Add logs

.gitignore

-deepgrail_Tagger
+SuperTagger
 Utils/silver
 Utils/gold
 .idea

Linker/Linker.py

@@ -6,7 +6,6 @@ import time
 
 import torch
 import torch.nn.functional as F
-from torch import Module
 from torch.nn import Sequential, LayerNorm, Dropout
 from torch.optim import AdamW
 from torch.utils.data import TensorDataset, random_split
@@ -23,7 +22,7 @@ from Linker.atom_map import atom_map
 from Linker.eval import mesure_accuracy, SinkhornLoss
 from Linker.utils_linker import find_pos_neg_idexes, get_atoms_batch, FFN, get_axiom_links, get_pos_encoding_for_s_idx, \
     get_neg_encoding_for_s_idx
-from Supertagger import *
+from SuperTagger import *
 from utils import pad_sequence
 
 def format_time(elapsed):

train.py

@@ -5,13 +5,13 @@ from utils import read_csv_pgbar
 
 torch.cuda.empty_cache()
 batch_size = int(Configuration.modelTrainingConfig['batch_size'])
-nb_sentences = batch_size * 40
+nb_sentences = batch_size * 2
 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)
 
 print("Linker")
-linker = Linker("models/model_supertagger.pt")
-print("Linker Training")
+linker = Linker("models/flaubert_super_98%_V2_50e.pt")
+print("\nLinker Training\n\n")
 linker.train_linker(df_axiom_links, validation_rate=0.1, epochs=epochs, batch_size=batch_size, checkpoint=False, tensorboard=True)