Update PrateritumGPT.py

PrateritumGPT.py

@@ -4,7 +4,6 @@ import torch.nn as nn
 from torch.utils.data import Dataset, DataLoader
 from torch.nn.utils.rnn import pad_sequence
 import math
-import os
 
 tokens = list("azertyuiopqsdfghjklmwxcvbnäüöß—– ")
 tokensdict = {}
@@ -13,7 +12,7 @@ for i in range(len(tokens)):
     tokensdict.update({tokens[i]: [0] * i + [0] * (len(tokens) - (i + 1))})
 
 # Ouvrir le fichier CSV
-with open(os.path.dirname(os.path.abspath(__file__))+"\\top-german-verbs.csv", 'r', encoding="utf-8") as file:
+with open("C:\\Users\\marc2\\Downloads\\7eaaf0e22461b505c749e268c0b72bc4-12ebe211a929f039791dfeaa1a019b64cadddaf1\\7eaaf0e22461b505c749e268c0b72bc4-12ebe211a929f039791dfeaa1a019b64cadddaf1\\top-german-verbs.csv", 'r', encoding="utf-8") as file:
     # Créer un objet lecteur CSV
     reader = [i for i in csv.reader(file)][1:]
 
@@ -38,12 +37,12 @@ for i in reader:
     for j in i[2]:
         k += [tokens.index(j)]
     k += [len(tokens) + 1] * (25 - len(k))
-    features += [torch.Tensor([k])]
+    features += [torch.Tensor(k)]
     k = []
     for j in i[8]:
         k += [tokens.index(j)]
     k += [len(tokens) + 1] * (25 - len(k))
-    labels += [torch.Tensor([k])]
+    labels += [torch.Tensor(k)]
 
 MyDataset = CSVDataset(features=features, labels=labels)
 
@@ -105,6 +104,9 @@ for epoch in range(epochs):
     total_loss = 0.0
 
     for batch_idx, (inputs, targets) in enumerate(train_loader):
+
+        print(inputs.shape,targets.shape)
+
         optimizer.zero_grad()
         output = model(inputs, targets[:, :-1])  # Shifted targets
         output = output.transpose(1, 2)  # Adjust shape for loss function