change for flaubert

SuperTagger/SuperTagger/SuperTagger.py

@@ -98,7 +98,9 @@ class SuperTagger:
             self.num_label = len(self.index_to_tags)
             self.model = Tagging_bert_model(self.bert_name, self.num_label)
             self.tags_tokenizer = SymbolTokenizer(self.index_to_tags)
-            self.sent_tokenizer = SentencesTokenizer(transformers.AutoTokenizer.from_pretrained(self.bert_name,do_lower_case=True), 
+            self.sent_tokenizer = SentencesTokenizer(transformers.AutoTokenizer.from_pretrained(
+                                                    self.bert_name,
+                                                    do_lower_case=True), 
                                                     self.max_len_sentence)
             self.model.load_state_dict(params['state_dict'])
             self.optimizer = params['optimizer']
@@ -138,9 +140,11 @@ class SuperTagger:
 
         self.index_to_tags = index_to_tags
         self.bert_name = bert_name
-        self.sent_tokenizer = SentencesTokenizer(AutoTokenizer.from_pretrained(bert_name,do_lower_case=True),
+        self.sent_tokenizer = SentencesTokenizer(AutoTokenizer.from_pretrained(
+                                                bert_name,
+                                                do_lower_case=True),
                                                 self.max_len_sentence)
-        self.optimizer = Adam(params=self.model.parameters(), lr=2e-4, eps=1e-8)
+        self.optimizer = Adam(params=self.model.parameters(), lr=1e-3, eps=1e-8)
         self.tags_tokenizer = SymbolTokenizer(index_to_tags)
         self.trainable = True
         self.model_load = True

predict_links.py

@@ -13,7 +13,7 @@ if __name__== '__main__':
       # region model
       model_tagger = "models/flaubert_super_98_V2_50e.pt"
       neuralproofnet = NeuralProofNet(model_tagger)
-      model = "Output/saved_linker.pt"
+      model = "models/saved_linker.pt"
       neuralproofnet.linker.load_weights(model)
       # endregion
 

train_neuralproofnet.py

@@ -7,7 +7,7 @@ torch.cuda.empty_cache()
 
 # region data
 file_path_axiom_links = 'Datasets/gold_dataset_links.csv'
-df_axiom_links = read_links_csv(file_path_axiom_links)[:32]
+df_axiom_links = read_links_csv(file_path_axiom_links)
 # endregion
 
 
@@ -16,7 +16,7 @@ print("#" * 20)
 print("#" * 20)
 model_tagger = "models/flaubert_super_98_V2_50e.pt"
 neural_proof_net = NeuralProofNet(model_tagger)
-neural_proof_net.train_neuralproofnet(df_axiom_links, validation_rate=0, epochs=5, pretrain_linker_epochs=5, batch_size=16,
+neural_proof_net.train_neuralproofnet(df_axiom_links, validation_rate=0.1, epochs=25, pretrain_linker_epochs=25, batch_size=16,
                                       checkpoint=True, tensorboard=True)
 print("#" * 20)
 print("#" * 20)

train_supertagger.py

@@ -15,10 +15,10 @@ index_to_super = load_obj('SuperTagger/Datasets/index_to_super')
 
 # region model
 tagger = SuperTagger()
-tagger.create_new_model(len(index_to_super),'camembert-base',index_to_super)
+tagger.create_new_model(len(index_to_super),"flaubert/flaubert_base_cased",index_to_super)
 ## If you want to upload a pretrained model
 # tagger.load_weights("models/model_check.pt")
-tagger.train(texts, tags, epochs=40, batch_size=16, validation_rate=0.1, 
+tagger.train(texts, tags, epochs=60, batch_size=16, validation_rate=0.1, 
             tensorboard=True, checkpoint=True)
 # endregion
 

utils.py

@@ -20,8 +20,12 @@ def read_links_csv(csv_path, nrows=float('inf'), chunksize=100):
     print("\n" + "#" * 20)
     print("Loading csv...")
 
+    rows = sum(1 for _ in open(csv_path, 'r', encoding="utf8")) - 1  # minus the header
     chunk_list = []
 
+    if rows > nrows:
+        rows = nrows
+
     with tqdm(total=nrows, desc='Rows read: ') as bar:
         for chunk in pd.read_csv(csv_path, header=0, converters={'Y': pd.eval, 'Z': pd.eval}, 
                                 chunksize=chunksize, nrows=nrows):
@@ -45,7 +49,12 @@ def read_supertags_csv(csv_path, nrows=float('inf'), chunksize=100):
     print("\n" + "#" * 20)
     print("Loading csv...")
 
+    rows = sum(1 for _ in open(csv_path, 'r', encoding="utf8")) - 1  # minus the header
     chunk_list = []
+
+    if rows > nrows:
+        rows = nrows
+
     with tqdm(total=nrows, desc='Rows read: ') as bar:
         for chunk in pd.read_csv(csv_path, header=0, converters={'Y1': pd.eval, 'Y2': pd.eval, 'Z': pd.eval}, 
                                 chunksize=chunksize, nrows=nrows):