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from transformers import PretrainedConfig
class ProtoConfig(PretrainedConfig):
model_type = "proto"
def __init__(self,
pretrained_model_name_or_path="xlm-roberta-base",
num_classes=10,
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,
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,
**kwargs):
super().__init__(**kwargs)
self.pretrained_model_name_or_path = pretrained_model_name_or_path
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.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
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