File size: 2,081 Bytes
d728ce3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 | from transformers.configuration_utils import PretrainedConfig
class SprotoConfig(PretrainedConfig):
model_type = "sproto"
def __init__(
self,
pretrained_model=None,
num_classes=None,
label_order_path=None,
use_sigmoid=False,
use_cuda=True,
lr_prototypes=5e-2,
lr_features=2e-6,
lr_others=2e-2,
num_training_steps=5000,
num_warmup_steps=1000,
loss="BCE",
save_dir="output",
use_attention=True,
use_global_attention=False,
dot_product=False,
normalize=None,
final_layer=False,
reduce_hidden_size=None,
use_prototype_loss=False,
prototype_vector_path=None,
attention_vector_path=None,
eval_buckets=None,
seed=7,
num_prototypes_per_class=1,
batch_size=10,
**kwargs,
):
super().__init__(**kwargs)
self.pretrained_model = pretrained_model
self.num_classes = num_classes
self.label_order_path = label_order_path
self.use_sigmoid = use_sigmoid
self.use_cuda = use_cuda
self.lr_prototypes = lr_prototypes
self.lr_features = lr_features
self.lr_others = lr_others
self.num_training_steps = num_training_steps
self.num_warmup_steps = num_warmup_steps
self.loss = loss
self.save_dir = save_dir
self.use_attention = use_attention
self.use_global_attention = use_global_attention
self.dot_product = dot_product
self.normalize = normalize
self.final_layer = final_layer
self.reduce_hidden_size = reduce_hidden_size
self.use_prototype_loss = use_prototype_loss
self.prototype_vector_path = prototype_vector_path
self.attention_vector_path = attention_vector_path
self.eval_buckets = eval_buckets
self.seed = seed
self.num_prototypes_per_class = num_prototypes_per_class
self.batch_size = batch_size |