Przetwarzanie_tekstu/projekt/FLAN_T5_sms_spam.ipynb

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            "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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            "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",
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            "\u001b[?25hCollecting huggingface-hub<1.0,>=0.11.0\n",
            "  Downloading huggingface_hub-0.12.1-py3-none-any.whl (190 kB)\n",
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            "\u001b[?25hCollecting responses<0.19\n",
            "  Downloading responses-0.18.0-py3-none-any.whl (38 kB)\n",
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            "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[31m7.0 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",
            "Installing collected packages: tokenizers, sentencepiece, xxhash, urllib3, multiprocess, responses, huggingface-hub, transformers, datasets\n",
            "  Attempting uninstall: urllib3\n",
            "    Found existing installation: urllib3 1.24.3\n",
            "    Uninstalling urllib3-1.24.3:\n",
            "      Successfully uninstalled urllib3-1.24.3\n",
            "Successfully installed datasets-2.9.0 huggingface-hub-0.12.1 multiprocess-0.70.14 responses-0.18.0 sentencepiece-0.1.97 tokenizers-0.13.2 transformers-4.26.1 urllib3-1.26.14 xxhash-3.2.0\n"
          ]
        }
      ],
      "source": [
        "!pip install transformers datasets torch sentencepiece"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Załadowanie datasetu"
      ],
      "metadata": {
        "id": "dhN0rmb5Oi3d"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset"
      ],
      "metadata": {
        "id": "tnaDkwZ2Pbnn"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = load_dataset(\"sms_spam\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 231,
          "referenced_widgets": [
            "ba71a0fb991849418b2e169e2774185b",
            "9b894a741cda4e9c963a77f4d03135f2",
            "9f744b38c6e74829ac78065a21ffdae9",
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            "193757f682be4f03a06c0b3db7db4b59",
            "11321931f47a4bc8bb08ca992367e8be",
            "ed2ca3627b1a467bbfa00b98ffd411a2",
            "c3249cbea42b402998cab8fe48bb38b3",
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            "fd0b0bebbdc04607ac6cf8f382c8a55e",
            "ae42f65f65e84663b0105e8e5e8cb660",
            "8252581cb36c4ede9e07f46a23bceab1"
          ]
        },
        "id": "cCiAuRqrOkvV",
        "outputId": "7694c09c-80a5-4b7f-dd31-57f5705242c8"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/3.21k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ba71a0fb991849418b2e169e2774185b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading metadata:   0%|          | 0.00/1.69k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4d90048e7c85439ab2010b70373f755e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/4.87k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7a479cffc6a644c2b50a696ff72d4e8e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset sms_spam/plain_text to /root/.cache/huggingface/datasets/sms_spam/plain_text/1.0.0/53f051d3b5f62d99d61792c91acefe4f1577ad3e4c216fb0ad39e30b9f20019c...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/203k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0cad760c82744f45a29dbf591953629d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/5574 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2181a8b3b8f349c4803784c65fa7f6d2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset sms_spam downloaded and prepared to /root/.cache/huggingface/datasets/sms_spam/plain_text/1.0.0/53f051d3b5f62d99d61792c91acefe4f1577ad3e4c216fb0ad39e30b9f20019c. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "02ae7dbb11194f0e962ef177ef7b07fb"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset['train'][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JKFHPko3OnAV",
        "outputId": "c54f2007-8d9b-4d81-fd18-9b8493f8b995"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'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": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Przygotowanie datasetu"
      ],
      "metadata": {
        "id": "l140vJrgYxPr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "parsed_dataset = []\n",
        "\n",
        "for row in dataset['train']:\n",
        "  text = row['sms'].replace(\"\\n\", \"\")\n",
        "  new_row = {}\n",
        "  new_row['sms'] = text\n",
        "  if row['label'] == 0:\n",
        "    new_row['label'] = \"False\"\n",
        "  else:\n",
        "    new_row['label'] = \"True\"\n",
        "  parsed_dataset.append(new_row)\n",
        "\n",
        "parsed_dataset[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1boUF-YiY3_y",
        "outputId": "671699cc-459f-49f4-85da-08e560e58a21"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'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": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Tokenizer FLAN-T5"
      ],
      "metadata": {
        "id": "O-J-jBDxPJcn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer"
      ],
      "metadata": {
        "id": "P23AYPX1PZ6g"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained('google/flan-t5-base')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "f69c7f8b53cd4a9aa1a44a2f220f8124",
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            "824ca8a5e51f4ab589b5b44b42c157be",
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            "87a84314626a466e915643b432be6d8a",
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            "91eaed8fe62e462f93a9ec637f3dab53",
            "cca593b390b043ff8dbb04136d572603",
            "f797765fe659426bbc997e4a5f0e19da",
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            "86a3b1fbab5246919d22237a024a0f00",
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            "a6d374adc0be4bdc8c60961905554825",
            "afeb47b4460443d2bbb485a4c0ed8673",
            "c1a73269aeae4b459dfe6cdc1fb1d0fc",
            "a93a627cf1404c6484006cb313bd561e",
            "654d770c66e04e70a1ec1a9ec313d8b7",
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            "61cd2f82626341e4bbdb09e90453b5fc",
            "81385a7ae9614d3fbb21bf94287bd27f",
            "af42d7ccf0064ca7938e0d6558b8b6f0",
            "7580b82f7a0a4974a400b6f3d891be2f",
            "6cc4b77c8ffe4057a107dd49c76e8b44",
            "6558a34168bd48569b3576d39b6ff8cc"
          ]
        },
        "id": "q5Jz0E_oPMBr",
        "outputId": "ada39869-6643-4925-aea5-11672a2569ce"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/2.54k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f69c7f8b53cd4a9aa1a44a2f220f8124"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)\"spiece.model\";:   0%|          | 0.00/792k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b5afbf6e7b2a4d6c89a696882c28d4ea"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/2.42M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c42d572cb8db484b862357539afcb3fe"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/2.20k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a93a627cf1404c6484006cb313bd561e"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "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)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dfxJQpoePsvI",
        "outputId": "35709b9e-7919-4dc5-eae4-142e3ffd6326"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "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"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Few shot learning"
      ],
      "metadata": {
        "id": "UpluhM8cU5Ir"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "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": "2851ec7c-5446-446e-8a32-56a4101ac745"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "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",
        "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": "bf21e565-f22d-48a6-d68b-422158de1f6b"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def check_class_balance(dataset):\n",
        "  spam_count = 0.0\n",
        "  not_spam_count = 0.0\n",
        "  for row in dataset:\n",
        "    if row['label'] == \"True\":\n",
        "      spam_count += 1.0\n",
        "    else:\n",
        "      not_spam_count += 1.0\n",
        "  return spam_count / not_spam_count"
      ],
      "metadata": {
        "id": "f2yGhxu80__k"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "parsed_dataset = parsed_dataset[1:123] + parsed_dataset[124:2000] + parsed_dataset[2001:3002] + parsed_dataset[3003:]\n",
        "print(\"Spam to not spam messages ratio: {}\\n\".format(check_class_balance(parsed_dataset)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "voYL96Ac1MQL",
        "outputId": "bef4b6f9-7b4f-49e0-ac96-c8797a4ecc97"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Spam to not spam messages ratio: 0.1544041450777202\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Load FLAN-T5 model"
      ],
      "metadata": {
        "id": "okTx_ynMV0rH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModelForSeq2SeqLM"
      ],
      "metadata": {
        "id": "Eu-7Eed8WgN0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = AutoModelForSeq2SeqLM.from_pretrained('google/flan-t5-base')\n",
        "\n",
        "model.cuda()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "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": "c902fc89-7c47-4eca-da89-411bc5b5e501"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/1.40k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8e60f2b1a9f7455db02eb846af52df63"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)\"pytorch_model.bin\";:   0%|          | 0.00/990M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3fcb6b4a0c454d59a6d5503cec84ba37"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)neration_config.json:   0%|          | 0.00/147 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c2a8099dd85e42599911991b467a7afe"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "T5ForConditionalGeneration(\n",
              "  (shared): Embedding(32128, 768)\n",
              "  (encoder): T5Stack(\n",
              "    (embed_tokens): Embedding(32128, 768)\n",
              "    (block): ModuleList(\n",
              "      (0): T5Block(\n",
              "        (layer): ModuleList(\n",
              "          (0): T5LayerSelfAttention(\n",
              "            (SelfAttention): T5Attention(\n",
              "              (q): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (k): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (v): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (o): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (relative_attention_bias): Embedding(32, 12)\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (1): T5LayerFF(\n",
              "            (DenseReluDense): T5DenseGatedActDense(\n",
              "              (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n",
              "              (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n",
              "              (wo): Linear(in_features=2048, out_features=768, bias=False)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "              (act): NewGELUActivation()\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (1): T5Block(\n",
              "        (layer): ModuleList(\n",
              "          (0): T5LayerSelfAttention(\n",
              "            (SelfAttention): T5Attention(\n",
              "              (q): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (k): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (v): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (o): Linear(in_features=768, out_features=768, bias=False)\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (1): T5LayerFF(\n",
              "            (DenseReluDense): T5DenseGatedActDense(\n",
              "              (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n",
              "              (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n",
              "              (wo): Linear(in_features=2048, out_features=768, bias=False)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "              (act): NewGELUActivation()\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (2): T5Block(\n",
              "        (layer): ModuleList(\n",
              "          (0): T5LayerSelfAttention(\n",
              "            (SelfAttention): T5Attention(\n",
              "              (q): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (k): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (v): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (o): Linear(in_features=768, out_features=768, bias=False)\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (1): T5LayerFF(\n",
              "            (DenseReluDense): T5DenseGatedActDense(\n",
              "              (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n",
              "              (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n",
              "              (wo): Linear(in_features=2048, out_features=768, bias=False)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "              (act): NewGELUActivation()\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (3): T5Block(\n",
              "        (layer): ModuleList(\n",
              "          (0): T5LayerSelfAttention(\n",
              "            (SelfAttention): T5Attention(\n",
              "              (q): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (k): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (v): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (o): Linear(in_features=768, out_features=768, bias=False)\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (1): T5LayerFF(\n",
              "            (DenseReluDense): T5DenseGatedActDense(\n",
              "              (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n",
              "              (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n",
              "              (wo): Linear(in_features=2048, out_features=768, bias=False)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "              (act): NewGELUActivation()\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (4): T5Block(\n",
              "        (layer): ModuleList(\n",
              "          (0): T5LayerSelfAttention(\n",
              "            (SelfAttention): T5Attention(\n",
              "              (q): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (k): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (v): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (o): Linear(in_features=768, out_features=768, bias=False)\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (1): T5LayerFF(\n",
              "            (DenseReluDense): T5DenseGatedActDense(\n",
              "              (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n",
              "              (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n",
              "              (wo): Linear(in_features=2048, out_features=768, bias=False)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "              (act): NewGELUActivation()\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (5): T5Block(\n",
              "        (layer): ModuleList(\n",
              "          (0): T5LayerSelfAttention(\n",
              "            (SelfAttention): T5Attention(\n",
              "              (q): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (k): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (v): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (o): Linear(in_features=768, out_features=768, bias=False)\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (1): T5LayerFF(\n",
              "            (DenseReluDense): T5DenseGatedActDense(\n",
              "              (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n",
              "              (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n",
              "              (wo): Linear(in_features=2048, out_features=768, bias=False)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "              (act): NewGELUActivation()\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (6): T5Block(\n",
              "        (layer): ModuleList(\n",
              "          (0): T5LayerSelfAttention(\n",
              "            (SelfAttention): T5Attention(\n",
              "              (q): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (k): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (v): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (o): Linear(in_features=768, out_features=768, bias=False)\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (1): T5LayerFF(\n",
              "            (DenseReluDense): T5DenseGatedActDense(\n",
              "              (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n",
              "              (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n",
              "              (wo): Linear(in_features=2048, out_features=768, bias=False)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "              (act): NewGELUActivation()\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (7): T5Block(\n",
              "        (layer): ModuleList(\n",
              "          (0): T5LayerSelfAttention(\n",
              "            (SelfAttention): T5Attention(\n",
              "              (q): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (k): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (v): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (o): Linear(in_features=768, out_features=768, bias=False)\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (1): T5LayerFF(\n",
              "            (DenseReluDense): T5DenseGatedActDense(\n",
              "              (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n",
              "              (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n",
              "              (wo): Linear(in_features=2048, out_features=768, bias=False)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "              (act): NewGELUActivation()\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (8): T5Block(\n",
              "        (layer): ModuleList(\n",
              "          (0): T5LayerSelfAttention(\n",
              "            (SelfAttention): T5Attention(\n",
              "              (q): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (k): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (v): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (o): Linear(in_features=768, out_features=768, bias=False)\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (1): T5LayerFF(\n",
              "            (DenseReluDense): T5DenseGatedActDense(\n",
              "              (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n",
              "              (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n",
              "              (wo): Linear(in_features=2048, out_features=768, bias=False)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "              (act): NewGELUActivation()\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (9): T5Block(\n",
              "        (layer): ModuleList(\n",
              "          (0): T5LayerSelfAttention(\n",
              "            (SelfAttention): T5Attention(\n",
              "              (q): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (k): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (v): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (o): Linear(in_features=768, out_features=768, bias=False)\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (1): T5LayerFF(\n",
              "            (DenseReluDense): T5DenseGatedActDense(\n",
              "              (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n",
              "              (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n",
              "              (wo): Linear(in_features=2048, out_features=768, bias=False)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "              (act): NewGELUActivation()\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (10): T5Block(\n",
              "        (layer): ModuleList(\n",
              "          (0): T5LayerSelfAttention(\n",
              "            (SelfAttention): T5Attention(\n",
              "              (q): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (k): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (v): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (o): Linear(in_features=768, out_features=768, bias=False)\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (1): T5LayerFF(\n",
              "            (DenseReluDense): T5DenseGatedActDense(\n",
              "              (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n",
              "              (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n",
              "              (wo): Linear(in_features=2048, out_features=768, bias=False)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "              (act): NewGELUActivation()\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (11): T5Block(\n",
              "        (layer): ModuleList(\n",
              "          (0): T5LayerSelfAttention(\n",
              "            (SelfAttention): T5Attention(\n",
              "              (q): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (k): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (v): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (o): Linear(in_features=768, out_features=768, bias=False)\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (1): T5LayerFF(\n",
              "            (DenseReluDense): T5DenseGatedActDense(\n",
              "              (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n",
              "              (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n",
              "              (wo): Linear(in_features=2048, out_features=768, bias=False)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "              (act): NewGELUActivation()\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (final_layer_norm): T5LayerNorm()\n",
              "    (dropout): Dropout(p=0.1, inplace=False)\n",
              "  )\n",
              "  (decoder): T5Stack(\n",
              "    (embed_tokens): Embedding(32128, 768)\n",
              "    (block): ModuleList(\n",
              "      (0): T5Block(\n",
              "        (layer): ModuleList(\n",
              "          (0): T5LayerSelfAttention(\n",
              "            (SelfAttention): T5Attention(\n",
              "              (q): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (k): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (v): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (o): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (relative_attention_bias): Embedding(32, 12)\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (1): T5LayerCrossAttention(\n",
              "            (EncDecAttention): T5Attention(\n",
              "              (q): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (k): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (v): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (o): Linear(in_features=768, out_features=768, bias=False)\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (2): T5LayerFF(\n",
              "            (DenseReluDense): T5DenseGatedActDense(\n",
              "              (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n",
              "              (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n",
              "              (wo): Linear(in_features=2048, out_features=768, bias=False)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "              (act): NewGELUActivation()\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (1): T5Block(\n",
              "        (layer): ModuleList(\n",
              "          (0): T5LayerSelfAttention(\n",
              "            (SelfAttention): T5Attention(\n",
              "              (q): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (k): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (v): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (o): Linear(in_features=768, out_features=768, bias=False)\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (1): T5LayerCrossAttention(\n",
              "            (EncDecAttention): T5Attention(\n",
              "              (q): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (k): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (v): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (o): Linear(in_features=768, out_features=768, bias=False)\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (2): T5LayerFF(\n",
              "            (DenseReluDense): T5DenseGatedActDense(\n",
              "              (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n",
              "              (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n",
              "              (wo): Linear(in_features=2048, out_features=768, bias=False)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "              (act): NewGELUActivation()\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (2): T5Block(\n",
              "        (layer): ModuleList(\n",
              "          (0): T5LayerSelfAttention(\n",
              "            (SelfAttention): T5Attention(\n",
              "              (q): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (k): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (v): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (o): Linear(in_features=768, out_features=768, bias=False)\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (1): T5LayerCrossAttention(\n",
              "            (EncDecAttention): T5Attention(\n",
              "              (q): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (k): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (v): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (o): Linear(in_features=768, out_features=768, bias=False)\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (2): T5LayerFF(\n",
              "            (DenseReluDense): T5DenseGatedActDense(\n",
              "              (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n",
              "              (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n",
              "              (wo): Linear(in_features=2048, out_features=768, bias=False)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "              (act): NewGELUActivation()\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (3): T5Block(\n",
              "        (layer): ModuleList(\n",
              "          (0): T5LayerSelfAttention(\n",
              "            (SelfAttention): T5Attention(\n",
              "              (q): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (k): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (v): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (o): Linear(in_features=768, out_features=768, bias=False)\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (1): T5LayerCrossAttention(\n",
              "            (EncDecAttention): T5Attention(\n",
              "              (q): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (k): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (v): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (o): Linear(in_features=768, out_features=768, bias=False)\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (2): T5LayerFF(\n",
              "            (DenseReluDense): T5DenseGatedActDense(\n",
              "              (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n",
              "              (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n",
              "              (wo): Linear(in_features=2048, out_features=768, bias=False)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "              (act): NewGELUActivation()\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (4): T5Block(\n",
              "        (layer): ModuleList(\n",
              "          (0): T5LayerSelfAttention(\n",
              "            (SelfAttention): T5Attention(\n",
              "              (q): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (k): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (v): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (o): Linear(in_features=768, out_features=768, bias=False)\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (1): T5LayerCrossAttention(\n",
              "            (EncDecAttention): T5Attention(\n",
              "              (q): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (k): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (v): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (o): Linear(in_features=768, out_features=768, bias=False)\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (2): T5LayerFF(\n",
              "            (DenseReluDense): T5DenseGatedActDense(\n",
              "              (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n",
              "              (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n",
              "              (wo): Linear(in_features=2048, out_features=768, bias=False)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "              (act): NewGELUActivation()\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (5): T5Block(\n",
              "        (layer): ModuleList(\n",
              "          (0): T5LayerSelfAttention(\n",
              "            (SelfAttention): T5Attention(\n",
              "              (q): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (k): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (v): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (o): Linear(in_features=768, out_features=768, bias=False)\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (1): T5LayerCrossAttention(\n",
              "            (EncDecAttention): T5Attention(\n",
              "              (q): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (k): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (v): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (o): Linear(in_features=768, out_features=768, bias=False)\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (2): T5LayerFF(\n",
              "            (DenseReluDense): T5DenseGatedActDense(\n",
              "              (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n",
              "              (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n",
              "              (wo): Linear(in_features=2048, out_features=768, bias=False)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "              (act): NewGELUActivation()\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (6): T5Block(\n",
              "        (layer): ModuleList(\n",
              "          (0): T5LayerSelfAttention(\n",
              "            (SelfAttention): T5Attention(\n",
              "              (q): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (k): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (v): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (o): Linear(in_features=768, out_features=768, bias=False)\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (1): T5LayerCrossAttention(\n",
              "            (EncDecAttention): T5Attention(\n",
              "              (q): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (k): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (v): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (o): Linear(in_features=768, out_features=768, bias=False)\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (2): T5LayerFF(\n",
              "            (DenseReluDense): T5DenseGatedActDense(\n",
              "              (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n",
              "              (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n",
              "              (wo): Linear(in_features=2048, out_features=768, bias=False)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "              (act): NewGELUActivation()\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (7): T5Block(\n",
              "        (layer): ModuleList(\n",
              "          (0): T5LayerSelfAttention(\n",
              "            (SelfAttention): T5Attention(\n",
              "              (q): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (k): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (v): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (o): Linear(in_features=768, out_features=768, bias=False)\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (1): T5LayerCrossAttention(\n",
              "            (EncDecAttention): T5Attention(\n",
              "              (q): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (k): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (v): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (o): Linear(in_features=768, out_features=768, bias=False)\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (2): T5LayerFF(\n",
              "            (DenseReluDense): T5DenseGatedActDense(\n",
              "              (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n",
              "              (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n",
              "              (wo): Linear(in_features=2048, out_features=768, bias=False)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "              (act): NewGELUActivation()\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (8): T5Block(\n",
              "        (layer): ModuleList(\n",
              "          (0): T5LayerSelfAttention(\n",
              "            (SelfAttention): T5Attention(\n",
              "              (q): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (k): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (v): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (o): Linear(in_features=768, out_features=768, bias=False)\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (1): T5LayerCrossAttention(\n",
              "            (EncDecAttention): T5Attention(\n",
              "              (q): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (k): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (v): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (o): Linear(in_features=768, out_features=768, bias=False)\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (2): T5LayerFF(\n",
              "            (DenseReluDense): T5DenseGatedActDense(\n",
              "              (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n",
              "              (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n",
              "              (wo): Linear(in_features=2048, out_features=768, bias=False)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "              (act): NewGELUActivation()\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (9): T5Block(\n",
              "        (layer): ModuleList(\n",
              "          (0): T5LayerSelfAttention(\n",
              "            (SelfAttention): T5Attention(\n",
              "              (q): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (k): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (v): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (o): Linear(in_features=768, out_features=768, bias=False)\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (1): T5LayerCrossAttention(\n",
              "            (EncDecAttention): T5Attention(\n",
              "              (q): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (k): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (v): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (o): Linear(in_features=768, out_features=768, bias=False)\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (2): T5LayerFF(\n",
              "            (DenseReluDense): T5DenseGatedActDense(\n",
              "              (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n",
              "              (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n",
              "              (wo): Linear(in_features=2048, out_features=768, bias=False)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "              (act): NewGELUActivation()\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (10): T5Block(\n",
              "        (layer): ModuleList(\n",
              "          (0): T5LayerSelfAttention(\n",
              "            (SelfAttention): T5Attention(\n",
              "              (q): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (k): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (v): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (o): Linear(in_features=768, out_features=768, bias=False)\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (1): T5LayerCrossAttention(\n",
              "            (EncDecAttention): T5Attention(\n",
              "              (q): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (k): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (v): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (o): Linear(in_features=768, out_features=768, bias=False)\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (2): T5LayerFF(\n",
              "            (DenseReluDense): T5DenseGatedActDense(\n",
              "              (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n",
              "              (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n",
              "              (wo): Linear(in_features=2048, out_features=768, bias=False)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "              (act): NewGELUActivation()\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (11): T5Block(\n",
              "        (layer): ModuleList(\n",
              "          (0): T5LayerSelfAttention(\n",
              "            (SelfAttention): T5Attention(\n",
              "              (q): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (k): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (v): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (o): Linear(in_features=768, out_features=768, bias=False)\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (1): T5LayerCrossAttention(\n",
              "            (EncDecAttention): T5Attention(\n",
              "              (q): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (k): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (v): Linear(in_features=768, out_features=768, bias=False)\n",
              "              (o): Linear(in_features=768, out_features=768, bias=False)\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (2): T5LayerFF(\n",
              "            (DenseReluDense): T5DenseGatedActDense(\n",
              "              (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n",
              "              (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n",
              "              (wo): Linear(in_features=2048, out_features=768, bias=False)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "              (act): NewGELUActivation()\n",
              "            )\n",
              "            (layer_norm): T5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (final_layer_norm): T5LayerNorm()\n",
              "    (dropout): Dropout(p=0.1, inplace=False)\n",
              "  )\n",
              "  (lm_head): Linear(in_features=768, out_features=32128, bias=False)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Helper functions"
      ],
      "metadata": {
        "id": "F_SDAwxoawDy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch"
      ],
      "metadata": {
        "id": "rdWMg_KJZEZH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def calculate_accuracy(preds, target):\n",
        "  results_ok = 0.0\n",
        "  results_false = 0.0\n",
        "\n",
        "  for idx, pred in enumerate(preds):\n",
        "    if pred == target[idx]:\n",
        "      results_ok += 1.0\n",
        "    else:\n",
        "      results_false += 1.0\n",
        "\n",
        "  return results_ok / (results_ok + results_false)"
      ],
      "metadata": {
        "id": "FzUi8908ax61"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "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": "86i7iRmtW-6L",
        "outputId": "109072a9-ff0c-4ac0-fc74-4de30065af95"
      },
      "execution_count": null,
      "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": [
        "# Predykcja"
      ],
      "metadata": {
        "id": "H_YI3bS3VHQE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = []\n",
        "expected = []\n",
        "\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",
        "  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",
        "acc = calculate_accuracy(predictions, expected)\n",
        "print(acc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vunLGuBGVGmh",
        "outputId": "07945f48-6cf3-4762-a91a-df68ec0d599b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.4601436265709156\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Sample prediction: {}, expected: {}\".format(predictions[101], expected[101]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "20WbYZmLaDl7",
        "outputId": "6a5eb397-39ed-4095-8640-d76b6cdab520"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sample prediction: False, expected: False\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# MCC Score"
      ],
      "metadata": {
        "id": "dLYc9WXz_B1o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import matthews_corrcoef\n",
        "\n",
        "print('Calculating Matthews Corr. Coef. for each batch...')\n",
        "matthews = matthews_corrcoef(expected, predictions)    \n",
        "print('Total MCC: %.3f' % matthews)"
      ],
      "metadata": {
        "id": "hPEPpXXX_DXR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9e0a324e-b880-4591-9243-dfb45ecb21cd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Calculating Matthews Corr. Coef. for each batch...\n",
            "Total MCC: -0.001\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Save model"
      ],
      "metadata": {
        "id": "GPhCp068_Iwq"
      }
    },
    {
      "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/FLAN-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)"
      ],
      "metadata": {
        "id": "avafCMoS_KDF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "16223c2c-299d-4059-8d6f-3ec2637f1185"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive/\n",
            "Saving model to /content/gdrive/My Drive/UAM/Przetwarzanie-tekstu/FLAN-T5_Model\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('/content/gdrive/My Drive/UAM/Przetwarzanie-tekstu/FLAN-T5_Model/tokenizer_config.json',\n",
              " '/content/gdrive/My Drive/UAM/Przetwarzanie-tekstu/FLAN-T5_Model/special_tokens_map.json',\n",
              " '/content/gdrive/My Drive/UAM/Przetwarzanie-tekstu/FLAN-T5_Model/spiece.model',\n",
              " '/content/gdrive/My Drive/UAM/Przetwarzanie-tekstu/FLAN-T5_Model/added_tokens.json',\n",
              " '/content/gdrive/My Drive/UAM/Przetwarzanie-tekstu/FLAN-T5_Model/tokenizer.json')"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Bibliografia\n",
        "- https://huggingface.co/docs/transformers/main/en/model_doc/flan-t5\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-"
      }
    }
  ]
}