Text-Summarizer/Text_Summarizer_Pegasus.ipynb

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            "Wed Sep 13 07:13:39 2023       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 525.105.17   Driver Version: 525.105.17   CUDA Version: 12.0     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   41C    P8     9W /  70W |      0MiB / 15360MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
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      ],
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        "pip install transformers[sentencepiece] datasets sacrebleu rouge_score py7zr -q"
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      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m3.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[?25h  Building wheel for rouge_score (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install --upgrade accelerate\n",
        "!pip install -y transformers accelerate\n",
        "!pip install transformers accelerate"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LTLdeihajRag",
        "outputId": "278e8580-9c46-42f3-a82a-aef48429b150"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: accelerate in /usr/local/lib/python3.10/dist-packages (0.22.0)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from accelerate) (1.23.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (23.1)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from accelerate) (5.9.5)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from accelerate) (6.0.1)\n",
            "Requirement already satisfied: torch>=1.10.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (2.0.1+cu118)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.12.2)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (4.5.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.1.2)\n",
            "Requirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2.0.0)\n",
            "Requirement already satisfied: cmake in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch>=1.10.0->accelerate) (3.27.4.1)\n",
            "Requirement already satisfied: lit in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch>=1.10.0->accelerate) (16.0.6)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.10.0->accelerate) (2.1.3)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.10.0->accelerate) (1.3.0)\n",
            "\n",
            "Usage:   \n",
            "  pip3 install [options] <requirement specifier> [package-index-options] ...\n",
            "  pip3 install [options] -r <requirements file> [package-index-options] ...\n",
            "  pip3 install [options] [-e] <vcs project url> ...\n",
            "  pip3 install [options] [-e] <local project path> ...\n",
            "  pip3 install [options] <archive url/path> ...\n",
            "\n",
            "no such option: -y\n",
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.33.1)\n",
            "Requirement already satisfied: accelerate in /usr/local/lib/python3.10/dist-packages (0.22.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.12.2)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.15.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.17.1)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.23.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (23.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2023.6.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.31.0)\n",
            "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.13.3)\n",
            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.3.3)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.1)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from accelerate) (5.9.5)\n",
            "Requirement already satisfied: torch>=1.10.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (2.0.1+cu118)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.15.1->transformers) (2023.6.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.15.1->transformers) (4.5.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.1.2)\n",
            "Requirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2.0.0)\n",
            "Requirement already satisfied: cmake in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch>=1.10.0->accelerate) (3.27.4.1)\n",
            "Requirement already satisfied: lit in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch>=1.10.0->accelerate) (16.0.6)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.2.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2023.7.22)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.10.0->accelerate) (2.1.3)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.10.0->accelerate) (1.3.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import pipeline, set_seed\n",
        "from datasets import load_dataset, load_from_disk\n",
        "import matplotlib.pyplot as plt\n",
        "from datasets import load_dataset\n",
        "import pandas as pd\n",
        "from datasets import load_dataset, load_metric\n",
        "\n",
        "from transformers import AutoModelForSeq2SeqLM, AutoTokenizer\n",
        "import nltk\n",
        "from nltk.tokenize import sent_tokenize\n",
        "\n",
        "from tqdm import tqdm\n",
        "import torch\n",
        "\n",
        "nltk.download(\"punkt\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pOAgn5hGjIjp",
        "outputId": "ee2e9518-392d-485e-ce43-a63c697751fd"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#download and unzip data\n",
        "\n",
        "!wget https://github.com/entbappy/Branching-tutorial/raw/master/summarizer-data.zip\n",
        "!unzip summarizer-data.zip"
      ],
      "metadata": {
        "id": "NBPaUiZrlGN6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fefe9e8b-5f11-41a9-a89a-ba7e082d0c38"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-09-13 07:14:30--  https://github.com/entbappy/Branching-tutorial/raw/master/summarizer-data.zip\n",
            "Resolving github.com (github.com)... 140.82.114.3\n",
            "Connecting to github.com (github.com)|140.82.114.3|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://raw.githubusercontent.com/entbappy/Branching-tutorial/master/summarizer-data.zip [following]\n",
            "--2023-09-13 07:14:30--  https://raw.githubusercontent.com/entbappy/Branching-tutorial/master/summarizer-data.zip\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 7903594 (7.5M) [application/zip]\n",
            "Saving to: ‘summarizer-data.zip’\n",
            "\n",
            "summarizer-data.zip 100%[===================>]   7.54M  --.-KB/s    in 0.09s   \n",
            "\n",
            "2023-09-13 07:14:30 (87.8 MB/s) - ‘summarizer-data.zip’ saved [7903594/7903594]\n",
            "\n",
            "Archive:  summarizer-data.zip\n",
            "  inflating: samsum-test.csv         \n",
            "  inflating: samsum-train.csv        \n",
            "  inflating: samsum-validation.csv   \n",
            "   creating: samsum_dataset/\n",
            " extracting: samsum_dataset/dataset_dict.json  \n",
            "   creating: samsum_dataset/test/\n",
            "  inflating: samsum_dataset/test/data-00000-of-00001.arrow  \n",
            "  inflating: samsum_dataset/test/dataset_info.json  \n",
            "  inflating: samsum_dataset/test/state.json  \n",
            "   creating: samsum_dataset/train/\n",
            "  inflating: samsum_dataset/train/data-00000-of-00001.arrow  \n",
            "  inflating: samsum_dataset/train/dataset_info.json  \n",
            "  inflating: samsum_dataset/train/state.json  \n",
            "   creating: samsum_dataset/validation/\n",
            "  inflating: samsum_dataset/validation/data-00000-of-00001.arrow  \n",
            "  inflating: samsum_dataset/validation/dataset_info.json  \n",
            "  inflating: samsum_dataset/validation/state.json  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 387,
          "referenced_widgets": [
            "ebc11621bb484f7d95f0270714ecc2e4",
            "49fe3281302545059dad1bd8fd59ce8a",
            "c15766951cdb4d8a8999a56c5c2d194f",
            "4693810b89024a51ba16e0403f6f2272",
            "f77b81f9d1734615b398f35033b32403",
            "4e550cbbaa564f4a8821ebb48f421313",
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            "5ca6ff5db8ab439f8b8076659b3692e5",
            "4a5993e83861493fa988233ca33d98b0",
            "fac357fd385648b6b88a8c84e6805d51",
            "2d6a8f32503846f3805f05426610931b",
            "4668082f8dd541f0be2ebd87bf48f726",
            "4027ad23cea6406ba3de394b5f7198cc",
            "ac32313a42bf4d8baf8aa54a16e6e0c3"
          ]
        },
        "id": "UVVkc2rN1Yk5",
        "outputId": "eac97b4d-bbc0-4099-a945-97b192c2fc14"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ebc11621bb484f7d95f0270714ecc2e4"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "\n",
        "model_ckpt = \"google/pegasus-cnn_dailymail\"\n",
        "token = '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'\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_ckpt)\n",
        "\n",
        "model_pegasus = AutoModelForSeq2SeqLM.from_pretrained(model_ckpt).to(device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264,
          "referenced_widgets": [
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            "c79ea0601f114f8ba22f6bc62d9d1b1d",
            "15b2437f7b2e45a89b30964a4c672bb1",
            "a19fdff231ca48fdb2fbf7b64a07a9e3",
            "ff624749c28e4d64bd3006c1441fe1c6"
          ]
        },
        "id": "DPHj1Thujrm1",
        "outputId": "17be173c-16ac-4cba-bdac-977991e37147"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "Downloading pytorch_model.bin:   0%|          | 0.00/2.28G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "017e0baca84a463eb7455874c752530c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of PegasusForConditionalGeneration were not initialized from the model checkpoint at google/pegasus-cnn_dailymail and are newly initialized: ['model.decoder.embed_positions.weight', 'model.encoder.embed_positions.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)neration_config.json:   0%|          | 0.00/280 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5c30337073564382a8387ba7026eb803"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_pegasus"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lOyxbE_oN501",
        "outputId": "a9428bf3-c5b6-49eb-e531-abd3c76a4a18"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PegasusForConditionalGeneration(\n",
              "  (model): PegasusModel(\n",
              "    (shared): Embedding(96103, 1024, padding_idx=0)\n",
              "    (encoder): PegasusEncoder(\n",
              "      (embed_tokens): Embedding(96103, 1024, padding_idx=0)\n",
              "      (embed_positions): PegasusSinusoidalPositionalEmbedding(1024, 1024)\n",
              "      (layers): ModuleList(\n",
              "        (0-15): 16 x PegasusEncoderLayer(\n",
              "          (self_attn): PegasusAttention(\n",
              "            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "          )\n",
              "          (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "          (activation_fn): ReLU()\n",
              "          (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
              "          (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
              "          (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "        )\n",
              "      )\n",
              "      (layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "    )\n",
              "    (decoder): PegasusDecoder(\n",
              "      (embed_tokens): Embedding(96103, 1024, padding_idx=0)\n",
              "      (embed_positions): PegasusSinusoidalPositionalEmbedding(1024, 1024)\n",
              "      (layers): ModuleList(\n",
              "        (0-15): 16 x PegasusDecoderLayer(\n",
              "          (self_attn): PegasusAttention(\n",
              "            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "          )\n",
              "          (activation_fn): ReLU()\n",
              "          (self_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "          (encoder_attn): PegasusAttention(\n",
              "            (k_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (v_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (q_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "            (out_proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
              "          )\n",
              "          (encoder_attn_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "          (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
              "          (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
              "          (final_layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "        )\n",
              "      )\n",
              "      (layer_norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
              "    )\n",
              "  )\n",
              "  (lm_head): Linear(in_features=1024, out_features=96103, bias=False)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_samsum = load_from_disk('samsum_dataset')\n",
        "\n",
        "dataset_samsum"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ukZx1EQtN7el",
        "outputId": "829604af-0f30-4403-f9c7-55b5ea4ff296"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['id', 'dialogue', 'summary'],\n",
              "        num_rows: 14732\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['id', 'dialogue', 'summary'],\n",
              "        num_rows: 819\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['id', 'dialogue', 'summary'],\n",
              "        num_rows: 818\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "split_lengths = [len(dataset_samsum[split]) for split in dataset_samsum]\n",
        "\n",
        "print(f\"Split lengths: {split_lengths}\")\n",
        "print(f\"Features: {dataset_samsum['train'].column_names}\")\n",
        "print(\"\\nDialogue:\")\n",
        "\n",
        "print(dataset_samsum[\"test\"][1][\"dialogue\"])\n",
        "\n",
        "print(\"\\n Summary: \")\n",
        "\n",
        "print(dataset_samsum[\"test\"][1][\"summary\"])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tZW9RWFIkEAF",
        "outputId": "36eab8ce-0d10-4a9c-e753-85f98bf4ff07"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Split lengths: [14732, 819, 818]\n",
            "Features: ['id', 'dialogue', 'summary']\n",
            "\n",
            "Dialogue:\n",
            "Eric: MACHINE!\r\n",
            "Rob: That's so gr8!\r\n",
            "Eric: I know! And shows how Americans see Russian ;)\r\n",
            "Rob: And it's really funny!\r\n",
            "Eric: I know! I especially like the train part!\r\n",
            "Rob: Hahaha! No one talks to the machine like that!\r\n",
            "Eric: Is this his only stand-up?\r\n",
            "Rob: Idk. I'll check.\r\n",
            "Eric: Sure.\r\n",
            "Rob: Turns out no! There are some of his stand-ups on youtube.\r\n",
            "Eric: Gr8! I'll watch them now!\r\n",
            "Rob: Me too!\r\n",
            "Eric: MACHINE!\r\n",
            "Rob: MACHINE!\r\n",
            "Eric: TTYL?\r\n",
            "Rob: Sure :)\n",
            "\n",
            " Summary: \n",
            "Eric and Rob are going to watch a stand-up on youtube.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def convert_examples_to_features(example_batch):\n",
        "  input_encodings = tokenizer(example_batch['dialogue'], max_length=1024, truncation=True)\n",
        "\n",
        "  with tokenizer.as_target_tokenizer():\n",
        "    target_encodings = tokenizer(example_batch['summary'], max_length =128, truncation=True)\n",
        "\n",
        "  return {\n",
        "      'input_ids' : input_encodings[\"input_ids\"],\n",
        "      'attention_mask' : input_encodings['attention_mask'],\n",
        "      'labels': target_encodings['input_ids']\n",
        "  }\n",
        "\n"
      ],
      "metadata": {
        "id": "ECrq9rsImJDc"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_samsum_pt = dataset_samsum.map(convert_examples_to_features, batched = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168,
          "referenced_widgets": [
            "5485782c41e443f08064b3ea084f3be1",
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            "011a248cfd194821ae923a305eb456f7",
            "8c02229560f14d3b83658c45afa2fcc9",
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            "2977f6bc5a204958a3c984f0a8912076",
            "761d8f6248cc44daa1a7fa9815045919",
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            "8a5952d24dd646aca0d0f841e41ac297",
            "3f2228f2c27f441ba5fdff41b88428a0",
            "db97dd09fb9246fd80ea68000eccd3d5",
            "e195dbc6495f4a7cbe79566b54958b93",
            "677ac049db244520ac125d8e4e31744a",
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            "e0c1d76fd9794565835a23637ae19ac0",
            "df3d5878483a4f5f816288deb646767d",
            "999d67019da843c5ae42cbc75350cbf1",
            "fed534eb403740ee91213e4e5691274d",
            "4794904aed0947799c7036a0813baf14"
          ]
        },
        "id": "gSzFog2ppFnL",
        "outputId": "a5b896cf-def8-4cb5-cce6-b2e2021a51f5"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/14732 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5485782c41e443f08064b3ea084f3be1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/819 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "db97dd09fb9246fd80ea68000eccd3d5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/818 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "93bb5ddfef2d40758cfe40bd991c3af4"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_samsum_pt[\"train\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M8pgrcthpThq",
        "outputId": "ff3052dd-f9d4-4240-fdf2-c06ada0863b9"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['id', 'dialogue', 'summary', 'input_ids', 'attention_mask', 'labels'],\n",
              "    num_rows: 14732\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_samsum_pt[\"train\"][1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s2FrzCARpu3L",
        "outputId": "ca801b71-3182-4a73-dad4-f0197260edf8"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'id': '13728867',\n",
              " 'dialogue': 'Olivia: Who are you voting for in this election? \\r\\nOliver: Liberals as always.\\r\\nOlivia: Me too!!\\r\\nOliver: Great',\n",
              " 'summary': 'Olivia and Olivier are voting for liberals in this election. ',\n",
              " 'input_ids': [18038,\n",
              "  151,\n",
              "  2632,\n",
              "  127,\n",
              "  119,\n",
              "  6228,\n",
              "  118,\n",
              "  115,\n",
              "  136,\n",
              "  2974,\n",
              "  152,\n",
              "  10463,\n",
              "  151,\n",
              "  35884,\n",
              "  130,\n",
              "  329,\n",
              "  107,\n",
              "  18038,\n",
              "  151,\n",
              "  2587,\n",
              "  314,\n",
              "  1242,\n",
              "  10463,\n",
              "  151,\n",
              "  1509,\n",
              "  1],\n",
              " 'attention_mask': [1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1],\n",
              " 'labels': [18038, 111, 34296, 127, 6228, 118, 33195, 115, 136, 2974, 107, 1]}"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Training\n",
        "\n",
        "from transformers import DataCollatorForSeq2Seq\n",
        "from transformers import TrainingArguments, Trainer\n",
        "\n",
        "seq2seq_data_collator = DataCollatorForSeq2Seq(tokenizer, model = model_pegasus)\n",
        "\n",
        "\n",
        "trainer_args = TrainingArguments(\n",
        "    output_dir = 'pegasus-samsum', num_train_epochs=1, warmup_steps= 500,\n",
        "    per_device_train_batch_size=1, per_device_eval_batch_size=1,\n",
        "    weight_decay=0.01, logging_steps=10, evaluation_strategy='steps',\n",
        "    eval_steps=500, save_steps=1e6, gradient_accumulation_steps=16\n",
        ")"
      ],
      "metadata": {
        "id": "UNfWlLl4p1xr"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer = Trainer(model=model_pegasus, args=trainer_args,\n",
        "                  tokenizer=tokenizer, data_collator=seq2seq_data_collator,\n",
        "                  train_dataset= dataset_samsum_pt[\"test\"],\n",
        "                  eval_dataset=dataset_samsum_pt[\"validation\"])\n",
        "\n"
      ],
      "metadata": {
        "id": "vWVGqU9FqkGS"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 165
        },
        "id": "mmE-3c0YvxDy",
        "outputId": "a02c60b3-0dd1-4aa3-d7e7-5e7e9593e802"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You're using a PegasusTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='51' max='51' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [51/51 02:33, Epoch 0/1]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=51, training_loss=3.0752516447329055, metrics={'train_runtime': 159.1657, 'train_samples_per_second': 5.146, 'train_steps_per_second': 0.32, 'total_flos': 313317832187904.0, 'train_loss': 3.0752516447329055, 'epoch': 1.0})"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluation\n",
        "\n",
        "def generate_batch_sized_chunks(list_of_elements, batch_size):\n",
        "  \"\"\" split the dataset into smaller batches that we can process simultaneously\n",
        "      Yield successive batch-sized chunks from list_of_elements.\"\"\"\n",
        "\n",
        "\n",
        "  for i in range(0, len(list_of_elements), batch_size):\n",
        "    yield list_of_elements[i: i+ batch_size]\n",
        "\n",
        "\n",
        "def calculate_metric_on_test_ds(dataset, metric, model, tokenizer,\n",
        "                                batch_size=16, device=device,\n",
        "                                column_text=\"article\",\n",
        "                                column_summary=\"highlights\"):\n",
        "\n",
        "\n",
        "  article_batches = list(generate_batch_sized_chunks(dataset[column_text], batch_size))\n",
        "  target_batches = list(generate_batch_sized_chunks(dataset[column_summary], batch_size))\n",
        "\n",
        "  for article_batch, target_batch in tqdm(\n",
        "      zip(article_batches, target_batches), total = len(article_batches)):\n",
        "\n",
        "      inputs = tokenizer(article_batch, max_length=1024, truncation=True,\n",
        "                         padding=\"max_length\", return_tensors=\"pt\")\n",
        "\n",
        "      summaries = model.generate(input_ids=inputs[\"input_ids\"].to(device),\n",
        "                        attention_mask= inputs[\"attention_mask\"].to(device),\n",
        "                        length_penalty=0.8, num_beams=8, max_length=128)\n",
        "\n",
        "      ''' parameter for length penalty ensures that the model does not generate sequences '''\n",
        "\n",
        "      # finally, we decode the generated texts,\n",
        "      # replace the token, and add the decoded texts with the references to the metric.\n",
        "      decoded_summaries = [tokenizer.decode(s, skip_special_tokens=True,\n",
        "                                            clean_up_tokenization_spaces= True)\n",
        "                          for s in summaries]\n",
        "\n",
        "\n",
        "      decoded_summaries = [d.replace(\"\", \" \") for d in decoded_summaries]\n",
        "\n",
        "      metric.add_batch(predictions = decoded_summaries, references= target_batch)\n",
        "\n",
        "  # Finally compute and return the ROUGE scores\n",
        "\n",
        "  score = metric.compute()\n",
        "\n",
        "  return score\n"
      ],
      "metadata": {
        "id": "jYdEDRmBwTPR"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rouge_names = [\"rouge1\", \"rouge2\", \"rougeL\", \"rougeLsum\"]\n",
        "\n",
        "rouge_metric = load_metric('rouge')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104,
          "referenced_widgets": [
            "d170cef345514174803b450ef4b67d21",
            "bc3d8668ca4743019b6e692a29ee7b12",
            "e59bf15f89044d74a752f107cd3470ed",
            "8351ce7772424b7d969984e4c278a66a",
            "569dd6f22fee4230ae1278004a97cb73",
            "8ba1678813ee455ba4150a9edc28517d",
            "dfd04680565d4400b3e0ab1caa17880a",
            "b2aa6f3a8122431a9ca0101ac1200195",
            "4d6637966ccf4f08834a56c1d8524547",
            "fe79cab9402042a68ff7599a3bc73541",
            "a9ba2e77b70b4bdda2a9e60e0c1a9051"
          ]
        },
        "id": "EmzZq6x15C7X",
        "outputId": "23afa279-3f16-4f54-8b13-c996a38f8fca"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-19-67199026c897>:3: FutureWarning: load_metric is deprecated and will be removed in the next major version of datasets. Use 'evaluate.load' instead, from the new library 🤗 Evaluate: https://huggingface.co/docs/evaluate\n",
            "  rouge_metric = load_metric('rouge')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/2.17k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d170cef345514174803b450ef4b67d21"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "score = calculate_metric_on_test_ds(\n",
        "    dataset_samsum['test'][0:10], rouge_metric, trainer.model, tokenizer, batch_size=2, column_text='dialogue', column_summary='summary'\n",
        ")\n",
        "\n",
        "rouge_dict = dict((rn, score[rn].mid.fmeasure) for rn in rouge_names)\n",
        "\n",
        "pd.DataFrame( rouge_dict, index=[f'pegasus'])\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 98
        },
        "id": "E94JefAV6yvT",
        "outputId": "cc0f2460-f287-4865-de71-f311cb20be77"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 5/5 [00:13<00:00,  2.72s/it]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           rouge1  rouge2    rougeL  rougeLsum\n",
              "pegasus  0.021232     0.0  0.018682   0.018764"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-60d7a901-9f99-4911-982c-eb9ea59ea0bb\" 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>rouge1</th>\n",
              "      <th>rouge2</th>\n",
              "      <th>rougeL</th>\n",
              "      <th>rougeLsum</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>pegasus</th>\n",
              "      <td>0.021232</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.018682</td>\n",
              "      <td>0.018764</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-60d7a901-9f99-4911-982c-eb9ea59ea0bb')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-60d7a901-9f99-4911-982c-eb9ea59ea0bb 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-60d7a901-9f99-4911-982c-eb9ea59ea0bb');\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",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Save model\n",
        "\n",
        "model_pegasus.save_pretrained(\"pegasus-samsum-model\")"
      ],
      "metadata": {
        "id": "vNEvnn8J74YM"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Save tokenizer\n",
        "\n",
        "tokenizer.save_pretrained(\"tokenizer\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xz8G8L058UQv",
        "outputId": "a21dfafe-70c4-41df-f833-0e5ff6365870"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('tokenizer/tokenizer_config.json',\n",
              " 'tokenizer/special_tokens_map.json',\n",
              " 'tokenizer/spiece.model',\n",
              " 'tokenizer/added_tokens.json',\n",
              " 'tokenizer/tokenizer.json')"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Just Prediction\n",
        "\n",
        "gen_kwargs = {\"length_penalty\": 0.8, \"num_beams\": 8, \"max_length\": 128}\n",
        "\n",
        "\n",
        "sample_text = dataset_samsum[\"test\"][0][\"dialogue\"]\n",
        "\n",
        "reference = dataset_samsum[\"test\"][0][\"summary\"]\n",
        "\n",
        "pipe = pipeline(\"summarization\", model=\"pegasus-samsum-model\", tokenizer=tokenizer)\n",
        "\n",
        "##\n",
        "print(\"\\n Dialogue: \")\n",
        "print(sample_text)\n",
        "\n",
        "print(\"\\n Reference Summary:\")\n",
        "print(reference)\n",
        "\n",
        "print(\"\\n Model Summary: \")\n",
        "print(pipe(sample_text, **gen_kwargs)[0][\"summary_text\"])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pali8GJs8BXe",
        "outputId": "6dfcf987-47ac-4e9b-e6ae-80cce5a2354c"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Your max_length is set to 128, but your input_length is only 122. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=61)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dialogue: \n",
            "Hannah: Hey, do you have Betty's number?\n",
            "Amanda: Lemme check\n",
            "Hannah: <file_gif>\n",
            "Amanda: Sorry, can't find it.\n",
            "Amanda: Ask Larry\n",
            "Amanda: He called her last time we were at the park together\n",
            "Hannah: I don't know him well\n",
            "Hannah: <file_gif>\n",
            "Amanda: Don't be shy, he's very nice\n",
            "Hannah: If you say so..\n",
            "Hannah: I'd rather you texted him\n",
            "Amanda: Just text him 🙂\n",
            "Hannah: Urgh.. Alright\n",
            "Hannah: Bye\n",
            "Amanda: Bye bye\n",
            "\n",
            " Reference Summary:\n",
            "Hannah needs Betty's number but Amanda doesn't have it. She needs to contact Larry.\n",
            "\n",
            " Model Summary: \n",
            "Amanda: Ask Larry Amanda: He called her last time we were at the park together .<n>Hannah: I'd rather you texted him .<n>Amanda: Just text him .\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "4Oop9RcIfYmM"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}