Flan_T5_XL(3B)_demo_Narrativa.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/msauhta/CoreNLP/blob/master/Flan_T5_XL(3B)_demo_Narrativa.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# [Narrativa](https://www.narrativa.com/) Flan-T5 Demo"
      ],
      "metadata": {
        "id": "ZJnTkf2vU8n7"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0O81om8SNaMZ"
      },
      "source": [
        "## Flan-T5-XL (3B)\n",
        "\n",
        "![flan-t5](https://s3.amazonaws.com/moonup/production/uploads/1666363435475-62441d1d9fdefb55a0b7d12c.png)\n",
        "\n",
        "If you already know T5, FLAN-T5 is just better at everything. For the same number of parameters, these models have been fine-tuned on more than 1000 additional tasks covering also more languages. As mentioned in the first few lines of the abstract :\n",
        "\n",
        ">  Flan-PaLM 540B achieves state-of-the-art performance on several benchmarks, such as 75.2% on five-shot MMLU. We also publicly release Flan-T5 checkpoints,1 which achieve strong few-shot performance even compared to much larger models, such as PaLM 62B. Overall, instruction finetuning is a general method for improving the performance and usability of pretrained language models."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Check the hardware"
      ],
      "metadata": {
        "id": "PJK-bz_lX2eO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "! nvidia-smi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jROkf9XoW7OJ",
        "outputId": "1e0a0b21-a679-41cd-b1e6-27967239aea3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Wed Nov  2 12:29:28 2022       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 460.32.03    Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   42C    P8    11W /  70W |      0MiB / 15109MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! free -h"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ol3QxxwQW-qM",
        "outputId": "3116c70a-17d3-4e07-f875-7579fbf67c0d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              total        used        free      shared  buff/cache   available\n",
            "Mem:            51G        865M         46G        1.3M        3.2G         49G\n",
            "Swap:            0B          0B          0B\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! lscpu"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IuLjqwE7XE7e",
        "outputId": "180095b5-b65d-4b06-c967-c0a665c0c9b7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Architecture:        x86_64\n",
            "CPU op-mode(s):      32-bit, 64-bit\n",
            "Byte Order:          Little Endian\n",
            "CPU(s):              8\n",
            "On-line CPU(s) list: 0-7\n",
            "Thread(s) per core:  2\n",
            "Core(s) per socket:  4\n",
            "Socket(s):           1\n",
            "NUMA node(s):        1\n",
            "Vendor ID:           GenuineIntel\n",
            "CPU family:          6\n",
            "Model:               63\n",
            "Model name:          Intel(R) Xeon(R) CPU @ 2.30GHz\n",
            "Stepping:            0\n",
            "CPU MHz:             2299.998\n",
            "BogoMIPS:            4599.99\n",
            "Hypervisor vendor:   KVM\n",
            "Virtualization type: full\n",
            "L1d cache:           32K\n",
            "L1i cache:           32K\n",
            "L2 cache:            256K\n",
            "L3 cache:            46080K\n",
            "NUMA node0 CPU(s):   0-7\n",
            "Flags:               fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm invpcid_single ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid xsaveopt arat md_clear arch_capabilities\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Install required libraries"
      ],
      "metadata": {
        "id": "RiiGcrdRX51E"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6MA0K9LZMMOj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "59444f4e-942f-4aa7-d49f-af043bfba859"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[K     |████████████████████████████████| 11.6 MB 4.2 MB/s \n",
            "\u001b[K     |████████████████████████████████| 212 kB 75.0 MB/s \n",
            "\u001b[K     |████████████████████████████████| 84 kB 3.7 MB/s \n",
            "\u001b[K     |████████████████████████████████| 56 kB 5.2 MB/s \n",
            "\u001b[K     |████████████████████████████████| 106 kB 74.4 MB/s \n",
            "\u001b[K     |████████████████████████████████| 272 kB 88.9 MB/s \n",
            "\u001b[K     |████████████████████████████████| 54 kB 3.6 MB/s \n",
            "\u001b[K     |████████████████████████████████| 2.3 MB 61.4 MB/s \n",
            "\u001b[K     |████████████████████████████████| 55 kB 3.5 MB/s \n",
            "\u001b[K     |████████████████████████████████| 84 kB 4.2 MB/s \n",
            "\u001b[K     |████████████████████████████████| 63 kB 2.3 MB/s \n",
            "\u001b[K     |████████████████████████████████| 80 kB 10.5 MB/s \n",
            "\u001b[K     |████████████████████████████████| 68 kB 7.6 MB/s \n",
            "\u001b[K     |████████████████████████████████| 46 kB 4.4 MB/s \n",
            "\u001b[K     |████████████████████████████████| 856 kB 78.7 MB/s \n",
            "\u001b[K     |████████████████████████████████| 593 kB 82.8 MB/s \n",
            "\u001b[K     |████████████████████████████████| 4.1 MB 76.8 MB/s \n",
            "\u001b[?25h  Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for python-multipart (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ],
      "source": [
        "! pip install -q transformers accelerate sentencepiece gradio"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Load the model\n",
        "\n",
        "it can take a while"
      ],
      "metadata": {
        "id": "ZAMb71BzX-N_"
      }
    },
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          "metadata": {}
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        {
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          "metadata": {}
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        {
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              "model_id": "7de734e69fe6410b96bdaff082854737"
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          },
          "metadata": {}
        }
      ],
      "source": [
        "from transformers import T5Tokenizer, T5ForConditionalGeneration\n",
        "\n",
        "tokenizer = T5Tokenizer.from_pretrained(\"google/flan-t5-xl\")\n",
        "model = T5ForConditionalGeneration.from_pretrained(\"google/flan-t5-xl\").to(\"cuda\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Show some examples"
      ],
      "metadata": {
        "id": "GDlskFoGYDVt"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zY6iR0hjM68K"
      },
      "outputs": [],
      "source": [
        "def generate(input_text):\n",
        "  input_ids = tokenizer(input_text, return_tensors=\"pt\").input_ids.to(\"cuda\")\n",
        "  output = model.generate(input_ids, max_length=100)\n",
        "  return tokenizer.decode(output[0], skip_special_tokens=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KWQhBrqfMa2d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "9613b9f4-2e1d-4393-d29a-ca76a8458cf4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Wie alt sind Sie?'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "input_text = \"\"\"translate English to German: How old are you?\"\"\"\n",
        "generate(input_text)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pDw7UbRHPDCk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "3285ed8f-588a-452b-f82c-b3e4f51d5ebf"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'212 degrees celsius'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "input_text = \"\"\"Please answer the following question. \n",
        "What is the boiling point of water?\"\"\"\n",
        "generate(input_text)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zyZU22eKPf81",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "b1b1da71-863d-4f02-f374-684bbb90c86b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'The cafeteria had 23 apples and used 20 for lunch so they have 23 - 20 = 3 apples. They bought 6 more apples so they have 3 + 6 = 9 apples. Therefore, the final answer is 9.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "input_text = \"\"\"\n",
        "Answer the following question by reasoning step by step.\n",
        "The cafeteria had 23 apples. If the used 20 for lunch and bougth 6 more, how many apples do they have?\n",
        "\"\"\"\n",
        "generate(input_text)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "oQD7QKHHWl4H",
        "outputId": "3b474f53-e1f3-4667-f7a0-d04c5c6cfd7c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'George Washington died in 1799. Obama was born in 1961. So the final answer is no.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "input_text = \"\"\"\n",
        "Q: Can Obama have a conversation with George Washington?\n",
        "Give the rationale before answering\n",
        "\"\"\"\n",
        "generate(input_text)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "MV7GKoHAJyRs",
        "outputId": "90697521-1013-4107-b3cf-3fd28bc4bd3a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"Monkeys can't write. Monkeys can't read. Monkeys can't write. The answer is no.\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "input_text = \"\"\"\n",
        "Q: Answer the following yes/no question by\n",
        "reasoning step-by-step.\n",
        "Could a dandelion suffer from hepatitis?\n",
        "A: Hepatitis only affects organisms with livers.\n",
        "Dandelions don’t have a liver. The answer is no.\n",
        "Q: Answer the following yes/no question by\n",
        "reasoning step-by-step.\n",
        "Can a monkey write a post?\n",
        "A: \n",
        "\"\"\"\n",
        "\n",
        "generate(input_text)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "ldZRnLFbNesm",
        "outputId": "1d0ac86a-6d61-465e-cc89-6ab1515fa2df"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'yes'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "input_text = \"\"\"\n",
        "\"Premise: Alberto is the CTO of a top NLP company. Hypothesis: Alberto is a tech expert. Does the premise entail the hypothesis?\"\n",
        "\"\"\"\n",
        "\n",
        "generate(input_text)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input_text = \"\"\"\n",
        "Q: Answer the following yes/no question by\n",
        "reasoning step-by-step.\n",
        "Could a dandelion suffer from hepatitis?\n",
        "A: Hepatitis only affects organisms with livers.\n",
        "Dandelions don’t have a liver. The answer is no.\n",
        "Q: Answer the following yes/no question by\n",
        "reasoning step-by-step.\n",
        "Can you write a whole Haiku in a single tweet?\n",
        "A:\"\"\"\n",
        "generate(input_text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "OD0TO0rcZC45",
        "outputId": "2ec021e5-e85d-47f8-978a-4e1a5be2d23b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Haiku is a Japanese poetry form that is composed of three lines of three syllables each. A single tweet is 140 characters. The answer is no.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "xPi98dThUEC7",
        "outputId": "9177b316-246a-4fca-e2f6-245598adbce6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'The Eiffel Tower is a steel tower in Paris, France.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "input_text = \"\"\"\n",
        "Summarize the following text: The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building, and the tallest structure in Paris. Its base is square, measuring 125 metres (410 ft) on each side. During its construction, the Eiffel Tower surpassed the Washington Monument to become the tallest man-made structure in the world, a title it held for 41 years until the Chrysler Building in New York City was finished in 1930. It was the first structure to reach a height of 300 metres. Due to the addition of a broadcasting aerial at the top of the tower in 1957, it is now taller than the Chrysler Building by 5.2 metres (17 ft). Excluding transmitters, the Eiffel Tower is the second tallest free-standing structure in France after the Millau Viaduct.\n",
        "\"\"\"\n",
        "generate(input_text)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "sqxb-9n5Ul3t",
        "outputId": "420f5957-ac34-4924-b165-2e3367294ae1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'How many feet tall is the Eiffel Tower?'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "input_text = \"\"\"\n",
        "Generate a question for the following text: The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building, and the tallest structure in Paris. Its base is square, measuring 125 metres (410 ft) on each side. During its construction, the Eiffel Tower surpassed the Washington Monument to become the tallest man-made structure in the world, a title it held for 41 years until the Chrysler Building in New York City was finished in 1930. It was the first structure to reach a height of 300 metres. Due to the addition of a broadcasting aerial at the top of the tower in 1957, it is now taller than the Chrysler Building by 5.2 metres (17 ft). Excluding transmitters, the Eiffel Tower is the second tallest free-standing structure in France after the Millau Viaduct.\n",
        "\"\"\"\n",
        "generate(input_text)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "mSZOTY5_VGAh",
        "outputId": "223b1130-fdd3-4fcb-b737-f6927bf1c7e9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'iron'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "input_text = \"\"\"\n",
        "The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building, and the tallest structure in Paris. Its base is square, measuring 125 metres (410 ft) on each side. During its construction, the Eiffel Tower surpassed the Washington Monument to become the tallest man-made structure in the world, a title it held for 41 years until the Chrysler Building in New York City was finished in 1930. It was the first structure to reach a height of 300 metres. Due to the addition of a broadcasting aerial at the top of the tower in 1957, it is now taller than the Chrysler Building by 5.2 metres (17 ft). Excluding transmitters, the Eiffel Tower is the second tallest free-standing structure in France after the Millau Viaduct.\n",
        "Q: Which material is the tower made of?\n",
        "\"\"\"\n",
        "generate(input_text)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "4i2tGwMbXM8G",
        "outputId": "8a91bb7a-4afa-46ca-c3d1-eed4f5a68d26"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"I've never seen a politician lying, but I've seen many politicians lying. The answer: (A).\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "input_text = \"\"\"\n",
        "Q: Which statement is sarcastic?\n",
        "Options:\n",
        "(A) Wow thats crazy, I've never seen a politician lying!\n",
        "(B) Wow thats crazy, I've never seen Obama lying!\n",
        "A: Let's think step by step.\n",
        "\"\"\"\n",
        "generate(input_text)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Interactive Demo"
      ],
      "metadata": {
        "id": "pEU4o7eje0sg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title GUI\n",
        "import gradio as gr\n",
        "examples = [\n",
        "  [\"Answer the following question by detailing your reasoning: Are Pokemons alive?\"],\n",
        "  [\"Q: Can Barack Obama have a conversation with George Washington? Give the rationale before answering.\"],\n",
        "  [\"Summarize the following text: Peter and Elizabeth took a taxi to attend the night party in the city. While in the party, Elizabeth collapsed and was rushed to the hospital. Since she was diagnosed with a brain injury, the doctor told Peter to stay besides her until she gets well. Therefore, Peter stayed with her at the hospital for 3 days without leaving.\"],\n",
        "  [\"Translate to German: I love eating flan!\"],\n",
        "  [\"Generate a cooking recipe to make a cheesecacke:\"],\n",
        "  [\"Premise:  At my age you will probably have learnt one lesson. Hypothesis:  It's not certain how many lessons you'll learn by your thirties. Does the premise entail the hypothesis?\"],\n",
        "  [\"Answer the following question by reasoning step by step. The cafeteria had 23 apples. If they used 20 for lunch and bought 6 more, how many apples do they have?\"],\n",
        "]\n",
        "\n",
        "title = \"Flan T5 GUI Demo\"\n",
        "\n",
        "def inference(text):\n",
        "  return generate(text)\n",
        "\n",
        "io = gr.Interface(\n",
        "  inference,\n",
        "  gr.Textbox(lines=3),\n",
        "  outputs=[\n",
        "    gr.Textbox(lines=3, label=\"Flan T5\")\n",
        "  ],\n",
        "  title=title,\n",
        "  examples=examples\n",
        ")\n",
        "io.launch()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 643
        },
        "cellView": "form",
        "id": "9LrfsxZ4YI0Q",
        "outputId": "755b29c1-31a3-48f6-aee1-bd3cc249e184"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Colab notebook detected. To show errors in colab notebook, set `debug=True` in `launch()`\n",
            "Running on public URL: https://00e6ebf838a6d8d2.gradio.app\n",
            "\n",
            "This share link expires in 72 hours. For free permanent hosting and GPU upgrades (NEW!), check out Spaces: https://huggingface.co/spaces\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<div><iframe src=\"https://00e6ebf838a6d8d2.gradio.app\" width=\"900\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<gradio.routes.App at 0x7f9b3a5cab50>,\n",
              " 'http:https://127.0.0.1:7862/',\n",
              " 'https://00e6ebf838a6d8d2.gradio.app')"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "DwokA6akYI9C"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## About Narrativa\n",
        "\n",
        "Narrativa is an internationally recognized **content services company** that uses its proprietary **artificial intelligence** and **machine learning** platforms to build and deploy **digital content solutions** for enterprises. Its technology suite, consisting of data extraction, data analysis, **natural language processing** (NLP) and **natural language generation** (NLG) tools, all seamlessly work together to power a lineup of smart content creation, automated business intelligence reporting and process optimization products for a variety of industries.\n",
        "\n",
        "[Contact us](https://www.narrativa.com/) to learn more about our solutions!  "
      ],
      "metadata": {
        "id": "fHDqbmVIYHvO"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Zx4xNbICYN9i"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
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