Upload proto_model/configuration_proto.py with huggingface_hub

proto_model/configuration_proto.py

@@ -0,0 +1,53 @@
+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