diff --git a/Training.ipynb b/Training.ipynb
index 38606b5..721a316 100644
--- a/Training.ipynb
+++ b/Training.ipynb
@@ -41,7 +41,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00,  9.21it/s]\n"
+      "100%|████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00,  9.31it/s]\n"
      ]
     }
    ],
@@ -177,7 +177,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Some weights of the model checkpoint at bert-base-multilingual-cased were not used when initializing BertModel: ['cls.predictions.transform.dense.bias', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.weight', 'cls.seq_relationship.weight', 'cls.predictions.decoder.weight', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias']\n",
+      "Some weights of the model checkpoint at bert-base-multilingual-cased were not used when initializing BertModel: ['cls.seq_relationship.weight', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.bias', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.LayerNorm.weight']\n",
       "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
       "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
      ]
@@ -582,8 +582,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "for param in bert.parameters():\n",
-    "    param.requires_grad = False"
+    "#for param in bert.parameters():\n",
+    "#    param.requires_grad = False"
    ]
   },
   {
@@ -618,7 +618,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 28,
    "id": "8844beef",
    "metadata": {},
    "outputs": [],
@@ -640,7 +640,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 29,
    "id": "c37c3c1b",
    "metadata": {},
    "outputs": [],
@@ -650,7 +650,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 30,
    "id": "261d4cc7",
    "metadata": {},
    "outputs": [
@@ -660,7 +660,7 @@
        "torch.Size([1, 22, 22])"
       ]
      },
-     "execution_count": 63,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -679,7 +679,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 31,
    "id": "c773fdba",
    "metadata": {},
    "outputs": [],
@@ -702,7 +702,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 32,
    "id": "0f97b71a",
    "metadata": {},
    "outputs": [
@@ -712,7 +712,7 @@
        "torch.Size([22, 22])"
       ]
      },
-     "execution_count": 67,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -723,17 +723,85 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 36,
+   "id": "2a35055b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([19, 13, 21, 21, 19, 21, 13, 13, 16, 16, 21, 16, 19, 13,  2, 13, 13, 16,\n",
+       "        13, 20, 19, 19], device='cuda:0')"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "predict = emb.view(-1,emb.size(-1)).argmax(dim=-1)\n",
+    "predict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "778c99b7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([False, False, False, False, False,  True, False, False, False, False,\n",
+       "         True, False, False, False, False, False, False, False, False, False,\n",
+       "        False, False], device='cuda:0')"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(predict == entity_ids)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "798091aa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],\n",
+       "       device='cuda:0')"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(predict == entity_ids) * inputs[\"attention_mask\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
    "id": "d7d0164a",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "tensor(0.4138, device='cuda:0', grad_fn=<NllLossBackward0>)"
+       "tensor(2.9564, device='cuda:0', grad_fn=<NllLossBackward0>)"
       ]
      },
-     "execution_count": 68,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -753,7 +821,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 40,
    "id": "197cfa62",
    "metadata": {},
    "outputs": [],
@@ -801,7 +869,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████████████████████████████████████████████████████████████████████| 4250/4250 [00:00<00:00, 265652.17it/s]"
+      "100%|██████████████████████████████████████████████████████████████████████████| 4250/4250 [00:00<00:00, 236146.99it/s]"
      ]
     },
     {
@@ -848,7 +916,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "BATCH_SIZE = 32\n",
+    "BATCH_SIZE = 4\n",
     "train_loader = DataLoader(\n",
     "        DatasetArray(datasetTrain),\n",
     "        batch_size=BATCH_SIZE,\n",
@@ -874,7 +942,8 @@
    "id": "7d45dd29",
    "metadata": {},
    "source": [
-    "bert paramter를 freeze 안했을땐 batch를 8 정도로 했어요. 그 이상은 메모리가 부족해서 돌아가지 않아요."
+    "BATCH_SIZE 를 4로 잡는다.\n",
+    "bert paramter를 freeze 안했을땐 batch를 8 정도로 했어요. 그 이상은 메모리가 부족해서 돌아가지 않아요.\n"
    ]
   },
   {
@@ -937,7 +1006,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "optimizer = AdamW(model.parameters(), lr=5.0e-5)\n",
+    "optimizer = AdamW(model.parameters(), lr=1.0e-5)\n",
     "CELoss = nn.CrossEntropyLoss(ignore_index=tagIdConverter.pad_id)"
    ]
   },
@@ -952,6 +1021,36 @@
   {
    "cell_type": "code",
    "execution_count": 27,
+   "id": "78e46670",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1, 2, 3, 4]\n",
+      "[5, 6, 7, 8]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from groupby_index import groupby_index\n",
+    "\n",
+    "for g in groupby_index([1,2,3,4,5,6,7,8],4):\n",
+    "        print([*g])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ed61ce06",
+   "metadata": {},
+   "source": [
+    "`groupby_index` 그룹으로 묶어서 실행"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
    "id": "109259b4",
    "metadata": {},
    "outputs": [
@@ -966,7 +1065,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 0: 100%|█████████████████████████████████████████| 133/133 [00:26<00:00,  4.98batch/s, accuracy=0.746, loss=1.88]\n"
+      "Epoch 0: 100%|███████████████████████████████████████| 1063/1063 [00:45<00:00, 23.15batch/s, accuracy=0.923, loss=2.24]\n"
      ]
     },
     {
@@ -980,7 +1079,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 1: 100%|█████████████████████████████████████████| 133/133 [00:26<00:00,  5.04batch/s, accuracy=0.814, loss=1.17]\n"
+      "Epoch 1: 100%|███████████████████████████████████████| 1063/1063 [00:46<00:00, 23.07batch/s, accuracy=0.961, loss=1.52]\n"
      ]
     },
     {
@@ -994,7 +1093,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 2: 100%|████████████████████████████████████████| 133/133 [00:26<00:00,  5.10batch/s, accuracy=0.821, loss=0.928]\n"
+      "Epoch 2: 100%|██████████████████████████████████████| 1063/1063 [00:46<00:00, 23.06batch/s, accuracy=0.976, loss=0.793]\n"
      ]
     },
     {
@@ -1008,7 +1107,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 3: 100%|████████████████████████████████████████| 133/133 [00:26<00:00,  5.05batch/s, accuracy=0.821, loss=0.795]\n"
+      "Epoch 3: 100%|███████████████████████████████████████| 1063/1063 [00:46<00:00, 23.07batch/s, accuracy=0.935, loss=1.88]\n"
      ]
     },
     {
@@ -1022,18 +1121,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 4:   5%|██▏                                       | 7/133 [00:01<00:30,  4.10batch/s, accuracy=0.853, loss=0.724]\n"
-     ]
-    },
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_28932/1927930699.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     21\u001b[0m             \u001b[0mprediction\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0moutput\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mview\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutput\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdim\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     22\u001b[0m             \u001b[1;31m#부정확 할 수 있지만 대충 맞음.[PAD]기호를 예측할 일은 없어야 함.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 23\u001b[1;33m             \u001b[0mcorrect\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mprediction\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mbatch_labels\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mview\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     24\u001b[0m             \u001b[0maccuracy\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcorrect\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mbatch_inputs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"attention_mask\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mview\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     25\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
+      "Epoch 4: 100%|███████████████████████████████████████| 1063/1063 [00:46<00:00, 23.09batch/s, accuracy=0.98, loss=0.524]\n"
      ]
     }
    ],
@@ -1043,39 +1131,46 @@
     "result = []\n",
     "iteration = 0\n",
     "\n",
+    "t = []\n",
+    "\n",
     "model.zero_grad()\n",
     "\n",
     "for epoch in range(TRAIN_EPOCH):\n",
     "    model.train()\n",
     "    print(f\"epoch {epoch} start:\")\n",
-    "    with tqdm(train_loader, unit=\"batch\") as tepoch:\n",
-    "        for batch_i,batch_l in tepoch:\n",
-    "            tepoch.set_description(f\"Epoch {epoch}\")\n",
-    "            \n",
-    "            batch_inputs = {k: v.cuda(device) for k, v in list(batch_i.items())}\n",
-    "            batch_labels = batch_l.cuda(device)\n",
-    "\n",
-    "            output = model(**batch_inputs)\n",
-    "            loss = CELoss(output.view(-1, output.size(-1)), batch_labels.view(-1))\n",
-    "            \n",
-    "            prediction = output.view(-1, output.size(-1)).argmax(dim=-1)\n",
-    "            #부정확 할 수 있지만 대충 맞음.[PAD]기호를 예측할 일은 없어야 함.\n",
-    "            correct = (prediction == batch_labels.view(-1)).sum().item()\n",
-    "            accuracy = correct / batch_inputs[\"attention_mask\"].view(-1).sum()\n",
+    "    with tqdm(train_loader, unit=\"minibatch\") as tepoch:\n",
+    "        tepoch.set_description(f\"Epoch {epoch}\")\n",
+    "        \n",
+    "        for batch in groupby_index(tepoch,8):\n",
+    "            corrects = 0\n",
+    "            totals = 0\n",
+    "            losses = 0\n",
     "            \n",
     "            optimizer.zero_grad()\n",
-    "            loss.backward()\n",
+    "            for mini_i,mini_l in batch:\n",
+    "                batch_inputs = {k: v.cuda(device) for k, v in list(mini_i.items())}\n",
+    "                batch_labels = mini_l.cuda(device)\n",
+    "                attention_mask = batch_inputs[\"attention_mask\"]\n",
+    "                \n",
+    "                output = model(**batch_inputs)\n",
+    "                loss = CELoss(output.view(-1, output.size(-1)), batch_labels.view(-1))\n",
+    "            \n",
+    "                prediction = output.view(-1, output.size(-1)).argmax(dim=-1)\n",
+    "                corrects += ((prediction == batch_labels.view(-1)) * attention_mask.view(-1)).sum().item()\n",
+    "                totals += attention_mask.view(-1).sum().item()\n",
+    "                losses += loss.item()\n",
+    "                loss.backward()\n",
     "\n",
     "            optimizer.step()\n",
-    "        \n",
-    "            result.append({\"iter\":iteration,\"loss\":loss.item(),\"accuracy\":accuracy})\n",
-    "            tepoch.set_postfix(loss=loss.item(), accuracy= accuracy.item())\n",
+    "            accuracy = corrects / totals\n",
+    "            result.append({\"iter\":iteration,\"loss\":losses,\"accuracy\":accuracy})\n",
+    "            tepoch.set_postfix(loss=losses, accuracy= accuracy)\n",
     "            iteration += 1"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 44,
    "id": "f0d9b2d7",
    "metadata": {},
    "outputs": [],
@@ -1085,7 +1180,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 45,
    "id": "19ca6da1",
    "metadata": {},
    "outputs": [],
@@ -1096,13 +1191,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 46,
    "id": "0bee685c",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -1132,7 +1227,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 47,
    "id": "0defca72",
    "metadata": {},
    "outputs": [],
@@ -1142,7 +1237,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 48,
    "id": "2f45cae0",
    "metadata": {},
    "outputs": [
@@ -1150,8 +1245,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "gpu allocated : 693 MB\n",
-      "gpu reserved : 756MB\n"
+      "gpu allocated : 2739 MB\n",
+      "gpu reserved : 2910MB\n"
      ]
     }
    ],
@@ -1170,7 +1265,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 49,
    "id": "73b01630",
    "metadata": {},
    "outputs": [],
@@ -1191,7 +1286,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 50,
    "id": "af93a3ec",
    "metadata": {},
    "outputs": [
@@ -1211,7 +1306,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 51,
    "id": "9b2cd5c4",
    "metadata": {},
    "outputs": [
@@ -1263,7 +1358,7 @@
        "          0]], device='cuda:0')}"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 51,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1276,7 +1371,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 52,
    "id": "6b31782c",
    "metadata": {},
    "outputs": [],
@@ -1290,7 +1385,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 53,
    "id": "7f4d43ce",
    "metadata": {},
    "outputs": [
@@ -1298,8 +1393,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "['O', 'O', 'I-PS', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O']\n",
-      "['O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O']\n"
+      "['O', 'B-PS', 'I-PS', 'I-PS', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'I-PS', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O']\n",
+      "['O', 'B-OG', 'I-OG', 'O', 'O', 'O', 'O', 'B-PS', 'I-PS', 'I-PS', 'O', 'B-PS', 'I-PS', 'I-PS', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-DT', 'I-DT', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-OG', 'I-OG', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O']\n"
      ]
     }
    ],
@@ -1310,7 +1405,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 54,
    "id": "5ade3317",
    "metadata": {},
    "outputs": [
@@ -1338,7 +1433,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 55,
    "id": "383dd24a",
    "metadata": {},
    "outputs": [
@@ -1348,7 +1443,7 @@
        "torch.Size([194, 1])"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1359,17 +1454,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 56,
    "id": "ff74fced",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "tensor(106)"
+       "tensor(120)"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1381,7 +1476,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 57,
    "id": "3f6ad5d8",
    "metadata": {},
    "outputs": [
@@ -1391,7 +1486,7 @@
        "tensor(120, device='cuda:0')"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 57,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1402,17 +1497,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 58,
    "id": "986fd52b",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "tensor(0.8833, device='cuda:0')"
+       "tensor(1., device='cuda:0')"
       ]
      },
-     "execution_count": 42,
+     "execution_count": 58,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1432,7 +1527,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 59,
    "id": "1f3f8666",
    "metadata": {},
    "outputs": [
@@ -1440,7 +1535,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|███████████████████████████████████████████████████████████████████████████████| 16/16 [00:02<00:00,  5.66batch/s]\n"
+      "100%|█████████████████████████████████████████████████████████████████████████████| 125/125 [00:01<00:00, 74.40batch/s]\n"
      ]
     }
    ],
@@ -1467,7 +1562,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 60,
    "id": "b7567f48",
    "metadata": {},
    "outputs": [],
@@ -1490,7 +1585,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 61,
    "id": "15cd73a5",
    "metadata": {},
    "outputs": [
@@ -1521,7 +1616,7 @@
        "        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])"
       ]
      },
-     "execution_count": 45,
+     "execution_count": 61,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1532,7 +1627,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 62,
    "id": "de9c7932",
    "metadata": {},
    "outputs": [
@@ -1540,7 +1635,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "average_loss : 0.7903580265045166, average_accuracy : 0.8021594285964966, size :500\n"
+      "average_loss : 0.16166621172241866, average_accuracy : 0.9605389833450317, size :500\n"
      ]
     }
    ],
@@ -1564,104 +1659,36 @@
    "id": "24539b98",
    "metadata": {},
    "source": [
-    "test로 보면 결과가 나왔어요. 84% 나와요. F1 스코어는 아직입니다."
+    "test로 보면 결과가 나왔어요. 96% 나와요. F1 스코어는 아직입니다."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 73,
    "id": "f6047991",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[    0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "             0,     0],\n",
-       "        [    0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "             0,     0],\n",
-       "        [    0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "             0,     0],\n",
-       "        [    0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "             0,     0],\n",
-       "        [    0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "             0,     0],\n",
-       "        [    0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "             0,     0],\n",
-       "        [    0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "             0,     0],\n",
-       "        [    0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "             0,     0],\n",
-       "        [    0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "             0,     0],\n",
-       "        [    0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "             0,     0],\n",
-       "        [    0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "             0,     0],\n",
-       "        [    0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "             0,     0],\n",
-       "        [    0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "             0,     0],\n",
-       "        [    0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "             0,     0],\n",
-       "        [    0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "             0,     0],\n",
-       "        [    0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "             0,     0],\n",
-       "        [    0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "             0,     0,     0,     0,     0,     0,     1,     0,     0,     0,\n",
-       "             0,     0],\n",
-       "        [    0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "             0,     0,     0,     0,     0,     2,     2,    27,     0,     0,\n",
-       "             0,     0],\n",
-       "        [    0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "             0,     0],\n",
-       "        [    0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "             0,     0],\n",
-       "        [    0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-       "             0,     0],\n",
-       "        [    0,     0,     1,     0,   167,   126,   466,   421,    40,     0,\n",
-       "             0,     0,     5,     0,   375,   166,  1005,  1154,   101,     0,\n",
-       "             0, 16409]])"
-      ]
-     },
-     "execution_count": 58,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "confusion"
+    "confusion = confusion[0:21][0:21]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4830938c",
+   "metadata": {},
+   "source": [
+    "Outside 토큰에 해당하는 곳을 짜르겠습니다."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 74,
    "id": "000d1e68",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -1676,7 +1703,7 @@
     "import itertools\n",
     "\n",
     "plt.title(\"confusion matrix\")\n",
-    "plt.imshow(confusion[:21,:21],cmap='Blues')\n",
+    "plt.imshow(confusion,cmap='Blues')\n",
     "\n",
     "plt.colorbar()\n",
     "for i,j in itertools.product(range(confusion.shape[0]),range(confusion.shape[1])):\n",
@@ -1713,106 +1740,6 @@
     "F1Score는 다음과 같이 주어집니다."
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "db75fb9f",
-   "metadata": {},
-   "source": [
-    "O 클래스에 대해서 계산해보면"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "id": "86f318c4",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor(16409)"
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "TP = confusion[21,21]\n",
-    "TP"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "id": "60f8af59",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor(4027)"
-      ]
-     },
-     "execution_count": 50,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "FP = confusion[21].sum() - TP\n",
-    "FP"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "id": "0b5d4cd9",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor(0)"
-      ]
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "FN = confusion[:,21].sum() - TP\n",
-    "FN"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "id": "5d88f758",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "precision : 0.8029457926750183\n",
-      "recall : 1.0\n",
-      "F1Score : 0.8907042741775513\n"
-     ]
-    }
-   ],
-   "source": [
-    "precision = TP / (TP + FP)\n",
-    "recall = TP / (TP + FN)\n",
-    "\n",
-    "f1Score = (2*precision*recall)/(precision + recall)\n",
-    "print(f\"precision : {precision}\")\n",
-    "print(f\"recall : {recall}\")\n",
-    "print(f\"F1Score : {f1Score}\")"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "0b23e7d5",
@@ -1823,7 +1750,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 75,
    "id": "38b3eee6",
    "metadata": {},
    "outputs": [],
@@ -1841,7 +1768,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 77,
    "id": "61fe2d6c",
    "metadata": {},
    "outputs": [
@@ -1853,29 +1780,28 @@
       "class 1 f1 score : nan\n",
       "class 2 f1 score : nan\n",
       "class 3 f1 score : nan\n",
-      "class 4 f1 score : nan\n",
-      "class 5 f1 score : nan\n",
-      "class 6 f1 score : nan\n",
-      "class 7 f1 score : nan\n",
-      "class 8 f1 score : nan\n",
+      "class 4 f1 score : 0.9583332538604736\n",
+      "class 5 f1 score : 0.9216590523719788\n",
+      "class 6 f1 score : 0.9232480525970459\n",
+      "class 7 f1 score : 0.9203747510910034\n",
+      "class 8 f1 score : 0.8780487179756165\n",
       "class 9 f1 score : nan\n",
       "class 10 f1 score : nan\n",
       "class 11 f1 score : nan\n",
       "class 12 f1 score : nan\n",
       "class 13 f1 score : nan\n",
-      "class 14 f1 score : nan\n",
-      "class 15 f1 score : nan\n",
-      "class 16 f1 score : 0.001982160611078143\n",
-      "class 17 f1 score : 0.0445544570684433\n",
-      "class 18 f1 score : nan\n",
+      "class 14 f1 score : 0.9240506887435913\n",
+      "class 15 f1 score : 0.7439999580383301\n",
+      "class 16 f1 score : 0.885114848613739\n",
+      "class 17 f1 score : 0.9293712377548218\n",
+      "class 18 f1 score : 0.932692289352417\n",
       "class 19 f1 score : nan\n",
-      "class 20 f1 score : nan\n",
-      "class 21 f1 score : 0.8907042741775513\n"
+      "class 20 f1 score : nan\n"
      ]
     }
    ],
    "source": [
-    "for i in range(22):\n",
+    "for i in range(21):\n",
     "    f1 = getF1Score(confusion,i)\n",
     "    print(f\"class {i} f1 score : {f1}\")"
    ]