Prompt_Engineering.ipynb

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        "<a href=\"https://colab.research.google.com/github/MansiShinde/Generative-AI-with-LLM/blob/master/Prompt_Engineering.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "\n",
        "\n",
        "*   Text feed into the model is called **Prompt**\n",
        "*   Act of generating text is called **Inference**\n",
        "*   The output text is called **Completion**\n",
        "*   The process to develop and improve the prompt is called **Prompt Engineering**\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        ""
      ],
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        "#### Transformer - Decoder Model\n",
        "Two major components -\n",
        "1. Feed Forward Neural Network Layer\n",
        "2. Self Attention layer\n",
        "\n",
        "\n",
        "* First Step: Convert words into tokens by assigning them numbers/ids based on the position of these words in the vocabulary\n",
        "* Second Step: These numbers do not have any meaning. So to capture the meaning behind these numbers, we perform embeddings, that is converting this numbers into vector representation. So, we create an embedding matrix here\n",
        "Matrix dimension (vocabulary size * dimension of model). So each word here is now represented as a vector. So the transformer model/components work on these embedding vectors\n",
        "* These vectors will pass through a series of hidden layers   \n",
        "\n",
        "\n",
        "Turning Computation into Language\n",
        "* Apply softmax to the generated logits\n",
        "\n",
        "AutoModels - https://huggingface.co/transformers/v3.5.1/model_doc/auto.html#automodelforcausallm\n",
        "\n",
        "Resources: http://jalammar.github.io/illustrated-transformer/\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "fKIKzjozH2R5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Upgrade pip version, and install transformers library. Hugging face have consolidated all the NLP datasets together and have created a library datasets"
      ],
      "metadata": {
        "id": "5mZFeETnVg_P"
      }
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      "source": [
        "%pip install --upgrade pip\n",
        "%pip install --disable-pip-version-check \\\n",
        "    torch==1.13.1 \\\n",
        "    torchdata==0.5.1 --quiet\n",
        "\n",
        "%pip install \\\n",
        "    transformers==4.27.2 \\\n",
        "    datasets==2.11.0  --quiet"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pf6uMXkhUDWx",
        "outputId": "c84c60e0-0bf3-419b-975c-74cf7e509078"
      },
      "execution_count": 1,
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            "Requirement already satisfied: pip in /usr/local/lib/python3.10/dist-packages (23.1.2)\n",
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            "\u001b[?25hInstalling collected packages: pip\n",
            "  Attempting uninstall: pip\n",
            "    Found existing installation: pip 23.1.2\n",
            "    Uninstalling pip-23.1.2:\n",
            "      Successfully uninstalled pip-23.1.2\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m317.1/317.1 MB\u001b[0m \u001b[31m3.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "torchaudio 2.0.2+cu118 requires torch==2.0.1, but you have torch 1.13.1 which is incompatible.\n",
            "torchtext 0.15.2 requires torch==2.0.1, but you have torch 1.13.1 which is incompatible.\n",
            "torchtext 0.15.2 requires torchdata==0.6.1, but you have torchdata 0.5.1 which is incompatible.\n",
            "torchvision 0.15.2+cu118 requires torch==2.0.1, but you have torch 1.13.1 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.8/6.8 MB\u001b[0m \u001b[31m49.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m468.7/468.7 kB\u001b[0m \u001b[31m42.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m115.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m110.5/110.5 kB\u001b[0m \u001b[31m13.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m212.5/212.5 kB\u001b[0m \u001b[31m22.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.3/134.3 kB\u001b[0m \u001b[31m14.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m268.8/268.8 kB\u001b[0m \u001b[31m27.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Import LLM model and tokenizor classes from transformer library"
      ],
      "metadata": {
        "id": "R3GZnbTNNbdF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "from transformers import AutoModelForSeq2SeqLM\n",
        "from transformers import AutoTokenizer\n",
        "from transformers import GenerationConfig"
      ],
      "metadata": {
        "id": "geZCnv5jWAR1"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Load the dataset"
      ],
      "metadata": {
        "id": "EnJ3cDp0NofM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "huggingface_dataset_name = \"knkarthick/dialogsum\"\n",
        "\n",
        "dataset = load_dataset(huggingface_dataset_name)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297,
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        },
        "id": "oNuc68LjYEpY",
        "outputId": "128c81ea-a368-4dfd-b0d1-6832f54e47c9"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/4.56k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f847114848d94f9287001ad69c9b8f3f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset csv/knkarthick--dialogsum to /root/.cache/huggingface/datasets/knkarthick___csv/knkarthick--dialogsum-c8fac5d84cd35861/0.0.0/6954658bab30a358235fa864b05cf819af0e179325c740e4bc853bcc7ec513e1...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data files:   0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "80b57b3172eb4c4eb885d4bfd237d072"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/11.3M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8192de14beab44b09896553e0559e80d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/1.35M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3b64232a86f3405994c67a5f733a924e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/442k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "00967f024d4a4b3c96b00d0c2cc13ec1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extracting data files:   0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "29f404ca8bba4bae9901fb1db01a684e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "31abfe3fd2e545d49e31b5b987c3cdf4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "73c93c3c176c4ef0a48ab50ae5c08f4e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9142d2f401514eb8a5df35b7af7154f8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/knkarthick___csv/knkarthick--dialogsum-c8fac5d84cd35861/0.0.0/6954658bab30a358235fa864b05cf819af0e179325c740e4bc853bcc7ec513e1. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "version_minor": 0,
              "model_id": "52e31805e3e643c18757556751035fd3"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Print few datapoints. We will be dealing with Dialogue Data, the task will be to summarize the given dialogue."
      ],
      "metadata": {
        "id": "Y-3pA4n3On-M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "example_indices = [40, 100]\n",
        "\n",
        "dash_line = '-'.join('' for x in range(100))\n",
        "\n",
        "for i, index in enumerate(example_indices):\n",
        "    print(dash_line)\n",
        "    print('Example ', i + 1)\n",
        "    print(dash_line)\n",
        "    print('INPUT DIALOGUE:')\n",
        "    print(dataset['test'][index]['dialogue'])\n",
        "    print(dash_line)\n",
        "    print('BASELINE HUMAN SUMMARY:')\n",
        "    print(dataset['test'][index]['summary'])\n",
        "    print(dash_line)\n",
        "    print()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cxzwjehRYbbv",
        "outputId": "add2d025-1d2c-452f-f3f2-50f0dff15ea3"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "---------------------------------------------------------------------------------------------------\n",
            "Example  1\n",
            "---------------------------------------------------------------------------------------------------\n",
            "INPUT DIALOGUE:\n",
            "#Person1#: What time is it, Tom?\n",
            "#Person2#: Just a minute. It's ten to nine by my watch.\n",
            "#Person1#: Is it? I had no idea it was so late. I must be off now.\n",
            "#Person2#: What's the hurry?\n",
            "#Person1#: I must catch the nine-thirty train.\n",
            "#Person2#: You've plenty of time yet. The railway station is very close. It won't take more than twenty minutes to get there.\n",
            "---------------------------------------------------------------------------------------------------\n",
            "BASELINE HUMAN SUMMARY:\n",
            "#Person1# is in a hurry to catch a train. Tom tells #Person1# there is plenty of time.\n",
            "---------------------------------------------------------------------------------------------------\n",
            "\n",
            "---------------------------------------------------------------------------------------------------\n",
            "Example  2\n",
            "---------------------------------------------------------------------------------------------------\n",
            "INPUT DIALOGUE:\n",
            "#Person1#: OK, that's a cut! Let's start from the beginning, everyone.\n",
            "#Person2#: What was the problem that time?\n",
            "#Person1#: The feeling was all wrong, Mike. She is telling you that she doesn't want to see you any more, but I want to get more anger from you. You're acting hurt and sad, but that's not how your character would act in this situation.\n",
            "#Person2#: But Jason and Laura have been together for three years. Don't you think his reaction would be one of both anger and sadness?\n",
            "#Person1#: At this point, no. I think he would react the way most guys would, and then later on, we would see his real feelings.\n",
            "#Person2#: I'm not so sure about that.\n",
            "#Person1#: Let's try it my way, and you can see how you feel when you're saying your lines. After that, if it still doesn't feel right, we can try something else.\n",
            "---------------------------------------------------------------------------------------------------\n",
            "BASELINE HUMAN SUMMARY:\n",
            "#Person1# and Mike have a disagreement on how to act out a scene. #Person1# proposes that Mike can try to act in #Person1#'s way.\n",
            "---------------------------------------------------------------------------------------------------\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "load flan-t5-base model.\n",
        "\n",
        "`class transformers.AutoModelForSeq2SeqLM` is a generic class that is instantiated as one of the model classes of the library with seq to seq language modeling head when created with from_pretrained() method.\n",
        "\n",
        "AutoModelForSeq2SeqLM is used for language models with encoder-decoder architecture like T5 and BART, while AutoModelForCausalLM is used for auto-regressive language models like all the GPT models.\n",
        "\n",
        "Below we are loading the FLAN-T5 model, creating an instance of the AutoModelForSeq2SeqLM class with the .from_pretrained() method"
      ],
      "metadata": {
        "id": "4U9LRmB4PaCK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_name='google/flan-t5-base'\n",
        "\n",
        "model = AutoModelForSeq2SeqLM.from_pretrained(model_name)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
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      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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        {
          "output_type": "display_data",
          "data": {
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          "data": {
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    {
      "cell_type": "markdown",
      "source": [
        "Download the tokenizer for the FLAN-T5 model using `AutoTokenizer.from_pretrained()` method"
      ],
      "metadata": {
        "id": "rPDTa9CemkLJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
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        "outputId": "8e0c29ff-e0f1-40c8-879a-5f8a8b18cdf7"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "Downloading (…)okenizer_config.json:   0%|          | 0.00/2.54k [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "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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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
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            ],
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              "version_major": 2,
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    },
    {
      "cell_type": "markdown",
      "source": [
        "testing tokenizer's encoding and decoding output"
      ],
      "metadata": {
        "id": "ozchMX99msKI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sentence = \"What time is it, Tom?\"\n",
        "\n",
        "sentence_encoded = tokenizer(sentence, return_tensors='pt')\n",
        "\n",
        "sentence_decoded = tokenizer.decode(\n",
        "        sentence_encoded[\"input_ids\"][0],\n",
        "        skip_special_tokens=True\n",
        "    )\n",
        "\n",
        "print('ENCODED SENTENCE:')\n",
        "print(sentence_encoded[\"input_ids\"][0])\n",
        "print('\\nDECODED SENTENCE:')\n",
        "print(sentence_decoded)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xOc-X_z2Y4os",
        "outputId": "9dd4fcab-4ea8-49bb-86fd-38f89a5a63a3"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ENCODED SENTENCE:\n",
            "tensor([ 363,   97,   19,   34,    6, 3059,   58,    1])\n",
            "\n",
            "DECODED SENTENCE:\n",
            "What time is it, Tom?\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### **Using Flan-t5 to summarize the dialogue without prompt engineering.**\n",
        "\n",
        "Prompt engineering is an act of a human changing the prompt (input) to improve the response for a given task."
      ],
      "metadata": {
        "id": "g-LUj5h_m6dw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for i, index in enumerate(example_indices):\n",
        "    dialogue = dataset['test'][index]['dialogue']\n",
        "    summary = dataset['test'][index]['summary']\n",
        "\n",
        "    inputs = tokenizer(dialogue, return_tensors='pt')\n",
        "\n",
        "    gen_tokens = model.generate(\n",
        "            inputs[\"input_ids\"],\n",
        "            max_new_tokens=50,\n",
        "        )\n",
        "\n",
        "    output = tokenizer.decode( gen_tokens[0], skip_special_tokens=True)\n",
        "\n",
        "    print(dash_line)\n",
        "    print('Example ', i + 1)\n",
        "    print(dash_line)\n",
        "    print(f'INPUT PROMPT:\\n{dialogue}')\n",
        "    print(dash_line)\n",
        "    print(f'BASELINE HUMAN SUMMARY:\\n{summary}')\n",
        "    print(dash_line)\n",
        "    print(f'MODEL GENERATION - WITHOUT PROMPT ENGINEERING:\\n{output}\\n')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OSApRQRmY-0R",
        "outputId": "c10556fe-2c01-4da2-965d-e5868790b644"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "---------------------------------------------------------------------------------------------------\n",
            "Example  1\n",
            "---------------------------------------------------------------------------------------------------\n",
            "INPUT PROMPT:\n",
            "#Person1#: What time is it, Tom?\n",
            "#Person2#: Just a minute. It's ten to nine by my watch.\n",
            "#Person1#: Is it? I had no idea it was so late. I must be off now.\n",
            "#Person2#: What's the hurry?\n",
            "#Person1#: I must catch the nine-thirty train.\n",
            "#Person2#: You've plenty of time yet. The railway station is very close. It won't take more than twenty minutes to get there.\n",
            "---------------------------------------------------------------------------------------------------\n",
            "BASELINE HUMAN SUMMARY:\n",
            "#Person1# is in a hurry to catch a train. Tom tells #Person1# there is plenty of time.\n",
            "---------------------------------------------------------------------------------------------------\n",
            "MODEL GENERATION - WITHOUT PROMPT ENGINEERING:\n",
            "Person1: It's ten to nine.\n",
            "\n",
            "---------------------------------------------------------------------------------------------------\n",
            "Example  2\n",
            "---------------------------------------------------------------------------------------------------\n",
            "INPUT PROMPT:\n",
            "#Person1#: OK, that's a cut! Let's start from the beginning, everyone.\n",
            "#Person2#: What was the problem that time?\n",
            "#Person1#: The feeling was all wrong, Mike. She is telling you that she doesn't want to see you any more, but I want to get more anger from you. You're acting hurt and sad, but that's not how your character would act in this situation.\n",
            "#Person2#: But Jason and Laura have been together for three years. Don't you think his reaction would be one of both anger and sadness?\n",
            "#Person1#: At this point, no. I think he would react the way most guys would, and then later on, we would see his real feelings.\n",
            "#Person2#: I'm not so sure about that.\n",
            "#Person1#: Let's try it my way, and you can see how you feel when you're saying your lines. After that, if it still doesn't feel right, we can try something else.\n",
            "---------------------------------------------------------------------------------------------------\n",
            "BASELINE HUMAN SUMMARY:\n",
            "#Person1# and Mike have a disagreement on how to act out a scene. #Person1# proposes that Mike can try to act in #Person1#'s way.\n",
            "---------------------------------------------------------------------------------------------------\n",
            "MODEL GENERATION - WITHOUT PROMPT ENGINEERING:\n",
            "#Person1#: I'm sorry, but I'm not sure what Jason and Laura are doing.\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### **Summarize Dialogue with Zero Shot Instruction Prompt**\n",
        " Here we will take the dialogue and convert it into prompt. That is, we will wrap the dialogue with the instruction to help the LLM understand what it needs to perform."
      ],
      "metadata": {
        "id": "aU3ZZiHZn00U"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for i, index in enumerate(example_indices):\n",
        "    dialogue = dataset['test'][index]['dialogue']\n",
        "    summary = dataset['test'][index]['summary']\n",
        "\n",
        "    prompt = f\"\"\" Summarize the following conversation.\n",
        "\n",
        "{dialogue}\n",
        "\n",
        "Summary:\n",
        "    \"\"\"\n",
        "\n",
        "    # Input constructed prompt instead of the dialogue.\n",
        "    inputs = tokenizer(prompt, return_tensors='pt')\n",
        "\n",
        "    gen_tokens =model.generate(\n",
        "            inputs[\"input_ids\"],\n",
        "            max_new_tokens=50,\n",
        "        )\n",
        "    output = tokenizer.decode( gen_tokens[0], skip_special_tokens=True )\n",
        "\n",
        "    print(dash_line)\n",
        "    print('Example ', i + 1)\n",
        "    print(dash_line)\n",
        "    print(f'INPUT PROMPT:\\n{prompt}')\n",
        "    print(dash_line)\n",
        "    print(f'BASELINE HUMAN SUMMARY:\\n{summary}')\n",
        "    print(dash_line)\n",
        "    print(f'MODEL GENERATION - ZERO SHOT:\\n{output}\\n')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-ZSS811mngCV",
        "outputId": "fc79fbc9-bace-45b2-ab18-b02fa077c460"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "---------------------------------------------------------------------------------------------------\n",
            "Example  1\n",
            "---------------------------------------------------------------------------------------------------\n",
            "INPUT PROMPT:\n",
            " Summarize the following conversation.\n",
            "\n",
            "#Person1#: What time is it, Tom?\n",
            "#Person2#: Just a minute. It's ten to nine by my watch.\n",
            "#Person1#: Is it? I had no idea it was so late. I must be off now.\n",
            "#Person2#: What's the hurry?\n",
            "#Person1#: I must catch the nine-thirty train.\n",
            "#Person2#: You've plenty of time yet. The railway station is very close. It won't take more than twenty minutes to get there.\n",
            "\n",
            "Summary:\n",
            "    \n",
            "---------------------------------------------------------------------------------------------------\n",
            "BASELINE HUMAN SUMMARY:\n",
            "#Person1# is in a hurry to catch a train. Tom tells #Person1# there is plenty of time.\n",
            "---------------------------------------------------------------------------------------------------\n",
            "MODEL GENERATION - ZERO SHOT:\n",
            "The train is about to leave.\n",
            "\n",
            "---------------------------------------------------------------------------------------------------\n",
            "Example  2\n",
            "---------------------------------------------------------------------------------------------------\n",
            "INPUT PROMPT:\n",
            " Summarize the following conversation.\n",
            "\n",
            "#Person1#: OK, that's a cut! Let's start from the beginning, everyone.\n",
            "#Person2#: What was the problem that time?\n",
            "#Person1#: The feeling was all wrong, Mike. She is telling you that she doesn't want to see you any more, but I want to get more anger from you. You're acting hurt and sad, but that's not how your character would act in this situation.\n",
            "#Person2#: But Jason and Laura have been together for three years. Don't you think his reaction would be one of both anger and sadness?\n",
            "#Person1#: At this point, no. I think he would react the way most guys would, and then later on, we would see his real feelings.\n",
            "#Person2#: I'm not so sure about that.\n",
            "#Person1#: Let's try it my way, and you can see how you feel when you're saying your lines. After that, if it still doesn't feel right, we can try something else.\n",
            "\n",
            "Summary:\n",
            "    \n",
            "---------------------------------------------------------------------------------------------------\n",
            "BASELINE HUMAN SUMMARY:\n",
            "#Person1# and Mike have a disagreement on how to act out a scene. #Person1# proposes that Mike can try to act in #Person1#'s way.\n",
            "---------------------------------------------------------------------------------------------------\n",
            "MODEL GENERATION - ZERO SHOT:\n",
            "The cut is over.\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### **Zero Shot Inference with the Prompt Template from FLAN-T5**\n",
        "\n",
        "We can have a different prompt statement. FLAN-T5 has many prompt templates that are published for certain tasks [here](https://github.com/google-research/FLAN/tree/main/flan/v2)\n",
        "\n",
        "We can use one of then and see the difference in the result"
      ],
      "metadata": {
        "id": "4ajYqATMpxTn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for i, index in enumerate(example_indices):\n",
        "    dialogue = dataset['test'][index]['dialogue']\n",
        "    summary = dataset['test'][index]['summary']\n",
        "\n",
        "    prompt = f\"\"\"\n",
        "Dialogue:\n",
        "\n",
        "{dialogue}\n",
        "\n",
        "What were they talking about?\n",
        "\"\"\"\n",
        "\n",
        "    inputs = tokenizer(prompt, return_tensors='pt')\n",
        "    output = tokenizer.decode(\n",
        "        model.generate(\n",
        "            inputs[\"input_ids\"],\n",
        "            max_new_tokens=50,\n",
        "        )[0],\n",
        "        skip_special_tokens=True\n",
        "    )\n",
        "\n",
        "    print(dash_line)\n",
        "    print('Example ', i + 1)\n",
        "    print(dash_line)\n",
        "    print(f'INPUT PROMPT:\\n{prompt}')\n",
        "    print(dash_line)\n",
        "    print(f'BASELINE HUMAN SUMMARY:\\n{summary}\\n')\n",
        "    print(dash_line)\n",
        "    print(f'MODEL GENERATION - ZERO SHOT:\\n{output}\\n')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jVP4EJ8To5Eq",
        "outputId": "c9c0acc2-641a-4e22-b5bb-c3d22177ea2e"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "---------------------------------------------------------------------------------------------------\n",
            "Example  1\n",
            "---------------------------------------------------------------------------------------------------\n",
            "INPUT PROMPT:\n",
            "\n",
            "Dialogue:\n",
            "\n",
            "#Person1#: What time is it, Tom?\n",
            "#Person2#: Just a minute. It's ten to nine by my watch.\n",
            "#Person1#: Is it? I had no idea it was so late. I must be off now.\n",
            "#Person2#: What's the hurry?\n",
            "#Person1#: I must catch the nine-thirty train.\n",
            "#Person2#: You've plenty of time yet. The railway station is very close. It won't take more than twenty minutes to get there.\n",
            "\n",
            "What were they talking about?\n",
            "\n",
            "---------------------------------------------------------------------------------------------------\n",
            "BASELINE HUMAN SUMMARY:\n",
            "#Person1# is in a hurry to catch a train. Tom tells #Person1# there is plenty of time.\n",
            "\n",
            "---------------------------------------------------------------------------------------------------\n",
            "MODEL GENERATION - ZERO SHOT:\n",
            "Tom is late for the train.\n",
            "\n",
            "---------------------------------------------------------------------------------------------------\n",
            "Example  2\n",
            "---------------------------------------------------------------------------------------------------\n",
            "INPUT PROMPT:\n",
            "\n",
            "Dialogue:\n",
            "\n",
            "#Person1#: OK, that's a cut! Let's start from the beginning, everyone.\n",
            "#Person2#: What was the problem that time?\n",
            "#Person1#: The feeling was all wrong, Mike. She is telling you that she doesn't want to see you any more, but I want to get more anger from you. You're acting hurt and sad, but that's not how your character would act in this situation.\n",
            "#Person2#: But Jason and Laura have been together for three years. Don't you think his reaction would be one of both anger and sadness?\n",
            "#Person1#: At this point, no. I think he would react the way most guys would, and then later on, we would see his real feelings.\n",
            "#Person2#: I'm not so sure about that.\n",
            "#Person1#: Let's try it my way, and you can see how you feel when you're saying your lines. After that, if it still doesn't feel right, we can try something else.\n",
            "\n",
            "What were they talking about?\n",
            "\n",
            "---------------------------------------------------------------------------------------------------\n",
            "BASELINE HUMAN SUMMARY:\n",
            "#Person1# and Mike have a disagreement on how to act out a scene. #Person1# proposes that Mike can try to act in #Person1#'s way.\n",
            "\n",
            "---------------------------------------------------------------------------------------------------\n",
            "MODEL GENERATION - ZERO SHOT:\n",
            "The two people are trying to figure out how to react to a cut.\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### **Summarize Dialogue with One Shot and Few Shot Inference**\n",
        "\n",
        "One shot and few shot inference are the practices of providing an LLM with either one or more full examples of prompt-response pairs that match your task - before your actual prompt that you want completed. This is called \"in-context learning\" and puts your model into a state that understands your specific task"
      ],
      "metadata": {
        "id": "kVMdZv02tI3F"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### **One shot inference**\n",
        "We will provide one dialogue along with its summary as an example for the model to determine the summary of another dialogue prompt."
      ],
      "metadata": {
        "id": "OBfYb-EEtlf5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def make_prompt(example_indices_full, example_index_to_summarize):\n",
        "    prompt = ''\n",
        "    for index in example_indices_full:\n",
        "        dialogue = dataset['test'][index]['dialogue']\n",
        "        summary = dataset['test'][index]['summary']\n",
        "\n",
        "        # The stop sequence '{summary}\\n\\n\\n' is important for FLAN-T5. Other models may have their own preferred stop sequence.\n",
        "        prompt += f\"\"\"\n",
        "Dialogue:\n",
        "\n",
        "{dialogue}\n",
        "\n",
        "What was going on?\n",
        "{summary}\n",
        "\n",
        "\n",
        "\"\"\"\n",
        "\n",
        "    dialogue = dataset['test'][example_index_to_summarize]['dialogue']\n",
        "\n",
        "    prompt += f\"\"\"\n",
        "Dialogue:\n",
        "\n",
        "{dialogue}\n",
        "\n",
        "What was going on?\n",
        "\"\"\"\n",
        "\n",
        "    return prompt"
      ],
      "metadata": {
        "id": "d3gceGtFtkoe"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "example_indices_full = [40]\n",
        "example_index_to_summarize = 100\n",
        "\n",
        "one_shot_prompt = make_prompt(example_indices_full, example_index_to_summarize)\n",
        "\n",
        "print(one_shot_prompt)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5wb4Mhvisucp",
        "outputId": "4d0ea72d-76dd-4ded-a748-f395c491887c"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Dialogue:\n",
            "\n",
            "#Person1#: What time is it, Tom?\n",
            "#Person2#: Just a minute. It's ten to nine by my watch.\n",
            "#Person1#: Is it? I had no idea it was so late. I must be off now.\n",
            "#Person2#: What's the hurry?\n",
            "#Person1#: I must catch the nine-thirty train.\n",
            "#Person2#: You've plenty of time yet. The railway station is very close. It won't take more than twenty minutes to get there.\n",
            "\n",
            "What was going on?\n",
            "#Person1# is in a hurry to catch a train. Tom tells #Person1# there is plenty of time.\n",
            "\n",
            "\n",
            "\n",
            "Dialogue:\n",
            "\n",
            "#Person1#: OK, that's a cut! Let's start from the beginning, everyone.\n",
            "#Person2#: What was the problem that time?\n",
            "#Person1#: The feeling was all wrong, Mike. She is telling you that she doesn't want to see you any more, but I want to get more anger from you. You're acting hurt and sad, but that's not how your character would act in this situation.\n",
            "#Person2#: But Jason and Laura have been together for three years. Don't you think his reaction would be one of both anger and sadness?\n",
            "#Person1#: At this point, no. I think he would react the way most guys would, and then later on, we would see his real feelings.\n",
            "#Person2#: I'm not so sure about that.\n",
            "#Person1#: Let's try it my way, and you can see how you feel when you're saying your lines. After that, if it still doesn't feel right, we can try something else.\n",
            "\n",
            "What was going on?\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "summary = dataset['test'][example_index_to_summarize]['summary']\n",
        "\n",
        "inputs = tokenizer(one_shot_prompt, return_tensors='pt')\n",
        "\n",
        "gen_tokens = model.generate(\n",
        "            inputs[\"input_ids\"],\n",
        "            max_new_tokens=50,\n",
        "        )\n",
        "\n",
        "output = tokenizer.decode( gen_tokens[0], skip_special_tokens=True)\n",
        "\n",
        "print(dash_line)\n",
        "print(f'BASELINE HUMAN SUMMARY:\\n{summary}\\n')\n",
        "print(dash_line)\n",
        "print(f'MODEL GENERATION - ONE SHOT:\\n{output}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JVQPye_Nt_5C",
        "outputId": "2aeb704d-34a7-4db6-b225-28be6860c9ea"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "---------------------------------------------------------------------------------------------------\n",
            "BASELINE HUMAN SUMMARY:\n",
            "#Person1# and Mike have a disagreement on how to act out a scene. #Person1# proposes that Mike can try to act in #Person1#'s way.\n",
            "\n",
            "---------------------------------------------------------------------------------------------------\n",
            "MODEL GENERATION - ONE SHOT:\n",
            "The two people are trying to figure out how to react to the cut.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#####**Few Shot inference**"
      ],
      "metadata": {
        "id": "FvlZtPmsugrK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "example_indices_full = [40, 80, 200]\n",
        "example_index_to_summarize = 100\n",
        "\n",
        "few_shot_prompt = make_prompt(example_indices_full, example_index_to_summarize)\n",
        "\n",
        "print(few_shot_prompt)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aElfWqVGuUoB",
        "outputId": "16fb39ec-ac73-42e6-a80f-96c9c2773f92"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Dialogue:\n",
            "\n",
            "#Person1#: What time is it, Tom?\n",
            "#Person2#: Just a minute. It's ten to nine by my watch.\n",
            "#Person1#: Is it? I had no idea it was so late. I must be off now.\n",
            "#Person2#: What's the hurry?\n",
            "#Person1#: I must catch the nine-thirty train.\n",
            "#Person2#: You've plenty of time yet. The railway station is very close. It won't take more than twenty minutes to get there.\n",
            "\n",
            "What was going on?\n",
            "#Person1# is in a hurry to catch a train. Tom tells #Person1# there is plenty of time.\n",
            "\n",
            "\n",
            "\n",
            "Dialogue:\n",
            "\n",
            "#Person1#: May, do you mind helping me prepare for the picnic?\n",
            "#Person2#: Sure. Have you checked the weather report?\n",
            "#Person1#: Yes. It says it will be sunny all day. No sign of rain at all. This is your father's favorite sausage. Sandwiches for you and Daniel.\n",
            "#Person2#: No, thanks Mom. I'd like some toast and chicken wings.\n",
            "#Person1#: Okay. Please take some fruit salad and crackers for me.\n",
            "#Person2#: Done. Oh, don't forget to take napkins disposable plates, cups and picnic blanket.\n",
            "#Person1#: All set. May, can you help me take all these things to the living room?\n",
            "#Person2#: Yes, madam.\n",
            "#Person1#: Ask Daniel to give you a hand?\n",
            "#Person2#: No, mom, I can manage it by myself. His help just causes more trouble.\n",
            "\n",
            "What was going on?\n",
            "Mom asks May to help to prepare for the picnic and May agrees.\n",
            "\n",
            "\n",
            "\n",
            "Dialogue:\n",
            "\n",
            "#Person1#: Have you considered upgrading your system?\n",
            "#Person2#: Yes, but I'm not sure what exactly I would need.\n",
            "#Person1#: You could consider adding a painting program to your software. It would allow you to make up your own flyers and banners for advertising.\n",
            "#Person2#: That would be a definite bonus.\n",
            "#Person1#: You might also want to upgrade your hardware because it is pretty outdated now.\n",
            "#Person2#: How can we do that?\n",
            "#Person1#: You'd probably need a faster processor, to begin with. And you also need a more powerful hard disc, more memory and a faster modem. Do you have a CD-ROM drive?\n",
            "#Person2#: No.\n",
            "#Person1#: Then you might want to add a CD-ROM drive too, because most new software programs are coming out on Cds.\n",
            "#Person2#: That sounds great. Thanks.\n",
            "\n",
            "What was going on?\n",
            "#Person1# teaches #Person2# how to upgrade software and hardware in #Person2#'s system.\n",
            "\n",
            "\n",
            "\n",
            "Dialogue:\n",
            "\n",
            "#Person1#: OK, that's a cut! Let's start from the beginning, everyone.\n",
            "#Person2#: What was the problem that time?\n",
            "#Person1#: The feeling was all wrong, Mike. She is telling you that she doesn't want to see you any more, but I want to get more anger from you. You're acting hurt and sad, but that's not how your character would act in this situation.\n",
            "#Person2#: But Jason and Laura have been together for three years. Don't you think his reaction would be one of both anger and sadness?\n",
            "#Person1#: At this point, no. I think he would react the way most guys would, and then later on, we would see his real feelings.\n",
            "#Person2#: I'm not so sure about that.\n",
            "#Person1#: Let's try it my way, and you can see how you feel when you're saying your lines. After that, if it still doesn't feel right, we can try something else.\n",
            "\n",
            "What was going on?\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "summary = dataset['test'][example_index_to_summarize]['summary']\n",
        "\n",
        "inputs = tokenizer(one_shot_prompt, return_tensors='pt')\n",
        "\n",
        "gen_tokens = model.generate(\n",
        "            inputs[\"input_ids\"],\n",
        "            max_new_tokens=50,\n",
        "        )\n",
        "\n",
        "output = tokenizer.decode( gen_tokens[0], skip_special_tokens=True)\n",
        "\n",
        "print(dash_line)\n",
        "print(f'BASELINE HUMAN SUMMARY:\\n{summary}\\n')\n",
        "print(dash_line)\n",
        "print(f'MODEL GENERATION - ONE SHOT:\\n{output}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qTXISVDNvrK5",
        "outputId": "344aa1b1-9dd9-4797-b68c-5d775ad9d614"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "---------------------------------------------------------------------------------------------------\n",
            "BASELINE HUMAN SUMMARY:\n",
            "#Person1# and Mike have a disagreement on how to act out a scene. #Person1# proposes that Mike can try to act in #Person1#'s way.\n",
            "\n",
            "---------------------------------------------------------------------------------------------------\n",
            "MODEL GENERATION - ONE SHOT:\n",
            "The two people are trying to figure out how to react to the cut.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### **Using Generative Configuration Parameters for Inference**\n",
        "\n",
        "\n",
        "*   **max_new_tokens** : number of tokens that the model can generate\n",
        "*   **do_sample = True** : select token using random weighted strategy across the probability of all tokens\n",
        "*   **top_k** : specify number of tokens to randomly choose from\n",
        "*   **top_p** : specify total probability (this is calculated by adding up the probabilities of tokens and then select those tokens that make up the probability value) that you want the model to choose from\n",
        "*   **temperature** : control the randomness of the model output. temperature ⬆, randomness ⬆ and vice versa.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "59t2eYFwwHXD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# generation_config = GenerationConfig(max_new_tokens=50)\n",
        "# generation_config = GenerationConfig(max_new_tokens=10)\n",
        "# generation_config = GenerationConfig(max_new_tokens=50, do_sample=True, temperature=0.1)\n",
        "generation_config = GenerationConfig(max_new_tokens=50, do_sample=True, temperature=0.5)\n",
        "# generation_config = GenerationConfig(max_new_tokens=50, do_sample=True, temperature=1.0)\n",
        "\n",
        "inputs = tokenizer(few_shot_prompt, return_tensors='pt')\n",
        "\n",
        "gen_tokens = model.generate(\n",
        "        inputs[\"input_ids\"],\n",
        "        generation_config=generation_config,\n",
        "    )\n",
        "output = tokenizer.decode( gen_tokens[0], skip_special_tokens=True )\n",
        "\n",
        "print(dash_line)\n",
        "print(f'MODEL GENERATION - FEW SHOT:\\n{output}')\n",
        "print(dash_line)\n",
        "print(f'BASELINE HUMAN SUMMARY:\\n{summary}\\n')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gmX8bBK9vugK",
        "outputId": "64b815e2-5751-4132-b283-cf190538aa41"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "---------------------------------------------------------------------------------------------------\n",
            "MODEL GENERATION - FEW SHOT:\n",
            "The situation was wrong and Mike is angry. Jason and Laura have been together for three years.\n",
            "---------------------------------------------------------------------------------------------------\n",
            "BASELINE HUMAN SUMMARY:\n",
            "#Person1# and Mike have a disagreement on how to act out a scene. #Person1# proposes that Mike can try to act in #Person1#'s way.\n",
            "\n"
          ]
        }
      ]
    }
  ]
}