Update documents and test components.

Author: Eleven1Liu

docs/examples/plot_KimCNN_quickstart.py

@@ -56,7 +56,6 @@
     model_name=model_name,
     network_config=network_config,
     classes=classes,
-    word_dict=word_dict,
     embed_vecs=embed_vecs,
     learning_rate=learning_rate,
     monitor_metrics=["Micro-F1", "Macro-F1", "P@1", "P@3", "P@5"],
@@ -66,7 +65,7 @@
 # * ``model_name`` leads ``init_model`` function to find a network model.
 # * ``network_config`` contains the configurations of a network model.
 # * ``classes`` is the label set of the data.
-# * ``init_weight``, ``word_dict`` and ``embed_vecs`` are not used on a bert-base model, so we can ignore them.
+# * ``embed_vecs`` is the the pre-trained word vectors.
 # * ``moniter_metrics`` includes metrics you would like to track.
 #
 #

docs/examples/plot_bert_quickstart.py

@@ -70,7 +70,6 @@
 # * ``model_name`` leads ``init_model`` function to find a network model.
 # * ``network_config`` contains the configurations of a network model.
 # * ``classes`` is the label set of the data.
-# * ``init_weight``, ``word_dict`` and ``embed_vecs`` are not used on a bert-base model, so we can ignore them.
 # * ``moniter_metrics`` includes metrics you would like to track.
 #
 #

tests/nn/components.py

@@ -20,7 +20,7 @@ def get_name(self):
         return "token_to_id"
 
     def get_from_trainer(self, trainer):
-        return trainer.model.word_dict
+        return trainer.word_dict
 
     def compare(self, a, b):
         return a == b
@@ -34,7 +34,7 @@ def get_name(self):
         return "embed_vecs"
 
     def get_from_trainer(self, trainer):
-        return trainer.model.embed_vecs
+        return trainer.embed_vecs
 
     def compare(self, a, b):
         return (a == b).all()