diff --git a/projekt/FLAN_T5_sms_spam.ipynb b/projekt/FLAN_T5_sms_spam.ipynb
index 68423c7..c3b132a 100644
--- a/projekt/FLAN_T5_sms_spam.ipynb
+++ b/projekt/FLAN_T5_sms_spam.ipynb
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@@ -4493,57 +4493,57 @@
             "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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             "\u001b[?25hCollecting datasets\n",
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             "\u001b[?25hRequirement already satisfied: torch in /usr/local/lib/python3.8/dist-packages (1.13.1+cu116)\n",
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-            "\u001b[?25hRequirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.8/dist-packages (from transformers) (6.0)\n",
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+            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.8/dist-packages (from transformers) (3.9.0)\n",
+            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.8/dist-packages (from transformers) (4.64.1)\n",
+            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.8/dist-packages (from transformers) (1.21.6)\n",
             "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.8/dist-packages (from transformers) (2022.6.2)\n",
+            "Requirement already satisfied: requests in /usr/local/lib/python3.8/dist-packages (from transformers) (2.25.1)\n",
+            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.8/dist-packages (from transformers) (6.0)\n",
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+            "Collecting multiprocess\n",
+            "  Downloading multiprocess-0.70.14-py38-none-any.whl (132 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m132.0/132.0 KB\u001b[0m \u001b[31m18.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests->transformers) (2022.12.7)\n",
             "Collecting urllib3<1.27,>=1.21.1\n",
             "  Downloading urllib3-1.26.14-py2.py3-none-any.whl (140 kB)\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m140.6/140.6 KB\u001b[0m \u001b[31m9.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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-            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas->datasets) (2022.7.1)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m140.6/140.6 KB\u001b[0m \u001b[31m19.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas->datasets) (2022.7.1)\n",
+            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.8/dist-packages (from pandas->datasets) (2.8.2)\n",
             "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.8/dist-packages (from python-dateutil>=2.7.3->pandas->datasets) (1.15.0)\n",
             "Installing collected packages: tokenizers, sentencepiece, xxhash, urllib3, multiprocess, responses, huggingface-hub, transformers, datasets\n",
             "  Attempting uninstall: urllib3\n",
@@ -4586,78 +4586,78 @@
       "metadata": {
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           "base_uri": "https://localhost:8080/",
-          "height": 244,
+          "height": 231,
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+            "5e394d73c2df4d34b39989cd1b24dc57"
           ]
         },
         "id": "cCiAuRqrOkvV",
-        "outputId": "394df630-e545-4005-b3a4-82b0341b210b"
+        "outputId": "0a6da048-12e3-41b6-b623-d21cb246030c"
       },
       "execution_count": 3,
       "outputs": [
@@ -4670,7 +4670,7 @@
             "application/vnd.jupyter.widget-view+json": {
               "version_major": 2,
               "version_minor": 0,
-              "model_id": "e4bb6f2f32de48d4b1f6d7ecf97ce376"
+              "model_id": "a8e120a9a97d45d59fcf275af25a591e"
             }
           },
           "metadata": {}
@@ -4684,7 +4684,7 @@
             "application/vnd.jupyter.widget-view+json": {
               "version_major": 2,
               "version_minor": 0,
-              "model_id": "4084f218fca64fc9b765e04d0cc073ac"
+              "model_id": "564f30aef6c34efa952fdd1402627d13"
             }
           },
           "metadata": {}
@@ -4698,7 +4698,7 @@
             "application/vnd.jupyter.widget-view+json": {
               "version_major": 2,
               "version_minor": 0,
-              "model_id": "8cae9c49e18f43248072b5e059429f6d"
+              "model_id": "ae89e755c6e64ab2a3101913d08820ef"
             }
           },
           "metadata": {}
@@ -4719,7 +4719,7 @@
             "application/vnd.jupyter.widget-view+json": {
               "version_major": 2,
               "version_minor": 0,
-              "model_id": "ec0f16c3cbb14d9287e148887127219b"
+              "model_id": "db79fbdb9e25432a8f95221cc0bed971"
             }
           },
           "metadata": {}
@@ -4733,7 +4733,7 @@
             "application/vnd.jupyter.widget-view+json": {
               "version_major": 2,
               "version_minor": 0,
-              "model_id": "699a1bdf38ca48e2affc3c6bd771852f"
+              "model_id": "6fd330314de74ccc983da44783bcac7d"
             }
           },
           "metadata": {}
@@ -4754,7 +4754,7 @@
             "application/vnd.jupyter.widget-view+json": {
               "version_major": 2,
               "version_minor": 0,
-              "model_id": "3c84850ebe3e45d297d3bfa8a12f1b86"
+              "model_id": "99cbe58514e3460398f032b93a57d068"
             }
           },
           "metadata": {}
@@ -4764,14 +4764,14 @@
     {
       "cell_type": "code",
       "source": [
-        "dataset['train'][123]"
+        "dataset['train'][0]"
       ],
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "JKFHPko3OnAV",
-        "outputId": "423375ec-0be7-431b-8a65-f531df1d94d8"
+        "outputId": "e9aeffd1-9049-48fc-e59c-0ff8c43cd0f4"
       },
       "execution_count": 4,
       "outputs": [
@@ -4779,8 +4779,8 @@
           "output_type": "execute_result",
           "data": {
             "text/plain": [
-              "{'sms': 'Todays Voda numbers ending 7548 are selected to receive a $350 award. If you have a match please call 08712300220 quoting claim code 4041 standard rates app\\n',\n",
-              " 'label': 1}"
+              "{'sms': 'Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...\\n',\n",
+              " 'label': 0}"
             ]
           },
           "metadata": {},
@@ -4791,7 +4791,7 @@
     {
       "cell_type": "markdown",
       "source": [
-        "# Modyfikacja datasetu - klasyfikacja zero-shot"
+        "# Przygotowanie datasetu"
       ],
       "metadata": {
         "id": "l140vJrgYxPr"
@@ -4803,13 +4803,13 @@
         "parsed_dataset = []\n",
         "\n",
         "for row in dataset['train']:\n",
-        "  text = \"Answer the question in one word - true if provided text is spam or false, if provided text is not spam.\\n Q: Is this text spam? \\nText: \" + row['sms'] + \"A: \"\n",
+        "  text = row['sms'].replace(\"\\n\", \"\")\n",
         "  new_row = {}\n",
         "  new_row['sms'] = text\n",
         "  if row['label'] == 0:\n",
-        "    new_row['label'] = \"true\"\n",
+        "    new_row['label'] = \"False\"\n",
         "  else:\n",
-        "    new_row['label'] = \"false\"\n",
+        "    new_row['label'] = \"True\"\n",
         "  parsed_dataset.append(new_row)\n",
         "\n",
         "parsed_dataset[0]"
@@ -4819,7 +4819,7 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "1boUF-YiY3_y",
-        "outputId": "a7ecfad8-7c97-4de2-8852-80b28ea7c965"
+        "outputId": "d0088428-9014-43bf-e5d1-920042374797"
       },
       "execution_count": 5,
       "outputs": [
@@ -4827,8 +4827,8 @@
           "output_type": "execute_result",
           "data": {
             "text/plain": [
-              "{'sms': 'Answer the question in one word - true if provided text is spam or false, if provided text is not spam.\\n Q: Is this text spam? \\nText: Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...\\nA: ',\n",
-              " 'label': 'true'}"
+              "{'sms': 'Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...',\n",
+              " 'label': 'False'}"
             ]
           },
           "metadata": {},
@@ -4866,54 +4866,54 @@
           "base_uri": "https://localhost:8080/",
           "height": 145,
           "referenced_widgets": [
-            "040698c1b4be4adebfe751751a64c11d",
-            "11b4ddede4494fe093329c50097b7df5",
-            "7986c625c9c34d039fad240333d0da08",
-            "b35a6f8e81184f81990a0111cc5c6ca8",
-            "2968992398a94adeb626eec2f45a8699",
-            "c9f8f67483fb45c8a6b59fceae40fe42",
-            "c5a862e7d71c430c865fa87dc21b1f6d",
-            "d69171bdd6544c8ab9e96f3f3dfe7f92",
-            "9cca59c67c5f452eb1d9e8dd22296968",
-            "0f18ab0690ce4bc3b9b7c2f4d10c2049",
-            "af26b98c1e3440daa11e104ed1c8b7e4",
-            "be9060616d68475ea2e4674f7e460566",
-            "264afd978de54dbeaa1b711e4c0b1c07",
-            "bbcefda6eea74b8ea8313e4f7167df65",
-            "68d85f6957ea4807a1fe7c82c9a7bc06",
-            "cdfaf060f34a4d23afe97243d5f4c709",
-            "9411023294ab44b284894aebf3f8f589",
-            "d8bf78a3d7ea47a9824d6e01e70b0237",
-            "b95ece9507db467fad710338bdf29177",
-            "34a31746a99c4916b1402a2829e71a98",
-            "29b2a7a233a9484c92056034cc70b8ca",
-            "c9a72e2599e94e9e9bc2b4912d25877b",
-            "2c24af439f3e45db8a0e004686e912ed",
-            "dcb13b35232145fd85be1b6e4442cc5a",
-            "58037f9984bb414a9c6e56337d86215c",
-            "6e9379baaf2049658790e176ca279531",
-            "772e39d516cf43fdbb1b3db001d53d77",
-            "c08c1e0783324c53b7fc02912fa14af9",
-            "9bcdda510c0147178f651b66450b48ac",
-            "18ab5e8088e04cdca93df7519156a59f",
-            "f502a26154d840b89b5c61a2aae4f827",
-            "7beb993a979443dcb93d26ec6e6718c3",
-            "62e60fc862054e4d8d0d18cb1957cf6d",
-            "bd697213d4fa4c1d9119657c537e78fe",
-            "67f9f97815354675bf3bd435f2ed0436",
-            "27bd6c78e7f74ff385509e9856c2b8d0",
-            "34f670acaa3542eebc8c19018e900ce3",
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-            "b21e65a9dff040f4a045580d9989ac20",
-            "0efa19170c9e4de891e328365543c916",
-            "8c6fa802ddd5491581c092e5422bfeab",
-            "42cfc2ada7ac408fab39251add66f6ef"
+            "6fb269ceeada49c19adf46819664fa98",
+            "341f67fcddff4a91813fb1331fd63c3e",
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+            "87d7f2540c724ba88b6f972049043702",
+            "36111c073ea34489886c767532713233",
+            "c05a5532720d47e39e6cb710617aab67",
+            "458619b05af848fe8f40fac408e4c521",
+            "3cf20c5c605b46e1bc4890769cdeffe3",
+            "15c19b89e55243cc88e77c56895d56b4",
+            "b4dd2e86dd6a4d579f315293d4b0ba21",
+            "60577d05c1ad4acea832bd4e34efa6f4",
+            "4f67d0fe28914c1bb3d1057225c409cd",
+            "f384fc9cd7f2428fad3b6d10ffac7efd",
+            "36838c7736594b1b83f3046c8f0cc265",
+            "b98e34b842054f99ae1ac5f96104321f",
+            "a4ce8092283747e89e730a7e39e64834",
+            "0aa55ed422a94e94b01c117598842a10",
+            "d54619fbb71e441eb90d9c42145ba07b",
+            "da3487b705264da9a8f92254e568b76e",
+            "9f378f21d7804f49ad9f72ccabb4a10b",
+            "ebb15c7aaf1d4b59a2186e2abbea2868",
+            "6510cda1ab33418882a1e6811f826ed4",
+            "0b3c6eb43a5c4644adbc17ee2b06b079",
+            "fcb7ce49c0c14dc4a5dbbd9870334921",
+            "2f58b16a741f4f1d822c71c17a1c5606",
+            "35cbd0d168714ecc9f0c9236b3707d0a",
+            "b9393265011640c493b2bd4de43a4aef",
+            "eaeeca2372be4fe3868d2d3f42839bc2",
+            "079b78ca1493431880754da6fc4ac127",
+            "6c7e4ab3930c4570a05a4e3d9db6782d",
+            "2ae3b00d50f04990b8c1839dfa705dc8",
+            "cfcae44ed5734d7086ecb5028281cf9a",
+            "281ca6dc827f41d3833ba1c60606529d",
+            "bce6a704b2924287b8b9279d791b633b",
+            "b1dae5dc568446b0ba896b3abe3afb49",
+            "29ff3a3e656a489182943da91cd73813",
+            "585cf66f9f3648e0b950ca04eb17cc63",
+            "71e629333d5d4009bc8072b9f12c0cee",
+            "d5438b0c54104ddd99ed097fa65bc21c",
+            "224c2c07bdc5444aaddf46ca6325e681",
+            "197d80f118764b65b29ad5049edd0087",
+            "e0326b59a1334117bbe6ccb06c1f4020",
+            "6fd86b989a354a2696c8b9e880109a95",
+            "2caff09a31ad46f59e717e25462b12d6"
           ]
         },
         "id": "q5Jz0E_oPMBr",
-        "outputId": "c71131a5-4737-4354-e58f-17aa88116348"
+        "outputId": "b3c09e2f-577b-49e9-8ae7-cc9cede74cc8"
       },
       "execution_count": 7,
       "outputs": [
@@ -4926,7 +4926,7 @@
             "application/vnd.jupyter.widget-view+json": {
               "version_major": 2,
               "version_minor": 0,
-              "model_id": "040698c1b4be4adebfe751751a64c11d"
+              "model_id": "6fb269ceeada49c19adf46819664fa98"
             }
           },
           "metadata": {}
@@ -4940,7 +4940,7 @@
             "application/vnd.jupyter.widget-view+json": {
               "version_major": 2,
               "version_minor": 0,
-              "model_id": "be9060616d68475ea2e4674f7e460566"
+              "model_id": "4f67d0fe28914c1bb3d1057225c409cd"
             }
           },
           "metadata": {}
@@ -4954,7 +4954,7 @@
             "application/vnd.jupyter.widget-view+json": {
               "version_major": 2,
               "version_minor": 0,
-              "model_id": "2c24af439f3e45db8a0e004686e912ed"
+              "model_id": "0b3c6eb43a5c4644adbc17ee2b06b079"
             }
           },
           "metadata": {}
@@ -4968,7 +4968,7 @@
             "application/vnd.jupyter.widget-view+json": {
               "version_major": 2,
               "version_minor": 0,
-              "model_id": "bd697213d4fa4c1d9119657c537e78fe"
+              "model_id": "bce6a704b2924287b8b9279d791b633b"
             }
           },
           "metadata": {}
@@ -4979,7 +4979,7 @@
       "cell_type": "code",
       "source": [
         "sms = parsed_dataset[0]['sms']\n",
-        "print('Original: \\n', sms)\n",
+        "print('Original: ', sms)\n",
         "print('Tokenized: ', tokenizer.tokenize(sms))\n",
         "print('Token IDs: ', tokenizer.convert_tokens_to_ids(tokenizer.tokenize(sms)))"
       ],
@@ -4988,7 +4988,7 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "dfxJQpoePsvI",
-        "outputId": "2ce963db-ed94-467a-e859-fd0515849ee2"
+        "outputId": "00e742fc-45d6-49d8-b5a5-f42f7c8f5037"
       },
       "execution_count": 8,
       "outputs": [
@@ -4996,13 +4996,9 @@
           "output_type": "stream",
           "name": "stdout",
           "text": [
-            "Original: \n",
-            " Answer the question in one word - true if provided text is spam or false, if provided text is not spam.\n",
-            " Q: Is this text spam? \n",
-            "Text: Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...\n",
-            "A: \n",
-            "Tokenized:  ['▁Answer', '▁the', '▁question', '▁in', '▁one', '▁word', '▁', '-', '▁true', '▁', 'if', '▁provided', '▁text', '▁is', '▁spam', '▁or', '▁false', ',', '▁', 'if', '▁provided', '▁text', '▁is', '▁not', '▁spam', '.', '▁Q', ':', '▁I', 's', '▁this', '▁text', '▁spam', '?', '▁Text', ':', '▁Go', '▁until', '▁jur', 'ong', '▁point', ',', '▁crazy', '.', '.', '▁Available', '▁only', '▁in', '▁bug', 'is', '▁', 'n', '▁great', '▁world', '▁la', '▁', 'e', '▁buffet', '...', '▁Cine', '▁there', '▁got', '▁', 'a', 'more', '▁wa', 't', '...', '▁A', ':', '▁']\n",
-            "Token IDs:  [11801, 8, 822, 16, 80, 1448, 3, 18, 1176, 3, 99, 937, 1499, 19, 13655, 42, 6136, 6, 3, 99, 937, 1499, 19, 59, 13655, 5, 1593, 10, 27, 7, 48, 1499, 13655, 58, 5027, 10, 1263, 552, 10081, 2444, 500, 6, 6139, 5, 5, 8144, 163, 16, 8143, 159, 3, 29, 248, 296, 50, 3, 15, 15385, 233, 17270, 132, 530, 3, 9, 3706, 8036, 17, 233, 71, 10, 3]\n"
+            "Original:  Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...\n",
+            "Tokenized:  ['▁Go', '▁until', '▁jur', 'ong', '▁point', ',', '▁crazy', '.', '.', '▁Available', '▁only', '▁in', '▁bug', 'is', '▁', 'n', '▁great', '▁world', '▁la', '▁', 'e', '▁buffet', '...', '▁Cine', '▁there', '▁got', '▁', 'a', 'more', '▁wa', 't', '...']\n",
+            "Token IDs:  [1263, 552, 10081, 2444, 500, 6, 6139, 5, 5, 8144, 163, 16, 8143, 159, 3, 29, 248, 296, 50, 3, 15, 15385, 233, 17270, 132, 530, 3, 9, 3706, 8036, 17, 233]\n"
           ]
         }
       ]
@@ -5010,7 +5006,7 @@
     {
       "cell_type": "markdown",
       "source": [
-        "# Check maximum lenght of a sentence"
+        "# Few shot learning"
       ],
       "metadata": {
         "id": "UpluhM8cU5Ir"
@@ -5019,28 +5015,28 @@
     {
       "cell_type": "code",
       "source": [
-        "max_len = 0\n",
-        "\n",
-        "for sentence in parsed_dataset:\n",
-        "    input_ids = tokenizer.encode(sentence['sms'], add_special_tokens=True)\n",
-        "    max_len = max(max_len, len(input_ids))\n",
-        "\n",
-        "print('Max sentence length: ', max_len)"
+        "print(parsed_dataset[0]) #0\n",
+        "print(parsed_dataset[123]) #1\n",
+        "print(parsed_dataset[2000]) #0\n",
+        "print(parsed_dataset[3002]) #1"
       ],
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "7uNUkixPU85O",
-        "outputId": "352b31bb-164e-42a8-a8b0-6be9d3eca0c4"
+        "outputId": "9c2c8ef6-30c3-4043-b1a8-f33715d10334"
       },
-      "execution_count": 12,
+      "execution_count": 9,
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
-            "Max sentence length:  377\n"
+            "{'sms': 'Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...', 'label': 'False'}\n",
+            "{'sms': 'Todays Voda numbers ending 7548 are selected to receive a $350 award. If you have a match please call 08712300220 quoting claim code 4041 standard rates app', 'label': 'True'}\n",
+            "{'sms': \"LMAO where's your fish memory when I need it?\", 'label': 'False'}\n",
+            "{'sms': 'This message is free. Welcome to the new & improved Sex & Dogging club! To unsubscribe from this service reply STOP. msgs@150p 18+only', 'label': 'True'}\n"
           ]
         }
       ]
@@ -5048,285 +5044,40 @@
     {
       "cell_type": "code",
       "source": [
-        "max_label_len = 0\n",
+        "non_spam_1 = \"SMS: \" + parsed_dataset[0]['sms'] + \"\\nSpam: False\\n\\n\"\n",
+        "spam_1 = \"SMS: \" + parsed_dataset[123]['sms'] + \"\\nSpam: True\\n\\n\"\n",
+        "non_spam_2 = \"SMS: \" + parsed_dataset[2000]['sms'] + \"\\nSpam: False\\n\\n\"\n",
+        "spam_2 = \"SMS: \" + parsed_dataset[3002]['sms'] + \"\\nSpam: True\\n\\n\"\n",
         "\n",
-        "for sentence in parsed_dataset:\n",
-        "    input_ids = tokenizer.encode(sentence['label'], add_special_tokens=True)\n",
-        "    max_label_len = max(max_label_len, len(input_ids))\n",
-        "\n",
-        "print('Max sentence length: ', max_label_len)"
+        "few_shot_prefix = non_spam_1 + spam_1 + non_spam_2 + spam_2 + \"SMS: \"\n",
+        "print(few_shot_prefix)"
       ],
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "lj0issBznZfK",
-        "outputId": "53defde8-ed8d-4927-add4-5d7d63f737df"
+        "outputId": "a8aa4038-4145-4950-9d9f-91593a550d12"
       },
-      "execution_count": 13,
+      "execution_count": 10,
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
-            "Max sentence length:  2\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# Pre train tokenization"
-      ],
-      "metadata": {
-        "id": "nfw62HdgSERb"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "import torch"
-      ],
-      "metadata": {
-        "id": "KTXYalS1VLqH"
-      },
-      "execution_count": 11,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "input_ids = []\n",
-        "target_ids = []\n",
-        "attention_masks = []\n",
-        "\n",
-        "for sentence in parsed_dataset:\n",
-        "    encoded_dict = tokenizer.encode_plus(\n",
-        "                        sentence['sms'],\n",
-        "                        add_special_tokens = True,\n",
-        "                        max_length = 380,\n",
-        "                        padding = 'max_length',\n",
-        "                        truncation=True,\n",
-        "                        return_attention_mask = True,\n",
-        "                        return_tensors = 'pt',\n",
-        "                   )\n",
-        "    \n",
-        "    encoded_target_dict = tokenizer.encode_plus(\n",
-        "                        sentence['label'],\n",
-        "                        add_special_tokens = True,\n",
-        "                        max_length = 2,\n",
-        "                        padding = 'max_length',\n",
-        "                        truncation=True,\n",
-        "                        return_attention_mask = True,\n",
-        "                        return_tensors = 'pt',\n",
-        "                   )\n",
-        "    \n",
-        "    input_ids.append(encoded_dict['input_ids'])\n",
-        "    target_ids.append(encoded_target_dict['input_ids'])\n",
-        "    attention_masks.append(encoded_dict['attention_mask'])\n",
-        "\n",
-        "input_ids = torch.cat(input_ids, dim=0)\n",
-        "target_ids = torch.cat(target_ids, dim=0)\n",
-        "attention_masks = torch.cat(attention_masks, dim=0)\n",
-        "\n",
-        "print('Original: ', parsed_dataset[0])\n",
-        "print('Token IDs:', input_ids[0])\n",
-        "print('Label token IDs:', target_ids[0])"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "Z28QYfLnSGxR",
-        "outputId": "eb9d48d0-cb2d-4596-c752-14b18f6e3590"
-      },
-      "execution_count": 14,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Original:  {'sms': 'Answer the question in one word - true if provided text is spam or false, if provided text is not spam.\\n Q: Is this text spam? \\nText: Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...\\nA: ', 'label': 'true'}\n",
-            "Token IDs: tensor([11801,     8,   822,    16,    80,  1448,     3,    18,  1176,     3,\n",
-            "           99,   937,  1499,    19, 13655,    42,  6136,     6,     3,    99,\n",
-            "          937,  1499,    19,    59, 13655,     5,  1593,    10,    27,     7,\n",
-            "           48,  1499, 13655,    58,  5027,    10,  1263,   552, 10081,  2444,\n",
-            "          500,     6,  6139,     5,     5,  8144,   163,    16,  8143,   159,\n",
-            "            3,    29,   248,   296,    50,     3,    15, 15385,   233, 17270,\n",
-            "          132,   530,     3,     9,  3706,  8036,    17,   233,    71,    10,\n",
-            "            3,     1,     0,     0,     0,     0,     0,     0,     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,     0,     0,     0,     0,     0,     0,     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,     0,     0,     0,     0,     0,     0,     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,     0,     0,     0,     0,     0,     0,     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,     0,     0,     0,     0,     0,     0,     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,     0,     0,     0,     0,     0,     0,     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,     0,     0,     0,     0,     0,     0,     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,     0,     0,     0,     0,     0,     0,     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,     0,     0,     0,     0,     0,     0,     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,     0,     0,     0,     0,     0,     0,     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,     0,     0,     0,     0,     0,     0,     0,     0])\n",
-            "Label token IDs: tensor([1176,    1])\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# Split dataset"
-      ],
-      "metadata": {
-        "id": "qD_t0y0KVVSy"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "from torch.utils.data import TensorDataset, random_split"
-      ],
-      "metadata": {
-        "id": "vN_SatRIVa4c"
-      },
-      "execution_count": 15,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "dataset = TensorDataset(input_ids, attention_masks, target_ids)\n",
-        "\n",
-        "test_size = 1000\n",
-        "dataset_len = len(dataset)\n",
-        "train_size = int(0.9 * (dataset_len-test_size))\n",
-        "val_size = (dataset_len-test_size) - train_size\n",
-        "\n",
-        "test_dataset, train_dataset, val_dataset = random_split(dataset, [test_size, train_size, val_size])\n",
-        "\n",
-        "print('{:>5,} test samples'.format(test_size))\n",
-        "print('{:>5,} training samples'.format(train_size))\n",
-        "print('{:>5,} validation samples'.format(val_size))"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "Mm6vc6lLVW3l",
-        "outputId": "023efb5b-eab3-4675-9900-3918aedae90f"
-      },
-      "execution_count": 16,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "1,000 test samples\n",
-            "4,116 training samples\n",
-            "  458 validation samples\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# Create train and validation loaders"
-      ],
-      "metadata": {
-        "id": "bmgQOP4EVfA1"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "from torch.utils.data import DataLoader, RandomSampler, SequentialSampler"
-      ],
-      "metadata": {
-        "id": "CxnQ3cmIVlNh"
-      },
-      "execution_count": 17,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "batch_size = 8\n",
-        "\n",
-        "train_dataloader = DataLoader(\n",
-        "            train_dataset,\n",
-        "            sampler = RandomSampler(train_dataset),\n",
-        "            batch_size = batch_size\n",
-        "        )\n",
-        "\n",
-        "validation_dataloader = DataLoader(\n",
-        "            val_dataset,\n",
-        "            sampler = SequentialSampler(val_dataset),\n",
-        "            batch_size = batch_size\n",
-        "        )"
-      ],
-      "metadata": {
-        "id": "0hcpO_onVjEC"
-      },
-      "execution_count": 18,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# Device check"
-      ],
-      "metadata": {
-        "id": "efwhqLyyVu9z"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "if torch.cuda.is_available():    \n",
-        "    device = torch.device(\"cuda\")\n",
-        "\n",
-        "    print('There are %d GPU(s) available.' % torch.cuda.device_count())\n",
-        "    print('We will use the GPU:', torch.cuda.get_device_name(0))\n",
-        "\n",
-        "else:\n",
-        "    print('No GPU available, using the CPU instead.')\n",
-        "    device = torch.device(\"cpu\")"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "ANBCfNGnVwVk",
-        "outputId": "6192e88f-5e61-4de6-b476-de9a6e3a59a6"
-      },
-      "execution_count": 19,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "There are 1 GPU(s) available.\n",
-            "We will use the GPU: Tesla T4\n"
+            "SMS: Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...\n",
+            "Spam: False\n",
+            "\n",
+            "SMS: Todays Voda numbers ending 7548 are selected to receive a $350 award. If you have a match please call 08712300220 quoting claim code 4041 standard rates app\n",
+            "Spam: True\n",
+            "\n",
+            "SMS: LMAO where's your fish memory when I need it?\n",
+            "Spam: False\n",
+            "\n",
+            "SMS: This message is free. Welcome to the new & improved Sex & Dogging club! To unsubscribe from this service reply STOP. msgs@150p 18+only\n",
+            "Spam: True\n",
+            "\n",
+            "SMS: \n"
           ]
         }
       ]
@@ -5348,7 +5099,7 @@
       "metadata": {
         "id": "Eu-7Eed8WgN0"
       },
-      "execution_count": 20,
+      "execution_count": 11,
       "outputs": []
     },
     {
@@ -5363,45 +5114,45 @@
           "base_uri": "https://localhost:8080/",
           "height": 1000,
           "referenced_widgets": [
-            "3747d0aa68d642449ff32b7efd47d497",
-            "2a84ccf3660a4e24b37bfc1d952f06d9",
-            "43b98452768846eb9b773839a4681cc6",
-            "2dfbcef644a84b558ce99b2ddaf091b6",
-            "9dd02373a1994433a49e0015b76dd858",
-            "42a620dfccb4416c95fbd7a76637dc73",
-            "9d953b41e24c4263b18e1ff2174eb797",
-            "cb409960af114fa690bac72c1d51d2ab",
-            "995a94c35b4c441b86598d1976281711",
-            "4ea2ccb5fa264dd5a042f570c22db7de",
-            "4d5668e5b66d4993b935b077a1dc281c",
-            "cf36c750119449e8a16aa97a9b66a124",
-            "0de691bf20804830ba5e6710c205c9ac",
-            "7ee532c002784554b12fede964c6c05a",
-            "25e5c76c84a8414495b61663250825aa",
-            "12bd0cd379754967bfd5481d5836b7a4",
-            "d55d326ffbdc420da0a399c141f7fb56",
-            "092323b59b734eada8d88197d9f0eb72",
-            "622df288b2ae4f62b776cb101de18b9f",
-            "c72abd216b9444ef9bdb0f8a0a6771b0",
-            "03d5f2402e464b7cb5244db0f824df1d",
-            "4a6e196f590d4b329e0edefc910d4730",
-            "9a04f6723e2a40288eb42eea0134dfaf",
-            "4065d0eed87e4d268de8f4536287e225",
-            "bebca147a83446abaf19c3e3db11744e",
-            "cf525a599ca245b19204bfff3fa1bd11",
-            "b60759f0730343849ef3e51d2c8be38c",
-            "dcc795e3fac0401b9a4d2aac3bd6e8cc",
-            "ed7ca6da408842b38a466097ec9d4616",
-            "0335f29f40564adf9b577707dfb40aa9",
-            "a200da1478fa46a19695a5b2f2c77fd1",
-            "e21e75bbbc4149b4ac85fece44aee355",
-            "c014c271cd374f62a0d113efca14001a"
+            "8e60f2b1a9f7455db02eb846af52df63",
+            "71ff180cc81a4376b6a27455ed5cba26",
+            "4dc7c026d3e5469da3ada03bf77d7019",
+            "279308acf5be418ab7f25c3501739c9d",
+            "5c4c0f7bfb6c4a518cdfd3f1d70e77d5",
+            "7182f8c40174456db2d6f974e1a672a9",
+            "f34bf1f4156b4dec8990f16cbceccb00",
+            "bcf3f636355347338e1d57c191e2349f",
+            "e36598bd61874b4eb737d248df7c1212",
+            "f9c113d3c4f64aee969c897451b4522e",
+            "fc647c0c0bd14b87b59d539db3793d0a",
+            "3fcb6b4a0c454d59a6d5503cec84ba37",
+            "17d4993dc58949ca87a852e7e6c02e0c",
+            "db24a85aa3e24a2c9275b9aa4ac78faa",
+            "48a5bb74641a4a2fba370c0ddad1f6cb",
+            "1b0971b224444e2c8ddbf817f2f02131",
+            "07c9e3207eb04c5fb004744c3e4eb3b2",
+            "957e7a116bad4dee93ab449d7766d8ba",
+            "1a2ed54b1c584fa3bd2e0517a878790f",
+            "444add600a24462aa2272f2758ddb99d",
+            "98a9c4dc9bf349d1a8cf985c6a132530",
+            "4d94cb68eed54ffd97cc8272f70a92f3",
+            "c2a8099dd85e42599911991b467a7afe",
+            "72d0effc5d0948378103da4b2c8da5ae",
+            "cf3ac6e0e69643639d6b0a2577fff71a",
+            "d8f4949af74342d3b72200484ee3f1fb",
+            "ff42d87d84694bb2a6a7e63e43750029",
+            "ae619521ac64447b91b9fb26c46e696a",
+            "62a20c9c11f4452389dfdde24fa90d03",
+            "cb94c6469891495db93c332e2b707299",
+            "4e0b6f1f2b2f497d9b6e0183b0bc7ade",
+            "999d0e74507b4acfae447018abb03323",
+            "eaa726bb3b9d45d29c011053e844a97e"
           ]
         },
         "id": "JKv9O8kfV2zZ",
-        "outputId": "6893e79a-48f7-4713-c4a4-a9558acbcf7c"
+        "outputId": "c902fc89-7c47-4eca-da89-411bc5b5e501"
       },
-      "execution_count": 21,
+      "execution_count": 12,
       "outputs": [
         {
           "output_type": "display_data",
@@ -5412,7 +5163,7 @@
             "application/vnd.jupyter.widget-view+json": {
               "version_major": 2,
               "version_minor": 0,
-              "model_id": "3747d0aa68d642449ff32b7efd47d497"
+              "model_id": "8e60f2b1a9f7455db02eb846af52df63"
             }
           },
           "metadata": {}
@@ -5426,7 +5177,7 @@
             "application/vnd.jupyter.widget-view+json": {
               "version_major": 2,
               "version_minor": 0,
-              "model_id": "cf36c750119449e8a16aa97a9b66a124"
+              "model_id": "3fcb6b4a0c454d59a6d5503cec84ba37"
             }
           },
           "metadata": {}
@@ -5440,7 +5191,7 @@
             "application/vnd.jupyter.widget-view+json": {
               "version_major": 2,
               "version_minor": 0,
-              "model_id": "9a04f6723e2a40288eb42eea0134dfaf"
+              "model_id": "c2a8099dd85e42599911991b467a7afe"
             }
           },
           "metadata": {}
@@ -6192,7 +5943,7 @@
             ]
           },
           "metadata": {},
-          "execution_count": 21
+          "execution_count": 12
         }
       ]
     },
@@ -6208,13 +5959,12 @@
     {
       "cell_type": "code",
       "source": [
-        "import datetime\n",
-        "import numpy as np"
+        "import torch"
       ],
       "metadata": {
-        "id": "s-q6_F38bLVA"
+        "id": "rdWMg_KJZEZH"
       },
-      "execution_count": 22,
+      "execution_count": 13,
       "outputs": []
     },
     {
@@ -6230,360 +5980,42 @@
         "    else:\n",
         "      results_false += 1.0\n",
         "\n",
-        "  return results_ok / (results_ok + results_false)\n",
-        "\n",
-        "def format_time(elapsed):\n",
-        "    '''\n",
-        "    Takes a time in seconds and returns a string hh:mm:ss\n",
-        "    '''\n",
-        "    elapsed_rounded = int(round((elapsed)))\n",
-        "    return str(datetime.timedelta(seconds=elapsed_rounded))"
+        "  return results_ok / (results_ok + results_false)"
       ],
       "metadata": {
         "id": "FzUi8908ax61"
       },
-      "execution_count": 23,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# Init training"
-      ],
-      "metadata": {
-        "id": "ucChBa-9bXJy"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "from transformers import get_linear_schedule_with_warmup"
-      ],
-      "metadata": {
-        "id": "c9e7rbGwbdEp"
-      },
-      "execution_count": 24,
+      "execution_count": 14,
       "outputs": []
     },
     {
       "cell_type": "code",
       "source": [
-        "optimizer = torch.optim.AdamW(model.parameters(),\n",
-        "                  lr = 3e-4,\n",
-        "                  eps = 1e-8\n",
-        "                )\n",
+        "if torch.cuda.is_available():     \n",
+        "    device = torch.device(\"cuda\")\n",
         "\n",
-        "epochs = 4\n",
+        "    print('There are %d GPU(s) available.' % torch.cuda.device_count())\n",
+        "    print('We will use the GPU:', torch.cuda.get_device_name(0))\n",
         "\n",
-        "total_steps = len(train_dataloader) * epochs\n",
-        "\n",
-        "scheduler = get_linear_schedule_with_warmup(optimizer, \n",
-        "                                            num_warmup_steps = 0,\n",
-        "                                            num_training_steps = total_steps)"
-      ],
-      "metadata": {
-        "id": "A7XUF4PNbYy8"
-      },
-      "execution_count": 25,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# Training"
-      ],
-      "metadata": {
-        "id": "DAzQWODja0A3"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "import random\n",
-        "import time"
-      ],
-      "metadata": {
-        "id": "Hoa7NlU0bI7G"
-      },
-      "execution_count": 26,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "# This training code is based on the `run_glue.py` script here:\n",
-        "# https://github.com/huggingface/transformers/blob/5bfcd0485ece086ebcbed2d008813037968a9e58/examples/run_glue.py#L128\n",
-        "\n",
-        "seed_val = 42\n",
-        "\n",
-        "random.seed(seed_val)\n",
-        "np.random.seed(seed_val)\n",
-        "torch.manual_seed(seed_val)\n",
-        "torch.cuda.manual_seed_all(seed_val)\n",
-        "\n",
-        "training_stats = []\n",
-        "total_t0 = time.time()\n",
-        "\n",
-        "for epoch_i in range(0, epochs):\n",
-        "    \n",
-        "    # ========================================\n",
-        "    #               Training\n",
-        "    # ========================================\n",
-        "\n",
-        "    print(\"\")\n",
-        "    print('======== Epoch {:} / {:} ========'.format(epoch_i + 1, epochs))\n",
-        "    print('Training...')\n",
-        "\n",
-        "    t0 = time.time()\n",
-        "\n",
-        "    total_train_loss = 0\n",
-        "    total_train_acc = 0\n",
-        "\n",
-        "    model.train()\n",
-        "\n",
-        "    for step, batch in enumerate(train_dataloader):\n",
-        "        if step % 40 == 0 and not step == 0:\n",
-        "            elapsed = format_time(time.time() - t0)\n",
-        "            print('  Batch {:>5,}  of  {:>5,}.    Elapsed: {:}.'.format(step, len(train_dataloader), elapsed))\n",
-        "\n",
-        "\n",
-        "        b_input_ids = batch[0].to(device)\n",
-        "        b_input_mask = batch[1].to(device)\n",
-        "\n",
-        "        y = batch[2].to(device)\n",
-        "        y_ids = y[:, :-1].contiguous()\n",
-        "        lm_labels = y[:, 1:].clone().detach()\n",
-        "        lm_labels[y[:, 1:] == tokenizer.pad_token_id] = -100\n",
-        "\n",
-        "        model.zero_grad()        \n",
-        "\n",
-        "        outputs = model(\n",
-        "            input_ids=b_input_ids,\n",
-        "            attention_mask=b_input_mask,\n",
-        "            decoder_input_ids=y_ids,\n",
-        "            labels=lm_labels\n",
-        "        )\n",
-        "\n",
-        "        generated_ids = model.generate(\n",
-        "            input_ids = b_input_ids,\n",
-        "            attention_mask = b_input_mask, \n",
-        "            max_length=2, \n",
-        "            num_beams=2,\n",
-        "            repetition_penalty=2.5, \n",
-        "            length_penalty=1.0, \n",
-        "            early_stopping=True\n",
-        "        )\n",
-        "\n",
-        "        preds = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True) for g in generated_ids]\n",
-        "        target = [tokenizer.decode(t, skip_special_tokens=True, clean_up_tokenization_spaces=True) for t in y]\n",
-        "        total_train_acc += calculate_accuracy(preds, target)   \n",
-        "\n",
-        "        loss = outputs['loss']\n",
-        "        total_train_loss += loss.item()\n",
-        "\n",
-        "        loss.backward()\n",
-        "        torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n",
-        "\n",
-        "        optimizer.step()\n",
-        "        scheduler.step()\n",
-        "\n",
-        "    avg_train_loss = total_train_loss / len(train_dataloader)            \n",
-        "    avg_train_acc = total_train_acc / len(train_dataloader)            \n",
-        "    \n",
-        "    training_time = format_time(time.time() - t0)\n",
-        "\n",
-        "    print(\"\")\n",
-        "    print(\"  Average training loss: {0:.2f}\".format(avg_train_loss))\n",
-        "    print(\"  Average training acc: {0:.2f}\".format(avg_train_acc))\n",
-        "    print(\"  Training epcoh took: {:}\".format(training_time))\n",
-        "        \n",
-        "    # ========================================\n",
-        "    #               Validation\n",
-        "    # ========================================\n",
-        "\n",
-        "    print(\"\")\n",
-        "    print(\"Running Validation...\")\n",
-        "\n",
-        "    t0 = time.time()\n",
-        "    model.eval()\n",
-        "\n",
-        "    total_eval_loss = 0\n",
-        "    total_eval_accuracy = 0\n",
-        "\n",
-        "    for batch in validation_dataloader:\n",
-        "\n",
-        "        b_input_ids = batch[0].to(device)\n",
-        "        b_input_mask = batch[1].to(device)\n",
-        "\n",
-        "        y = batch[2].to(device)\n",
-        "        y_ids = y[:, :-1].contiguous()\n",
-        "        lm_labels = y[:, 1:].clone().detach()\n",
-        "        lm_labels[y[:, 1:] == tokenizer.pad_token_id] = -100\n",
-        "        \n",
-        "        with torch.no_grad():        \n",
-        "\n",
-        "          outputs = model(\n",
-        "            input_ids=b_input_ids,\n",
-        "            attention_mask=b_input_mask,\n",
-        "            decoder_input_ids=y_ids,\n",
-        "            labels=lm_labels\n",
-        "          )\n",
-        "\n",
-        "          loss = outputs['loss']\n",
-        "          total_eval_loss += loss.item()\n",
-        "\n",
-        "          generated_ids = model.generate(\n",
-        "            input_ids = b_input_ids,\n",
-        "            attention_mask = b_input_mask, \n",
-        "            max_length=2, \n",
-        "            num_beams=2,\n",
-        "            repetition_penalty=2.5, \n",
-        "            length_penalty=1.0, \n",
-        "            early_stopping=True\n",
-        "          )\n",
-        "\n",
-        "          preds = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True) for g in generated_ids]\n",
-        "          target = [tokenizer.decode(t, skip_special_tokens=True, clean_up_tokenization_spaces=True) for t in y]\n",
-        "          total_eval_accuracy += calculate_accuracy(preds, target)        \n",
-        "\n",
-        "    avg_val_loss = total_eval_loss / len(validation_dataloader)\n",
-        "\n",
-        "    avg_val_accuracy = total_eval_accuracy / len(validation_dataloader)\n",
-        "    print(\"  Accuracy: {0:.2f}\".format(avg_val_accuracy))\n",
-        "    \n",
-        "    validation_time = format_time(time.time() - t0)\n",
-        "    print(\"  Validation took: {:}\".format(validation_time))\n",
-        "    print(\"  Validation Loss: {0:.2f}\".format(avg_val_loss))\n",
-        "\n",
-        "    training_stats.append(\n",
-        "        {\n",
-        "            'epoch': epoch_i + 1,\n",
-        "            'Training Loss': avg_train_loss,\n",
-        "            'Training Accur.': avg_train_acc,\n",
-        "            'Valid. Loss': avg_val_loss,\n",
-        "            'Valid. Accur.': avg_val_accuracy,\n",
-        "            'Training Time': training_time,\n",
-        "            'Validation Time': validation_time\n",
-        "        }\n",
-        "    )\n",
-        "\n",
-        "print(\"\")\n",
-        "print(\"Training complete!\")\n",
-        "\n",
-        "print(\"Total training took {:} (h:mm:ss)\".format(format_time(time.time()-total_t0)))"
+        "else:\n",
+        "    print('No GPU available, using the CPU instead.')\n",
+        "    device = torch.device(\"cpu\")"
       ],
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "id": "xsHxfslka1u5",
-        "outputId": "28c30ee0-6f41-4ede-eb3a-eebd4269c332"
+        "id": "86i7iRmtW-6L",
+        "outputId": "109072a9-ff0c-4ac0-fc74-4de30065af95"
       },
-      "execution_count": 27,
+      "execution_count": 15,
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
-            "\n",
-            "======== Epoch 1 / 4 ========\n",
-            "Training...\n",
-            "  Batch    40  of    515.    Elapsed: 0:00:46.\n",
-            "  Batch    80  of    515.    Elapsed: 0:01:30.\n",
-            "  Batch   120  of    515.    Elapsed: 0:02:14.\n",
-            "  Batch   160  of    515.    Elapsed: 0:02:59.\n",
-            "  Batch   200  of    515.    Elapsed: 0:03:44.\n",
-            "  Batch   240  of    515.    Elapsed: 0:04:28.\n",
-            "  Batch   280  of    515.    Elapsed: 0:05:13.\n",
-            "  Batch   320  of    515.    Elapsed: 0:05:57.\n",
-            "  Batch   360  of    515.    Elapsed: 0:06:42.\n",
-            "  Batch   400  of    515.    Elapsed: 0:07:26.\n",
-            "  Batch   440  of    515.    Elapsed: 0:08:11.\n",
-            "  Batch   480  of    515.    Elapsed: 0:08:55.\n",
-            "\n",
-            "  Average training loss: 0.01\n",
-            "  Average training acc: 0.59\n",
-            "  Training epcoh took: 0:09:34\n",
-            "\n",
-            "Running Validation...\n",
-            "  Accuracy: 0.47\n",
-            "  Validation took: 0:00:31\n",
-            "  Validation Loss: 0.00\n",
-            "\n",
-            "======== Epoch 2 / 4 ========\n",
-            "Training...\n",
-            "  Batch    40  of    515.    Elapsed: 0:00:44.\n",
-            "  Batch    80  of    515.    Elapsed: 0:01:29.\n",
-            "  Batch   120  of    515.    Elapsed: 0:02:13.\n",
-            "  Batch   160  of    515.    Elapsed: 0:02:58.\n",
-            "  Batch   200  of    515.    Elapsed: 0:03:42.\n",
-            "  Batch   240  of    515.    Elapsed: 0:04:27.\n",
-            "  Batch   280  of    515.    Elapsed: 0:05:11.\n",
-            "  Batch   320  of    515.    Elapsed: 0:05:56.\n",
-            "  Batch   360  of    515.    Elapsed: 0:06:40.\n",
-            "  Batch   400  of    515.    Elapsed: 0:07:25.\n",
-            "  Batch   440  of    515.    Elapsed: 0:08:09.\n",
-            "  Batch   480  of    515.    Elapsed: 0:08:54.\n",
-            "\n",
-            "  Average training loss: 0.00\n",
-            "  Average training acc: 0.59\n",
-            "  Training epcoh took: 0:09:32\n",
-            "\n",
-            "Running Validation...\n",
-            "  Accuracy: 0.46\n",
-            "  Validation took: 0:00:31\n",
-            "  Validation Loss: 0.00\n",
-            "\n",
-            "======== Epoch 3 / 4 ========\n",
-            "Training...\n",
-            "  Batch    40  of    515.    Elapsed: 0:00:44.\n",
-            "  Batch    80  of    515.    Elapsed: 0:01:34.\n",
-            "  Batch   120  of    515.    Elapsed: 0:02:20.\n",
-            "  Batch   160  of    515.    Elapsed: 0:03:05.\n",
-            "  Batch   200  of    515.    Elapsed: 0:03:49.\n",
-            "  Batch   240  of    515.    Elapsed: 0:04:34.\n",
-            "  Batch   280  of    515.    Elapsed: 0:05:18.\n",
-            "  Batch   320  of    515.    Elapsed: 0:06:03.\n",
-            "  Batch   360  of    515.    Elapsed: 0:06:47.\n",
-            "  Batch   400  of    515.    Elapsed: 0:07:32.\n",
-            "  Batch   440  of    515.    Elapsed: 0:08:16.\n",
-            "  Batch   480  of    515.    Elapsed: 0:09:00.\n",
-            "\n",
-            "  Average training loss: 0.00\n",
-            "  Average training acc: 0.59\n",
-            "  Training epcoh took: 0:09:39\n",
-            "\n",
-            "Running Validation...\n",
-            "  Accuracy: 0.46\n",
-            "  Validation took: 0:00:31\n",
-            "  Validation Loss: 0.00\n",
-            "\n",
-            "======== Epoch 4 / 4 ========\n",
-            "Training...\n",
-            "  Batch    40  of    515.    Elapsed: 0:00:45.\n",
-            "  Batch    80  of    515.    Elapsed: 0:01:29.\n",
-            "  Batch   120  of    515.    Elapsed: 0:02:14.\n",
-            "  Batch   160  of    515.    Elapsed: 0:02:58.\n",
-            "  Batch   200  of    515.    Elapsed: 0:03:42.\n",
-            "  Batch   240  of    515.    Elapsed: 0:04:27.\n",
-            "  Batch   280  of    515.    Elapsed: 0:05:11.\n",
-            "  Batch   320  of    515.    Elapsed: 0:05:56.\n",
-            "  Batch   360  of    515.    Elapsed: 0:06:40.\n",
-            "  Batch   400  of    515.    Elapsed: 0:07:24.\n",
-            "  Batch   440  of    515.    Elapsed: 0:08:09.\n",
-            "  Batch   480  of    515.    Elapsed: 0:08:53.\n",
-            "\n",
-            "  Average training loss: 0.00\n",
-            "  Average training acc: 0.58\n",
-            "  Training epcoh took: 0:09:32\n",
-            "\n",
-            "Running Validation...\n",
-            "  Accuracy: 0.46\n",
-            "  Validation took: 0:00:31\n",
-            "  Validation Loss: 0.00\n",
-            "\n",
-            "Training complete!\n",
-            "Total training took 0:40:22 (h:mm:ss)\n"
+            "There are 1 GPU(s) available.\n",
+            "We will use the GPU: Tesla T4\n"
           ]
         }
       ]
@@ -6591,341 +6023,46 @@
     {
       "cell_type": "markdown",
       "source": [
-        "# Train summary"
+        "# Predykcja"
       ],
       "metadata": {
-        "id": "xIpFPoRb91Or"
+        "id": "H_YI3bS3VHQE"
       }
     },
     {
       "cell_type": "code",
       "source": [
-        "import pandas as pd\n",
+        "parsed_dataset = parsed_dataset[1:123] + parsed_dataset[124:2000] + parsed_dataset[2001:3002] + parsed_dataset[3003:]\n",
+        "predictions = []\n",
+        "expected = []\n",
         "\n",
-        "pd.set_option('precision', 2)\n",
-        "df_stats = pd.DataFrame(data=training_stats)\n",
+        "for row in parsed_dataset:\n",
+        "  input_text = few_shot_prefix + row['sms'] + \"\\nSpam: \"\n",
+        "  input_ids = tokenizer(input_text, return_tensors=\"pt\").input_ids.to(device)\n",
         "\n",
-        "df_stats = df_stats.set_index('epoch')\n",
-        "df_stats"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 206
-        },
-        "id": "GjYqBrrO93Oh",
-        "outputId": "0087ee68-c017-41fd-db84-ca6e0d25fb12"
-      },
-      "execution_count": 28,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "       Training Loss  Training Accur.  Valid. Loss  Valid. Accur.  \\\n",
-              "epoch                                                               \n",
-              "1           5.27e-03             0.59          0.0           0.47   \n",
-              "2           2.74e-08             0.59          0.0           0.46   \n",
-              "3           1.58e-08             0.59          0.0           0.46   \n",
-              "4           1.55e-08             0.58          0.0           0.46   \n",
-              "\n",
-              "      Training Time Validation Time  \n",
-              "epoch                                \n",
-              "1           0:09:34         0:00:31  \n",
-              "2           0:09:32         0:00:31  \n",
-              "3           0:09:39         0:00:31  \n",
-              "4           0:09:32         0:00:31  "
-            ],
-            "text/html": [
-              "\n",
-              "  <div id=\"df-ca1b78cf-f532-4d4d-b437-30d6fbef7e05\">\n",
-              "    <div class=\"colab-df-container\">\n",
-              "      <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>Training Loss</th>\n",
-              "      <th>Training Accur.</th>\n",
-              "      <th>Valid. Loss</th>\n",
-              "      <th>Valid. Accur.</th>\n",
-              "      <th>Training Time</th>\n",
-              "      <th>Validation Time</th>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>epoch</th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>5.27e-03</td>\n",
-              "      <td>0.59</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.47</td>\n",
-              "      <td>0:09:34</td>\n",
-              "      <td>0:00:31</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>2.74e-08</td>\n",
-              "      <td>0.59</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.46</td>\n",
-              "      <td>0:09:32</td>\n",
-              "      <td>0:00:31</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>1.58e-08</td>\n",
-              "      <td>0.59</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.46</td>\n",
-              "      <td>0:09:39</td>\n",
-              "      <td>0:00:31</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>1.55e-08</td>\n",
-              "      <td>0.58</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.46</td>\n",
-              "      <td>0:09:32</td>\n",
-              "      <td>0:00:31</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>\n",
-              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ca1b78cf-f532-4d4d-b437-30d6fbef7e05')\"\n",
-              "              title=\"Convert this dataframe to an interactive table.\"\n",
-              "              style=\"display:none;\">\n",
-              "        \n",
-              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-              "       width=\"24px\">\n",
-              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
-              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
-              "  </svg>\n",
-              "      </button>\n",
-              "      \n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      flex-wrap:wrap;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "      <script>\n",
-              "        const buttonEl =\n",
-              "          document.querySelector('#df-ca1b78cf-f532-4d4d-b437-30d6fbef7e05 button.colab-df-convert');\n",
-              "        buttonEl.style.display =\n",
-              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "        async function convertToInteractive(key) {\n",
-              "          const element = document.querySelector('#df-ca1b78cf-f532-4d4d-b437-30d6fbef7e05');\n",
-              "          const dataTable =\n",
-              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                     [key], {});\n",
-              "          if (!dataTable) return;\n",
-              "\n",
-              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "            + ' to learn more about interactive tables.';\n",
-              "          element.innerHTML = '';\n",
-              "          dataTable['output_type'] = 'display_data';\n",
-              "          await google.colab.output.renderOutput(dataTable, element);\n",
-              "          const docLink = document.createElement('div');\n",
-              "          docLink.innerHTML = docLinkHtml;\n",
-              "          element.appendChild(docLink);\n",
-              "        }\n",
-              "      </script>\n",
-              "    </div>\n",
-              "  </div>\n",
-              "  "
-            ]
-          },
-          "metadata": {},
-          "execution_count": 28
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "import matplotlib.pyplot as plt\n",
-        "%matplotlib inline\n",
+        "  generated_ids = model.generate(input_ids, do_sample=True, temperature=0.9, max_length=200)\n",
+        "  generated_text = tokenizer.decode(generated_ids[0], skip_special_tokens=True, clean_up_tokenization_spaces=True)\n",
+        "  \n",
+        "  predictions.append(generated_text)\n",
+        "  expected.append(row['label'])\n",
         "\n",
-        "import seaborn as sns\n",
-        "\n",
-        "sns.set(style='darkgrid')\n",
-        "\n",
-        "sns.set(font_scale=1.5)\n",
-        "plt.rcParams[\"figure.figsize\"] = (12,6)\n",
-        "\n",
-        "plt.plot(df_stats['Training Loss'], 'b-o', label=\"Training\")\n",
-        "plt.plot(df_stats['Valid. Loss'], 'g-o', label=\"Validation\")\n",
-        "\n",
-        "plt.title(\"Training & Validation Loss\")\n",
-        "plt.xlabel(\"Epoch\")\n",
-        "plt.ylabel(\"Loss\")\n",
-        "plt.legend()\n",
-        "plt.xticks([1, 2, 3, 4])\n",
-        "\n",
-        "plt.show()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 427
-        },
-        "id": "Xk3gzkeU96v3",
-        "outputId": "aa447af5-09f3-4bc2-e234-8a74bba87c05"
-      },
-      "execution_count": 29,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 864x432 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# Create test loader"
-      ],
-      "metadata": {
-        "id": "UJlKxl0r-W-m"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "prediction_dataloader = DataLoader(\n",
-        "            test_dataset,\n",
-        "            sampler = SequentialSampler(test_dataset),\n",
-        "            batch_size = batch_size\n",
-        "        )"
-      ],
-      "metadata": {
-        "id": "eQGsEEDh-YxG"
-      },
-      "execution_count": 30,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# Evaluate on test dataset"
-      ],
-      "metadata": {
-        "id": "gHSDNWvA-aq9"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "print('Predicting labels for {:,} test sentences...'.format(len(test_dataset)))\n",
-        "\n",
-        "model.eval()\n",
-        "predictions , true_labels = [], []\n",
-        "\n",
-        "for batch in prediction_dataloader:\n",
-        "\n",
-        "    b_input_ids = batch[0].to(device)\n",
-        "    b_input_mask = batch[1].to(device)\n",
-        "    y = batch[2].to(device)\n",
-        "    \n",
-        "    with torch.no_grad():        \n",
-        "\n",
-        "      generated_ids = model.generate(\n",
-        "        input_ids = b_input_ids,\n",
-        "        attention_mask = b_input_mask, \n",
-        "        max_length=2, \n",
-        "        num_beams=2,\n",
-        "        repetition_penalty=2.5, \n",
-        "        length_penalty=1.0, \n",
-        "        early_stopping=True\n",
-        "      )\n",
-        "        \n",
-        "    preds = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True) for g in generated_ids]\n",
-        "    target = [tokenizer.decode(t, skip_special_tokens=True, clean_up_tokenization_spaces=True) for t in y]\n",
-        "\n",
-        "    predictions.append(preds)\n",
-        "    true_labels.append(target)\n",
-        "\n",
-        "print('    DONE.')"
+        "acc = calculate_accuracy(predictions, expected)\n",
+        "print(acc)"
       ],
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "id": "OPcQkHnJ-c9A",
-        "outputId": "768230ca-117a-423c-9e5d-058a63fa8838"
+        "id": "vunLGuBGVGmh",
+        "outputId": "07945f48-6cf3-4762-a91a-df68ec0d599b"
       },
-      "execution_count": 31,
+      "execution_count": 16,
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
-            "Predicting labels for 1,000 test sentences...\n",
-            "    DONE.\n"
+            "0.4601436265709156\n"
           ]
         }
       ]
@@ -6933,54 +6070,22 @@
     {
       "cell_type": "code",
       "source": [
-        "results_ok = 0\n",
-        "results_false = 0\n",
-        "for idx, true_labels_batch in enumerate(true_labels):\n",
-        "  for bidx, true_label in enumerate(true_labels_batch):\n",
-        "    if true_label == predictions[idx][bidx]:\n",
-        "      results_ok += 1\n",
-        "    else:\n",
-        "      results_false += 1\n",
-        "\n",
-        "print(\"Correct predictions: {}, incorrect results: {}, accuracy: {}\".format(results_ok, results_false, float(results_ok) / (results_ok + results_false)))"
+        "print(\"Sample prediction: {}, expected: {}\".format(predictions[101], expected[101]))"
       ],
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "id": "ifz56jYW-zBN",
-        "outputId": "0d8585e1-c0d7-4cca-a0d9-2f1c0f3dd1a9"
+        "id": "20WbYZmLaDl7",
+        "outputId": "6a5eb397-39ed-4095-8640-d76b6cdab520"
       },
-      "execution_count": 32,
+      "execution_count": 18,
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
-            "Correct predictions: 431, incorrect results: 569, accuracy: 0.431\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "print(\"Sample prediction: {}, expected: {}\".format(predictions[2][0], true_labels[2][0]))"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "1LqVo4wW-2g-",
-        "outputId": "f777b5ba-8c10-466b-c9be-d61382478d77"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Sample prediction: I, expected: true\n"
+            "Sample prediction: False, expected: False\n"
           ]
         }
       ]
@@ -6999,87 +6104,25 @@
       "source": [
         "from sklearn.metrics import matthews_corrcoef\n",
         "\n",
-        "matthews_set = []\n",
         "print('Calculating Matthews Corr. Coef. for each batch...')\n",
-        "\n",
-        "for i in range(len(true_labels)):\n",
-        "  matthews = matthews_corrcoef(true_labels[i], predictions[i])                \n",
-        "  matthews_set.append(matthews)"
+        "matthews = matthews_corrcoef(expected, predictions)    \n",
+        "print('Total MCC: %.3f' % matthews)"
       ],
       "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
         "id": "hPEPpXXX_DXR",
-        "outputId": "215f4970-5e0d-4477-cf5b-95ec1b27317f"
-      },
-      "execution_count": 33,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Calculating Matthews Corr. Coef. for each batch...\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "ax = sns.barplot(x=list(range(len(matthews_set))), y=matthews_set, ci=None)\n",
-        "\n",
-        "plt.title('MCC Score per Batch')\n",
-        "plt.ylabel('MCC Score (-1 to +1)')\n",
-        "plt.xlabel('Batch #')\n",
-        "\n",
-        "plt.show()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 427
-        },
-        "id": "qjtAYcme_EyM",
-        "outputId": "00d36441-d6fb-4398-dad1-dfed14b8c7e6"
-      },
-      "execution_count": 34,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 864x432 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "flat_predictions = np.concatenate(predictions, axis=0)\n",
-        "flat_true_labels = np.concatenate(true_labels, axis=0)\n",
-        "\n",
-        "mcc = matthews_corrcoef(flat_true_labels, flat_predictions)\n",
-        "print('Total MCC: %.3f' % mcc)"
-      ],
-      "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "id": "rkonN244_HPz",
-        "outputId": "cbf3d43c-f453-4146-ee38-58e45475aeb2"
+        "outputId": "9e0a324e-b880-4591-9243-dfb45ecb21cd"
       },
-      "execution_count": 35,
+      "execution_count": 22,
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
-            "Total MCC: -0.033\n"
+            "Calculating Matthews Corr. Coef. for each batch...\n",
+            "Total MCC: -0.001\n"
           ]
         }
       ]
@@ -7108,13 +6151,13 @@
         "tokenizer.save_pretrained(output_dir)"
       ],
       "metadata": {
+        "id": "avafCMoS_KDF",
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "id": "avafCMoS_KDF",
-        "outputId": "c1148369-1c8e-4448-de94-af6ea058c171"
+        "outputId": "16223c2c-299d-4059-8d6f-3ec2637f1185"
       },
-      "execution_count": 36,
+      "execution_count": 23,
       "outputs": [
         {
           "output_type": "stream",
@@ -7136,7 +6179,7 @@
             ]
           },
           "metadata": {},
-          "execution_count": 36
+          "execution_count": 23
         }
       ]
     },
diff --git a/projekt/README.md b/projekt/README.md
index 40b2554..1b9156f 100644
--- a/projekt/README.md
+++ b/projekt/README.md
@@ -1,16 +1,19 @@
 # Projekt
 Wykrywanie czy podany SMS to spam - klasyfikacja.
 
+
 ## Zbiór danych
-Wykorzystaliśmy zbiór danych [sms spam](https://huggingface.co/datasets/sms_spam). Dataset posiada tylko zbiór treningowy dlatego w trakcie uczenia modeli podzielilśmy go losowo na 3 podzbiory:
+Wykorzystaliśmy zbiór danych [sms spam](https://huggingface.co/datasets/sms_spam). Dataset posiada tylko zbiór treningowy dlatego w trakcie uczenia modeli podzielilśmy go losowo na 3 podzbiory (wyjątek few-shot learning):
 - zbiór testowy 1 000 przykładów
 - zbiór treningowy 4 116 przykładów
 - zbiór walidacyjny 458 przykładów
 
+
 ## Ewaluacja
 Ewaluacja modeli występuje po etapie trenowania na zbiorze testowym. Metryki:
 - accuracy 0-100%
-- Matthews’s correlation coefficient - w skrócie accuracy, tylko bierze pod uwagę zbalansowanie zbioru, wyniki: -1 przeciwne predykcje, 0 losowe, 1 100% dokładności.
+- Matthews’s correlation coefficient - w skrócie accuracy, tylko bierze pod uwagę zbalansowanie zbioru, wyniki: -1 przeciwne predykcje, 0 losowe, 1 idealna predykcja.
+
 
 ## Rozwiązania
 Wykorzystaliśmy 4 modele - BERT, GPT2, T5 oraz FLAN-T5
@@ -48,26 +51,49 @@ Najważniejsze cechy:
 - wytrenowany model t5-base
 - typ modelu transformers.T5ForConditionalGeneration
 - input modelu - 'binary classification: ' + treść smsa
-- output modelu - tekstowo '1' lub '0'
+- output modelu - tekstowo '0' lub '1'
 - finetuning na zbiorze treningowym
   - adamW optimizer
   - learning rate 3e-4
   - 16 batch size
   - 4 epoch
-- Accuracy: 74%
-- MCC: 0.190
+- Accuracy: 87%
+- MCC: 0.042
+- Ciekawostka - accuracy bez finetuningu to 30% (dla klas wyjściowych True False)
 
-### Zero-shot Transformer Encoder-Decoder - FLAN-T5
+### Few-shot Transformer Encoder-Decoder - FLAN-T5
 Najważniejsze cechy:
 - wytrenowany model google/flan-t5-base
 - typ modelu transformers.AutoModelForSeq2SeqLM
-- input modelu - Opis zadania + treść smsa
-  - Przykład: "Answer the question in one word - true if provided text is spam or false, if provided text is not spam. \nQ: Is this text spam? \nText: treść smsa \nA:"
-- output modelu - tekstowo klasa 1 'true' lub klasa 2 'false'
-- finetuning na zbiorze treningowym
-  - adamW optimizer
-  - learning rate 3e-4
-  - 8 batch size
-  - 4 epoch
-- Accauracy: 43%
-- MCC: -0.033
\ No newline at end of file
+- input modelu - 4 przykłady + docelowy sms
+  ```
+  SMS: Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...
+  Spam: False
+
+  SMS: Todays Voda numbers ending 7548 are selected to receive a $350 award. If you have a match please call 08712300220 quoting claim code 4041 standard rates app
+  Spam: True
+
+  SMS: LMAO where's your fish memory when I need it?
+  Spam: False
+
+  SMS: This message is free. Welcome to the new & improved Sex & Dogging club! To unsubscribe from this service reply STOP. msgs@150p 18+only
+  Spam: True
+
+  SMS: treść docelowego smsa
+  Spam: 
+  ```
+- output modelu - tekstowo 'True' lub 'False'
+- Ewaluacja na całym zbiorze bez 4 przykładów
+- Accauracy: 46%
+- MCC: -0.001
+
+
+## Wnioski
+
+### BERT oraz GPT2
+Dzięki głowie modelu, która jest przeznaczona specjalnie do zadania klasyfikacji tekstu, modele osiągnęły prawie perfekcyjny wynik accuracy i MCC. Dalsze modyfikacje są zbędne, ponieważ nie poprawiłby znacząco rezultatów.
+
+### T5 oraz FLAN-T5
+Modele T5 oraz FLAN-T5 to modele text-2-text, których głównym celem jest multizadaniowość. Nie są zoptymalizowane do zadania klasyfikacji, ale mimo to osiągnęły dobry wynik. W modelu T5 bardzo ważne jest określnie zadania w prefixie inputu, które będzie wykonywał model. Testowaliśmy różne kombinacje - brak prefixu, dokładny opis zadania, różne klasy wyjściowe, ale najlepsze rezultaty uzyskał prefix 'binary classification: ' z klasami wyjściowymi '0' lub '1'. Niski MCC świadczy o niezbalansowaniu predykcji.
+<br/><br/>
+FLAN-T5 to zoptymalizowany model T5. Został dodatkowo finetunowany na większym zbiorze danych. Przy uczeniu FLAN-T5 zastosowaliśmy metodę few-shot learning. Model mimo bardzo małej ilości przykładów osiągnął accuracy na poziomie 46%, jednak wynik MCC wskazuje na duże niezbalansowanie i losowe predykcje. Jest to jednak bardzo ciekawe, że wykorzystując tylko 4 przykady do uczenia, model może odpowiadać z pasującymi wynikami (niekoniecznie prawdziwymi).
\ No newline at end of file
diff --git a/projekt/T5_sms_spam.ipynb b/projekt/T5_sms_spam.ipynb
index cc354b3..50c41db 100644
--- a/projekt/T5_sms_spam.ipynb
+++ b/projekt/T5_sms_spam.ipynb
@@ -1,3453 +1,13 @@
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   "cells": [
     {
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-      "source": [
-        "# Instalacja pakietów"
-      ],
       "metadata": {
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-      }
+      },
+      "source": [
+        "# Instalacja pakietów"
+      ]
     },
     {
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@@ -3457,7 +17,7 @@
           "base_uri": "https://localhost:8080/"
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         {
@@ -3467,55 +27,55 @@
             "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
             "Collecting transformers\n",
             "  Downloading transformers-4.26.1-py3-none-any.whl (6.3 MB)\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.3/6.3 MB\u001b[0m \u001b[31m45.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.3/6.3 MB\u001b[0m \u001b[31m47.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
             "\u001b[?25hCollecting datasets\n",
             "  Downloading datasets-2.9.0-py3-none-any.whl (462 kB)\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m462.8/462.8 KB\u001b[0m \u001b[31m28.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m462.8/462.8 KB\u001b[0m \u001b[31m23.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
             "\u001b[?25hRequirement already satisfied: torch in /usr/local/lib/python3.8/dist-packages (1.13.1+cu116)\n",
             "Collecting sentencepiece\n",
             "  Downloading sentencepiece-0.1.97-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m40.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[?25hRequirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.8/dist-packages (from transformers) (4.64.1)\n",
-            "Requirement already satisfied: filelock in /usr/local/lib/python3.8/dist-packages (from transformers) (3.9.0)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m13.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.8/dist-packages (from transformers) (2022.6.2)\n",
             "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1\n",
             "  Downloading tokenizers-0.13.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.6 MB)\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.6/7.6 MB\u001b[0m \u001b[31m59.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.8/dist-packages (from transformers) (2.25.1)\n",
-            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.8/dist-packages (from transformers) (2022.6.2)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.6/7.6 MB\u001b[0m \u001b[31m43.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.8/dist-packages (from transformers) (6.0)\n",
             "Collecting huggingface-hub<1.0,>=0.11.0\n",
             "  Downloading huggingface_hub-0.12.0-py3-none-any.whl (190 kB)\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m190.3/190.3 KB\u001b[0m \u001b[31m10.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[?25hRequirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.8/dist-packages (from transformers) (23.0)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m190.3/190.3 KB\u001b[0m \u001b[31m7.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.8/dist-packages (from transformers) (4.64.1)\n",
+            "Requirement already satisfied: requests in /usr/local/lib/python3.8/dist-packages (from transformers) (2.25.1)\n",
+            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.8/dist-packages (from transformers) (23.0)\n",
+            "Requirement already satisfied: filelock in /usr/local/lib/python3.8/dist-packages (from transformers) (3.9.0)\n",
             "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.8/dist-packages (from transformers) (1.21.6)\n",
-            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.8/dist-packages (from transformers) (6.0)\n",
             "Collecting xxhash\n",
             "  Downloading xxhash-3.2.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (213 kB)\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m213.0/213.0 KB\u001b[0m \u001b[31m1.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m132.0/132.0 KB\u001b[0m \u001b[31m8.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[?25hRequirement already satisfied: aiohttp in /usr/local/lib/python3.8/dist-packages (from datasets) (3.8.3)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m213.0/213.0 KB\u001b[0m \u001b[31m9.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: pyarrow>=6.0.0 in /usr/local/lib/python3.8/dist-packages (from datasets) (9.0.0)\n",
+            "Requirement already satisfied: aiohttp in /usr/local/lib/python3.8/dist-packages (from datasets) (3.8.3)\n",
             "Collecting responses<0.19\n",
             "  Downloading responses-0.18.0-py3-none-any.whl (38 kB)\n",
-            "Requirement already satisfied: pandas in /usr/local/lib/python3.8/dist-packages (from datasets) (1.3.5)\n",
+            "Collecting multiprocess\n",
+            "  Downloading multiprocess-0.70.14-py38-none-any.whl (132 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m132.0/132.0 KB\u001b[0m \u001b[31m2.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.8/dist-packages (from datasets) (1.3.5)\n",
+            "Requirement already satisfied: fsspec[http]>=2021.11.1 in /usr/local/lib/python3.8/dist-packages (from datasets) (2023.1.0)\n",
+            "Requirement already satisfied: dill<0.3.7 in /usr/local/lib/python3.8/dist-packages (from datasets) (0.3.6)\n",
             "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.8/dist-packages (from torch) (4.4.0)\n",
+            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (6.0.4)\n",
             "Requirement already satisfied: charset-normalizer<3.0,>=2.0 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (2.1.1)\n",
+            "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (4.0.2)\n",
+            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (1.3.3)\n",
+            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (1.3.1)\n",
             "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (1.8.2)\n",
             "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (22.2.0)\n",
-            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (6.0.4)\n",
-            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (1.3.1)\n",
-            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (1.3.3)\n",
-            "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (4.0.2)\n",
-            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests->transformers) (2022.12.7)\n",
             "Requirement already satisfied: chardet<5,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests->transformers) (4.0.0)\n",
-            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests->transformers) (2.10)\n",
             "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests->transformers) (1.24.3)\n",
+            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests->transformers) (2.10)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests->transformers) (2022.12.7)\n",
             "Collecting urllib3<1.27,>=1.21.1\n",
             "  Downloading urllib3-1.26.14-py2.py3-none-any.whl (140 kB)\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m140.6/140.6 KB\u001b[0m \u001b[31m4.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m140.6/140.6 KB\u001b[0m \u001b[31m9.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
             "\u001b[?25hRequirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.8/dist-packages (from pandas->datasets) (2.8.2)\n",
             "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas->datasets) (2022.7.1)\n",
             "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.8/dist-packages (from python-dateutil>=2.7.3->pandas->datasets) (1.15.0)\n",
@@ -3534,106 +94,103 @@
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "# Załadowanie datasetu"
-      ],
       "metadata": {
         "id": "dhN0rmb5Oi3d"
-      }
+      },
+      "source": [
+        "# Załadowanie datasetu"
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "from datasets import load_dataset"
-      ],
+      "execution_count": 2,
       "metadata": {
         "id": "tnaDkwZ2Pbnn"
       },
-      "execution_count": 2,
-      "outputs": []
+      "outputs": [],
+      "source": [
+        "from datasets import load_dataset"
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "dataset = load_dataset(\"sms_spam\")"
-      ],
+      "execution_count": 3,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 231,
           "referenced_widgets": [
-            "137fa30b14f34f57a0beb8a6c6e60cf5",
-            "755a0a2d6a154feab203d06a6daf9848",
-            "c0c63e8ee5e84757a66dc953b11116d7",
-            "cf211c4bc94c47e2a9b752d4a9890271",
-            "070471ec9b8d4523be8e8779c87e7d9e",
-            "c22bd9c427b34a7dad89b1293da20e65",
-            "39348508617043c8a3cbec645ef49331",
-            "d1f02d9191c6458a9fc42f5ebb2d5961",
-            "11b4792c447c4b56acbdc5b3f7427c54",
-            "633950f724a8490b9425bb2fa2e4e84a",
-            "fe588dfbdc0f4fabaf54a1c3e02c0be3",
-            "47463520a286401ba3d5d29fce07ede5",
-            "f26179b81ddd477588764f8262560e2f",
-            "5d6c567d1f2a430c9632cf063572c8de",
-            "25f45b49e5f643ee994fbb5998807676",
-            "6030ad10c42c4993ac7d6610c8f0d77f",
-            "945360806d6445418fd779166c114994",
-            "d24b647f4a1542e5a4ebfa59bcf2bb2b",
-            "1bc411a48f1c4f849351440d4e9c646f",
-            "dd1c430002dd4fd6a08cb5b576bf0290",
-            "2d551e5c6a4548ae82c34d827f919d18",
-            "02cb3347a1be49bf8808e95a45a34801",
-            "0926a24353a94b82a8e405ec72bef775",
-            "7e44b8cbc06f4d3bb5f073fad6a0b151",
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-            "0d26ce82d9f1424dbe8d08ce7b7e5b0b",
-            "9bdccfd08b814f4fb1f53c5a8a0bded0",
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-            "f01fad907cf742f6aae936051f36579d",
-            "86661cdb45b44daeaf1581ca1b8d4cc6",
-            "04f96b4447a547b8ba8ff13b9bbedc04",
-            "dbdeaaff21924caca89dc32b633f80da",
-            "320ba3e1e74541288c307eedbd5e2754",
-            "e9675016075e4ec89c891033452ec11c",
-            "305a6be9c48c4381983307f584c5c6c8",
-            "cbfc39d75c494de284e2b8a3c95e6057",
-            "6bc6798a84944c9e8dad7391d1baa997",
-            "86c211b9dac34fd0ad51d81385d59fd8",
-            "21df98aa3cdc403f91acd1c21f4b4e9d",
-            "eeb508fdb9064c09b8152b800fe61214",
-            "ce0d0132b7904f1794037e81880a563a",
-            "113ab2100a894a16b74040611824c91d",
-            "834964317e404dd5a9a28c63a58ce6c9",
-            "a950c7427a174b22abdd17fb7710ece7",
-            "b72789fe47914768ba2808926ad54c86",
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-            "1c38d46a00224aa791cad25fdd4d33e2"
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+            "af29ca374d42465b93525a7cb54c7869",
+            "f6c0f046a62a489497e659d6a786324a",
+            "0d1267b2242d4a93ab5791a0f45aaa84",
+            "2af172c9becf4b24a12afa75f7c23bfa",
+            "9c9c5376fbed445dbea430c02cff9194",
+            "ca515e1f8f154ec398c8a823629a5622",
+            "c93b2a9a14134fccb9b815e3228ed56a",
+            "90d9bc90d3944b4297b13af2ba123f99",
+            "7f68486bff314b178c4fdb5b54cca92e",
+            "e11096eae0ef432dabb00cb49c5e96b8",
+            "84576cd6a7c64d6f809dc708b137134c",
+            "c096402cab6e4e4b911c4aa41b285e1b",
+            "556b1cb4381b473db666c64ca193585c",
+            "9213dd04267b42ffb51918b5a6e8bfe0",
+            "69b79fd9c03b4801bef03d27604ef51c",
+            "4e6240934876400db18bacae780b9839",
+            "616c196921514481aef009cde26fe0a8",
+            "6ac66769ad604b66aa658bf52b11914a",
+            "75db994dd2c24faaa36a2fdddcd11fb9",
+            "0cdcd5131b5148bf8ef7130e741e7f9b",
+            "296cc18ff4b5480e9cf22136589b23b2",
+            "1c465c9fe6314a75aefcf6293c79f800",
+            "03cc9c6818ff45c1b791557da43d5d77",
+            "19d794a37566410592951ac2f7d6f8d4",
+            "7a965267d797415a94c4b1d08eb11e63",
+            "1ea948fb5ea54ae3ae24fc124d64ac45",
+            "454abc04b69a46b5b28c10be09db3adf",
+            "41b58dbbb09c4b9497b0acb4a7484f45",
+            "7a0a7e8ab334489d8d363459b9e0bd37",
+            "7071959c1e2141d58bceb19e1e0759f3"
           ]
         },
         "id": "cCiAuRqrOkvV",
-        "outputId": "b8dfca85-8b7a-4321-da77-ec7eea1843e9"
+        "outputId": "22cf5f97-411a-4e6c-b0bf-28b477607386"
       },
-      "execution_count": 3,
       "outputs": [
         {
           "output_type": "display_data",
@@ -3644,7 +201,7 @@
             "application/vnd.jupyter.widget-view+json": {
               "version_major": 2,
               "version_minor": 0,
-              "model_id": "137fa30b14f34f57a0beb8a6c6e60cf5"
+              "model_id": "d481c2d945214c45b716d266d5d75184"
             }
           },
           "metadata": {}
@@ -3658,7 +215,7 @@
             "application/vnd.jupyter.widget-view+json": {
               "version_major": 2,
               "version_minor": 0,
-              "model_id": "47463520a286401ba3d5d29fce07ede5"
+              "model_id": "0a4967eb8656482988be61b4e884ab6b"
             }
           },
           "metadata": {}
@@ -3672,7 +229,7 @@
             "application/vnd.jupyter.widget-view+json": {
               "version_major": 2,
               "version_minor": 0,
-              "model_id": "0926a24353a94b82a8e405ec72bef775"
+              "model_id": "36974fdfee5141b7850289e25b23df3c"
             }
           },
           "metadata": {}
@@ -3693,7 +250,7 @@
             "application/vnd.jupyter.widget-view+json": {
               "version_major": 2,
               "version_minor": 0,
-              "model_id": "320ba3e1e74541288c307eedbd5e2754"
+              "model_id": "4bbbc1eaabb0460cba315a082cd69783"
             }
           },
           "metadata": {}
@@ -3707,7 +264,7 @@
             "application/vnd.jupyter.widget-view+json": {
               "version_major": 2,
               "version_minor": 0,
-              "model_id": "a950c7427a174b22abdd17fb7710ece7"
+              "model_id": "7f68486bff314b178c4fdb5b54cca92e"
             }
           },
           "metadata": {}
@@ -3728,26 +285,26 @@
             "application/vnd.jupyter.widget-view+json": {
               "version_major": 2,
               "version_minor": 0,
-              "model_id": "fddd4ee4bc054b0f90ed88018fc3e3a0"
+              "model_id": "0cdcd5131b5148bf8ef7130e741e7f9b"
             }
           },
           "metadata": {}
         }
+      ],
+      "source": [
+        "dataset = load_dataset(\"sms_spam\")"
       ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "dataset['train'][0]"
-      ],
+      "execution_count": 4,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "JKFHPko3OnAV",
-        "outputId": "2048bc4f-4d5f-45e4-e5c9-0be61d9d7349"
+        "outputId": "2682981e-b242-4563-8e88-21f1b56f2d15"
       },
-      "execution_count": 4,
       "outputs": [
         {
           "output_type": "execute_result",
@@ -3760,19 +317,43 @@
           "metadata": {},
           "execution_count": 4
         }
+      ],
+      "source": [
+        "dataset['train'][0]"
       ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "# Modyfikacja datasetu - klasyfikacja"
-      ],
       "metadata": {
         "id": "l140vJrgYxPr"
-      }
+      },
+      "source": [
+        "# Modyfikacja datasetu - klasyfikacja"
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "1boUF-YiY3_y",
+        "outputId": "f031453d-54d7-4fba-abc4-26ff8e110d96"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "{'sms': 'binary classification: Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...',\n",
+              " 'label': '0'}"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ],
       "source": [
         "parsed_dataset = []\n",
         "\n",
@@ -3787,87 +368,63 @@
         "  parsed_dataset.append(new_row)\n",
         "\n",
         "parsed_dataset[0]"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "1boUF-YiY3_y",
-        "outputId": "fed6fa9c-8699-4727-ae1b-37475f831b61"
-      },
-      "execution_count": 5,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "{'sms': 'binary classification: Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...',\n",
-              " 'label': '0'}"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 5
-        }
       ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "# Tokenizer T5"
-      ],
       "metadata": {
         "id": "O-J-jBDxPJcn"
-      }
+      },
+      "source": [
+        "# Tokenizer T5"
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "from transformers import T5Tokenizer"
-      ],
+      "execution_count": 6,
       "metadata": {
         "id": "P23AYPX1PZ6g"
       },
-      "execution_count": 6,
-      "outputs": []
+      "outputs": [],
+      "source": [
+        "from transformers import T5Tokenizer"
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "tokenizer = T5Tokenizer.from_pretrained('t5-base')"
-      ],
+      "execution_count": 7,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 203,
           "referenced_widgets": [
-            "6e2b903343ad49c89339a38a1c626619",
-            "b1e96f5c00d048c69c1c0fadeb31dcd9",
-            "ec994f6daafb4ef8a08371f2394918dd",
-            "699d9f4479854372ada35ab38fe80352",
-            "cb36ca390eac4c76ad8cfbcbdb5b6950",
-            "5ffe2a0b8e3342a9b764fbbdf1395f1c",
-            "ea92e7a968d4479e842c368ece4b60c1",
-            "95a4496f03414cf8a8d7cf5e6cc3f37b",
-            "7b1d15df592048fe8a8c043e7a8461ad",
-            "cc73442788a3462a8d5d53c9c799df7a",
-            "ee75218b2c4047fdb265df7a54feea78",
-            "e720c7e5ef0849918eb6e7123673c95e",
-            "8b2ff14cab9941388b547140d06e1dd5",
-            "0eb67bf20ecf4cc0b5f3bd0589440e6b",
-            "4e976d7959e640f4b098d9a02320f228",
-            "48de3465dc194fff9903fd3813aae91a",
-            "29558f7cc7574024879d274548ac4cd7",
-            "ce5531904574465d84d65365b0fc2951",
-            "10cc03c556cf4fb791697277b3deef35",
-            "2113acabfb014e7ab55d099d28845914",
-            "504b320daae543b78b8777cebbe65dea",
-            "5ea6f5fa80184fe58ef86a536ec0f8f0"
+            "e9563b43a79542b3ac4607b3b32fef36",
+            "dd79782a047542c79cf638d50c4fb6b5",
+            "ecada9c5179b40cfa2aba932c5a9aedd",
+            "e64dfd8ce12043a48eb24ee8fafaaa5e",
+            "fb537b30046a4b699cd0ac5d21c0cb5e",
+            "e63a74cb438b488a9f1f5e340842e5e0",
+            "4c3f3eeffbc946428cd198bf16fbdac0",
+            "5f0ceff818cf43308975724aabc9d799",
+            "e1e65e6fe54c4d60a6c93573d3cd1fd0",
+            "b163c0caacc547199a2372e03bdabcce",
+            "faf1774fb05940b8908415eafe3a1070",
+            "bf509a7ff72a4e40bfea72feb958477c",
+            "1479e73733d548d4881d163983549014",
+            "1eff9f6872ca41feb2a67795d6b2af20",
+            "054b198ab5174073bfaa2a951410dc37",
+            "2f029974bf9643dab5e96eb15c6834b8",
+            "28036b83d1fc438e913f8f3f4cb19391",
+            "ef17ea825afa4cf8898f13c1cefa8475",
+            "b31a9cf7195b47faaa22deb09fe1f0d1",
+            "2c056fb1574a42eaaeb4d64c45b9e884",
+            "28f716fbfec540819b13602a705e69cd",
+            "5a0c5d19389e4ccbb552ed41111dfa2c"
           ]
         },
         "id": "q5Jz0E_oPMBr",
-        "outputId": "bbe8a564-fee5-42ef-da4d-04ae4af111db"
+        "outputId": "2a01700a-67b3-4347-afda-a2763b30c714"
       },
-      "execution_count": 7,
       "outputs": [
         {
           "output_type": "display_data",
@@ -3878,7 +435,7 @@
             "application/vnd.jupyter.widget-view+json": {
               "version_major": 2,
               "version_minor": 0,
-              "model_id": "6e2b903343ad49c89339a38a1c626619"
+              "model_id": "e9563b43a79542b3ac4607b3b32fef36"
             }
           },
           "metadata": {}
@@ -3892,7 +449,7 @@
             "application/vnd.jupyter.widget-view+json": {
               "version_major": 2,
               "version_minor": 0,
-              "model_id": "e720c7e5ef0849918eb6e7123673c95e"
+              "model_id": "bf509a7ff72a4e40bfea72feb958477c"
             }
           },
           "metadata": {}
@@ -3909,24 +466,21 @@
             "  warnings.warn(\n"
           ]
         }
+      ],
+      "source": [
+        "tokenizer = T5Tokenizer.from_pretrained('t5-base')"
       ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "sms = parsed_dataset[0]['sms']\n",
-        "print('Original: ', sms)\n",
-        "print('Tokenized: ', tokenizer.tokenize(sms))\n",
-        "print('Token IDs: ', tokenizer.convert_tokens_to_ids(tokenizer.tokenize(sms)))"
-      ],
+      "execution_count": 8,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "dfxJQpoePsvI",
-        "outputId": "fa44a9cd-aff1-4b64-957e-52595dad7472"
+        "outputId": "305252ef-73f2-4677-d7c9-31efc4d249a6"
       },
-      "execution_count": 8,
       "outputs": [
         {
           "output_type": "stream",
@@ -3937,36 +491,33 @@
             "Token IDs:  [14865, 13774, 10, 1263, 552, 10081, 2444, 500, 6, 6139, 5, 5, 8144, 163, 16, 8143, 159, 3, 29, 248, 296, 50, 3, 15, 15385, 233, 17270, 132, 530, 3, 9, 3706, 8036, 17, 233]\n"
           ]
         }
+      ],
+      "source": [
+        "sms = parsed_dataset[0]['sms']\n",
+        "print('Original: ', sms)\n",
+        "print('Tokenized: ', tokenizer.tokenize(sms))\n",
+        "print('Token IDs: ', tokenizer.convert_tokens_to_ids(tokenizer.tokenize(sms)))"
       ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "# Check maximum lenght of a sentence"
-      ],
       "metadata": {
         "id": "UpluhM8cU5Ir"
-      }
+      },
+      "source": [
+        "# Check maximum lenght of a sentence"
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "max_len = 0\n",
-        "\n",
-        "for sentence in parsed_dataset:\n",
-        "    input_ids = tokenizer.encode(sentence['sms'], add_special_tokens=True)\n",
-        "    max_len = max(max_len, len(input_ids))\n",
-        "\n",
-        "print('Max sentence length: ', max_len)"
-      ],
+      "execution_count": 9,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "7uNUkixPU85O",
-        "outputId": "2ec78c60-f5ae-4201-c8e5-30208c94efab"
+        "outputId": "6812abc8-d279-4ee8-9947-9c9a5932e47c"
       },
-      "execution_count": 9,
       "outputs": [
         {
           "output_type": "stream",
@@ -3975,27 +526,27 @@
             "Max sentence length:  341\n"
           ]
         }
+      ],
+      "source": [
+        "max_len = 0\n",
+        "\n",
+        "for sentence in parsed_dataset:\n",
+        "    input_ids = tokenizer.encode(sentence['sms'], add_special_tokens=True)\n",
+        "    max_len = max(max_len, len(input_ids))\n",
+        "\n",
+        "print('Max sentence length: ', max_len)"
       ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "max_label_len = 0\n",
-        "\n",
-        "for sentence in parsed_dataset:\n",
-        "    input_ids = tokenizer.encode(sentence['label'], add_special_tokens=True)\n",
-        "    max_label_len = max(max_label_len, len(input_ids))\n",
-        "\n",
-        "print('Max sentence length: ', max_label_len)"
-      ],
+      "execution_count": 10,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "lj0issBznZfK",
-        "outputId": "2fb86f95-c0a0-45ea-a36d-a6b174f32aac"
+        "outputId": "9dccb0f2-6452-460b-b955-83468285a635"
       },
-      "execution_count": 10,
       "outputs": [
         {
           "output_type": "stream",
@@ -4004,76 +555,47 @@
             "Max sentence length:  3\n"
           ]
         }
+      ],
+      "source": [
+        "max_label_len = 0\n",
+        "\n",
+        "for sentence in parsed_dataset:\n",
+        "    input_ids = tokenizer.encode(sentence['label'], add_special_tokens=True)\n",
+        "    max_label_len = max(max_label_len, len(input_ids))\n",
+        "\n",
+        "print('Max sentence length: ', max_label_len)"
       ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "# Pre train tokenization"
-      ],
       "metadata": {
         "id": "nfw62HdgSERb"
-      }
+      },
+      "source": [
+        "# Pre train tokenization"
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "import torch"
-      ],
+      "execution_count": 11,
       "metadata": {
         "id": "KTXYalS1VLqH"
       },
-      "execution_count": 11,
-      "outputs": []
+      "outputs": [],
+      "source": [
+        "import torch"
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "input_ids = []\n",
-        "target_ids = []\n",
-        "attention_masks = []\n",
-        "\n",
-        "for sentence in parsed_dataset:\n",
-        "    encoded_dict = tokenizer.encode_plus(\n",
-        "                        sentence['sms'],\n",
-        "                        add_special_tokens = True,\n",
-        "                        max_length = 341,\n",
-        "                        padding = 'max_length',\n",
-        "                        truncation=True,\n",
-        "                        return_attention_mask = True,\n",
-        "                        return_tensors = 'pt',\n",
-        "                   )\n",
-        "    \n",
-        "    encoded_target_dict = tokenizer.encode_plus(\n",
-        "                        sentence['label'],\n",
-        "                        add_special_tokens = True,\n",
-        "                        max_length = 3,\n",
-        "                        padding = 'max_length',\n",
-        "                        truncation=True,\n",
-        "                        return_attention_mask = True,\n",
-        "                        return_tensors = 'pt',\n",
-        "                   )\n",
-        "      \n",
-        "    input_ids.append(encoded_dict['input_ids'])\n",
-        "    target_ids.append(encoded_target_dict['input_ids'])\n",
-        "    attention_masks.append(encoded_dict['attention_mask'])\n",
-        "\n",
-        "input_ids = torch.cat(input_ids, dim=0)\n",
-        "target_ids = torch.cat(target_ids, dim=0)\n",
-        "attention_masks = torch.cat(attention_masks, dim=0)\n",
-        "\n",
-        "print('Original: ', parsed_dataset[0])\n",
-        "print('Token IDs:', input_ids[0])\n",
-        "print('Label token IDs:', target_ids[0])"
-      ],
+      "execution_count": 12,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "Z28QYfLnSGxR",
-        "outputId": "e90e2369-25d1-4fc1-b7fd-b805eaf1f5de"
+        "outputId": "bcb84fca-c7e0-4e4a-a8f6-ff3db86e143d"
       },
-      "execution_count": 12,
       "outputs": [
         {
           "output_type": "stream",
@@ -4118,30 +640,87 @@
             "Label token IDs: tensor([  3, 632,   1])\n"
           ]
         }
+      ],
+      "source": [
+        "input_ids = []\n",
+        "target_ids = []\n",
+        "attention_masks = []\n",
+        "\n",
+        "for sentence in parsed_dataset:\n",
+        "    encoded_dict = tokenizer.encode_plus(\n",
+        "                        sentence['sms'],\n",
+        "                        add_special_tokens = True,\n",
+        "                        max_length = 341,\n",
+        "                        padding = 'max_length',\n",
+        "                        truncation=True,\n",
+        "                        return_attention_mask = True,\n",
+        "                        return_tensors = 'pt',\n",
+        "                   )\n",
+        "    \n",
+        "    encoded_target_dict = tokenizer.encode_plus(\n",
+        "                        sentence['label'],\n",
+        "                        add_special_tokens = True,\n",
+        "                        max_length = 3,\n",
+        "                        padding = 'max_length',\n",
+        "                        truncation=True,\n",
+        "                        return_attention_mask = True,\n",
+        "                        return_tensors = 'pt',\n",
+        "                   )\n",
+        "      \n",
+        "    input_ids.append(encoded_dict['input_ids'])\n",
+        "    target_ids.append(encoded_target_dict['input_ids'])\n",
+        "    attention_masks.append(encoded_dict['attention_mask'])\n",
+        "\n",
+        "input_ids = torch.cat(input_ids, dim=0)\n",
+        "target_ids = torch.cat(target_ids, dim=0)\n",
+        "attention_masks = torch.cat(attention_masks, dim=0)\n",
+        "\n",
+        "print('Original: ', parsed_dataset[0])\n",
+        "print('Token IDs:', input_ids[0])\n",
+        "print('Label token IDs:', target_ids[0])"
       ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "# Split dataset"
-      ],
       "metadata": {
         "id": "qD_t0y0KVVSy"
-      }
+      },
+      "source": [
+        "# Split dataset"
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "from torch.utils.data import TensorDataset, random_split"
-      ],
+      "execution_count": 13,
       "metadata": {
         "id": "vN_SatRIVa4c"
       },
-      "execution_count": 13,
-      "outputs": []
+      "outputs": [],
+      "source": [
+        "from torch.utils.data import TensorDataset, random_split"
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Mm6vc6lLVW3l",
+        "outputId": "2ff9533f-7117-4492-dae2-5a8dada86e41"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "1,000 test samples\n",
+            "4,116 training samples\n",
+            "  458 validation samples\n"
+          ]
+        }
+      ],
       "source": [
         "dataset = TensorDataset(input_ids, attention_masks, target_ids)\n",
         "\n",
@@ -4155,49 +734,35 @@
         "print('{:>5,} test samples'.format(test_size))\n",
         "print('{:>5,} training samples'.format(train_size))\n",
         "print('{:>5,} validation samples'.format(val_size))"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "Mm6vc6lLVW3l",
-        "outputId": "af8a7007-791f-426c-c277-1c77a1fd9d78"
-      },
-      "execution_count": 14,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "1,000 test samples\n",
-            "4,116 training samples\n",
-            "  458 validation samples\n"
-          ]
-        }
       ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "# Create train and validation loaders"
-      ],
       "metadata": {
         "id": "bmgQOP4EVfA1"
-      }
+      },
+      "source": [
+        "# Create train and validation loaders"
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "from torch.utils.data import DataLoader, RandomSampler, SequentialSampler"
-      ],
+      "execution_count": 15,
       "metadata": {
         "id": "CxnQ3cmIVlNh"
       },
-      "execution_count": 15,
-      "outputs": []
+      "outputs": [],
+      "source": [
+        "from torch.utils.data import DataLoader, RandomSampler, SequentialSampler"
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {
+        "id": "0hcpO_onVjEC"
+      },
+      "outputs": [],
       "source": [
         "batch_size = 16\n",
         "\n",
@@ -4212,24 +777,37 @@
         "            sampler = SequentialSampler(val_dataset),\n",
         "            batch_size = batch_size\n",
         "        )"
-      ],
-      "metadata": {
-        "id": "0hcpO_onVjEC"
-      },
-      "execution_count": 16,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "# Device check"
-      ],
       "metadata": {
         "id": "efwhqLyyVu9z"
-      }
+      },
+      "source": [
+        "# Device check"
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ANBCfNGnVwVk",
+        "outputId": "ff2ff959-f0e9-47f3-d504-9daa45f870c2"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "There are 1 GPU(s) available.\n",
+            "We will use the GPU: Tesla T4\n"
+          ]
+        }
+      ],
       "source": [
         "if torch.cuda.is_available():     \n",
         "    device = torch.device(\"cuda\")\n",
@@ -4240,86 +818,63 @@
         "else:\n",
         "    print('No GPU available, using the CPU instead.')\n",
         "    device = torch.device(\"cpu\")"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "ANBCfNGnVwVk",
-        "outputId": "02086b95-30b8-4be0-aa4b-ac0041b4b007"
-      },
-      "execution_count": 17,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "There are 1 GPU(s) available.\n",
-            "We will use the GPU: Tesla T4\n"
-          ]
-        }
       ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "# Load T5 model"
-      ],
       "metadata": {
         "id": "okTx_ynMV0rH"
-      }
+      },
+      "source": [
+        "# Load T5 model"
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "from transformers import T5ForConditionalGeneration"
-      ],
+      "execution_count": 18,
       "metadata": {
         "id": "Eu-7Eed8WgN0"
       },
-      "execution_count": 18,
-      "outputs": []
+      "outputs": [],
+      "source": [
+        "from transformers import T5ForConditionalGeneration"
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "model = T5ForConditionalGeneration.from_pretrained('t5-base')\n",
-        "\n",
-        "model.cuda()"
-      ],
+      "execution_count": 19,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 1000,
           "referenced_widgets": [
-            "b1d7a5cf900b48408e515baa4c66a1cd",
-            "6846a2acd95b45a3a0e2cb79f552f0c0",
-            "fce901d34cc34feeb92854999e98c0f9",
-            "5a89ce40643247fda326742531912a01",
-            "bcc7a0cd035e485680b41e7c4a78b8f8",
-            "bd0a578fefb44fb4b2662d59fd2ff12e",
-            "6585bd6115c047fd881c0bfd323142f0",
-            "dbc7f7aa90174ff68b5cc829a6fd8690",
-            "ca3a8e4611c6422380351b947882876a",
-            "2470365762844b62a09dc6fa818c4a09",
-            "3f2489ce0ae941a1a720c60a3052ee70",
-            "0795a8385c68409fb5539b9ea6756a47",
-            "05dfc6dc9f78483da34b2c6513315e7d",
-            "5cfe28a638cb42fc914dc81eb02a46f4",
-            "d061dcb2f3e840ec9ba6a6ec4d972619",
-            "df418dee3efd4da8aa57ca0044190b2e",
-            "9d3d394c756d4eabb0f3fd66ba8ef05a",
-            "00612595fa42467a83aa6e4b55343339",
-            "33521be9887b4c368915b4f8f2438440",
-            "990a862f07894fa9b9f08d3bb7e082ca",
-            "1b793ae9c46740bdbbec5e617a899683",
-            "cbfde7f5f0204417abdced523c5621e9"
+            "68418b4f08654a2c8a19bdefa31ef7e2",
+            "f59f1fe74df84329baa0137729651d7e",
+            "4e6666f32de94c14973b2f5895c4f4ec",
+            "9a8b0e9cf614453789dceff586f47682",
+            "a4e1407e1a42416087a3138812851afa",
+            "1813bc00d8db4de5a7bb7cd276346312",
+            "ab6b0613a4934f34aad4d28cd855362d",
+            "7514dfc8c5c34f29ab9a246ba6b45dc2",
+            "017b00a3a26743d3a761a5b05f72fe73",
+            "1cfe23326f964bb0a2925456aea14ad5",
+            "384aac4ea3274eebbb43ea847036793a",
+            "17986d272156460f8e9bcee2559088d9",
+            "f1c7c8e7770848dabf155be27b342c6f",
+            "719b8ebc46884edd9b36829f49680c98",
+            "f28050af08f947678a41e1ea5611067f",
+            "2ff5d9e91bf64330a2747c9c518ba31c",
+            "85bd410d586b4ac98b8df72f980c0194",
+            "feb7905c359e4acd9c9f848fb63d5d55",
+            "b1d4154a8b054c8380a9ac70c311755b",
+            "2fee9e3e54ae41c8977beaae6802010f",
+            "42dc0f0578ed4105abeee4362667a98a",
+            "04bb3488deec4565a0864049b122437d"
           ]
         },
         "id": "JKv9O8kfV2zZ",
-        "outputId": "b4b823d6-f7dc-4b78-a12b-4a2bae4e463f"
+        "outputId": "ad88a39b-bdc7-4325-b588-ed5feb453c3e"
       },
-      "execution_count": 19,
       "outputs": [
         {
           "output_type": "display_data",
@@ -4330,7 +885,7 @@
             "application/vnd.jupyter.widget-view+json": {
               "version_major": 2,
               "version_minor": 0,
-              "model_id": "b1d7a5cf900b48408e515baa4c66a1cd"
+              "model_id": "68418b4f08654a2c8a19bdefa31ef7e2"
             }
           },
           "metadata": {}
@@ -4344,7 +899,7 @@
             "application/vnd.jupyter.widget-view+json": {
               "version_major": 2,
               "version_minor": 0,
-              "model_id": "0795a8385c68409fb5539b9ea6756a47"
+              "model_id": "17986d272156460f8e9bcee2559088d9"
             }
           },
           "metadata": {}
@@ -5074,31 +1629,41 @@
           "metadata": {},
           "execution_count": 19
         }
+      ],
+      "source": [
+        "model = T5ForConditionalGeneration.from_pretrained('t5-base')\n",
+        "\n",
+        "model.cuda()"
       ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "# Helper functions"
-      ],
       "metadata": {
         "id": "F_SDAwxoawDy"
-      }
+      },
+      "source": [
+        "# Helper functions"
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "import datetime\n",
-        "import numpy as np"
-      ],
+      "execution_count": 20,
       "metadata": {
         "id": "s-q6_F38bLVA"
       },
-      "execution_count": 20,
-      "outputs": []
+      "outputs": [],
+      "source": [
+        "import datetime\n",
+        "import numpy as np"
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 21,
+      "metadata": {
+        "id": "FzUi8908ax61"
+      },
+      "outputs": [],
       "source": [
         "def calculate_accuracy(preds, target):\n",
         "  results_ok = 0.0\n",
@@ -5118,35 +1683,24 @@
         "    '''\n",
         "    elapsed_rounded = int(round((elapsed)))\n",
         "    return str(datetime.timedelta(seconds=elapsed_rounded))"
-      ],
-      "metadata": {
-        "id": "FzUi8908ax61"
-      },
-      "execution_count": 21,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "# Init training"
-      ],
       "metadata": {
         "id": "ucChBa-9bXJy"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "from transformers import get_linear_schedule_with_warmup"
-      ],
-      "metadata": {
-        "id": "c9e7rbGwbdEp"
       },
-      "execution_count": 22,
-      "outputs": []
+      "source": [
+        "# Init training"
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 22,
+      "metadata": {
+        "id": "A7XUF4PNbYy8"
+      },
+      "outputs": [],
       "source": [
         "optimizer = torch.optim.AdamW(model.parameters(),\n",
         "                  lr = 3e-4,\n",
@@ -5154,42 +1708,123 @@
         "                )\n",
         "\n",
         "epochs = 4\n",
-        "\n",
-        "total_steps = len(train_dataloader) * epochs\n",
-        "\n",
-        "scheduler = get_linear_schedule_with_warmup(optimizer, \n",
-        "                                            num_warmup_steps = 0,\n",
-        "                                            num_training_steps = total_steps)"
-      ],
-      "metadata": {
-        "id": "A7XUF4PNbYy8"
-      },
-      "execution_count": 23,
-      "outputs": []
+        "total_steps = len(train_dataloader) * epochs"
+      ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "# Training"
-      ],
       "metadata": {
         "id": "DAzQWODja0A3"
-      }
+      },
+      "source": [
+        "# Training"
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "import random\n",
-        "import time"
-      ],
+      "execution_count": 23,
       "metadata": {
         "id": "Hoa7NlU0bI7G"
       },
-      "execution_count": 24,
-      "outputs": []
+      "outputs": [],
+      "source": [
+        "import random\n",
+        "import time"
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 24,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "xsHxfslka1u5",
+        "outputId": "e40d00a1-baf8-4554-e5ec-aeb87ee35f66"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "======== Epoch 1 / 4 ========\n",
+            "Training...\n",
+            "  Batch    40  of    258.    Elapsed: 0:01:12.\n",
+            "  Batch    80  of    258.    Elapsed: 0:02:20.\n",
+            "  Batch   120  of    258.    Elapsed: 0:03:28.\n",
+            "  Batch   160  of    258.    Elapsed: 0:04:36.\n",
+            "  Batch   200  of    258.    Elapsed: 0:05:45.\n",
+            "  Batch   240  of    258.    Elapsed: 0:06:53.\n",
+            "\n",
+            "  Average training loss: 0.09\n",
+            "  Average training acc: 0.81\n",
+            "  Training epcoh took: 0:07:23\n",
+            "\n",
+            "Running Validation...\n",
+            "  Accuracy: 0.83\n",
+            "  Validation took: 0:00:27\n",
+            "  Validation Loss: 0.00\n",
+            "\n",
+            "======== Epoch 2 / 4 ========\n",
+            "Training...\n",
+            "  Batch    40  of    258.    Elapsed: 0:01:09.\n",
+            "  Batch    80  of    258.    Elapsed: 0:02:17.\n",
+            "  Batch   120  of    258.    Elapsed: 0:03:25.\n",
+            "  Batch   160  of    258.    Elapsed: 0:04:33.\n",
+            "  Batch   200  of    258.    Elapsed: 0:05:42.\n",
+            "  Batch   240  of    258.    Elapsed: 0:06:50.\n",
+            "\n",
+            "  Average training loss: 0.00\n",
+            "  Average training acc: 0.86\n",
+            "  Training epcoh took: 0:07:19\n",
+            "\n",
+            "Running Validation...\n",
+            "  Accuracy: 0.83\n",
+            "  Validation took: 0:00:26\n",
+            "  Validation Loss: 0.00\n",
+            "\n",
+            "======== Epoch 3 / 4 ========\n",
+            "Training...\n",
+            "  Batch    40  of    258.    Elapsed: 0:01:08.\n",
+            "  Batch    80  of    258.    Elapsed: 0:02:16.\n",
+            "  Batch   120  of    258.    Elapsed: 0:03:24.\n",
+            "  Batch   160  of    258.    Elapsed: 0:04:32.\n",
+            "  Batch   200  of    258.    Elapsed: 0:05:41.\n",
+            "  Batch   240  of    258.    Elapsed: 0:06:49.\n",
+            "\n",
+            "  Average training loss: 0.00\n",
+            "  Average training acc: 0.85\n",
+            "  Training epcoh took: 0:07:18\n",
+            "\n",
+            "Running Validation...\n",
+            "  Accuracy: 0.83\n",
+            "  Validation took: 0:00:26\n",
+            "  Validation Loss: 0.00\n",
+            "\n",
+            "======== Epoch 4 / 4 ========\n",
+            "Training...\n",
+            "  Batch    40  of    258.    Elapsed: 0:01:08.\n",
+            "  Batch    80  of    258.    Elapsed: 0:02:16.\n",
+            "  Batch   120  of    258.    Elapsed: 0:03:24.\n",
+            "  Batch   160  of    258.    Elapsed: 0:04:32.\n",
+            "  Batch   200  of    258.    Elapsed: 0:05:41.\n",
+            "  Batch   240  of    258.    Elapsed: 0:06:49.\n",
+            "\n",
+            "  Average training loss: 0.00\n",
+            "  Average training acc: 0.86\n",
+            "  Training epcoh took: 0:07:18\n",
+            "\n",
+            "Running Validation...\n",
+            "  Accuracy: 0.83\n",
+            "  Validation took: 0:00:26\n",
+            "  Validation Loss: 0.00\n",
+            "\n",
+            "Training complete!\n",
+            "Total training took 0:31:02 (h:mm:ss)\n"
+          ]
+        }
+      ],
       "source": [
         "# This training code is based on the `run_glue.py` script here:\n",
         "# https://github.com/huggingface/transformers/blob/5bfcd0485ece086ebcbed2d008813037968a9e58/examples/run_glue.py#L128\n",
@@ -5231,9 +1866,7 @@
         "        y = batch[2].to(device)\n",
         "        y_ids = y[:, :-1].contiguous()\n",
         "        lm_labels = y[:, 1:].clone().detach()\n",
-        "        lm_labels[y[:, 1:] == tokenizer.pad_token_id] = -100\n",
-        "\n",
-        "        model.zero_grad()        \n",
+        "        lm_labels[y[:, 1:] == tokenizer.pad_token_id] = -100      \n",
         "\n",
         "        outputs = model(\n",
         "            input_ids=b_input_ids,\n",
@@ -5245,7 +1878,7 @@
         "        generated_ids = model.generate(\n",
         "            input_ids = b_input_ids,\n",
         "            attention_mask = b_input_mask, \n",
-        "            max_length=2, \n",
+        "            max_length=3, \n",
         "            num_beams=2,\n",
         "            repetition_penalty=2.5, \n",
         "            length_penalty=1.0, \n",
@@ -5256,14 +1889,13 @@
         "        target = [tokenizer.decode(t, skip_special_tokens=True, clean_up_tokenization_spaces=True) for t in y]\n",
         "        total_train_acc += calculate_accuracy(preds, target)   \n",
         "\n",
-        "        loss = outputs['loss']\n",
-        "        total_train_loss += loss.item()\n",
+        "        loss = outputs[0]\n",
         "\n",
+        "        optimizer.zero_grad()\n",
         "        loss.backward()\n",
-        "        torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n",
-        "\n",
         "        optimizer.step()\n",
-        "        scheduler.step()\n",
+        "\n",
+        "        total_train_loss += loss.item()\n",
         "\n",
         "    avg_train_loss = total_train_loss / len(train_dataloader)    \n",
         "    avg_train_acc = total_train_acc / len(train_dataloader)          \n",
@@ -5305,13 +1937,13 @@
         "            labels=lm_labels\n",
         "          )\n",
         "\n",
-        "          loss = outputs['loss']\n",
+        "          loss = outputs[0]\n",
         "          total_eval_loss += loss.item()\n",
         "\n",
         "          generated_ids = model.generate(\n",
         "            input_ids = b_input_ids,\n",
         "            attention_mask = b_input_mask, \n",
-        "            max_length=2, \n",
+        "            max_length=3, \n",
         "            num_beams=2,\n",
         "            repetition_penalty=2.5, \n",
         "            length_penalty=1.0, \n",
@@ -5347,128 +1979,28 @@
         "print(\"Training complete!\")\n",
         "\n",
         "print(\"Total training took {:} (h:mm:ss)\".format(format_time(time.time()-total_t0)))"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "xsHxfslka1u5",
-        "outputId": "60bea81f-a963-4599-ca22-b1992c14a3e5"
-      },
-      "execution_count": 25,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "\n",
-            "======== Epoch 1 / 4 ========\n",
-            "Training...\n",
-            "  Batch    40  of    258.    Elapsed: 0:01:06.\n",
-            "  Batch    80  of    258.    Elapsed: 0:02:13.\n",
-            "  Batch   120  of    258.    Elapsed: 0:03:22.\n",
-            "  Batch   160  of    258.    Elapsed: 0:04:32.\n",
-            "  Batch   200  of    258.    Elapsed: 0:05:42.\n",
-            "  Batch   240  of    258.    Elapsed: 0:06:52.\n",
-            "\n",
-            "  Average training loss: 0.09\n",
-            "  Average training acc: 0.42\n",
-            "  Training epcoh took: 0:07:22\n",
-            "\n",
-            "Running Validation...\n",
-            "  Accuracy: 0.68\n",
-            "  Validation took: 0:00:25\n",
-            "  Validation Loss: 0.00\n",
-            "\n",
-            "======== Epoch 2 / 4 ========\n",
-            "Training...\n",
-            "  Batch    40  of    258.    Elapsed: 0:01:10.\n",
-            "  Batch    80  of    258.    Elapsed: 0:02:19.\n",
-            "  Batch   120  of    258.    Elapsed: 0:03:29.\n",
-            "  Batch   160  of    258.    Elapsed: 0:04:39.\n",
-            "  Batch   200  of    258.    Elapsed: 0:05:48.\n",
-            "  Batch   240  of    258.    Elapsed: 0:06:58.\n",
-            "\n",
-            "  Average training loss: 0.00\n",
-            "  Average training acc: 0.49\n",
-            "  Training epcoh took: 0:07:28\n",
-            "\n",
-            "Running Validation...\n",
-            "  Accuracy: 0.72\n",
-            "  Validation took: 0:00:25\n",
-            "  Validation Loss: 0.00\n",
-            "\n",
-            "======== Epoch 3 / 4 ========\n",
-            "Training...\n",
-            "  Batch    40  of    258.    Elapsed: 0:01:10.\n",
-            "  Batch    80  of    258.    Elapsed: 0:02:19.\n",
-            "  Batch   120  of    258.    Elapsed: 0:03:29.\n",
-            "  Batch   160  of    258.    Elapsed: 0:04:39.\n",
-            "  Batch   200  of    258.    Elapsed: 0:05:49.\n",
-            "  Batch   240  of    258.    Elapsed: 0:06:58.\n",
-            "\n",
-            "  Average training loss: 0.00\n",
-            "  Average training acc: 0.50\n",
-            "  Training epcoh took: 0:07:29\n",
-            "\n",
-            "Running Validation...\n",
-            "  Accuracy: 0.72\n",
-            "  Validation took: 0:00:25\n",
-            "  Validation Loss: 0.00\n",
-            "\n",
-            "======== Epoch 4 / 4 ========\n",
-            "Training...\n",
-            "  Batch    40  of    258.    Elapsed: 0:01:10.\n",
-            "  Batch    80  of    258.    Elapsed: 0:02:19.\n",
-            "  Batch   120  of    258.    Elapsed: 0:03:29.\n",
-            "  Batch   160  of    258.    Elapsed: 0:04:39.\n",
-            "  Batch   200  of    258.    Elapsed: 0:05:49.\n",
-            "  Batch   240  of    258.    Elapsed: 0:06:58.\n",
-            "\n",
-            "  Average training loss: 0.00\n",
-            "  Average training acc: 0.50\n",
-            "  Training epcoh took: 0:07:29\n",
-            "\n",
-            "Running Validation...\n",
-            "  Accuracy: 0.72\n",
-            "  Validation took: 0:00:25\n",
-            "  Validation Loss: 0.00\n",
-            "\n",
-            "Training complete!\n",
-            "Total training took 0:31:29 (h:mm:ss)\n"
-          ]
-        }
       ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "# Train summary"
-      ],
       "metadata": {
         "id": "xIpFPoRb91Or"
-      }
+      },
+      "source": [
+        "# Train summary"
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "import pandas as pd\n",
-        "\n",
-        "pd.set_option('precision', 2)\n",
-        "df_stats = pd.DataFrame(data=training_stats)\n",
-        "\n",
-        "df_stats = df_stats.set_index('epoch')\n",
-        "df_stats"
-      ],
+      "execution_count": 25,
       "metadata": {
+        "id": "GjYqBrrO93Oh",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 204
         },
-        "id": "GjYqBrrO93Oh",
-        "outputId": "d5742682-1cb4-4910-ab30-9424671b31e4"
+        "outputId": "326edb05-56a5-4376-d793-424e5e122507"
       },
-      "execution_count": 26,
       "outputs": [
         {
           "output_type": "execute_result",
@@ -5476,21 +2008,21 @@
             "text/plain": [
               "       Training Loss  Training Accur.  Valid. Loss  Valid. Accur.  \\\n",
               "epoch                                                               \n",
-              "1           8.67e-02             0.42     1.46e-08           0.68   \n",
-              "2           2.02e-06             0.49     2.65e-10           0.72   \n",
-              "3           1.50e-06             0.50     0.00e+00           0.72   \n",
-              "4           1.10e-06             0.50     0.00e+00           0.72   \n",
+              "1           9.03e-02             0.81     9.89e-07           0.83   \n",
+              "2           1.30e-05             0.86     2.26e-08           0.83   \n",
+              "3           3.05e-06             0.85     0.00e+00           0.83   \n",
+              "4           5.13e-06             0.86     0.00e+00           0.83   \n",
               "\n",
               "      Training Time Validation Time  \n",
               "epoch                                \n",
-              "1           0:07:22         0:00:25  \n",
-              "2           0:07:28         0:00:25  \n",
-              "3           0:07:29         0:00:25  \n",
-              "4           0:07:29         0:00:25  "
+              "1           0:07:23         0:00:27  \n",
+              "2           0:07:19         0:00:26  \n",
+              "3           0:07:18         0:00:26  \n",
+              "4           0:07:18         0:00:26  "
             ],
             "text/html": [
               "\n",
-              "  <div id=\"df-4b30f186-a8f8-4cc3-ab45-b65be984cbf4\">\n",
+              "  <div id=\"df-970792fa-8287-4ad4-ac66-0d66603dad55\">\n",
               "    <div class=\"colab-df-container\">\n",
               "      <div>\n",
               "<style scoped>\n",
@@ -5530,44 +2062,44 @@
               "  <tbody>\n",
               "    <tr>\n",
               "      <th>1</th>\n",
-              "      <td>8.67e-02</td>\n",
-              "      <td>0.42</td>\n",
-              "      <td>1.46e-08</td>\n",
-              "      <td>0.68</td>\n",
-              "      <td>0:07:22</td>\n",
-              "      <td>0:00:25</td>\n",
+              "      <td>9.03e-02</td>\n",
+              "      <td>0.81</td>\n",
+              "      <td>9.89e-07</td>\n",
+              "      <td>0.83</td>\n",
+              "      <td>0:07:23</td>\n",
+              "      <td>0:00:27</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>2</th>\n",
-              "      <td>2.02e-06</td>\n",
-              "      <td>0.49</td>\n",
-              "      <td>2.65e-10</td>\n",
-              "      <td>0.72</td>\n",
-              "      <td>0:07:28</td>\n",
-              "      <td>0:00:25</td>\n",
+              "      <td>1.30e-05</td>\n",
+              "      <td>0.86</td>\n",
+              "      <td>2.26e-08</td>\n",
+              "      <td>0.83</td>\n",
+              "      <td>0:07:19</td>\n",
+              "      <td>0:00:26</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>3</th>\n",
-              "      <td>1.50e-06</td>\n",
-              "      <td>0.50</td>\n",
+              "      <td>3.05e-06</td>\n",
+              "      <td>0.85</td>\n",
               "      <td>0.00e+00</td>\n",
-              "      <td>0.72</td>\n",
-              "      <td>0:07:29</td>\n",
-              "      <td>0:00:25</td>\n",
+              "      <td>0.83</td>\n",
+              "      <td>0:07:18</td>\n",
+              "      <td>0:00:26</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>4</th>\n",
-              "      <td>1.10e-06</td>\n",
-              "      <td>0.50</td>\n",
+              "      <td>5.13e-06</td>\n",
+              "      <td>0.86</td>\n",
               "      <td>0.00e+00</td>\n",
-              "      <td>0.72</td>\n",
-              "      <td>0:07:29</td>\n",
-              "      <td>0:00:25</td>\n",
+              "      <td>0.83</td>\n",
+              "      <td>0:07:18</td>\n",
+              "      <td>0:00:26</td>\n",
               "    </tr>\n",
               "  </tbody>\n",
               "</table>\n",
               "</div>\n",
-              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4b30f186-a8f8-4cc3-ab45-b65be984cbf4')\"\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-970792fa-8287-4ad4-ac66-0d66603dad55')\"\n",
               "              title=\"Convert this dataframe to an interactive table.\"\n",
               "              style=\"display:none;\">\n",
               "        \n",
@@ -5618,12 +2150,12 @@
               "\n",
               "      <script>\n",
               "        const buttonEl =\n",
-              "          document.querySelector('#df-4b30f186-a8f8-4cc3-ab45-b65be984cbf4 button.colab-df-convert');\n",
+              "          document.querySelector('#df-970792fa-8287-4ad4-ac66-0d66603dad55 button.colab-df-convert');\n",
               "        buttonEl.style.display =\n",
               "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "\n",
               "        async function convertToInteractive(key) {\n",
-              "          const element = document.querySelector('#df-4b30f186-a8f8-4cc3-ab45-b65be984cbf4');\n",
+              "          const element = document.querySelector('#df-970792fa-8287-4ad4-ac66-0d66603dad55');\n",
               "          const dataTable =\n",
               "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
               "                                                     [key], {});\n",
@@ -5646,12 +2178,42 @@
             ]
           },
           "metadata": {},
-          "execution_count": 26
+          "execution_count": 25
         }
+      ],
+      "source": [
+        "import pandas as pd\n",
+        "\n",
+        "pd.set_option('precision', 2)\n",
+        "df_stats = pd.DataFrame(data=training_stats)\n",
+        "\n",
+        "df_stats = df_stats.set_index('epoch')\n",
+        "df_stats"
       ]
     },
     {
       "cell_type": "code",
+      "execution_count": 26,
+      "metadata": {
+        "id": "Xk3gzkeU96v3",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 427
+        },
+        "outputId": "0fab6cbe-dd1c-4bdd-a986-24ae72d30aec"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 864x432 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
       "source": [
         "import matplotlib.pyplot as plt\n",
         "%matplotlib inline\n",
@@ -5673,69 +2235,67 @@
         "plt.xticks([1, 2, 3, 4])\n",
         "\n",
         "plt.show()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 427
-        },
-        "id": "Xk3gzkeU96v3",
-        "outputId": "11211824-04f4-43b2-984f-56f9965d38bc"
-      },
-      "execution_count": 27,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 864x432 with 1 Axes>"
-            ],
-            "image/png": 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lIyJ6RVOmTIFCoUBERAS+/vpr2NjYoH///hg+fDiCgoKELk+NVCrFxo0bsXjxYsTExODQoUPw8vJCeHg4Fi5ciKKiIo3G+frrr9GpUyfs2LED69atQ2lpKZo2bYrAwEBMnjwZUqkUo0aNwocffghTU1P4+flpNO7AgQOxePFiFBcXq928CtTc+71w4UJYW1tj586dWLx4MVq0aIFPP/0Ud+7cUbtxdMmSJfjuu+9w4sQJREVFoUWLFnj33XchkUiwYMEClX3bt2+PDz74ADt27MAnn3wCmUyG2bNnVxrgTU1NsX37dqxYsQInTpxAZGQkrKysMHr0aLz99ttVfvqvpq5du1bhCj5SqRTTp0+Hp6cnduzYgRUrVmD79u14+vQpHBwc8MEHH2Dy5MnK/V1dXdG3b19cuHAB+/fvh1wuh729PWbMmKGy3+TJk3H69Gls3rwZeXl5sLKygre3N2bMmKGy0g0RCUukqI07i4iIqFrKysrQuXNneHl5VfthSEREVL9wDjwRkY6o6Cr7jh078OTJkwrXPSciooaJU2iIiHTExx9/jJKSErRr1w5SqRRXrlzBgQMH4OjoiJEjRwpdHhER6QhOoSEi0hF79uzB1q1bcfv2bTx9+hRWVlbo0aMH5s6dC2tra6HLIyIiHcEAT0RERERUh3AOPBERERFRHcIAT0RERERUh/Am1ip6/LgAcnntzzqysjJBdnZ+rR+XqK7huUKkGZ4rRJoR4lwRi0WwtDSudDsDfBXJ5QpBAnz5sYno5XiuEGmG5wqRZnTtXOEUGiIiIiKiOoQBnoiIiIioDmGAJyIiIiKqQxjgiYiIiIjqEAZ4IiIiIqI6hKvQEBEREWlBYWEB8vNzUVZWKnQppEUPHoghl8u1Np6enj5MTMxhZFT5MpEvwwBPRERE9IpKS0uQl/cYFhbW0Nc3gEgkErok0hKJRAyZTDsBXqFQoLS0GDk5DyGR6ENfX1qtcTiFhoiIiOgV5eXlwMTEHFKpIcM7VUokEkEqNYSxsTny83OqPQ4DPBEREdErkslKYGBgJHQZVEcYGhqhtLSk2v05hUbHnf37PiJPJ+PRk2I0NjPAsB7O6NLaTuiyiIiI6B/k8jKIxXpCl0F1hFisB7m8rNr9GeB12Nm/72PjoUSU/P95V9lPirHxUCIAMMQTERHpGE6dIU296t8Kp9DosMjTycrwXq5EJkfk6WSBKiIiIiIioTHA67DsJ8VVaiciIiKqa2bPno7Zs6fXet+6jFNodJiVmUGFYd3KzECAaoiIiKgh8fProNF+u3btg739azVcDf0TA7wOG9bDWWUOPACIxSIM6+EsYFVERETUEHzyyZcqP+/cuR2ZmRl4++33VNotLCxf6TjLlv0kSN+6jAFeh5XfqFq+Co1UX4ziUjlszLlMFREREdWsgIAglZ9PnYpBbm6OWvvzioqKYGhoqPFx9PX1q1Xfq/atyzgHXsd1aW2HJbO6Yd93g/H9bD9YmRki7GA8ikuqv/QQERERkTbMnj0dEyeORXx8HN58cwr8/bth69aNAIDffjuFDz+ci8GDA9GrVxeMHDkY4eFhKCsrUxvjn/PYL1++CD+/Djh9+gTCw8MwZEh/+Pt3xdy5byItLVVrfQFg9+6dGDFiMPz9u2HatFBcu3alTsyr5xX4OsTIQIIpA9yxePsV7Dx1E+P7uQpdEhEREdWQ8mfBZD8phpUOPwsmJ+cxPvroXfTrF4jAwAFo0uRZjdHRB2Bk1AijRo1Do0ZGuHTpIsLCfkZBQQHeemvuS8fduHEdxGI9jB0biry8J9i+fTO++OJjrF27USt9o6IisGzZYrRt64NRo8YgIyMDCxZ8AFNTU9jY2Fb/DakFDPB1jJujJfp2cMCxi6nwed0GrZ0aC10SERERaVldehbMw4dZmD//EwQHD1Zp//zz/8DA4H9TaYYMCcGSJYsQFbUL06a9CalU+sJxZTIZ1q/fCInkWVw1MzPHDz8sRUrKTbRs2eqV+paWliIsbDVat/bE8uWrlPu1avU6vv76cwZ40r7hPVoi7lY21kcn4KspndDIsGHO/yIiItJ1v/+VgTOxGVXul5yeC1mZQqWtRCbHhugE/Ho1vcrj+XnZo5unfZX7acLQ0BCBgQPU2v8Z3p8+LUBJSSm8vdth795I3LlzG6+/7vLCcQcMGKQM1gDg7d0WAJCefu+lAf5lfRMT45Gbm4tZs4aq7Ne3byBWrPj+hWPrAgb4Okiqr4epwR74etMlbD12A9MGeghdEhEREWnR8+H9Ze1CsrGxVQnB5VJSkrF27WpcvvwnCgoKVLYVFOS/dNzyqTjlTE3NAAB5eXmv3Pf+/Wcfqpo1c1DZTyKRwN6+Zj7oaBMDfB3lZG+G4K6O2Pf7bfi4WKO9q25/1UNERNQQdfOs3pXvD1f9XumzYOaN89FGaVrzzyvt5fLy8vD229PRqJEJpkyZiaZNm0EqleL69USsXr0Scrm8gpFUicV6FbYrFC//EPMqfesCrkJThwV3bQHHJqbYeDgJuQUlQpdDREREWjKshzOkEtWYJpWI68yzYK5cuYTc3FwsXPgZRo4cg27d3kDHjr7KK+FCs7N79qHq+ZVpZDIZMjKqPuWptjHA12ESPTGmBrujqKQMmw4n1ptPlURERA1dl9Z2mNDfTfn0dSszA0zo76ZzN7BWRix+FjH/mU1KS0sRFbVLqJJUuLl5wNzcHPv2RUEmkynbjx07jLy8JwJWphlOoanjmtqYYFj3lth58ib+iLtfYzeoEBERUe3q0tquzgT253l6esHU1Axff/05QkJGQSQS4ciRaOjKtUZ9fX1Mnjwdy5YtwTvvzEKvXr2RkZGBQ4f2o2nTZhCJREKX+EK8Al8P9OvoAJdm5th2/Dqyc4uELoeIiIgaOHNzCyxevAxWVtZYu3Y1tm/fgg4dfDFr1hyhS1MaPnwU3nnnA9y/n4GffvoB165dwf/93/cwMTGFVGogdHkvJFJw3kWVZGfnQy6v/bfMxsYUWVmV33X9IKcQn627gJavmeH90W0h1vFPjkQ15WXnChE9w3NFu+7fvwM7O0ehy6BXJJfLERzcFz169MK8eR8DACQSMWSyl990W1Uv+psRi0WwsjKptC+vwNcTthZGGNW7FRLuPMbJy/eELoeIiIhIpxUXq6/yc/jwQTx5kot27doLUJHmOAe+Hunh/RquXH+IXSdvorVTY9g1biR0SUREREQ6KTb2KlavXomePf1hZmaO69cTcfDgPrRs6YxevfoIXd4LCXoFvqSkBEuWLIGfnx+8vLwwcuRInD17VqO+mZmZmDt3Ljp06AAfHx/MmjULqampavvl5eXh22+/Rb9+/eDl5QV/f398+umnyMzM1PbLEZxIJMLE/m7Ql4gRdiAeZRqssUpERETUEL32WlNYW9sgIuIXLF++BGfO/IrAwAH44YfV0NfX7afcCzoH/r333sPRo0cRGhoKR0dHREVFIS4uDps3b0a7du0q7VdQUIBhw4ahoKAAEydOhEQiQXh4OEQiEfbs2QNzc3MAz+YxjR49Gjdu3MCYMWPg5OSEW7duYfv27bCxscGBAwcglUqrVLOuzoH/p/Pxmfjvvr8xrHtLBHdtUbOFEekYzusl0gzPFe3iHPj6SxfnwAs2hSY2NhYHDx7EggULMHHiRADAkCFDEBwcjKVLl2Lr1q2V9t22bRvu3LmDyMhIeHh4AADeeOMNDBw4EOHh4Zg7dy4A4K+//sK1a9fw6aefYty4ccr+r732Gr766itcvnwZnTt3rrkXKRBfjya4fD0Le8/cgpezFZo3MRW6JCIiIiLSEsGm0Bw+fBj6+voYMWKEss3AwAAhISG4dOkSHjx4UGnfI0eOoG3btsrwDgDOzs7o0qULDh06pGzLz88HAFhZWan0t7a2BgAYGqo/+re+GB/gChMjfYQdiEdpDXxqJCIiIiJhCBbgExIS4OTkBGNjY5V2Ly8vKBQKJCQkVNhPLpcjKSkJbdq0Udvm6emJ27dvo7CwEADQunVrNGrUCD/88APOnj2LzMxMnD17Fj/88AN8fX3h7e2t/RemI0yM9DGxvxvSsgqw50yK0OUQERERkZYIFuCzsrJga2ur1m5jYwMAlV6Bz8nJQUlJiXK/5/sqFApkZWUBACwsLLBs2TLk5eVh4sSJ6N69OyZOnAhHR0esWbNG55+y9aq8W1mju7c9Dp+/i5tpuUKXQ0RERERaINgc+KKiogrv8DUwePbkq4rW5vxne0U3n5b3LSr639NIGzdujDZt2qBdu3ZwdnZGYmIiwsLC8O9//xvff/99let+0Q0FNc3Gpupz2d8a2Q6JqbnYcCgRK97vCUMDrhxK9V91zhWihojnivY8eCCGRMLH69RXNfG7FYvF1T4HBUtzhoaGKC0tVWsvD+jlYfx55e0lJSWV9i2f256amorQ0FAsXboUffo8W8+zT58+aNq0KebPn4/hw4ejW7duVaq7LqxC87xJga5YvO0KVkVcxfh+rlqujEi3cGUNIs3wXNEuuVxeIyuVkPBqahUauVxe6Tmos09itbGxqXCaTPn0l4qm1wDPpsVIpVLlfs/3FYlEyuk1kZGRKCkpQY8ePVT28/f3BwBcvnz5lV5DXeHa3BJ9Ozrg5OV7iLuVLXQ5RERERPQKBAvwbm5uuHXrFgoKClTar127ptxeEbFYDBcXF8TFxalti42NhaOjI4yMjAAA2dnZUCgUeH6pe5lMpvLPhmBY95awt2qEDdGJKChS/+aDiIiIqCZFR++Hn18HZGSkK9tCQgbi668/r1bfV3X58kX4+XXA5csXtTZmbREswAcGBqK0tBS7du1StpWUlCAyMhI+Pj5o0qQJACA9PR3JyckqfQMCAnD16lXEx8cr21JSUnDu3DkEBgYq21q0aAG5XK6ytCQAHDhwAABUlqGs76T6epga7IHc/BJsO3Zd6HKIiIhIx3300bvo08dPubpfRd57bzYCAnpUeu+iLjh+/Ah27twmdBlaJdgceG9vbwQGBmLp0qXIyspC8+bNERUVhfT0dHzzzTfK/ebNm4cLFy4gKSlJ2TZ27Fjs2rUL06dPx6RJk6Cnp4fw8HDY2NgoHwoFAEOHDsX69euxcOFCxMXFoVWrVvj7778REREBV1dX5VSahsLJ3gzBXR2x7/fb8HGxQXvXiqcpEREREfXtG4A//vgNZ86cRt++gWrbHz9+hEuX/kS/fv0rvXfxZbZt2w2xuGavJ8fEHMWNG9cxcuRYlfa2bX0QE/N7hYuq6DpBb5devHgxxo8fj7179+I///kPZDIZ1qxZg/bt27+wn4mJCTZv3gwfHx+sWrUKP/zwA9zc3LBlyxZYWloq97O0tMTu3bsxaNAgnDhxAl999RVOnDiBkJAQbNy4sU7+wl5VcNcWcLQzxcbDScgtUL8RmIiIiAgA3nijJ4yMGuH48SMVbj9x4jjKysrQr596uNeUVCqFRCLM9WSxWAwDA4Ma/wBREwRdU9DAwADz5s3DvHnzKt1n8+bNFbbb2dlhxYoVLz1GkyZNsGjRomrXWN9I9MSYGuyBLzb8iU2HEzF7mGe9Xw+fiIiIqs7Q0BBvvNEDJ08ex5MnT2BmZqay/fjxI7CysoKDgyOWLv0/XLp0AZmZmTA0NISPTwe89dZc2Nu/9sJjhIQMRLt27bFw4efKtpSUZCxfvgRxcX/B3NwcgwcPg7W1+vN/fvvtFPbti8L160l48iQXNja2CAoaiPHjn83OAIDZs6fj6tVni5b4+XUAANjZ2SMiYj8uX76IOXNmYsWKn+Hj00E5bkzMUWzZEo47d26jUSNjvPFGd8yY8TYsLCyU+8yePR35+fn49NMv8f33i5GQ8DdMTc0wYsRojBs3oWpvdDVwUfAGqKm1MYZ1b4mdJ2/i97/uw8/LXuiSiIiI6DkX7l/GvuTDeFycA0sDCwxyDkQnO59araFv30AcPXoIp07FYNCgocr2+/czEBcXi5CQ0UhI+BtxcbHo0ycANja2yMhIx549u/H22zOwZcsu5fLemsjOfog5c2ZCLpfjX/+aAENDI+zbF1XhFJ3o6AMwMmqEUaPGoVEjI1y6dBFhYT+joKAAb701FwAwYcJkFBYWIjMzA2+//UFPJJ4AACAASURBVB4AwMioUaXHj47ej0WLvkDr1p548805ePAgE7t3/4K//47D2rWbVOp48iQX778/B7169Ubv3v1w8uRxrF69Ei1btkKXLlVbpryqGOAbqH4dHXD1Rha2x1yHu6MlrMw1P7mIiIioZl24fxnbEnejVP5s5bjHxTnYlrgbAGo1xHfs6AsLC0scP35EJcAfP34ECoUCffsGwNm5FXr16qPSr1u37pg5cxJOnYpBYOAAjY+3detG5ObmICxsM1xdn61I2L9/MMaMGaq27+ef/wcGBv/LL0OGhGDJkkWIitqFadPehFQqRceOnREZuQu5uTkICAh64bFlMhlWr16JVq1csHLlf5UPDfXw8MAnnyzA/v1RCAkZrdz/wYNMfPbZf5T3BwQHD0ZISDAOHtzLAE81QywWYXKwBz5bfwHroxPw/ui2EHMqDRERkVadz7iEsxl/Vrnfrdy7kClUl7sulZdia0IE/ki/UOXxuth3hK/9i+8xrIhEIoG/fx/s2bMbDx8+hLW1NQDg+PGjaNbMAR4ebVT2l8lkKCjIR7NmDjAxMcX164lVCvBnz/4OT09vZXgHnt3T2Ldvf0RF7VLZ95/h/enTApSUlMLbux327o3EnTu38frrLlV6rYmJ8Xj8+JEy/Jfr3bsvVqxYhj/++F0lwJuYmKBPnwDlz/r6+nB3b4309HtVOm51MMA3YLYWRhjt3wobDyfhxKU09OngIHRJREREBKiF95e116S+fQMRGbkLJ04cxciRY3H79i3cvHkdkyZNAwAUFxdh8+ZwREfvR1bWA5Xn7+Tn51fpWJmZ9+Hp6a3W3ry5o1pbSkoy1q5djcuX/1R7rlBBQdWOCzybFlTRscRiMZo1c0BmZoZKu61tE7X7CE1NzZCcfLPKx64qBvgGrrv3a7h8/SEiTiWjtVNj2FsZC10SERFRveFr375aV74//n0RHhfnqLVbGljgHZ+Z2ihNY56e3rC3b4pjxw5j5MixOHbsMAAop44sW7YE0dH7MWLEGLRp4wkTExMAInz++b/VHqapLXl5eXj77elo1MgEU6bMRNOmzSCVSnH9eiJWr14JuVxeI8f9J7FYr8L2mnrNKseu8SOQThOJRJjY3w36EjHCDiSgrBb+4ImIiOjFBjkHQl+suty1vlgfg5yrv2Tjq+jTpx8SEuKRlpaKmJijcHV1V16pLp/n/vbb76JXrz7o2LEzvLzaVvnqOwA0aWKHtLRUtfa7d++o/HzlyiXk5uZi4cLPMHLkGHTr9gY6dvSFqamZWl9AsynCdnb2FR5LoVAgLS0VTZrozqIfDPAES1MD/KufK25lPEH0ubtCl0NERNTgdbLzwVi34bA0eLZ0oaWBBca6Da/1VWjK9evXHwDw44/LkJaWqrL2e0VXonfv/gVlZWVVPk6XLt3w11/XkJSUqGx7/Pgxjh07pLJf+drt/7zaXVpaqjZPHgCMjIw0+jDh5uYBS8vG2LMnAqWlpcr2EyeOIyvrAbp2rdkbU6uCU2gIAODr0QRXbmRh35lb8Ha2QvMmpkKXRERE1KB1svMRLLA/z8mpJVq1csGZM79CLBajd+//3bzZtasfjhyJhrGxCVq0cMLff/+FixcvwNzcvMrHGTt2Ao4cicZ7772FkJDRMDAwxL59UWjSxB75+TeU+3l6esHU1Axff/05QkJGQSQS4ciRaFQ0e8XV1Q1Hjx7CypXfw83NA0ZGjeDn111tP4lEgjfffBuLFn2Bt9+egT59+uHBg0xERPyCli2dMXCg+ko4QuEVeFL6Vz9XmBjpY+2BeJTKOJWGiIiI/qf8qnu7du2Vq9EAwNy5HyAgIAjHjh3Cjz8ux8OHD7F8+U8vXG+9MtbW1lix4r9wcnLG5s3h2LVrOwIDgzBixGiV/czNLbB48TJYWVlj7drV2L59Czp08MWsWXPUxhw8eDgCAvojOvoAvvjiYyxfvqTS4wcFDcTnn3+N4uIi/PTTD4iO3o+AgP744YefK1yLXigiRW3MtK9HsrPzIZfX/ltmY2OKrKy8Gj9ObPJDLN8Vi/6dm2NEz1Y1fjwibautc4WoruO5ol3379+BnZ36SilU90kkYshq4MLmi/5mxGIRrKxMKu3LK/CkwsvZGt297XH4/F3cSFO/+52IiIiIhMUAT2pG+b8OKzNDrDuQgKKS2l9vloiIiIgqxwBPaowMJJgywB1ZOYXYdTJZ6HKIiIiI6B8Y4KlCrs0t0bejA05euYe4W9lCl0NERERE/x8DPFVqeI+WsLdqhA3RiSgoKn15ByIiIiKqcQzwVCl9iR6mBnsgN78E245dF7ocIiIiIgIDPL2Ek70Zgrs64uzfmbiY+EDocoiIiIgaPAZ4eqngri3gaGeKTUeSkFtQInQ5REREOomP1iFNverfCgM8vZRET4ypwR4oKinDxkOJ/BcUERHRc/T0JCgt5UUu0kxpaQn09CTV7s8ATxppam2M4T1a4urNh/j9r/tCl0NERKRTTEwskJOThZKSYl7ookopFAqUlBQjJycLJiYW1R6n+tGfGpy+HR1w5cZDbI+5DjdHC1ibGwldEhERkU4wMjIGAOTmPkRZGR+CWJ+IxWLI5XKtjaenJ4GpqaXyb6Y6GOBJY2KRCFMGuOPT9Rew/mACPhjTDmKRSOiyiIiIdIKRkfErhTLSTTY2psjKyhO6DBWcQkNVYmNhhDG9X0fi3RzEXEoTuhwiIiKiBocBnqrsDS97eDlbIeJUMjKyC4Quh4iIiKhBYYCnKhOJRJjY3w1SiRhhBxJQpsV5YURERET0YgzwVC0WJgYYH+CKWxlPEH3urtDlEBERETUYDPBUbZ3cm6CTuy32nbmFO/d16+YOIiIiovqKAZ5eyb/6ucKkkT7CDsajVMapNEREREQ1jQGeXomJkT4m9XfDvawC7PktRehyiIiIiOo9Bnh6ZV7O1uju/RoOn7+LG2k5QpdDREREVK8xwJNWjPJvBStzQ6w7kICiEj6BjoiIiKimMMCTVhgZSDBlgDuycgqx82Sy0OUQERER1VsM8KQ1rs0t0bejA05duYe4lGyhyyEiIiKqlxjgSauG92iJ16yNsT46AQVFpUKXQ0RERFTvMMCTVulL9DA12B15T0ux9dh1ocshIiIiqncY4EnrWtiZIbhrC5z7OxMXEx8IXQ4RERFRvcIATzViQBdHONqZYtORJOTmFwtdDhEREVG9wQBPNUKiJ8bUYA8UlZRh4+EkKBQKoUsiIiIiqhcY4KnGNLU2RkiPlrh68yHO/JUhdDlERERE9QIDPNWoPh0d4Opgge3Hb+BhbqHQ5RARERHVeQzwVKPEIhEmD3CHAsD6gwmQcyoNERER0SthgKcaZ2NhhDG9X0fi3RzEXEoTuhwiIiKiOo0BnmrFG1728HK2QsSpZGRkFwhdDhEREVGdxQBPtUIkEmFifzdIJWKEHUhAmVwudElEREREdRIDPNUaCxMDjA9wxa2MJ4g+e0focoiIiIjqJAZ4qlWd3Jugk7st9v1+G3fu5wldDhEREVGdwwBPte5f/Vxh0kgfYQfjUSorE7ocIiIiojqFAZ5qnYmRPib1d8O9rAJE/XZL6HKIiIiI6hQGeBKEl7M1erR9DUfO38X11ByhyyEiIiKqMxjgSTAje7WClbkh1h2MR1GJTOhyiIiIiOoEBngSjJGBBFODPfAwpwg7TyYLXQ4RERFRncAAT4JycbBAv04OOHXlHuJSsoUuh4iIiEjnMcCT4IZ1b4nXrI2xPjoBBUWlQpdDREREpNMY4Elw+hI9TA12R97TUmw9el3ocoiIiIh0mqABvqSkBEuWLIGfnx+8vLwwcuRInD17VqO+mZmZmDt3Ljp06AAfHx/MmjULqampFe774MEDLFy4EH5+fvD09ESfPn3wzTffaPOl0CtqYWeGgV1b4Fx8Ji4mPhC6HCIiIiKdJRHy4PPnz8fRo0cRGhoKR0dHREVFYdq0adi8eTPatWtXab+CggKEhoaioKAAM2fOhEQiQXh4OEJDQ7Fnzx6Ym5sr97137x7GjBkDExMThIaGwtLSEvfv38etW1x/XNcEdXHE1ZsPselIEl5vZg5zEwOhSyIiIiLSOSKFQqEQ4sCxsbEYMWIEFixYgIkTJwIAiouLERwcDFtbW2zdurXSvmvXrsV3332HyMhIeHh4AACSk5MxcOBAzJgxA3PnzlXuO2XKFOTl5WHTpk0wNDR85bqzs/Mhl9f+W2ZjY4qsrLxaP25tS39YgM83/Ik2To3x9nBPiEQioUuiOqahnCtEr4rnCpFmhDhXxGIRrKxMKt9ei7WoOHz4MPT19TFixAhlm4GBAUJCQnDp0iU8eFD5NIojR46gbdu2yvAOAM7OzujSpQsOHTqkbEtOTsaZM2fw1ltvwdDQEIWFhZDJuN64LnvN2hghPVri6s2HOBObIXQ5RERERDpHsACfkJAAJycnGBsbq7R7eXlBoVAgISGhwn5yuRxJSUlo06aN2jZPT0/cvn0bhYWFAIA//vgDACCVSjFs2DC0bdsWbdu2xZw5c/Do0SMtvyLSlj4dHeDqYIHtMTfwMKdQ6HKIiIiIdIpgAT4rKwu2trZq7TY2NgBQ6RX4nJwclJSUKPd7vq9CoUBWVhYA4M6dOwCAd955B05OTlixYgXefPNNnDx5ElOnTkVZWZm2Xg5pkVgkwpQB7gCA9dEJkAszy4uIiIhIJwl2E2tRURH09fXV2g0Mnt24WFxcXGG/8napVFpp36KiIgDA06dPATy7Mv/dd98BAAICAmBhYYEvv/wSJ0+eRJ8+fapU94vmI9U0GxtTwY5d22xsTDFtiCdW7ryK84lZGNTdWeiSqA5pSOcK0avguUKkGV07VwQL8IaGhigtVX9oT3lALw/jzytvLykpqbRv+c2q5f8MDg5W2W/QoEH48ssvcfny5SoHeN7EWnvaOlnCy9kK4Qfj0cLWGPZWxi/vRA1eQzxXiKqD5wqRZngT6z/Y2NhUOE2mfPpLRdNrAMDCwgJSqVS53/N9RSKRcnpN+T+trKxU9jM1NYVUKsWTJ09e6TVQzRKJRJjY3w1SiRhhB+JRJpcLXRIRERGR4AQL8G5ubrh16xYKCgpU2q9du6bcXhGxWAwXFxfExcWpbYuNjYWjoyOMjIwAAK1btwbw7KFP//To0SOUlJSgcePGr/w6qGZZmBhgfIArbmXk4eDZO0KXQ0RERCQ4wQJ8YGAgSktLsWvXLmVbSUkJIiMj4ePjgyZNmgAA0tPTkZycrNI3ICAAV69eRXx8vLItJSUF586dQ2BgoLLN19cXlpaWiIyMhPwfV2/Lj9mlS5caeW2kXZ3cm8DXown2/34bd+7z614iIiJq2AR7kBMAzJ07FzExMZgwYQKaN2+OqKgoxMXFYePGjWjfvj0AYPz48bhw4QKSkpKU/fLz8zF06FAUFhZi0qRJ0NPTQ3h4OBQKBfbs2QNLS0vlvhEREVi4cCG6du2KPn36IDk5Gdu3b0f37t3x3//+t8o1cw68MPILS/HJuvMwMdTHpxM7QF+iJ3RJpKMa+rlCpCmeK0Sa0cU58IIG+OLiYixfvhz79+9Hbm4uXF1d8d5776Fr167KfSoK8ABw//59LFq0CL///jvkcjl8fX2xcOFCODg4qB1n7969CAsLw61bt2BhYYHg4GC888471XoyKwO8cGKTs7F81zUE+jbHyF6thC6HdBTPFSLN8Fwh0gwDfD3AAC+sjYcT8evVdMwb5wMXBwuhyyEdxHOFSDM8V4g0o4sBXrA58ETVMbJXK1iZG2LdwXgUlciELoeIiIio1jHAU51iZCDB1GAPPMwpws4TN4Uuh4iIiKjWMcBTnePiYIGATs1x6mo6/krJFrocIiIiolrFAE910tDuTmhqbYwN0QkoKFJ/oi8RERFRfcUAT3WSvkQPU4M9kPe0FFuPXhe6HCIiIqJawwBPdZajnSkGdm2Bc/GZ+DPxgdDlEBEREdUKBniq04K6OMLJ3hSbjyQhN79Y6HKIiIiIahwDPNVpEj0xpgZ7oLi0DOGHEsHHGhAREVF9xwBPdZ69lTGG93DGteRsnInNELocIiIiohrFAE/1Qp8OzeDW3ALbY27gYU6h0OUQERER1RgGeKoXxCIRJge5AwDWHUyAnFNpiIiIqJ5igKd6w9rCCGN6v46k1Bwcv5gmdDlERERENYIBnuoVPy97eDtbYffpZGRkFwhdDhEREZHWMcBTvSISiTCxvxsM9PUQdiAeZXK50CURERERaRUDPNU75iYGGB/gilsZeTh49o7Q5RARERFpFQM81Usd3Wzh69EE+3+/jdv3nwhdDhEREZHWMMBTvTWurwtMG+kj7EACSmVlQpdDREREpBUM8FRvmRjpY1KQO9IfFiDq11tCl0NERESkFQzwVK95trRCz7av4ciFu7iemiN0OURERESvjAGe6r2R/q1gbWGIsAPxKCyWCV0OERER0SthgKd6z1AqwZQBHsjOLcLOkzeFLoeIiIjolTDAU4Pg4mCBAN/mOH01HbHJ2UKXQ0RERFRtDPDUYAx9wwlNrY2x4VAC8gtLhS6HiIiIqFoY4KnB0JfoYWqwB/KflmLrsetCl0NERERULQzw1KA42pliYLcWOB+fiQsJmUKXQ0RERFRlDPDU4Azo4ggne1NsPpKEnPxiocshIiIiqhIGeGpw9MRiTA32QIlMjvBDiVAoFEKXRERERKQxBnhqkOytjBHSwxmxydn4LTZD6HKIiIiINMYATw1W7w7N4NbcAttjbuBhTqHQ5RARERFphAGeGiyxSITJA9whArDuYALknEpDREREdQADPDVo1uZGGNP7dSSl5uD4xTShyyEiIiJ6KQZ4avD8vOzh7WyFiFPJSH9YIHQ5RERERC/EAE8NnkgkwsT+bjCU6iHsQDxkZXKhSyIiIiKqlFYCvEwmw5EjR7Bz505kZWVpY0iiWmVuYoDQAFfcvp+H6LN3hC6HiIiIqFKSqnZYvHgxzp8/j927dwMAFAoFJk2ahIsXL0KhUMDCwgI7d+5E8+bNtV4sUU3q4GaLzh5NsP+P2/BqZYUWdmZCl0RERESkpspX4H/77Td06NBB+fOJEyfw559/YsqUKfjuu+8AAGvWrNFehUS1aFw/F5g20kfYgQSUysqELoeIiIhITZUD/P379+Ho6Kj8+eTJk2jWrBk++OADDBgwAKNHj8bZs2e1WiRRbTE21MekIHekPyxA5K8pQpdDREREpKbKAb60tBQSyf9m3pw/fx5du3ZV/uzg4MB58FSneba0Qs92TXH0QiqS7j4WuhwiIiIiFVUO8HZ2drhy5QoA4MaNG0hNTUXHjh2V27Ozs9GoUSPtVUgkgJG9nGFtYYh1BxNQWCwTuhwiIiIipSoH+AEDBmDPnj2YMWMGZsyYARMTE/To0UO5PSEhgTewUp1nKJVgygAPZOcWYefJm0KXQ0RERKRU5QA/Y8YMDB06FFevXoVIJMK3334LM7Nnq3Xk5eXhxIkT6NKli9YLJaptLg4WCPBtjtNX0xGbnC10OUREREQAAJFCoVBoazC5XI6CggIYGhpCX19fW8PqlOzsfMjlWnvLNGZjY4qsrLxaP25DVyorw5fhF5FfVIqvpvjCxKh+/l3XJzxXiDTDc4VIM0KcK2KxCFZWJpVv1+bBZDIZTE1N6214p4ZHX6KHqcEeyH9aii1Hk4Quh4iIiKjqAf706dNYuXKlStvWrVvh4+ODtm3b4v3330dpaanWCiQSmqOdKQZ1a4ELCQ9wISFT6HKIiIiogatygF+3bh1SUv63PnZycjIWLVoEW1tbdO3aFdHR0di6datWiyQSWlAXRzjZm2HzkSTk5BcLXQ4RERE1YFUO8CkpKWjTpo3y5+joaBgYGCAiIgJhYWEICgrCnj17tFokkdD0xGJMDXZHiUyO8EOJ0OKtI0RERERVUuUAn5ubC0tLS+XPf/zxBzp37gwTk2cT7Tt16oS0tDTtVUikI+ytjBHSwxmxydn4LTZD6HKIiIiogapygLe0tER6ejoAID8/H3/99Rc6dOig3C6TyVBWVqa9Col0SO8OzeDW3ALbY24gK6dQ6HKIiIioAapygG/bti127NiBw4cPY9GiRSgrK0P37t2V2+/cuQNbW1utFkmkK8QiESYPcIcIwPqDCZBzKg0RERHVsioH+Dlz5kAul+Odd95BZGQkhgwZglatWgEAFAoFjh8/Dh8fH60XSqQrrM2NMKbP60hKzcHxP1OFLoeIiIgaGElVO7Rq1QrR0dG4fPkyTE1N0bFjR+W2J0+eYMKECfD19dVqkUS6xs/THleuP0TE6RS0bmmFptbGQpdEREREDYRWn8TaEPBJrFQuN78Yn6y7ACtzQywc3x4SPa0+F42qiecKkWZ4rhBpRhefxFrlK/Dl7t69i5iYGKSmPptC4ODggN69e6N58+bVHZKoTjE3MUBogCtW7YnDwbN3MNjPSeiSiIiIqAGoVoBfvnw51q5dq7bazJIlSzBjxgzMnTtXK8UR6boObrbo3LoJDvxxG96trNDCzkzokoiIiKieq/J3/hEREfj555/h5eWFn376CUePHsXRo0fx008/oW3btvj5558RGRlZE7US6aRxfV1gZixF2IEElMq4hCoRERHVrCoH+G3btsHb2xubN29WTplp3rw5evfujU2bNsHLywtbtmzRaKySkhIsWbIEfn5+8PLywsiRI3H27FmN+mZmZmLu3Lno0KEDfHx8MGvWLOV0nspcu3YNbm5ucHV1xZMnTzQ6DtHLGBvqY1J/N6Q/LEDkrylCl0NERET1XJUDfHJyMoKCgiCRqM++kUgkCAoKQnJyskZjzZ8/Hxs3bsSgQYOwcOFCiMViTJs2DVeuXHlhv4KCAoSGhuLSpUuYOXMm5syZg/j4eISGhiI3N7fCPgqFAv/5z39gZGSkUW1EVdGmpRV6tmuKoxdSkXT3sdDlEBERUT1W5QCvr6+Pp0+fVrq9oKAA+vr6Lx0nNjYWBw8exAcffICPPvoIo0aNwsaNG2Fvb4+lS5e+sO+2bdtw584drFmzBlOnTsXEiROxbt06ZGZmIjw8vMI+UVFRuHv3LoYPH/7S2oiqY2QvZ9hYGGHdwQQUFsuELoeIiIjqqSoHeE9PT/zyyy94+PCh2rbs7Gzs3LkT3t7eLx3n8OHD0NfXx4gRI5RtBgYGCAkJwaVLl/DgwYNK+x45cgRt27aFh4eHss3Z2RldunTBoUOH1PbPz8/H999/j9mzZ8Pc3PyltRFVh6FUginB7sjOLcIvJ24KXQ4RERHVU1UO8LNmzUJWVhaCgoLw7bffYvfu3di9eze+/fZbBAUF4eHDh3jzzTdfOk5CQgKcnJxgbKz6ABwvLy8oFAokJCRU2E8ulyMpKQlt2rRR2+bp6Ynbt2+jsLBQpX3VqlUwMTHBmDFjqvBKiaru9WYWCPRtjl+vpSM2Wf1DLhEREdGrqvIykh07dsTKlSvx1VdfYcOGDSrbXnvtNXz77bfo0KHDS8fJyspCkyZN1NptbGwAoNIr8Dk5OSgpKVHu93xfhUKBrKws5Xr0t2/fxqZNm7By5coK5+0TaduQN1oiNiUbGw4l4qspvjAxevmUMiIiIiJNVSvR+vv7o2fPnoiLi0NaWhqAZw9yat26NXbu3ImgoCBER0e/cIyioqIK58obGBgAAIqLiyvsV94ulUor7VtUVKRs++abb9CxY0f06tVLg1f2ci96KlZNs7ExFezYVDUf/qsD3v/hV0ScTsGH41/+gZa0i+cKkWZ4rhBpRtfOlWpfkhaLxfDy8oKXl5dK++PHj3Hr1q2X9jc0NERpaalae3lALw/jzytvLykpqbSvoaEhAODXX3/Fb7/9hqioqJfWo6ns7HzI5QqtjacpPvK6bjEz0MOgbi0Q9dsteDhaoJO7+rdNVDN4rhBphucKkWaEOFfEYtELLxpXeQ68ttjY2FQ4TSYrKwsAYGtrW2E/CwsLSKVS5X7P9xWJRMrpNUuWLIG/vz+MjY2RlpaGtLQ05frv6enpL7xRluhVBXVxhJO9GTYfSUJOfsXfKBERERFVlWAB3s3NDbdu3UJBQYFK+7Vr15TbKyIWi+Hi4oK4uDi1bbGxsXB0dFSu9Z6RkYFjx46hd+/eyv9t2rQJADB48GDMnj1bmy+JSIWeWIypwe4olckRfigRCkXtf3NDRERE9Y9gd3UGBgZi/fr12LVrFyZOnAjg2bSYyMhI+Pj4KG9wTU9PR2FhIZydnZV9AwIC8P333yM+Pl65lGRKSgrOnTuHadOmKfdbunQpZDLV9bgPHjyI6OhoLFmyBPb29jX8Kqmhs7cyxvCezth+/AZ+i81Ad+/XhC6JiIiI6jjBAry3tzcCAwOxdOlS5aoxUVFRSE9PxzfffKPcb968ebhw4QKSkpKUbWPHjsWuXbswffp0TJo0CXp6eggPD4eNjY3ywwAA9OzZU+245ctT9uzZE2ZmZjX2+ojK9W7fDFeuZ2F7zA24O1rCxoJPAyYiIqLq0yjAP79c5ItcvnxZ430XL16M5cuXY+/evcjNzYWrqyvWrFmD9u3bv7CfiYkJNm/ejEWLFmHVqlWQy+Xw9fXFwoULYWlpqfHxiWqDWCTC5AHu+HTdBaw7mICPxraDWCQSuiwiIiKqo0QKDSbmVjYfvdJBRaJKH8RU13EVGqquM7EZWB+dgFH+rRDQqbnQ5dRbPFeINMNzhUgzurgKjUZX4Mtv/CSi6uvmaYfL17Ow+3QK2rS0QlNr45d3IiIiInqORgG+U6dONV0HUb0nEokwob8bPgk7j7AD8Vg4vj0keoItBEVERER1FNMDUS0yN5YiNMAVd+7n4eDZO0KXQ0RERHUQAzxRLevgZovOrZtg/++3cSvjidDlEBERUR3DAE8kgHF9XWBuIkXYgXiUlJYJXQ4RERHVIQzwRAIwNtTHpCA3ZGQ/ReSvKUKXsMX9QQAAIABJREFUQ0RERHUIAzyRQNo4WaFXu6Y49mcqku4+FrocIiIiqiMY4IkENLJXK9hYGGHdwQQUFsuELoeIiIjqAAZ4IgEZSPUwJdgd2blF+OXEDaHLISIiojqAAZ5IYK83s0Bg5+b49VoGrt18KHQ5REREpOMY4Il0wBC/lmhmY4zwQ4nILywVuhwiIiLSYQzwRDpAXyLG1GAP5BeWYsvRJKHLISIiIh3GAE+kI5o3McUgPydcSHiA8/GZQpdDREREOooBnkiHBHVujpavmWHL0SQ8zisWuhwiIiLSQQzwRDpETyzGlAHuKJXJEX4oEQqFQuiSiIiISMcwwBPpGHsrY4T0dMZfKdn49Vq60OUQERGRjmGAJ9JB/u2bwd3REjtO3ERWTqHQ5RAREZEOYYAn0kFikQiTg9whFgHrDsRDLudUGiIiInqGAZ5IR1mZG2JMbxdcT8vF0T9ThS6HiIiIdAQDPJEO6+Zph3avWyPy1xTcy8oXuhwiIiLSAQzwRDpMJBJhQqAbDKV6CDuQAFmZXOiSiIiISGAM8EQ6zsxYigmBrriTmYcD/6+9O4+uqjrYP/7cGzISIAnegGUIGCADYQoKMqggsIgMQpcgLRAUBKVgBbpoq6LrXa9tHcFlS8UqgYIUtS8YDMZCQKBQQUGQ0SQMCSAxQK6BQObp3t8f/rg1JsGgJPve5Pv5x5V99uE8h7W2eTjZ92TPGdNxAACAYRR4wAP0jQjVgO5tlLznrE6fv2o6DgAAMIgCD3iIKSO6qVWgjxKSU1VWXmk6DgAAMIQCD3iIAD9vTR8VqfO5RUrclWk6DgAAMIQCD3iQmM6tNTS2nbZ+fk7Hv7psOg4AADCAAg94mAeHdJEt2F8rPkpTcWmF6TgAAKCBUeABD+Pr46WZo6OVe7VE/9x+0nQcAADQwCjwgAfq0r6V4vp31K7D53X41Dem4wAAgAZEgQc81PjBt6m9rblWbUpXQXG56TgAAKCBUOABD+XdzKqZY6JVUFyuNSnHTccBAAANhAIPeLCObVpo3ODO+jw9R3tTL5qOAwAAGgAFHvBw993ZUeE/a6l/bDmuy/mlpuMAAIB6RoEHPJyX1apHxkSrvMKhVZvS5XQ6TUcCAAD1iAIPNAJtQwI0YUi4jmbmaufhbNNxAABAPaLAA43EvX3bKyosWP/cdko5ecWm4wAAgHpCgQcaCavFokdGR8lqlVYmp8rhYCsNAACNEQUeaERCWvpp8vBuOpF1RVs+P2c6DgAAqAcUeKCRGRjTVn263qLEXZn62l5gOg4AALjJKPBAI2OxWPRQXKT8fLyUkJymikqH6UgAAOAmosADjVDL5j56KC5SZy/mK3nPGdNxAADATUSBBxqpvhE2DejeVsl7zur0+aum4wAAgJuEAg80YlNGdFWrQB8lJKeqrLzSdBwAAHATUOCBRizAz1szRkXpfG6REndlmo4DAABuAgo80Mh17xyiobHttOXzc0o/e9l0HAAA8BNR4IEm4MEhXRQa7K8VH6WpuLTCdBwAAPATUOCBJsDXx0szx0TrUn6J3tt20nQcAADwE1DggSaiS7tWuq9/mP5z5LwOn/rGdBwAAPAjUeCBJmTc4M5qbwvU3zelK7+ozHQcAADwI1DggSbEu5lVM8dEqbC4XGu2nJDT6TQdCQAA3CAKPNDEdGzTQuPv6qz96Tnam3bRdBwAAHCDKPBAExTXv6PCf9ZSa7ec0OX8UtNxAADADaDAA02Ql9WqmWOiVV7h0N83pbGVBgAAD0KBB5qoNiEBmji0i45lXtLOw9mm4wAAgDoyWuDLysr0yiuvaPDgwerZs6cefPBBffrpp3U69+LFi5o3b55uv/12xcbGas6cOTp37lyVOefPn9fSpUs1YcIE3XHHHerfv7/i4+PrfA2gsRsa205RYcH657ZTyskrNh0HAADUgdEC/+STT2r16tW6//77tWjRIlmtVs2aNUsHDx687nmFhYWaNm2aDhw4oNmzZ+uJJ55Qamqqpk2bpitXrrjmbdu2TQkJCQoLC9P8+fM1Z84cFRYW6uGHH9YHH3xQ37cHuD2rxaJHRkfJarVoRXKqHA620gAA4O4sTkObX48cOaKJEyfqqaee0sMPPyxJKi0t1ZgxYxQaGqq1a9fWeu7y5cu1ZMkSJSYmKjo6WpKUkZGhsWPH6rHHHtO8efMkSSdPnlTr1q0VEhLiOresrEzjxo1TaWmptm/ffsO5c3MLjJQcm62F7Pb8Br8umobdR89rxUdpenBoF8X172g6zk/CWgHqhrUC1I2JtWK1WtS6dWDtxxswSxWbN2+Wt7e3Jk6c6Brz9fXVhAkTdODAAeXk5NR6bkpKinr37u0q75IUHh6uAQMGaNOmTa6xrl27VinvkuTj46N77rlHX3/9tUpKSm7iHQGea2BMW/XpeosSd2Xoa3uB6TgAAOA6jBX4tLQ0de7cWc2bN68y3rNnTzmdTqWlpdV4nsPh0PHjxxUTE1PtWI8ePXTmzBkVF19/L6/dbldAQIB8fX1//A0AjYjFYtFDcZHy922mhOQ0VVQ6TEcCAAC1MFbg7Xa7QkNDq43bbDZJqvUJfF5ensrKylzzvn+u0+mU3W6v9bpnz57V1q1bFRcXJ4vF8iPTA41Py+Y+mjYyUmcv5it5zxnTcQAAQC2ambpwSUmJvL29q41feypeWlrzL5e5Nu7j41PrubVtjSkuLta8efPk7++vBQsW/Kjc19uPVN9sthbGro2mIc7WQqlfXVbyp2d1z+0d1a1jsOlIPwprBagb1gpQN+62VowVeD8/P5WXl1cbv1bQa9vecm28rKys1nP9/PyqHausrNSCBQuUkZGhFStW1Pj0vy74ECsauwfu6qxDJ+xa/I/9+p+H75CPt5fpSDeEtQLUDWsFqBs+xPodNputxm0y17a/1Fawg4KC5OPjU+M2GbvdLovFUuP2mmeeeUY7d+7USy+9pH79+v3E9EDjFeDnrRmjo3Q+t0jv78w0HQcAAHyPsQIfGRmp06dPq7CwsMr44cOHXcdrYrVa1a1bNx07dqzasSNHjigsLEz+/v5Vxl966SUlJibq6aef1qhRo27SHQCNV/dOIbo3tp227j+n9LOXTccBAADfYazAx8XFqby8XOvWrXONlZWVKTExUbGxsWrTpo0kKTs7WxkZGVXOHTlypA4dOqTU1FTXWGZmpj777DPFxcVVmZuQkKCVK1dq9uzZio+Pr8c7AhqXiUO6KDTYXys+SlNxaYXpOAAA4P8z9oucJGnevHnatm2bHnroIXXs2FEbNmzQsWPHtHr1avXt21eSFB8fr3379un48eOu8woKCvTzn/9cxcXFmj59ury8vLRq1So5nU598MEHCg7+9oN3W7du1eOPP65OnTppzpw51a4/YsQIBQQE3FBm9sCjKTn19RW98I8DGtTjVs0YFWU6Tp2wVoC6Ya0AdeOOe+CNfYhVkl5++WW99tprSkpK0pUrVxQREaG33nrLVd5rExgYqDVr1uj555/XsmXL5HA41L9/fy1atMhV3iUpPT1dknTmzBn97ne/q/bnbNu27YYLPNCUdGnXSqPuDNNHn55VbFebene9xXQkAACaPKNP4D0RT+DR1JRXOPSH1ft1tahMf3ikn1oEVH+FqzthrQB1w1oB6sYdn8Ab2wMPwDN4N7Nq1thoFRaXa82WE+Lf/AAAmEWBB/CDOoQGavxdnbU/PUd70y6ajgMAQJNGgQdQJ3H9Oyr8Zy31j5QTupxf829KBgAA9Y8CD6BOvKxWzRwTrQqHQ3/flMZWGgAADKHAA6izNiEBmjiki45lXtLOQ9mm4wAA0CRR4AHckKGx7RTdKVj/3H5KOZeLTMcBAKDJocADuCFWi0UzRkXJarVoxUdpRl6rCgBAU0aBB3DDQlr6afLwrjqZdUUpn39lOg4AAE0KBR7AjzIwpq1iu9m0YVemsuwFpuMAANBkUOAB/CgWi0XT4iLk79tMCcmpqqh0mI4EAECTQIEH8KO1DPDRQ3GR+upigT7cfcZ0HAAAmgQKPICfJLabTQNj2uqjT8/q9PmrpuMAANDoUeAB/GSTh3dVq0AfLf8wVWXllabjAADQqFHgAfxkAX7emjE6ShcuFWn9zgzTcQAAaNQo8ABuiu6dQjQstr0+3p+ltLOXTccBAKDRosADuGkmDA1Xm2B/rfwoTcWlFabjAADQKFHgAdw0vt5emjkmWpfyS/TutpOm4wAA0ChR4AHcVOHtWmnUnWH65Mh5HTr5jek4AAA0OhR4ADfduMGd1SE0UKs2pyu/qMx0HAAAGhUKPICbrpmXVTPHRKuwuFxrUo7L6XSajgQAQKNBgQdQLzqEBmr8XZ21/7hde1Mvmo4DAECjQYEHUG/u6x+m8HYt9Y8tJ3Q5v9R0HAAAGgUKPIB6Y7VaNHN0tCocDv39X2lspQEA4CagwAOoV21CAjRxSBcdO31J/z6UbToOAAAejwIPoN4NjW2n7p2C9X/bTynncpHpOAAAeDQKPIB6Z7VYNH1UlKxWixI+SpPDwVYaAAB+LAo8gAYR0tJPU0Z01amsK0r5/CvTcQAA8FgUeAANZkD3tortZtOGXZnKsheYjgMAgEeiwANoMBaLRdPiIuTv20wJH6aqotJhOhIAAB6HAg+gQbUM8NHDcZH6KqdAG3efMR0HAACPQ4EH0OD6dLNpUExb/evTs8rMvmo6DgAAHoUCD8CIXw7vpqAWPkpITlVZeaXpOAAAeAwKPAAjAvyaacaoKF24VKT1OzNMxwEAwGNQ4AEYE90pRMNi2+vj/VlKO3PJdBwAADwCBR6AUROGhqtNSIBW/itNRSUVpuMAAOD2KPAAjPL19tLM0VG6lF+q97adNB0HAAC3R4EHYFx4u1YadWeYPjl6XodOfmM6DgAAbo0CD8AtjBvcWR1CA7VqU5quFpWZjgMAgNuiwANwC828rJo5JlqFJRVak3JcTqfTdCQAANwSBR6A2+gQGqif332bDhy367PUi6bjAADglijwANxKXL+O6tKuldZuOaHL+aWm4wAA4HYo8ADcitVq0SNjolThcOjv/0pjKw0AAN9DgQfgdtoEB+jBoV107PQl/ftQtuk4AAC4FQo8ALc0tE87de8UrH9uP6mLl4tMxwEAwG1Q4AG4JYvFoumjouRltWrFR2lyONhKAwCARIEH4MZCWvpp6ohuOpV1RSn7vjIdBwAAt0CBB+DW7uzeRn272bThP5nKyikwHQcAAOMo8ADcmsViUXxchAJ8mykhOVUVlQ7TkQAAMIoCD8DttQzw0UNxkfoqp0Abd58xHQcAAKMo8AA8Qp9uNg3q0VYffXpGGdlXTMcBAMAYCjwAj/HLYd0U0sJXCclpKi2vNB0HAAAjKPAAPEaAXzPNGBWli5eK9P6/M0zHAQDACAo8AI8S1SlEw/q218cHspR25pLpOAAANDgKPACPM2FIuNqEBGjlv9JUVFJhOg4AAA2KAg/A4/h6e2nm6Chdyi/Vu9tOmI4DAECDMlrgy8rK9Morr2jw4MHq2bOnHnzwQX366ad1OvfixYuaN2+ebr/9dsXGxmrOnDk6d+5cjXPXrVun++67Tz169NDIkSO1du3am3kbAAwIb9dKoweEaffRCzp40m46DgAADcZogX/yySe1evVq3X///Vq0aJGsVqtmzZqlgwcPXve8wsJCTZs2TQcOHNDs2bP1xBNPKDU1VdOmTdOVK1VfL/fee+/pmWeeUbdu3fTss8+qV69eeu6557Ry5cr6vDUADeD+QZ3VMTRQqzel62pRmek4AAA0CIvT6XSauPCRI0c0ceJEPfXUU3r44YclSaWlpRozZoxCQ0Ov+5R8+fLlWrJkiRITExUdHS1JysjI0NixY/XYY49p3rx5kqSSkhLdc8896tu3r5YtW+Y6f+HChdq+fbt27typFi1a3FDu3NwCORwN91e278IX2pixWXmleQryDdL94XHq1za2wa4PuLusnAI9t/pztQ7LVX6ro3I0K5a1wl8DWw/R5NuHmo4HuJ139u/Qntx/s1aAH2ByrVitFrVuHVj78QZJUYPNmzfL29tbEydOdI35+vpqwoQJOnDggHJycmo9NyUlRb1793aVd0kKDw/XgAEDtGnTJtfY3r17lZeXp8mTJ1c5f8qUKSosLNSuXbtu4h3dfPsufKF30t/X5dI8OSVdLs3TO+nva9+FL0xHA9xG+9BAdY7O15Xg/XJ6F8tikZzexfrkcore2b/DdDzArbyzf4c+uZzCWgF+gLuvlWamLpyWlqbOnTurefPmVcZ79uwpp9OptLQ0hYaGVjvP4XDo+PHjmjRpUrVjPXr00O7du1VcXCx/f3+lpqZKkmJiYqrM6969u6xWq1JTUzV69OibeFc318aMzSp3lFcZK3eUa23aeu3J3mcoFeB+snzPyGJ1VBmzeDm0Oy9FmbuPmQkFuKHzxV/L4sVaAX5IbWtlT+6/NVnmf2JlrMDb7Xa1adOm2rjNZpOkWp/A5+XlqayszDXv++c6nU7Z7XZ17NhRdrtdPj4+CgoKqjLv2tj1nvLX5no/zrjZ8krzahyvcFbI29urwXIA7s5pcchSy3iWvaDB8wDuytqCtQLURW1rxdGsWDbbjW2/rg/GCnxJSYm8vb2rjfv6+kr6dj98Ta6N+/j41HpuSUnJda9xbW5t17iehtwDH+QbpMs1lPhg3yDN7TGrQTIAnuDxlP+R07u42ril3F+/vWOOgUSAe3r16KuSD2sF+CG1rRVrhb/s9vx6v/4P7YE3VuD9/PxUXl5ebfxaqb5Wxr/v2nhZWfU3Tlw718/Pz/XfmuZdm1vbNdzF/eFxeif9/SrbaLyt3ro/PM5gKsD9DGw9RJ9cTqny405npVWDbxmi8HatDCYD3Mug86wVoC5qWyuDWg8xF+o7jH2I1Waz1biFxW7/9n3ONe1/l6SgoCD5+Pi45n3/XIvF4tpeY7PZVF5erry8qk+xy8rKlJeXV+s13EW/trGaHPmAgn2DZNG3T94nRz7AW2iA75l8+1ANDh4pS7m/nM5vnyYODh7JmzWA72GtAHXj7mvF2BP4yMhIrVmzRoWFhVU+yHr48GHX8ZpYrVZ169ZNx45V/7DNkSNHFBYWJn9/f0lSVFSUJOnYsWMaPHiwa96xY8fkcDhcx91Zv7ax6tc2VjZbiwb5kQ3gqSbfPlSTNZS1AvwA1gpQN+68Vow9gY+Li1N5ebnWrVvnGisrK1NiYqJiY2NdH3DNzs5WRkZGlXNHjhypQ4cOud4yI0mZmZn67LPPFBf33+0ld955p4KCgvTOO+9UOf/dd99VQECA7r777vq4NQAAAKDeGHsC36tXL8XFxWnx4sWut8Zs2LBB2dnZeuGFF1zzfv/732vfvn06fvy4a2zy5Mlat26dHn30UU2fPl1eXl5atWqVbDab65dCSd/ugX/iiSf03HPPad68eRo8eLD279+vjRs3auHChWrZsmVD3jIAAADwkxkr8JL08ssv67XXXlNSUpKuXLmiiIgIvfXWW+rbt+91zwsMDNSaNWv0/PPPa9myZXI4HOrfv78WLVqk4ODgKnOnTJkib29vrVy5Utu2bdOtt96qRYsWadq0afV5awAAAEC9sDidzoZ5J2Ij0ZCvkfwud9x/Bbgj1gpQN6wVoG5MrJUfeo2ksT3wAAAAAG4cBR4AAADwIBR4AAAAwINQ4AEAAAAPQoEHAAAAPIjR10h6IqvV0iSvDXgS1gpQN6wVoG4aeq380PV4jSQAAADgQdhCAwAAAHgQCjwAAADgQSjwAAAAgAehwAMAAAAehAIPAAAAeBAKPAAAAOBBKPAAAACAB6HAAwAAAB6EAg8AAAB4EAo8AAAA4EGamQ6AmuXk5Ojtt9/W4cOHdezYMRUVFentt99W//79TUcD3MqRI0e0YcMG7d27V9nZ2QoKClKfPn00f/58hYWFmY4HuI2jR4/qb3/7m1JTU5Wbm6sWLVooMjJSc+fOVWxsrOl4gNtavny5Fi9erMjISCUlJZmOI4kC77ZOnz6t5cuXKywsTBERETp48KDpSIBbSkhI0BdffKG4uDhFRETIbrdr7dq1Gj9+vNavX6/w8HDTEQG3cO7cOVVWVmrixImy2WzKz8/Xhx9+qKlTp2r58uUaNGiQ6YiA27Hb7XrjjTcUEBBgOkoVFqfT6TQdAtUVFBSovLxcwcHB+vjjjzV37lyewAM1+OKLLxQTEyMfHx/X2JkzZzR27FiNHj1aL774osF0gHsrLi7W8OHDFRMTozfffNN0HMDtPPnkk8rOzpbT6dTVq1fd5gk8e+DdVGBgoIKDg03HANxebGxslfIuSZ06dVLXrl2VkZFhKBXgGfz9/RUSEqKrV6+ajgK4nSNHjmjjxo166qmnTEephgIPoNFxOp365ptv+EcwUIOCggJdunRJmZmZevXVV3XixAkNGDDAdCzArTidTv3hD3/Q+PHjFRUVZTpONeyBB9DobNy4URcvXtSCBQtMRwHcztNPP62UlBRJkre3t37xi19o9uzZhlMB7uWDDz7QqVOn9Prrr5uOUiMKPIBGJSMjQ88995z69u2rcePGmY4DuJ25c+dq0qRJunDhgpKSklRWVqby8vJqW9GApqqgoEBLlizRo48+qtDQUNNxasQWGgCNht1u12OPPaZWrVrpz3/+s6xW/hcHfF9ERIQGDRqkBx54QCtWrNCXX37plnt8AVPeeOMNeXt7a/r06aaj1IrvbgAahfz8fM2aNUv5+flKSEiQzWYzHQlwe97e3ho2bJi2bNmikpIS03EA43JycrR69WpNnjxZ33zzjbKyspSVlaXS0lKVl5crKytLV65cMR2TLTQAPF9paalmz56tM2fOaNWqVbrttttMRwI8RklJiZxOpwoLC+Xn52c6DmBUbm6uysvLtXjxYi1evLja8WHDhmnWrFlauHChgXT/RYEH4NEqKys1f/58HTp0SMuWLVPv3r1NRwLc0qVLlxQSElJlrKCgQCkpKbr11lvVunVrQ8kA99G+ffsaP7j62muvqaioSE8//bQ6derU8MG+hwLvxpYtWyZJrndZJyUl6cCBA2rZsqWmTp1qMhrgNl588UVt375dQ4cOVV5eXpVfstG8eXMNHz7cYDrAfcyfP1++vr7q06ePbDabzp8/r8TERF24cEGvvvqq6XiAW2jRokWN3zdWr14tLy8vt/mewm9idWMRERE1jrdr107bt29v4DSAe4qPj9e+fftqPMZaAf5r/fr1SkpK0qlTp3T16lW1aNFCvXv31owZM9SvXz/T8QC3Fh8f71a/iZUCDwAAAHgQ3kIDAAAAeBAKPAAAAOBBKPAAAACAB6HAAwAAAB6EAg8AAAB4EAo8AAAA4EEo8AAAAIAHocADANxefHy87r33XtMxAMAtNDMdAABgxt69ezVt2rRaj3t5eSk1NbUBEwEA6oICDwBN3JgxY3T33XdXG7da+SEtALgjCjwANHHR0dEaN26c6RgAgDri8QoA4LqysrIUERGhpUuXKjk5WWPHjlWPHj00ZMgQLV26VBUVFdXOSU9P19y5c9W/f3/16NFDo0aN0vLly1VZWVltrt1u1x//+EcNGzZMMTExGjBggKZPn67du3dXm3vx4kX95je/0R133KFevXrpkUce0enTp+vlvgHAXfEEHgCauOLiYl26dKnauI+PjwIDA11fb9++XefOndOUKVN0yy23aPv27frrX/+q7OxsvfDCC655R48eVXx8vJo1a+aau2PHDi1evFjp6elasmSJa25WVpZ++ctfKjc3V+PGjVNMTIyKi4t1+PBh7dmzR4MGDXLNLSoq0tSpU9WrVy8tWLBAWVlZevvttzVnzhwlJyfLy8urnv6GAMC9UOABoIlbunSpli5dWm18yJAhevPNN11fp6ena/369erevbskaerUqXr88ceVmJioSZMmqXfv3pKkP/3pTyorK9N7772nyMhI19z58+crOTlZEyZM0IABAyRJ//u//6ucnBwlJCTorrvuqnJ9h8NR5evLly/rkUce0axZs1xjISEheuWVV7Rnz55q5wNAY0WBB4AmbtKkSYqLi6s2HhISUuXrgQMHusq7JFksFs2cOVMff/yxtm7dqt69eys3N1cHDx7UiBEjXOX92txf/epX2rx5s7Zu3aoBAwYoLy9P//nPf3TXXXfVWL6//yFaq9Va7a05d955pyTp7NmzFHgATQYFHgCauLCwMA0cOPAH54WHh1cb69KliyTp3Llzkr7dEvPd8e+67bbbZLVaXXO/+uorOZ1ORUdH1ylnaGiofH19q4wFBQVJkvLy8ur0ZwBAY8CHWAEAHuF6e9ydTmcDJgEAsyjwAIA6ycjIqDZ26tQpSVKHDh0kSe3bt68y/l2ZmZlyOByuuR07dpTFYlFaWlp9RQaARokCDwCokz179ujLL790fe10OpWQkCBJGj58uCSpdevW6tOnj3bs2KETJ05UmfvWW29JkkaMGCHp2+0vd999t3bt2qU9e/ZUux5P1QGgZuyBB4AmLjU1VUlJSTUeu1bMJSkyMlIPPfSQpkyZIpvNpm3btmnPnj0aN26c+vTp45q3aNEixcfHa8qUKZo8ebJsNpt27NihTz75RGPGjHG9gUaSnn32WaWmpmrWrFkaP368unfvrtLSUh0+fFjt2rXTb3/72/q7cQDwUBR4AGjikpOTlZycXOOxLVu2uPae33vvvercubPefPNNnT59Wq1bt9acOXM0Z86cKuf06NFD7733nv7yl7/o3XffVVFRkTp06KCFCxdqxowZVeZ26NBB77//vl5//XXt2rVLSUlJatmypSIjIzVp0qT6uWEA8HAWJz+jBABcR1ZWloYNG6bHH39cv/71r03HAYAmjz3wAAAAgAehwAMAAAAehAIPAAAAeBD2wAMAAAAehCfwAAAAgAehwAMAAAAehAIPAAAAeBBGG+55AAAAHElEQVQKPAAAAOBBKPAAAACAB6HAAwAAAB7k/wE9KnI746BFMQAAAABJRU5ErkJggg==\n"
-          },
-          "metadata": {}
-        }
       ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "# Create test loader"
-      ],
       "metadata": {
         "id": "UJlKxl0r-W-m"
-      }
+      },
+      "source": [
+        "# Create test loader"
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 27,
+      "metadata": {
+        "id": "eQGsEEDh-YxG"
+      },
+      "outputs": [],
       "source": [
         "prediction_dataloader = DataLoader(\n",
         "            test_dataset,\n",
         "            sampler = SequentialSampler(test_dataset),\n",
         "            batch_size = batch_size\n",
         "        )"
-      ],
-      "metadata": {
-        "id": "eQGsEEDh-YxG"
-      },
-      "execution_count": 28,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "# Evaluate on test dataset"
-      ],
       "metadata": {
         "id": "gHSDNWvA-aq9"
-      }
+      },
+      "source": [
+        "# Evaluate on test dataset"
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 28,
+      "metadata": {
+        "id": "OPcQkHnJ-c9A",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "d5f3a63f-f9f6-4e68-b9d8-39e826ac1a3d"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Predicting labels for 1,000 test sentences...\n",
+            "    DONE.\n"
+          ]
+        }
+      ],
       "source": [
         "print('Predicting labels for {:,} test sentences...'.format(len(test_dataset)))\n",
         "\n",
         "model.eval()\n",
         "predictions , true_labels = [], []\n",
+        "total_test_acc = 0\n",
         "\n",
         "for batch in prediction_dataloader:\n",
         "\n",
@@ -5748,7 +2308,7 @@
         "      generated_ids = model.generate(\n",
         "        input_ids = b_input_ids,\n",
         "        attention_mask = b_input_mask, \n",
-        "        max_length=2, \n",
+        "        max_length=3, \n",
         "        num_beams=2,\n",
         "        repetition_penalty=2.5, \n",
         "        length_penalty=1.0, \n",
@@ -5757,76 +2317,51 @@
         "        \n",
         "    preds = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True) for g in generated_ids]\n",
         "    target = [tokenizer.decode(t, skip_special_tokens=True, clean_up_tokenization_spaces=True) for t in y]\n",
+        "    total_test_acc += calculate_accuracy(preds, target)  \n",
         "\n",
         "    predictions.append(preds)\n",
         "    true_labels.append(target)\n",
         "\n",
         "print('    DONE.')"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "OPcQkHnJ-c9A",
-        "outputId": "ba586e95-e91f-4424-aee0-67493db5a99a"
-      },
+      ]
+    },
+    {
+      "cell_type": "code",
       "execution_count": 29,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Predicting labels for 1,000 test sentences...\n",
-            "    DONE.\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "results_ok = 0\n",
-        "results_false = 0\n",
-        "for idx, true_labels_batch in enumerate(true_labels):\n",
-        "  for bidx, true_label in enumerate(true_labels_batch):\n",
-        "    if true_label == predictions[idx][bidx]:\n",
-        "      results_ok += 1\n",
-        "    else:\n",
-        "      results_false += 1\n",
-        "\n",
-        "print(\"Correct predictions: {}, incorrect results: {}, accuracy: {}\".format(results_ok, results_false, float(results_ok) / (results_ok + results_false)))"
-      ],
       "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
         "id": "ifz56jYW-zBN",
-        "outputId": "005fd31f-28de-422a-9ccf-d1a15568c706"
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "7f68ebc4-1205-4522-f5b5-74fb49d63565"
       },
-      "execution_count": 30,
       "outputs": [
         {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Correct predictions: 738, incorrect results: 262, accuracy: 0.738\n"
-          ]
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.873015873015873"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 29
         }
+      ],
+      "source": [
+        "avg_test_accuracy = total_test_acc / len(prediction_dataloader)\n",
+        "avg_test_accuracy"
       ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "print(\"Sample prediction: {}, expected: {}\".format(predictions[2][0], true_labels[2][0]))"
-      ],
+      "execution_count": 30,
       "metadata": {
+        "id": "1LqVo4wW-2g-",
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "id": "1LqVo4wW-2g-",
-        "outputId": "084746aa-4677-4c54-b2ac-9d9af4a2488c"
+        "outputId": "9c4820aa-4d8d-41dd-d3c4-d751927fcac0"
       },
-      "execution_count": 31,
       "outputs": [
         {
           "output_type": "stream",
@@ -5835,19 +2370,39 @@
             "Sample prediction: 0, expected: 0\n"
           ]
         }
+      ],
+      "source": [
+        "print(\"Sample prediction: {}, expected: {}\".format(predictions[2][10], true_labels[2][10]))"
       ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "# MCC Score"
-      ],
       "metadata": {
         "id": "dLYc9WXz_B1o"
-      }
+      },
+      "source": [
+        "# MCC Score"
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 31,
+      "metadata": {
+        "id": "hPEPpXXX_DXR",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "5ce9215b-0d24-4126-f80e-4bfa831b48bb"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Calculating Matthews Corr. Coef. for each batch...\n"
+          ]
+        }
+      ],
       "source": [
         "from sklearn.metrics import matthews_corrcoef\n",
         "\n",
@@ -5857,27 +2412,31 @@
         "for i in range(len(true_labels)):\n",
         "  matthews = matthews_corrcoef(true_labels[i], predictions[i])                \n",
         "  matthews_set.append(matthews)"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "hPEPpXXX_DXR",
-        "outputId": "eaac6737-fc5b-4aee-c7e3-3c2cffedb073"
-      },
-      "execution_count": 32,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Calculating Matthews Corr. Coef. for each batch...\n"
-          ]
-        }
       ]
     },
     {
       "cell_type": "code",
+      "execution_count": 32,
+      "metadata": {
+        "id": "qjtAYcme_EyM",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 427
+        },
+        "outputId": "4728121a-b291-4ff8-ff2e-2197d04810ce"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 864x432 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
       "source": [
         "ax = sns.barplot(x=list(range(len(matthews_set))), y=matthews_set, ci=None)\n",
         "\n",
@@ -5886,87 +2445,54 @@
         "plt.xlabel('Batch #')\n",
         "\n",
         "plt.show()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 427
-        },
-        "id": "qjtAYcme_EyM",
-        "outputId": "697cb818-7af9-4237-e9c4-1712222ec85b"
-      },
-      "execution_count": 33,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 864x432 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
       ]
     },
     {
       "cell_type": "code",
+      "execution_count": 33,
+      "metadata": {
+        "id": "rkonN244_HPz",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "0f08315b-7d90-4d6e-ad6f-559cf4573fa8"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Total MCC: 0.042\n"
+          ]
+        }
+      ],
       "source": [
         "flat_predictions = np.concatenate(predictions, axis=0)\n",
         "flat_true_labels = np.concatenate(true_labels, axis=0)\n",
         "\n",
         "mcc = matthews_corrcoef(flat_true_labels, flat_predictions)\n",
         "print('Total MCC: %.3f' % mcc)"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "rkonN244_HPz",
-        "outputId": "83396102-49b8-4351-ea83-92231720881e"
-      },
-      "execution_count": 34,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Total MCC: 0.190\n"
-          ]
-        }
       ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "# Save model"
-      ],
       "metadata": {
         "id": "GPhCp068_Iwq"
-      }
+      },
+      "source": [
+        "# Save model"
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "from google.colab import drive\n",
-        "\n",
-        "drive.mount('/content/gdrive/', force_remount=True)\n",
-        "\n",
-        "output_dir = '/content/gdrive/My Drive/UAM/Przetwarzanie-tekstu/T5_Model'\n",
-        "print(\"Saving model to %s\" % output_dir)\n",
-        "\n",
-        "model_to_save = model.module if hasattr(model, 'module') else model \n",
-        "model_to_save.save_pretrained(output_dir)\n",
-        "tokenizer.save_pretrained(output_dir)"
-      ],
+      "execution_count": 34,
       "metadata": {
+        "id": "avafCMoS_KDF",
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "id": "avafCMoS_KDF",
-        "outputId": "baf501e6-b6c2-4394-db6c-4a971328d464"
+        "outputId": "e7983c91-ecab-4b7c-de48-9c5db5737ca9"
       },
-      "execution_count": 35,
       "outputs": [
         {
           "output_type": "stream",
@@ -5987,21 +2513,3473 @@
             ]
           },
           "metadata": {},
-          "execution_count": 35
+          "execution_count": 34
         }
+      ],
+      "source": [
+        "from google.colab import drive\n",
+        "\n",
+        "drive.mount('/content/gdrive/', force_remount=True)\n",
+        "\n",
+        "output_dir = '/content/gdrive/My Drive/UAM/Przetwarzanie-tekstu/T5_Model'\n",
+        "print(\"Saving model to %s\" % output_dir)\n",
+        "\n",
+        "model_to_save = model.module if hasattr(model, 'module') else model \n",
+        "model_to_save.save_pretrained(output_dir)\n",
+        "tokenizer.save_pretrained(output_dir)"
       ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {
+        "id": "wHzm2_nDA6i-"
+      },
       "source": [
         "# Bibliografia\n",
         "- https://github.com/Shivanandroy/T5-Finetuning-PyTorch/blob/main/notebook/T5_Fine_tuning_with_PyTorch.ipynb\n",
         "- https://mccormickml.com/2019/07/22/BERT-fine-tuning/#a1-saving--loading-fine-tuned-model\n",
         "- https://huggingface.co/docs/transformers/model_doc/t5#training"
-      ],
-      "metadata": {
-        "id": "wHzm2_nDA6i-"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "provenance": []
+    },
+    "gpuClass": "standard",
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    },
+    "widgets": {
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+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
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+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "HBoxView",
+            "box_style": "",
+            "children": [
+              "IPY_MODEL_b59689c435074e14a5ffab86f7358d79",
+              "IPY_MODEL_8e53b6fe3c9b4ddb95c6bc13431f330e",
+              "IPY_MODEL_a8e97add56944729a2ead40f46e0acd8"
+            ],
+            "layout": "IPY_MODEL_5e960877834348318c208650676ac592"
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