@@ -17,11 +17,11 @@ def __init__(self, opt ):
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self .encoder .weight = nn .Parameter (opt .embeddings ,requires_grad = opt .embedding_training )
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self .fc = nn .Linear (opt .embedding_dim , opt .label_size )
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- print ( self . __class__ . __name__ )
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+
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self .properties = {"model_name" :self .__class__ .__name__ ,
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- "embedding_dim" :self .opt .embedding_dim ,
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- "embedding_training" :self .opt .embedding_training ,
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- "max_seq_len" :self .opt .max_seq_len ,
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+ # "embedding_dim":self.opt.embedding_dim,
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+ # "embedding_training":self.opt.embedding_training,
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+ # "max_seq_len":self.opt.max_seq_len,
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"batch_size" :self .opt .batch_size ,
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"learning_rate" :self .opt .learning_rate ,
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"keep_dropout" :self .opt .keep_dropout ,
@@ -39,7 +39,7 @@ def save(self,save_dir="saved_model",metric=None):
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os .mkdir (save_dir )
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self .model_info = "__" .join ([k + "_" + str (v ) if type (v )!= list else k + "_" + str (v )[1 :- 1 ].replace ("," ,"_" ).replace ("," ,"" ) for k ,v in self .properties .items () ])
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if metric :
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- path = os .path .join (save_dir , str (metric ) + "__ " + self .model_info )
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+ path = os .path .join (save_dir , str (metric )[ 2 :] + "_ " + self .model_info )
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else :
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path = os .path .join (save_dir ,self .model_info )
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t .save (self ,path )
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