transformer_pegasus_paraphrase.ipynb

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    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JlN09HAEHIth"
      },
      "source": [
        "# **How to paraphrase text using transformers in Python**\n",
        "\n",
        "Chanin Nantasenamat\n",
        "\n",
        "[Data Professor](http://youtube.com/dataprofessor), http://youtube.com/dataprofessor\n",
        "\n",
        "**Notes and References:**\n",
        "- PEGASUS is an acronym for Pre-training with Extracted Gap-sentences for Abstractive SUmmarization Sequence-to-sequence models\n",
        "- [PEGASUS](https://huggingface.co/tuner007/pegasus_paraphrase) model used herein is from *Huggingface's* **transformers** library\n",
        "- [PEGASUS model from Google Research](https://github.com/google-research/pegasus)\n",
        "- Read the original paper [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://arxiv.org/abs/1912.08777)\n",
        "- Check out the book [Transformers for Natural Language Processing](https://amzn.to/39IC6E6)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4njR9yRpVLYY"
      },
      "source": [
        "# **Install library**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a3VswpH3ftGo",
        "outputId": "646d751f-dbb9-4de5-ad18-889899a406dd"
      },
      "source": [
        "! pip install sentence-splitter"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: sentence-splitter in /usr/local/lib/python3.7/dist-packages (1.4)\n",
            "Requirement already satisfied: regex>=2017.12.12 in /usr/local/lib/python3.7/dist-packages (from sentence-splitter) (2019.12.20)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "asu-lhsoV1KZ",
        "outputId": "3a10c57a-8254-45e2-dd51-5bb5f7c5772f"
      },
      "source": [
        "! pip install transformers"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: transformers in /usr/local/lib/python3.7/dist-packages (4.4.2)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.19.5)\n",
            "Requirement already satisfied: sacremoses in /usr/local/lib/python3.7/dist-packages (from transformers) (0.0.44)\n",
            "Requirement already satisfied: tokenizers<0.11,>=0.10.1 in /usr/local/lib/python3.7/dist-packages (from transformers) (0.10.1)\n",
            "Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from transformers) (3.8.1)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers) (3.0.12)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers) (2.23.0)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from transformers) (20.9)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers) (4.41.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (2019.12.20)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.15.0)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.0.1)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (7.1.2)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->transformers) (3.4.1)\n",
            "Requirement already satisfied: typing-extensions>=3.6.4; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->transformers) (3.7.4.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2020.12.5)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2.10)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (3.0.4)\n",
            "Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->transformers) (2.4.7)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0oFm6iQpyMve",
        "outputId": "17771cff-a791-4015-ed0e-d3b90e362ffe"
      },
      "source": [
        "! pip install SentencePiece"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: SentencePiece in /usr/local/lib/python3.7/dist-packages (0.1.95)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_yCRu_3CNaTE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 165,
          "referenced_widgets": [
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            "6dcab5852f99408ba1e177185843fa6b",
            "1830e657159044b3ba9d411136f0f90d",
            "ec24a0e1eb0b4e57987d0610c2941d71",
            "501b90a0a74041189e38c080eebae9eb",
            "dc0db6639cc64b3daed8149ca090a978",
            "a35b8a96d72d4c7e881bc9a696e13835",
            "02ad3e7df3d64bbc8b1ae397e12162f3",
            "fb9c5b1db4bb49eeb640a2a24d9b17a6",
            "d1903fc67e9a4ae288ea933dc2f632d7",
            "6d323ec59a304e8abb1ed5fc58159505"
          ]
        },
        "outputId": "444e39c0-ad06-4145-b7e5-ac873e44723d"
      },
      "source": [
        "# https://huggingface.co/tuner007/pegasus_paraphrase\n",
        "\n",
        "import torch\n",
        "from transformers import PegasusForConditionalGeneration, PegasusTokenizer\n",
        "\n",
        "model_name = 'tuner007/pegasus_paraphrase'\n",
        "torch_device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
        "tokenizer = PegasusTokenizer.from_pretrained(model_name)\n",
        "model = PegasusForConditionalGeneration.from_pretrained(model_name).to(torch_device)\n",
        "\n",
        "def get_response(input_text,num_return_sequences):\n",
        "  batch = tokenizer.prepare_seq2seq_batch([input_text],truncation=True,padding='longest',max_length=60, return_tensors=\"pt\").to(torch_device)\n",
        "  translated = model.generate(**batch,max_length=60,num_beams=10, num_return_sequences=num_return_sequences, temperature=1.5)\n",
        "  tgt_text = tokenizer.batch_decode(translated, skip_special_tokens=True)\n",
        "  return tgt_text"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8f516d93300d49cbad04a67e1be9d32b",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1912529.0, style=ProgressStyle(descript…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "063b18d513424f1183737e93c264cb8f",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=65.0, style=ProgressStyle(description_w…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ec24a0e1eb0b4e57987d0610c2941d71",
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              "version_major": 2
            },
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=86.0, style=ProgressStyle(description_w…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lva62WD3gScS"
      },
      "source": [
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6A44V5YUTdHP"
      },
      "source": [
        "## **Processing a single sentence**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zzrMc_I2TgV9"
      },
      "source": [
        "text = \"In this video, I will be showing you how to build a stock price web application in Python using the Streamlit and yfinance library.\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CBbJmP4jTrne",
        "outputId": "ca776c9a-bbf7-407a-cd9d-8a3758b78332"
      },
      "source": [
        "get_response(text, 5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:3221: FutureWarning: `prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` context manager to prepare your targets. See the documentation of your specific tokenizer for more details\n",
            "  FutureWarning,\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['In this video, I will show you how to use the Streamlit and yfinance libraries to build a stock price web application.',\n",
              " 'In this video, I will show you how to build a stock price web application in Python using the Streamlit and yfinance libraries.',\n",
              " 'In this video, I will show you how to build a stock price web application using the Streamlit and yfinance libraries.',\n",
              " 'In this video, I will show you how to use the Streamlit and yfinance libraries to build a stock price web application in Python.',\n",
              " 'In this video, I will show you how to use the Streamlit and yfinance library to build a stock price web application.']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3xmOh_62Tujy",
        "outputId": "0f5a855c-11c2-4fad-af68-309d39741137"
      },
      "source": [
        "get_response(text, 1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:3221: FutureWarning: `prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` context manager to prepare your targets. See the documentation of your specific tokenizer for more details\n",
            "  FutureWarning,\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['In this video, I will show you how to use the Streamlit and yfinance libraries to build a stock price web application.']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HXeaFgNcTV9B"
      },
      "source": [
        "## **Processing a paragraph of text**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yOiKx9cPNanF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "56e927c5-d1df-4d36-b327-2075bec11fe4"
      },
      "source": [
        "# Paragraph of text\n",
        "context = \"In this video, I will be showing you how to build a stock price web application in Python using the Streamlit and yfinance library. The app will be able to retrieve company information as well as the stock price data for S and P 500 companies. All of this in less than 50 lines of code.\"\n",
        "print(context)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "In this video, I will be showing you how to build a stock price web application in Python using the Streamlit and yfinance library. The app will be able to retrieve company information as well as the stock price data for S and P 500 companies. All of this in less than 50 lines of code.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fk-oJbtUNxkP",
        "outputId": "75a26119-ddca-4c7c-d247-9de23e08ef45"
      },
      "source": [
        "# Takes the input paragraph and splits it into a list of sentences\n",
        "from sentence_splitter import SentenceSplitter, split_text_into_sentences\n",
        "\n",
        "splitter = SentenceSplitter(language='en')\n",
        "\n",
        "sentence_list = splitter.split(context)\n",
        "sentence_list"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['In this video, I will be showing you how to build a stock price web application in Python using the Streamlit and yfinance library.',\n",
              " 'The app will be able to retrieve company information as well as the stock price data for S and P 500 companies.',\n",
              " 'All of this in less than 50 lines of code.']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QdE0CH6agx8H",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "44a7c9dd-9080-46f9-953d-7af2bedf6f09"
      },
      "source": [
        "# Do a for loop to iterate through the list of sentences and paraphrase each sentence in the iteration\n",
        "paraphrase = []\n",
        "\n",
        "for i in sentence_list:\n",
        "  a = get_response(i,1)\n",
        "  paraphrase.append(a)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:3221: FutureWarning: `prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` context manager to prepare your targets. See the documentation of your specific tokenizer for more details\n",
            "  FutureWarning,\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i1zyKct5jM3C",
        "outputId": "4acdc64c-59cb-4369-97c1-1506a8d9fce0"
      },
      "source": [
        "# This is the paraphrased text\n",
        "paraphrase"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[['In this video, I will show you how to use the Streamlit and yfinance libraries to build a stock price web application.'],\n",
              " ['The stock price data for S and P 500 companies will be retrieved by the app.'],\n",
              " ['This is in less than 50 lines of code.']]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fSOQwP_hiPjk",
        "outputId": "e5b9261b-07fc-4b47-b889-2ff343bc3f65"
      },
      "source": [
        "paraphrase2 = [' '.join(x) for x in paraphrase]\n",
        "paraphrase2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['In this video, I will show you how to use the Streamlit and yfinance libraries to build a stock price web application.',\n",
              " 'The stock price data for S and P 500 companies will be retrieved by the app.',\n",
              " 'This is in less than 50 lines of code.']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "id": "fJ5tDLzokYCd",
        "outputId": "eca018b6-c0c2-4c6f-db92-5cdc6c56d862"
      },
      "source": [
        "# Combines the above list into a paragraph\n",
        "paraphrase3 = [' '.join(x for x in paraphrase2) ]\n",
        "paraphrased_text = str(paraphrase3).strip('[]').strip(\"'\")\n",
        "paraphrased_text"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'In this video, I will show you how to use the Streamlit and yfinance libraries to build a stock price web application. The stock price data for S and P 500 companies will be retrieved by the app. This is in less than 50 lines of code.'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8XNC-TJQpZWW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dd95d96b-9f27-4076-a90e-d4c4f49bd2fe"
      },
      "source": [
        "# Comparison of the original (context variable) and the paraphrased version (paraphrase3 variable)\n",
        "\n",
        "print(context)\n",
        "print(paraphrased_text)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "In this video, I will be showing you how to build a stock price web application in Python using the Streamlit and yfinance library. The app will be able to retrieve company information as well as the stock price data for S and P 500 companies. All of this in less than 50 lines of code.\n",
            "In this video, I will show you how to use the Streamlit and yfinance libraries to build a stock price web application. The stock price data for S and P 500 companies will be retrieved by the app. This is in less than 50 lines of code.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xFUouTov0v9_"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}