", "average") # avg or max pool or last
hparams.add_hparam("last_k", 1) # number of last indices for averaging
hparams.add_hparam("max_triple_length", 30) # max length of every triple
hparams.add_hparam("train_triple_num",
5000) # max number of triples during training
hparams.add_hparam("attend_kb", True) # if False, it's a transformer model
hparams.add_hparam("kb_loss_weight", 0.0) # weight for distant supervision
hparams.add_hparam("test_triple_num", 28483) # max triples of KB
return hparams
def neural_assistant_tiny():
"""HParams for tiny neural_assistant model."""
hparams = transformer.transformer_tiny_tpu()
hparams.add_hparam("pos_weight", 1.0) # weight for positive triples
hparams.add_hparam("similarity_fuction",
"bilinear") # dot_product or bilinear
hparams.add_hparam("pool_technique", "average") # avg or max pool or last
hparams.add_hparam("last_k", 1) # number of last indices for averaging
hparams.add_hparam("max_triple_length", 30) # max length of every triple
hparams.add_hparam("train_triple_num",
5000) # max number of triples during training
hparams.add_hparam("attend_kb", True) # if False, it's a transformer model
hparams.add_hparam("kb_loss_weight", 0.0) # weight for distant supervision
hparams.add_hparam("test_triple_num", 28483) # max triples of KB
hparams.add_hparam("margin", 1.0) # KB training max-margin loss
hparams.add_hparam(
"num_negative_samples",
1) # Sampling number of different adversarial training examples
hparams.add_hparam("kb_train_weight", 0.0)
# KB_training loss weight which combines Language model and KB selection loss
return hparams