diff --git a/push pull texte b/push pull texte
deleted file mode 100644
index f80b3ef41b4ce5aa94eed2543895c4d8e50613b9..0000000000000000000000000000000000000000
--- a/push pull texte	
+++ /dev/null
@@ -1 +0,0 @@
- https://gitlab-ci-token:glpat-AZdpzmAPDFCSK8nPZxCw@gitlab.irit.fr/pnria/global-helper/deepgrail-rnn.git
\ No newline at end of file
diff --git a/test.py b/test.py
deleted file mode 100644
index 92a7e1e6042192e7a5fb3fd3d77626cf417909ef..0000000000000000000000000000000000000000
--- a/test.py
+++ /dev/null
@@ -1,4 +0,0 @@
-import regex
-import re
-
-print(re.match(r'([a-zA-Z|_]+)_\d+', "cl_r_1").group(1))
\ No newline at end of file
diff --git a/test_linker.py b/test_linker.py
deleted file mode 100644
index d64d171c6a39ca243c7a91533eec0faf0cfe31cb..0000000000000000000000000000000000000000
--- a/test_linker.py
+++ /dev/null
@@ -1,32 +0,0 @@
-import pickle
-import time
-
-import numpy as np
-import torch
-import torch.nn.functional as F
-from torch.optim import SGD, Adam, AdamW
-from torch.utils.data import Dataset, TensorDataset, random_split
-from transformers import get_cosine_schedule_with_warmup
-
-from Configuration import Configuration
-from SuperTagger.Linker.Linker import Linker
-from SuperTagger.Linker.atom_map import atom_map
-from SuperTagger.eval import SinkhornLoss
-from SuperTagger.utils import format_time, read_csv_pgbar
-
-file_path_validation = 'Datasets/aa1_links_dataset_links.csv'
-file_path = "Datasets/m2_dataset_V2.csv"
-
-df_axiom_links = read_csv_pgbar(file_path, 10)
-
-data = [['dr(0,np,n)', "n", 'dr(0,dl(0,np,np),n)', 'n', 'dr(0,dl(0,n,n),n)', 'n', 'dl(0,np,txt)']]
-
-linker = Linker()
-result = linker(data, [])
-
-print(result.shape)
-print(result)
-
-axiom_links_pred = torch.argmax(F.softmax(result, dim=3), dim=3)
-print(axiom_links_pred.shape)
-print(axiom_links_pred)
\ No newline at end of file
diff --git a/train.py b/train.py
index 619c632ec381f743acbfdf694c859b2e38ec4721..f8290f8554a28f22598b5a8780abcdb1d3ca1470 100644
--- a/train.py
+++ b/train.py
@@ -31,7 +31,7 @@ atom_vocab_size = int(Configuration.datasetConfig['atom_vocab_size'])
 # region ParamsTraining
 
 batch_size = int(Configuration.modelTrainingConfig['batch_size'])
-nb_sentences = batch_size * 2
+nb_sentences = batch_size * 10
 epochs = int(Configuration.modelTrainingConfig['epoch'])
 seed_val = int(Configuration.modelTrainingConfig['seed_val'])
 learning_rate = float(Configuration.modelTrainingConfig['learning_rate'])
@@ -151,7 +151,6 @@ def run_epochs(epochs):
 
             # Run the kinker on the categories predictions
             logits_predictions = linker(batch_atoms, batch_polarity, [])
-            print(logits_predictions.permute(1, 0, 2, 3).shape)
 
             linker_loss = cross_entropy_loss(logits_predictions.permute(1, 0, 2, 3), batch_true_links)
             # Perform a backward pass to calculate the gradients.