Generating_Blog_Posts.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f5ppztgbHKMu"
      },
      "source": [
        "# 0. Install and Import Dependencies"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "colab": {
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        },
        "id": "-07KrP3THKMz",
        "outputId": "fb47fce8-ae25-4850-92c7-1b4bd100ad07"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting transformers\n",
            "  Downloading transformers-4.29.2-py3-none-any.whl (7.1 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.1/7.1 MB\u001b[0m \u001b[31m63.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.12.0)\n",
            "Collecting huggingface-hub<1.0,>=0.14.1 (from transformers)\n",
            "  Downloading huggingface_hub-0.14.1-py3-none-any.whl (224 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m224.5/224.5 kB\u001b[0m \u001b[31m19.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.22.4)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (23.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2022.10.31)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.27.1)\n",
            "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1 (from transformers)\n",
            "  Downloading tokenizers-0.13.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m69.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.65.0)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.14.1->transformers) (2023.4.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.14.1->transformers) (4.5.0)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (1.26.15)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2022.12.7)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.12)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.4)\n",
            "Installing collected packages: tokenizers, huggingface-hub, transformers\n",
            "Successfully installed huggingface-hub-0.14.1 tokenizers-0.13.3 transformers-4.29.2\n"
          ]
        }
      ],
      "source": [
        "!pip install transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6C17AjSDHKM2"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "from transformers import GPT2LMHeadModel, GPT2Tokenizer"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Load model"
      ],
      "metadata": {
        "id": "7p9F95iBHj5N"
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          "data": {
            "text/plain": [
              "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/1.04M [00:00<?, ?B/s]"
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              "Downloading (…)olve/main/merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
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              "Downloading (…)lve/main/config.json:   0%|          | 0.00/666 [00:00<?, ?B/s]"
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        "tokenizer = GPT2Tokenizer.from_pretrained(\"gpt2-large\")"
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        "id": "oVEyWP3sHKM5",
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      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/3.25G [00:00<?, ?B/s]"
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        {
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            "text/plain": [
              "Downloading (…)neration_config.json:   0%|          | 0.00/124 [00:00<?, ?B/s]"
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              "model_id": "2c166799947c44ca9273bd8ae466c3a1"
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      ],
      "source": [
        "model = GPT2LMHeadModel.from_pretrained(\"gpt2-large\", pad_token_id=tokenizer.eos_token_id)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer.decode(tokenizer.eos_token_id)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "5znmeQNjJALF",
        "outputId": "caf34535-92bd-40cd-9dd7-386261d33df3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'<|endoftext|>'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Tokenize sentences"
      ],
      "metadata": {
        "id": "BmcjTLnMILYV"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nTu8a2TsHKM7"
      },
      "outputs": [],
      "source": [
        "sentence = 'No-code needs to adapt to specialists'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TgBpw5SZHKM8"
      },
      "outputs": [],
      "source": [
        "input_ids = tokenizer.encode(sentence, return_tensors='pt')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R332vc5OHKM9",
        "outputId": "a0c78435-2ec9-44c6-f460-73938b239e79"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[ 2949,    12,  8189,  2476,   284,  6068,   284, 22447]])"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "input_ids"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer.decode(input_ids[0][0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "zMyQG0MaImNM",
        "outputId": "7e140091-c19e-4ff4-8aa4-8d61ae9f2e43"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'No'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer.decode(input_ids[0][1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "cKEtVk0XLQ53",
        "outputId": "8ddd6b92-c859-47fc-f02e-495d1f30c23f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'-'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer.decode(input_ids[0][2])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "9IBItkgUMlgy",
        "outputId": "d6a47aec-6255-4006-dcba-0885fe767415"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'code'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Generate and decode text"
      ],
      "metadata": {
        "id": "mx2iKXWzJUjm"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RpXAdQbMHKM_"
      },
      "outputs": [],
      "source": [
        "# generate text until the output length (which includes the context length) reaches 50\n",
        "output = model.generate(input_ids, max_length=150, num_beams=5, no_repeat_ngram_size=2, early_stopping=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "colab": {
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        },
        "id": "NeinzdCvHKNA",
        "outputId": "3f52fcce-0b17-4821-d444-f925026628da"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[ 2949,    12,  8189,  2476,   284,  6068,   284, 22447,     6,  2476,\n",
              "            13,   198,   198,     1,  1135,   761,   284,   787,  1654,   326,\n",
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              "          2637,   366, 50256]])"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ],
      "source": [
        "output"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text = tokenizer.decode(output[0], skip_special_tokens= True)"
      ],
      "metadata": {
        "id": "JM9eZhlEJpQk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZB05dSfbHKNC",
        "outputId": "9a8f52f3-489a-4187-d8f2-21cb308ae7e3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No-code needs to adapt to specialists' needs.\n",
            "\n",
            "\"We need to make sure that we have the right people in place to do that,\" he said. \"We have to be able to say, 'This is what we're doing, and this is how it's going to work.' \"\n"
          ]
        }
      ],
      "source": [
        "print(tokenizer.decode(output[0], skip_special_tokens=True))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "with open('blog.txt', 'w') as f:\n",
        "     f.write(text)"
      ],
      "metadata": {
        "id": "PaXYfrG6Mu83"
      },
      "execution_count": null,
      "outputs": []
    }
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