4 Summarization | NLP with HuggingFace Tutorial.ipynb

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      "source": [
        "# Announcement-on-ML\n",
        "<a href='http://www.kgptalkie.com' target=\"_blank\"> <img src='https://github.com/laxmimerit/Important-Announcement-on-ML/raw/master/kgptalkie_strips.png'/></a>\n",
        "\n",
        "# ML Resources\n",
        "|  ML Course | Description |\n",
        "|:---|:---|\n",
        "| [**Data Visualization in Python Masterclass™: Beginners to Pro**](https://bit.ly/udemy95off_kgptalkie) |  Learn to build Machine Learning and Deep Learning models using Python and its libraries like Scikit-Learn, Keras, and TensorFlow. |\n",
        "| [**Python for Machine Learning: A Step-by-Step Guide**](https://bit.ly/ml-ds-project) | Learn to build Machine Learning and Deep Learning models using Python and its libraries like Scikit-Learn, Keras, and TensorFlow. |\n",
        "| [**Deep Learning for Beginners with Python**](https://bit.ly/dl-with-python) | Neural Networks, TensorFlow, ANN, CNN, RNN, LSTM, Transfer Learning and Much More. |\n",
        "| [**Python for Linear Regression in Machine Learning**](https://bit.ly/regression-python) | Learn to build Linear Regression models using Python and its libraries like Scikit-Learn. |\n",
        "| [**Introduction to Spacy 3 for Natural Language Processing**](https://bit.ly/spacy-intro) | Learn to build Natural Language Processing models using Python and its libraries like Spacy. |\n",
        "| [**Advanced Machine Learning and Deep Learning Projects**](https://bit.ly/kgptalkie_ml_projects) | Learn to build Advanced Machine Learning and Deep Learning models using Python and transformer models like BERT, GPT-2, and XLNet. |\n",
        "| [**Natural Language Processing in Python for Beginners**](https://bit.ly/intro_nlp) | Learn to build Natural Language Processing Projects using Spacy, NLTK, and Gensim, and transformer models like BERT, GPT-2, and XLNet. |\n",
        "| [**Deployment of Machine Learning Models in Production in Python**](https://bit.ly/bert_nlp) |  Learn to deploy Machine Learning and Deep Learning models using Python and its libraries like Flask, Streamlit, and NGINX. |\n",
        "| [**R 4.0 Programming for Data Science - Beginners to Pro**](https://bit.ly/r4-ml) | Learn to build Machine Learning and Deep Learning models using R and its libraries like caret, tidyverse, and keras. |\n"
      ]
    },
    {
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      "metadata": {
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      "source": [
        "## Summarization with HuggingFace | NLP Projects"
      ]
    },
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      "cell_type": "markdown",
      "metadata": {
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      },
      "source": [
        "Summarization in Natural Language Processing (NLP) involves the automatic generation of concise and coherent summaries from longer texts or documents.\n"
      ]
    },
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "op_Xq8q5BEQk"
      },
      "source": [
        "## Coding"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ex89mjU3ZOAp"
      },
      "source": [
        "https://huggingface.co/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QTw4CXbwzbLd",
        "outputId": "4aadf01f-5993-4bd1-fc18-aca4484f7197"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting transformers\n",
            "  Downloading transformers-4.33.2-py3-none-any.whl (7.6 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.6/7.6 MB\u001b[0m \u001b[31m59.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.12.2)\n",
            "Collecting huggingface-hub<1.0,>=0.15.1 (from transformers)\n",
            "  Downloading huggingface_hub-0.17.2-py3-none-any.whl (294 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m294.9/294.9 kB\u001b[0m \u001b[31m31.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement 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",
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            "Successfully installed brotli-1.1.0 inflate64-0.3.1 multivolumefile-0.2.3 py7zr-0.20.6 pybcj-1.0.1 pycryptodomex-3.19.0 pyppmd-1.0.0 pyzstd-0.15.9 texttable-1.6.7\n"
          ]
        }
      ],
      "source": [
        "!pip install -U transformers\n",
        "!pip install -U accelerate\n",
        "!pip install -U datasets\n",
        "!pip install -U bertviz\n",
        "!pip install -U umap-learn\n",
        "!pip install -U sentencepiece\n",
        "!pip install -U urllib3\n",
        "!pip install py7zr"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aSThJCcXYEPE"
      },
      "outputs": [],
      "source": [
        "# The CNN/DailyMail Dataset\n",
        "# The CNN/DailyMail dataset consists of around 300,000 pairs of news articles and\n",
        "# their corresponding summaries, composed from the bullet points that CNN and the\n",
        "# DailyMail attach to their articles.\n",
        "# summaries are abstractive and not extractive\n"
      ]
    },
    {
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            },
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/15.1k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1f9435a872c54ca69b370ac54e78fd86",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading data files:   0%|          | 0/5 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ca4aae6f7d6e4cb8b1bc388a68e59050",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading data:   0%|          | 0.00/159M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "42a0db02bcd34eddad9b3c2d2427f453",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading data:   0%|          | 0.00/376M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "322fdb9624bb4fd988b577e8a98ee24f",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading data:   0%|          | 0.00/12.3M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "cdbb283249bf4b7f84742a3a7913dee2",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading data:   0%|          | 0.00/661k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6a78cd857f99435998e120875dec23e4",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading data:   0%|          | 0.00/572k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8c6741a97e4e407ca6d348a3a809f287",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e74db8b345de47e19e1ee10306657d8e",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating validation split: 0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "28384d42277b436e97709c9ce60e72f0",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating test split: 0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "dataset = load_dataset(\"cnn_dailymail\", version=\"3.0.0\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "czPWe-6G5ZNR",
        "outputId": "a4655ab2-d70b-4795-ce8e-9fd6bc96398e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['article', 'highlights', 'id'],\n",
              "    num_rows: 287113\n",
              "})"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataset['train']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "id": "EFfO69sE5ZJv",
        "outputId": "9d16ef86-d0c0-4f21-ad42-4acb389888ee"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Editor\\'s note: In our Behind the Scenes series, CNN correspondents share their experiences in covering news and analyze the stories behind the events. Here, Soledad O\\'Brien takes users inside a jail where many of the inmates are mentally ill. An inmate housed on the \"forgotten floor,\" where many men'"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataset['train'][1]['article'][:300]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "id": "nYGxXYP25ZGl",
        "outputId": "e381e7b1-12f8-40c9-90ea-c01b45885902"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Mentally ill inmates in Miami are housed on the \"forgotten floor\"\\nJudge Steven Leifman says most are there as a result of \"avoidable felonies\"\\nWhile CNN tours facility, patient shouts: \"I am the son of the president\"\\nLeifman says the system is unjust and he\\'s fighting for change .'"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataset['train'][1]['highlights']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1zUXbfbv5ZDE"
      },
      "outputs": [],
      "source": [
        "# we will be using models like GPT2, T5, BART, PEGASUS"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 209,
          "referenced_widgets": [
            "1dd02d203bd14f81a202b91ac1bf4b49",
            "461dd3e9ee3844d1b22886a33e548783",
            "1e68415de97a4ec88c57042fa2c35c82",
            "2ff5bc7434724486b35f0d36b7f66984",
            "1ce1a5133f174aa590709466a8db755a",
            "7d079b6618a340598cf5e12cecc2d418",
            "79618799fba04d8aa066dca78d655921",
            "8cf52c5403ae4ee0b05ddbcd6d681ae1",
            "0245ba6dd41244c396460877a2c526cd",
            "ec299fea5408464281b22e3ae22c7282",
            "a28c1dacd30c4d97a60a19dddb1401a3",
            "25a923ef3ed34130905cab808815f7e4",
            "f70cadcae3094cf495459242b297ee18",
            "7d498f20bd044f19ac18eb6fabbaa2fa",
            "29f866a594c0457ea5cc767072103aec",
            "b7715099f066415fb6c33651b24ae4d8",
            "0fa1af631af046c5a58235abb0f06f6a",
            "32ba74148d534f1581b0a9d47c14c039",
            "fc402eb6abaf41a7a1789b1da98d9afb",
            "dcfebaeedcb54527aa757ee7c5707d2d",
            "adaf52fa951b48d1ade670bf49811ff1",
            "5ca2a84a30644e858909206bcf91eaa6",
            "b754cb9b3d9941369bab69f32345a345",
            "ace6a54b3e2e4c409ee7516c2ff586b0",
            "982fac3488ca402394670c70c8de8174",
            "afdd4014ca654fa79b55f7eb56edef25",
            "8024b2bd737b4f61b7116fad66bd4f23",
            "96e34c948c3a4f20852e64945247a34b",
            "3b27e4c79f66417881e0a8ab2494d7ab",
            "26e440f7e5fd48469a543218591d1ee7",
            "704a65f566f64c18a46549de95144508",
            "147dc6f37d104f45970a9219aa7c795e",
            "e2efa6d1c2dc4440a37cfc22e8c20aa3",
            "8e831aa3cd334188801f18c535f40bc2",
            "5bf990974b0346b98124c0087eb803de",
            "42296ea64e2d4bd8ad91469f71e7b1d1",
            "627eabf73e774544a5d55ed5dd78763f",
            "316d1eee590c4b3887a8103acbe57e41",
            "9820c6fb68744960a51b6617e480455b",
            "64ed1d03859246baaaddab1d533ade1e",
            "5ef2e56cccf94515b0b505066cdc9f61",
            "49e7db36f7384a57a417efc27d958aeb",
            "e84a282ab1ef49b79dfb5934bdcfaaf3",
            "79572b4c6cbd4debaf37c466cc64a10d",
            "9fc18aaa98d64b5ea7786ddc10b53a7d",
            "26cad57b61114ad1ac1a92c5c90e32b9",
            "27ff202a5b82462786f94e1f4b6a9c4c",
            "dcfd8decdf91474ea9fa69bd65e790dc",
            "408e34e61a66402787bbbd0adfc547d3",
            "dac808a273e34d518599ae9a6d65fe15",
            "47dcce753b1d42bfa3adc96a3af2bbbc",
            "09d6389fed6c460fafcb54ec76ea85cd",
            "c02d0c7361d14b9b85fa046e6982be3e",
            "5994523a30ef4997a9838bb25878be02",
            "7f750176c27f443981ee82c144875de8",
            "b97630eaf3384e23b9e6589200119074",
            "1134d04a31f94d30bc92ae69e2db8eac",
            "76669c47e9da4c59a85cc428296f9c79",
            "688e15d9a3c14cd58794478a60367bdb",
            "5e282e8235114d3aa264a7f9193a531b",
            "c362e4663b1e438fb290f41837dd431c",
            "3005fc058d864e5ba16403b89455d2e2",
            "35fd75197a604ee69239684b636557dc",
            "e16decbc56e245bb9b309bbb704587ad",
            "d3a932506d1547a69cdc82ba849e521e",
            "810eb116e29b4a4eb75a9efcf443421a"
          ]
        },
        "id": "umYzJ0D55Y_d",
        "outputId": "e4d1e415-d73a-4c41-9534-770406994914"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1dd02d203bd14f81a202b91ac1bf4b49",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/718 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "25a923ef3ed34130905cab808815f7e4",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading model.safetensors:   0%|          | 0.00/1.52G [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b754cb9b3d9941369bab69f32345a345",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)neration_config.json:   0%|          | 0.00/124 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8e831aa3cd334188801f18c535f40bc2",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/1.04M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9fc18aaa98d64b5ea7786ddc10b53a7d",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)olve/main/merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b97630eaf3384e23b9e6589200119074",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "pipe = pipeline(\"text-generation\", model=\"gpt2-medium\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "68g-Oh6Y5Y8E"
      },
      "outputs": [],
      "source": [
        "# lets use text summarization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_DiBDu0k5Y0g",
        "outputId": "60b5d72a-60ea-4889-c9c2-367dde92309d"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
          ]
        }
      ],
      "source": [
        "dataset['train'][1]['article'][:1000]\n",
        "input_text = dataset['train'][1]['article'][:2000]\n",
        "\n",
        "query = input_text + \"\\nTL;DR:\\n\"\n",
        "\n",
        "pipe_out = pipe(query, max_length=512, clean_up_tokenization_spaces=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PUVTBT0yweAE",
        "outputId": "4a19ff96-4e85-4dde-e94b-1f699074046a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "2000"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(input_text)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 193
        },
        "id": "Da8kP_ch5YxB",
        "outputId": "458d321f-fd1c-4ac1-8f2e-7f17bcbba1ec"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Editor\\'s note: In our Behind the Scenes series, CNN correspondents share their experiences in covering news and analyze the stories behind the events. Here, Soledad O\\'Brien takes users inside a jail where many of the inmates are mentally ill. An inmate housed on the \"forgotten floor,\" where many mentally ill inmates are housed in Miami before trial. MIAMI, Florida (CNN) -- The ninth floor of the Miami-Dade pretrial detention facility is dubbed the \"forgotten floor.\" Here, inmates with the most severe mental illnesses are incarcerated until they\\'re ready to appear in court. Most often, they face drug charges or charges of assaulting an officer --charges that Judge Steven Leifman says are usually \"avoidable felonies.\" He says the arrests often result from confrontations with police. Mentally ill people often won\\'t do what they\\'re told when police arrive on the scene -- confrontation seems to exacerbate their illness and they become more paranoid, delusional, and less likely to follow directions, according to Leifman. So, they end up on the ninth floor severely mentally disturbed, but not getting any real help because they\\'re in jail. We toured the jail with Leifman. He is well known in Miami as an advocate for justice and the mentally ill. Even though we were not exactly welcomed with open arms by the guards, we were given permission to shoot videotape and tour the floor.  Go inside the \\'forgotten floor\\' » . At first, it\\'s hard to determine where the people are. The prisoners are wearing sleeveless robes. Imagine cutting holes for arms and feet in a heavy wool sleeping bag -- that\\'s kind of what they look like. They\\'re designed to keep the mentally ill patients from injuring themselves. That\\'s also why they have no shoes, laces or mattresses. Leifman says about one-third of all people in Miami-Dade county jails are mentally ill. So, he says, the sheer volume is overwhelming the system, and the result is what we see on the ninth floor. Of course, it is a jail, so it\\'s \\nTL;DR:\\n1. On the ninth floor, people who commit crimes are incarcerated from a prison in which they are mentally ill.\\n2. The mentally ill inmates have no rights.\\n3. The mentally ill inmates live in the basement not because they\\'re mentally ill, but because their families are too far away.\\n4. Here, there are no mattresses because their families live more than'"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pipe_out[0]['generated_text']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        },
        "id": "ceGXYr-v5Ytk",
        "outputId": "bd87c10e-2cc6-45d4-8f77-512c4e52aee8"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "\"1. On the ninth floor, people who commit crimes are incarcerated from a prison in which they are mentally ill.\\n2. The mentally ill inmates have no rights.\\n3. The mentally ill inmates live in the basement not because they're mentally ill, but because their families are too far away.\\n4. Here, there are no mattresses because their families live more than\""
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pipe_out[0]['generated_text'][len(query):]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qMAzQQB25YqL"
      },
      "outputs": [],
      "source": [
        "summaries = {}\n",
        "summaries['gpt2-medium-380M'] = pipe_out[0]['generated_text'][len(query):]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302,
          "referenced_widgets": [
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        },
        "id": "yCCwRgbJ5Ym2",
        "outputId": "3e00f776-03f9-4566-baa6-1b660f13681b"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2096ef54fca54c9bbaddc148156781e6",
              "version_major": 2,
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          },
          "metadata": {},
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              "model_id": "1b2d9db65ae14aaea6b542ccbea5c124",
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
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              "model_id": "dec0e108d0f04d2b823796655c46e87f",
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        },
        {
          "data": {
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              "model_id": "9744add4ac6541d7a9ac95a42cb98731",
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        },
        {
          "data": {
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              "model_id": "337d2ee58dea4fcb95e7b168f843d6a2",
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/models/t5/tokenization_t5_fast.py:158: FutureWarning: This tokenizer was incorrectly instantiated with a model max length of 512 which will be corrected in Transformers v5.\n",
            "For now, this behavior is kept to avoid breaking backwards compatibility when padding/encoding with `truncation is True`.\n",
            "- Be aware that you SHOULD NOT rely on t5-base automatically truncating your input to 512 when padding/encoding.\n",
            "- If you want to encode/pad to sequences longer than 512 you can either instantiate this tokenizer with `model_max_length` or pass `max_length` when encoding/padding.\n",
            "- To avoid this warning, please instantiate this tokenizer with `model_max_length` set to your preferred value.\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "# Try out T5 Transformers\n",
        "\n",
        "pipe = pipeline('summarization', model='t5-base')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_fuouCBE5YkM"
      },
      "outputs": [],
      "source": [
        "pipe_out = pipe(input_text)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TeycLyr55Ygr"
      },
      "outputs": [],
      "source": [
        "summaries['t5-base-223M'] = pipe_out[0]['summary_text']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Q0VE6p1p5Ydb"
      },
      "outputs": [],
      "source": [
        "# lets try out to use BART model\n",
        "\n",
        "# pipe = pipeline('summarization', model='facebook/bart-large-cnn')\n",
        "# pipe_out = pipe(input_text)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "c-YvKu6J5YaS"
      },
      "outputs": [],
      "source": [
        "summaries['bart-large-cnn-400M'] = pipe_out[0]['summary_text']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PyHy9OzJ5YW-"
      },
      "outputs": [],
      "source": [
        "# PEGASUS Model\n",
        "\n",
        "# pipe = pipeline('summarization', model='google/pegasus-cnn_dailymail')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7Zy19MwG5YQn"
      },
      "outputs": [],
      "source": [
        "pipe_out = pipe(input_text)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sF09l8AM5YKP"
      },
      "outputs": [],
      "source": [
        "summaries['pegasus-cnn-568M'] = pipe_out[0]['summary_text']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lgkcKvsW5YGc",
        "outputId": "3557f42d-b5af-4606-bf83-835db9e18125"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "GPT2-MEDIUM-380M\n",
            "1. On the ninth floor, people who commit crimes are incarcerated from a prison in which they are mentally ill.\n",
            "2. The mentally ill inmates have no rights.\n",
            "3. The mentally ill inmates live in the basement not because they're mentally ill, but because their families are too far away.\n",
            "4. Here, there are no mattresses because their families live more than\n",
            "\n",
            "T5-BASE-223M\n",
            "mentally ill inmates housed on the \"forgotten floor\" of a pretrial detention facility in florida . inmates with the most severe mental illnesses are incarcerated until they're ready to appear . a judge says the arrests often result from confrontations with police . mentally ill people often won't do what police say, judge says .\n",
            "\n",
            "BART-LARGE-CNN-400M\n",
            "mentally ill inmates housed on the \"forgotten floor\" of a pretrial detention facility in florida . inmates with the most severe mental illnesses are incarcerated until they're ready to appear . a judge says the arrests often result from confrontations with police . mentally ill people often won't do what police say, judge says .\n",
            "\n",
            "PEGASUS-CNN-568M\n",
            "mentally ill inmates housed on the \"forgotten floor\" of a pretrial detention facility in florida . inmates with the most severe mental illnesses are incarcerated until they're ready to appear . a judge says the arrests often result from confrontations with police . mentally ill people often won't do what police say, judge says .\n",
            "\n"
          ]
        }
      ],
      "source": [
        "for model in summaries:\n",
        "  print(model.upper())\n",
        "  print(summaries[model])\n",
        "  print(\"\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JShvc31cvGdo"
      },
      "source": [
        "### Fine-Tuning Summarization Model on Custom Dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d1VBUQ5I5MS5"
      },
      "source": [
        "https://huggingface.co/datasets/samsum"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "E3WPp4IXpL6m"
      },
      "outputs": [],
      "source": [
        "from datasets import load_dataset\n",
        "from transformers import pipeline\n",
        "\n",
        "from transformers import AutoModelForSeq2SeqLM, AutoTokenizer\n",
        "import torch"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e750518483d84543922e697cf8979ef6",
              "version_major": 2,
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          "metadata": {},
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        },
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          },
          "metadata": {},
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        },
        {
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          },
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        },
        {
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
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            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/1.63G [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "device = 'gpu'\n",
        "model_ckpt = 'facebook/bart-large-cnn'\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_ckpt)\n",
        "model = AutoModelForSeq2SeqLM.from_pretrained(model_ckpt)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DlG5qKvY5bMR",
        "outputId": "4d3dbb0c-bcbc-47c0-f57a-bf3259e9be8b"
      },
      "outputs": [
        {
          "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",
              "})"
            ]
          },
          "execution_count": 39,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "samsum = load_dataset('samsum')\n",
        "samsum"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5MooFi6m5bIA",
        "outputId": "0be826aa-f278-4715-ddf8-33323fa8f5a5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'id': '13818513',\n",
              " 'dialogue': \"Amanda: I baked  cookies. Do you want some?\\r\\nJerry: Sure!\\r\\nAmanda: I'll bring you tomorrow :-)\",\n",
              " 'summary': 'Amanda baked cookies and will bring Jerry some tomorrow.'}"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "samsum['train'][0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uedM-XH35bEY"
      },
      "outputs": [],
      "source": [
        "dialogue_len = [len(x['dialogue'].split()) for x in samsum['train']]\n",
        "summary_len = [len(x['summary'].split()) for x in samsum['train']]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "0qs2VQYh5a-l",
        "outputId": "ae490410-2515-4fbd-a5e4-dc649ce6c690"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[<Axes: title={'center': 'Dialogue Length'}>,\n",
              "        <Axes: title={'center': 'Summary Length'}>]], dtype=object)"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
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            "text/plain": [
              "<Figure size 1500x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "data = pd.DataFrame([dialogue_len, summary_len]).T\n",
        "data.columns = ['Dialogue Length', 'Summary Length']\n",
        "\n",
        "data.hist(figsize=(15,5))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "p1ZW9Gjk5a7I"
      },
      "outputs": [],
      "source": [
        "# lets build Data Collator\n",
        "\n",
        "def get_feature(batch):\n",
        "  encodings = tokenizer(batch['dialogue'], text_target=batch['summary'],\n",
        "                        max_length=1024, truncation=True)\n",
        "\n",
        "  encodings = {'input_ids': encodings['input_ids'],\n",
        "               'attention_mask': encodings['attention_mask'],\n",
        "               'labels': encodings['labels']}\n",
        "\n",
        "  return encodings"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "eca7ef19c5a64a30934537c0e7bdb6b6",
            "a19a0235bc2048dda34a636803fec4e2",
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            "0ae44c0d625c4ad8afbe1500f7b0b1b3",
            "cf8708503f79406dafb7f96b1c46d7e2",
            "aad5e200e09241c9951cc3dcffd2a26b",
            "a0c48a8dc6474db38d4be5f49171b7bd",
            "c75d0c5f715544349cd4ed183e4ba12c",
            "7a4bbbad44f44a7988182ce8f432ff24",
            "6b40b350968e40f7a54497145ced43a5",
            "d77d93e758c24822be68b8c1177f56b1",
            "04604ee1226943f4a0780ef6a7056c14",
            "2b4ae22e291f4b46a603d426215f2f46",
            "46522a3a35774a398782a78f79646ad5",
            "97edcf34f45840bc99fe2909b31ab684",
            "246436abd090439fa1e360f2344057c4",
            "c22389852e694fe686e94a9ec05f272f",
            "dfd80b10fb2f4d0b8d12b1b0efa460b6",
            "d0ca703536444cf0bd1f4f5e8c91ea73",
            "7365ab000fea41c1beb0b68872f97642",
            "fc5116cae7794711b1519e407d1cadbb",
            "a12f9a992c6747d9906f91ef78fdd4fe",
            "cb3a918611d64b0b97f30763aa9d7545",
            "43fa173bca294f859f0947d41f0bc24c",
            "e723c8b98fa24d4b8f82ec562d04a2ab",
            "ac61b242c9064090a5d982507bfd04dd",
            "64a4723de7ed4f54b0b7f9b499d25109",
            "cc5ce55ff9844890894d2af4bb009349",
            "834c1bb582344185a1b971d8137278a8",
            "ab11c545c737438c8261f6419b850b82"
          ]
        },
        "id": "lBAWX5ft5a3v",
        "outputId": "10f79718-d14f-44cc-b800-38b70ebbeb6c"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "eca7ef19c5a64a30934537c0e7bdb6b6",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/14732 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7a4bbbad44f44a7988182ce8f432ff24",
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              "version_minor": 0
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              "Map:   0%|          | 0/819 [00:00<?, ? examples/s]"
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      ],
      "source": [
        "samsum_pt = samsum.map(get_feature, batched=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2bTY1OZ95a0J",
        "outputId": "2242d6b6-93cb-4e5f-9e9f-e39108de6870"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['id', 'dialogue', 'summary', 'input_ids', 'attention_mask', 'labels'],\n",
              "        num_rows: 14732\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['id', 'dialogue', 'summary', 'input_ids', 'attention_mask', 'labels'],\n",
              "        num_rows: 819\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['id', 'dialogue', 'summary', 'input_ids', 'attention_mask', 'labels'],\n",
              "        num_rows: 818\n",
              "    })\n",
              "})"
            ]
          },
          "execution_count": 42,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "samsum_pt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UczCvbAQ5awz"
      },
      "outputs": [],
      "source": [
        "columns = ['input_ids', 'labels', 'attention_mask']\n",
        "samsum_pt.set_format(type='torch', columns=columns)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dQo7-8yH5dFd"
      },
      "outputs": [],
      "source": [
        "from transformers import DataCollatorForSeq2Seq\n",
        "data_collator = DataCollatorForSeq2Seq(tokenizer, model=model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7F4_2D765dCJ"
      },
      "outputs": [],
      "source": [
        "from transformers import TrainingArguments, Trainer\n",
        "\n",
        "training_args = TrainingArguments(\n",
        "    output_dir = 'bart_samsum',\n",
        "    num_train_epochs=1,\n",
        "    warmup_steps = 500,\n",
        "    per_device_train_batch_size=4,\n",
        "    per_device_eval_batch_size=4,\n",
        "    weight_decay = 0.01,\n",
        "    logging_steps = 10,\n",
        "    evaluation_strategy = 'steps',\n",
        "    eval_steps=500,\n",
        "    save_steps=1e6,\n",
        "    gradient_accumulation_steps=16\n",
        ")\n",
        "\n",
        "trainer = Trainer(model=model, args=training_args, tokenizer=tokenizer, data_collator=data_collator,\n",
        "                  train_dataset = samsum_pt['train'], eval_dataset = samsum_pt['validation'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "j9Y81KGM5c_L",
        "outputId": "f3989e8f-4d23-428e-a724-7d756051e7e2"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "You're using a BartTokenizerFast 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"
          ]
        },
        {
          "data": {
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              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='160' max='230' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [160/230 20:07 < 08:55, 0.13 it/s, Epoch 0.69/1]\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>"
            ],
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              "<IPython.core.display.HTML object>"
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              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='230' max='230' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [230/230 29:20, 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>"
            ],
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              "<IPython.core.display.HTML object>"
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          },
          "metadata": {},
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        {
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            "text/plain": [
              "TrainOutput(global_step=230, training_loss=1.4829248884449835, metrics={'train_runtime': 1770.2346, 'train_samples_per_second': 8.322, 'train_steps_per_second': 0.13, 'total_flos': 8823772719611904.0, 'train_loss': 1.4829248884449835, 'epoch': 1.0})"
            ]
          },
          "execution_count": 49,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aWJVNdeX5atl"
      },
      "outputs": [],
      "source": [
        "trainer.save_model('bart_samsum_model')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RMo0JLJ-5aqT",
        "outputId": "a6d78adb-da02-46dd-84ca-0c48e1faa633"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Your max_length is set to 128, but your input_length is only 72. 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=36)\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[{'summary_text': \"Juli wants to send Tom on a business trip first. She will assign him a new project once he is back. Tom did excellent in last quarter and she wants to give him more work. Laxmi Kant doesn't have any suitable work for him.\"}]\n"
          ]
        }
      ],
      "source": [
        "# custome Dialogue Prediction\n",
        "\n",
        "pipe = pipeline('summarization', model='bart_samsum_model')\n",
        "gen_kwargs = {'length_penalty': 0.8, 'num_beams': 8, \"max_length\": 128}\n",
        "\n",
        "custom_dialogue=\"\"\"\n",
        "Laxmi Kant: what work you planning to give Tom?\n",
        "Juli: i was hoping to send him on a business trip first.\n",
        "Laxmi Kant: cool. is there any suitable work for him?\n",
        "Juli: he did excellent in last quarter. i will assign new project, once he is back.\n",
        "\"\"\"\n",
        "print(pipe(custom_dialogue, **gen_kwargs))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LingPybB5anF",
        "outputId": "f679d659-8df9-4fed-9166-8acbe8280bf4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "  adding: bart_samsum_model/ (stored 0%)\n",
            "  adding: bart_samsum_model/tokenizer_config.json (deflated 50%)\n",
            "  adding: bart_samsum_model/pytorch_model.bin (deflated 7%)\n",
            "  adding: bart_samsum_model/special_tokens_map.json (deflated 52%)\n",
            "  adding: bart_samsum_model/config.json (deflated 61%)\n",
            "  adding: bart_samsum_model/training_args.bin (deflated 49%)\n",
            "  adding: bart_samsum_model/generation_config.json (deflated 47%)\n",
            "  adding: bart_samsum_model/vocab.json (deflated 59%)\n",
            "  adding: bart_samsum_model/merges.txt (deflated 53%)\n",
            "  adding: bart_samsum_model/tokenizer.json (deflated 72%)\n"
          ]
        }
      ],
      "source": [
        "!zip bart_samsum.zip -r bart_samsum_model/"
      ]
    },
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