diff --git a/Machine_translation_Pytorch.ipynb b/Machine_translation_Pytorch.ipynb
index 33bb875..0baf508 100644
--- a/Machine_translation_Pytorch.ipynb
+++ b/Machine_translation_Pytorch.ipynb
@@ -97,6 +97,18 @@
     "collapsed": false
    }
   },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "outputs": [],
+   "source": [],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
   {
    "cell_type": "code",
    "execution_count": 2,
@@ -524,11 +536,13 @@
     "    return [token.text for token in spacy_english.tokenizer(text)]\n",
     "\n",
     "train_iter, valid_iter, test_iter, = IWSLT2017(root='.pytorch/.data/', language_pair=('en','de'))\n",
-    "\n",
+    "train_data = list(train_iter)\n",
+    "valid_data = list(valid_iter)\n",
+    "test_data = list(test_iter)\n",
     "en_counter = Counter()\n",
     "de_counter = Counter()\n",
     "\n",
-    "for (de, en) in train_iter:\n",
+    "for (en, de) in train_data:\n",
     "    en_counter.update(tokenize_english(en))\n",
     "    de_counter.update(tokenize_german(de))\n",
     "\n",
@@ -550,8 +564,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Unique tokens in source (de) vocabs 14101\n",
-      "Unique tokens in source (en) vocabs 17069\n"
+      "Unique tokens in source (de) vocabs 16712\n",
+      "Unique tokens in source (en) vocabs 14018\n"
      ]
     }
    ],
@@ -574,7 +588,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "output of the text_transform: [1, 0, 7366, 39, 0, 2]\n"
+      "output of the text_transform: [1, 92, 15, 54, 241, 2]\n"
      ]
     }
    ],
@@ -589,6 +603,63 @@
      "name": "#%%\n"
     }
    }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Generating Batch iterator"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "(tensor([[   1,    1,    1,  ...,    1,    1,    1],\n         [ 343,   61,   59,  ...,  141,  113,   20],\n         [2537,  219,  139,  ..., 4919,   44,   45],\n         ...,\n         [   3,    3,    3,  ...,    5,    3,    3],\n         [   3,    3,    3,  ...,    2,    3,    3],\n         [   3,    3,    3,  ...,    3,    3,    3]]),\n tensor([[   1,    1,    1,  ...,    1,    1,    1],\n         [   0,    0,    0,  ...,  141,    0,    0],\n         [   0, 4791,    0,  ...,    0,    0,    0],\n         ...,\n         [   3,    3,    3,  ...,  141,    3,    3],\n         [   3,    3,    3,  ...,    5,    3,    3],\n         [   3,    3,    3,  ...,    2,    3,    3]]))"
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from torch.utils.data import DataLoader\n",
+    "from torch.nn.utils.rnn import pad_sequence\n",
+    "BATCH_SIZE=32\n",
+    "def collate_batch(batch):\n",
+    "    de_list, en_list = [], []\n",
+    "    for (_en, _de) in batch:\n",
+    "        en_list.append(torch.tensor(english_transform(_en)))\n",
+    "        de_list.append(torch.tensor(english_transform(_de)))\n",
+    "    return pad_sequence(en_list, padding_value=3.0), pad_sequence(de_list, padding_value=3.0)\n",
+    "\n",
+    "train_dataloader = DataLoader(train_data, batch_size=BATCH_SIZE, shuffle=True, collate_fn=collate_batch)\n",
+    "test_dataloader = DataLoader(test_data, batch_size=BATCH_SIZE, shuffle=True, collate_fn=collate_batch)\n",
+    "valid_dataloader = DataLoader(valid_data, batch_size=BATCH_SIZE, shuffle=True, collate_fn=collate_batch)\n",
+    "next(iter(train_dataloader))"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
   }
  ],
  "metadata": {