Update config.py

config.py

@@ -1,28 +1,28 @@
-from transformers import PretrainedConfig, AutoConfig
-
-class BERTMultiAttentionConfig(PretrainedConfig):
-    model_type = "bert_multi_attention"
-    keys_to_ignore_at_inference = ["dropout"]
-
-    def __init__(
-        self,
-        transformer="bert-base-uncased",
-        hidden_size=768,
-        num_heads=8,
-        dropout=0.1,
-        rnn_hidden_size=128,
-        rnn_num_layers=2,
-        rnn_bidirectional=True,
-        **kwargs
-    ):
-        super().__init__(**kwargs)
-        self.transformer = transformer
-        self.hidden_size = hidden_size
-        self.num_heads = num_heads
-        self.dropout = dropout
-        self.rnn_hidden_size = rnn_hidden_size
-        self.rnn_num_layers = rnn_num_layers
-        self.rnn_bidirectional = rnn_bidirectional
-
-
-AutoConfig.register("bert_multi_attention", BERTMultiAttentionConfig)
+from transformers import PretrainedConfig, AutoConfig
+
+class BERTMultiAttentionConfig(PretrainedConfig):
+    model_type = "bert_multi_attention"
+   # keys_to_ignore_at_inference = ["dropout"]
+
+    def __init__(
+        self,
+        transformer="bert-base-uncased",
+        hidden_size=768,
+        num_heads=8,
+        dropout=0.1,
+        rnn_hidden_size=128,
+        rnn_num_layers=2,
+        rnn_bidirectional=True,
+        **kwargs
+    ):
+        super().__init__(**kwargs)
+        self.transformer = transformer
+        self.hidden_size = hidden_size
+        self.num_heads = num_heads
+        self.dropout = dropout
+        self.rnn_hidden_size = rnn_hidden_size
+        self.rnn_num_layers = rnn_num_layers
+        self.rnn_bidirectional = rnn_bidirectional
+
+
+AutoConfig.register("bert_multi_attention", BERTMultiAttentionConfig)