git ignore

.gitignore

@@ -3,3 +3,6 @@ Utils/silver
 Utils/gold
 .idea
 *.pt
+Linker/__pycache__
+Configuration/__pycache__
+__pycache__

Configuration/config.ini

@@ -28,7 +28,7 @@ sinkhorn_iters=3
 
 [MODEL_TRAINING]
 device=cpu
-batch_size=32
+batch_size=16
 epoch=20
 seed_val=42
-learning_rate=0.005
\ No newline at end of file
+learning_rate=2e-5
\ No newline at end of file

Linker/Linker.py

@@ -60,7 +60,6 @@ class Linker(Module):
 
         self.cross_entropy_loss = SinkhornLoss()
         self.optimizer = AdamW(self.parameters(),
-                               weight_decay=1e-5,
                                lr=learning_rate)
         self.scheduler = get_cosine_schedule_with_warmup(self.optimizer,
                                                          num_warmup_steps=0,
@@ -106,7 +105,7 @@ class Linker(Module):
         return training_dataloader, validation_dataloader
 
     def make_decoder_mask(self, atoms_token):
-        decoder_attn_mask = torch.ones_like(atoms_token, dtype=torch.float64)
+        decoder_attn_mask = torch.ones_like(atoms_token, dtype=torch.float64, device=self.device)
         decoder_attn_mask[atoms_token.eq(self.padding_id)] = 0.0
         return decoder_attn_mask.unsqueeze(1).repeat(1, atoms_token.shape[1], 1).repeat(self.nhead, 1, 1)
 

Utils/PostpreprocesTXT.py

@@ -32,12 +32,44 @@ def sub_tree_line(line_with_data: str):
     return sentence, list(itertools.chain(*sub_trees))
 
 
-def Txt_to_csv(file_name: str):
+def Txt_to_csv(file_name: str, result_name):
     file = open(file_name, "r", encoding="utf8")
     text = file.readlines()
     sub = [sub_tree_line(data) for data in text]
     df = pd.DataFrame(data=sub, columns=['Sentences', 'sub_tree'])
-    df.to_csv("../Datasets/" + file_name[:-4] + "_dataset_links.csv", index=False)
+    df.to_csv("../Datasets/" + result_name + "_dataset_links.csv", mode='a', index=False, header=False)
 
+def Txt_to_csv_header(file_name: str, result_name):
+    file = open(file_name, "r", encoding="utf8")
+    text = file.readlines()
+    sub = [sub_tree_line(data) for data in text]
+    df = pd.DataFrame(data=sub, columns=['Sentences', 'sub_tree'])
+    df.to_csv("../Datasets/" + result_name + "_dataset_links.csv", index=False)
+
+# import os
+# i = 0
+# path = "gold"
+# for filename in os.listdir(path):
+#     if i == 0:
+#         Txt_to_csv_header(os.path.join(path, filename),path)
+#     else :
+#         Txt_to_csv(os.path.join(path, filename),path)
+#     i+=1
+#
+# i = 0
+# path = "silver"
+# for filename in os.listdir(path):
+#     if i == 0:
+#         Txt_to_csv_header(os.path.join(path, filename),path)
+#     else :
+#         Txt_to_csv(os.path.join(path, filename),path)
+#     i+=1
 
-Txt_to_csv("aa1_links.txt")
+# # reading csv files
+# data1 = pd.read_csv('../Datasets/gold_dataset_links.csv')
+# data2 = pd.read_csv('../Datasets/silver_dataset_links.csv')
+#
+# # using merge function by setting how='left'
+# df = pd.merge(data1, data2,how='outer')
+#
+# df.to_csv("../Datasets/goldANDsilver_dataset_links.csv", index=False)

train.py

 import torch
 from Configuration import Configuration
 from Linker import *
-from Supertagger import *
+from deepgrail_Tagger.SuperTagger.SuperTagger import SuperTagger
 from utils import read_csv_pgbar
 
 torch.cuda.empty_cache()
@@ -10,15 +10,21 @@ nb_sentences = batch_size * 200
 epochs = int(Configuration.modelTrainingConfig['epoch'])
 
 file_path_axiom_links = 'Datasets/goldANDsilver_dataset_links.csv'
+nb_sentences = batch_size * 20
+epochs = int(Configuration.modelTrainingConfig['epoch'])
+
+file_path_axiom_links = 'Datasets/gold_dataset_links.csv'
 df_axiom_links = read_csv_pgbar(file_path_axiom_links, nb_sentences)
 
 sentences_batch = df_axiom_links["Sentences"].tolist()
 supertagger = SuperTagger()
-supertagger.load_weights("models/model_supertagger.pt")
+supertagger.load_weights("models/flaubert_super_98%_V2_50e.pt")
+
+
 sents_tokenized, sents_mask = supertagger.sent_tokenizer.fit_transform_tensors(sentences_batch)
 
 print("Linker")
 linker = Linker(supertagger)
 linker = linker.to(torch.device("cuda" if torch.cuda.is_available() else "cpu"))
 print("Linker Training")
-linker.train_linker(df_axiom_links, sents_tokenized, sents_mask, validation_rate=0.1, epochs=epochs, batch_size=batch_size, checkpoint=True, validate=True)
+linker.train_linker(df_axiom_links, sents_tokenized, sents_mask, validation_rate=0.1, epochs=epochs, batch_size=batch_size, checkpoint=False, validate=True)