WorkshopNotebook.ipynb

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)\r\n",
        "\r\n",
        "\r\n",
        "\r\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-mq73onfnjyS"
      },
      "source": [
        "# Using Hugging Face\r\n",
        "\r\n",
        "State-of-the-art Natural Language Processing for PyTorch and TensorFlow 2.0\r\n",
        "\r\n",
        "🤗 Transformers provides thousands of pretrained models to perform tasks on texts such as classification, information extraction, question answering, summarization, translation, text generation, etc in 100+ languages. Its aim is to make cutting-edge NLP easier to use for everyone.\r\n",
        "\r\n",
        "🤗 Transformers provides APIs to quickly download and use those pretrained models on a given text, fine-tune them on your own datasets then share them with the community on our model hub. At the same time, each python module defining an architecture can be used as a standalone and modified to enable quick research experiments.\r\n",
        "\r\n",
        "🤗 Transformers is backed by the two most popular deep learning libraries, PyTorch and TensorFlow, with a seamless integration between them, allowing you to train your models with one then load it for inference with the other."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7vmTxuhAnmnd",
        "outputId": "bda86853-c10b-4286-be7a-31d79c79bdc5"
      },
      "source": [
        "!pip install transformers"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting transformers\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/98/87/ef312eef26f5cecd8b17ae9654cdd8d1fae1eb6dbd87257d6d73c128a4d0/transformers-4.3.2-py3-none-any.whl (1.8MB)\n",
            "\u001b[K     |████████████████████████████████| 1.8MB 9.4MB/s \n",
            "\u001b[?25hRequirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.6/dist-packages (from transformers) (2019.12.20)\n",
            "Requirement already satisfied: dataclasses; python_version < \"3.7\" in /usr/local/lib/python3.6/dist-packages (from transformers) (0.8)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.6/dist-packages (from transformers) (3.0.12)\n",
            "Collecting sacremoses\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/7d/34/09d19aff26edcc8eb2a01bed8e98f13a1537005d31e95233fd48216eed10/sacremoses-0.0.43.tar.gz (883kB)\n",
            "\u001b[K     |████████████████████████████████| 890kB 47.4MB/s \n",
            "\u001b[?25hRequirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from transformers) (3.4.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from transformers) (2.23.0)\n",
            "Collecting tokenizers<0.11,>=0.10.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/fd/5b/44baae602e0a30bcc53fbdbc60bd940c15e143d252d658dfdefce736ece5/tokenizers-0.10.1-cp36-cp36m-manylinux2010_x86_64.whl (3.2MB)\n",
            "\u001b[K     |████████████████████████████████| 3.2MB 25.8MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.6/dist-packages (from transformers) (1.19.5)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.6/dist-packages (from transformers) (20.9)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.6/dist-packages (from transformers) (4.41.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from sacremoses->transformers) (1.15.0)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.6/dist-packages (from sacremoses->transformers) (7.1.2)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.6/dist-packages (from sacremoses->transformers) (1.0.0)\n",
            "Requirement already satisfied: typing-extensions>=3.6.4; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from importlib-metadata; python_version < \"3.8\"->transformers) (3.7.4.3)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata; python_version < \"3.8\"->transformers) (3.4.0)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/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/python3.6/dist-packages (from requests->transformers) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->transformers) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->transformers) (2020.12.5)\n",
            "Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.6/dist-packages (from packaging->transformers) (2.4.7)\n",
            "Building wheels for collected packages: sacremoses\n",
            "  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sacremoses: filename=sacremoses-0.0.43-cp36-none-any.whl size=893261 sha256=018f0b743bb907abfbb4a7ddf74cac7d74282f2ab4ed7dc980b8317facf28f97\n",
            "  Stored in directory: /root/.cache/pip/wheels/29/3c/fd/7ce5c3f0666dab31a50123635e6fb5e19ceb42ce38d4e58f45\n",
            "Successfully built sacremoses\n",
            "Installing collected packages: sacremoses, tokenizers, transformers\n",
            "Successfully installed sacremoses-0.0.43 tokenizers-0.10.1 transformers-4.3.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hd-eB_uVoLMM"
      },
      "source": [
        "# Pipeline"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lggXcH_ZoNxR"
      },
      "source": [
        "from transformers import pipeline"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8X-BoSAnoZuD"
      },
      "source": [
        "## Sentiment Analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233,
          "referenced_widgets": [
            "d1a1466f49a849a1b17bdeaceb62fad6",
            "0b8ff32f1401443a8bf9fe83da767a8c",
            "39b8c5606ac04c45b4a086fb592bcb13",
            "88ae24f9a5a1499694a907c282183a56",
            "cc0ddb28e517488aadbf0a7a77cec7bb",
            "dc97b406a19f47468473589b8cb543fd",
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        "id": "4NjLFUM8ocCn",
        "outputId": "4a2526c0-62bd-4fa5-8a3b-b79459875ab8"
      },
      "source": [
        "nlp = pipeline('sentiment-analysis')\r\n",
        "nlp('We are very happy to include pipeline into the transformers repository.')"
      ],
      "execution_count": null,
      "outputs": [
        {
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          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
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          "output_type": "stream",
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            "\n"
          ],
          "name": "stdout"
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        },
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          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=48.0, style=ProgressStyle(description_w…"
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        },
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          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'label': 'POSITIVE', 'score': 0.9978193640708923}]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5Kj7x37BpsVd"
      },
      "source": [
        "## Question Answering"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233,
          "referenced_widgets": [
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        },
        "id": "KHx7rnEKpq0o",
        "outputId": "6232de26-c150-446a-c6db-9ee309d935f8"
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      "source": [
        "nlp = pipeline('question-answering')\r\n",
        "nlp({\r\n",
        "    'question': 'What is my name ?',\r\n",
        "    'context': 'My name is Gagan, I work at Google'\r\n",
        "})"
      ],
      "execution_count": null,
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          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
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          "metadata": {
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          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
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          ],
          "name": "stdout"
        },
        {
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        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
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        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'answer': 'Gagan', 'end': 16, 'score': 0.9914252161979675, 'start': 11}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gdDD0Rejq2WM"
      },
      "source": [
        "## Mask Fill"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 640,
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        },
        "id": "NzFnYtlCp5d8",
        "outputId": "d748fe45-18a9-4a50-ede4-ffa5cd9b66f2"
      },
      "source": [
        "unmasker = pipeline('fill-mask', model='bert-base-uncased')\r\n",
        "unmasker(\"Hello I'm a [MASK] model.\")"
      ],
      "execution_count": null,
      "outputs": [
        {
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          ],
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        },
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            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForMaskedLM: ['cls.seq_relationship.weight', 'cls.seq_relationship.bias']\n",
            "- This IS expected if you are initializing BertForMaskedLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertForMaskedLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'score': 0.10731077939271927,\n",
              "  'sequence': \"hello i'm a fashion model.\",\n",
              "  'token': 4827,\n",
              "  'token_str': 'fashion'},\n",
              " {'score': 0.08774469047784805,\n",
              "  'sequence': \"hello i'm a role model.\",\n",
              "  'token': 2535,\n",
              "  'token_str': 'role'},\n",
              " {'score': 0.053383756428956985,\n",
              "  'sequence': \"hello i'm a new model.\",\n",
              "  'token': 2047,\n",
              "  'token_str': 'new'},\n",
              " {'score': 0.0466720387339592,\n",
              "  'sequence': \"hello i'm a super model.\",\n",
              "  'token': 3565,\n",
              "  'token_str': 'super'},\n",
              " {'score': 0.02709590084850788,\n",
              "  'sequence': \"hello i'm a fine model.\",\n",
              "  'token': 2986,\n",
              "  'token_str': 'fine'}]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
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        "id": "07PTg2WuqbAf",
        "outputId": "3c9c837c-df48-41c4-bde8-7f7f86530e76"
      },
      "source": [
        "nlp = pipeline('fill-mask')\r\n",
        "nlp('I hope you <mask> this video')"
      ],
      "execution_count": null,
      "outputs": [
        {
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            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
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            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=456318.0, style=ProgressStyle(descripti…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1355863.0, style=ProgressStyle(descript…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'score': 0.7073914408683777,\n",
              "  'sequence': 'I hope you enjoyed this video',\n",
              "  'token': 3776,\n",
              "  'token_str': ' enjoyed'},\n",
              " {'score': 0.13673578202724457,\n",
              "  'sequence': 'I hope you enjoy this video',\n",
              "  'token': 2254,\n",
              "  'token_str': ' enjoy'},\n",
              " {'score': 0.1335313767194748,\n",
              "  'sequence': 'I hope you liked this video',\n",
              "  'token': 6640,\n",
              "  'token_str': ' liked'},\n",
              " {'score': 0.0057791294530034065,\n",
              "  'sequence': 'I hope you like this video',\n",
              "  'token': 101,\n",
              "  'token_str': ' like'},\n",
              " {'score': 0.005615198519080877,\n",
              "  'sequence': 'I hope you appreciated this video',\n",
              "  'token': 10874,\n",
              "  'token_str': ' appreciated'}]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6mSgKs2Kq6g2"
      },
      "source": [
        "## NER (Named-entity recognition)\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 638,
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            "768e276e6c174eaf88716bbfb60c8742",
            "eff7a4d544854f40b9e9685f7845b30c",
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            "d6a484b42fe1447e985e522aa34d5b3f"
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        },
        "id": "uWv4RXVpqukc",
        "outputId": "29a3dec6-0f4f-468d-92ca-747f3c643125"
      },
      "source": [
        "nlp = pipeline('ner')\r\n",
        "nlp('It is me, Gagan, I work at DSC')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=998.0, style=ProgressStyle(description_…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f0d5f865a2ce4ddd94ef5c6e9c04ced8",
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1334448817.0, style=ProgressStyle(descr…"
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          },
          "metadata": {
            "tags": []
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        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=213450.0, style=ProgressStyle(descripti…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "768e276e6c174eaf88716bbfb60c8742",
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              "version_major": 2
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=60.0, style=ProgressStyle(description_w…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'end': 14,\n",
              "  'entity': 'I-PER',\n",
              "  'index': 5,\n",
              "  'score': 0.994879424571991,\n",
              "  'start': 10,\n",
              "  'word': 'Gaga'},\n",
              " {'end': 15,\n",
              "  'entity': 'I-PER',\n",
              "  'index': 6,\n",
              "  'score': 0.9487237930297852,\n",
              "  'start': 14,\n",
              "  'word': '##n'},\n",
              " {'end': 29,\n",
              "  'entity': 'I-ORG',\n",
              "  'index': 11,\n",
              "  'score': 0.9993436932563782,\n",
              "  'start': 27,\n",
              "  'word': 'DS'},\n",
              " {'end': 30,\n",
              "  'entity': 'I-ORG',\n",
              "  'index': 12,\n",
              "  'score': 0.9922718405723572,\n",
              "  'start': 29,\n",
              "  'word': '##C'}]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l5kE32CdrE10"
      },
      "source": [
        "nlp.model.save_pretrained('...')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vgzjtdC0sSgC"
      },
      "source": [
        "# Text Generation (GPT-2)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320,
          "referenced_widgets": [
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            "b065c5699a96487d8c4778bad3f2ef92"
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        },
        "id": "h3Rv5mJpslWE",
        "outputId": "fa96bfb9-82a5-4e24-cf42-8e8d68c28420"
      },
      "source": [
        "gpt2 = pipeline('text-generation')"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=665.0, style=ProgressStyle(description_…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=548118077.0, style=ProgressStyle(descri…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Some weights of GPT2Model were not initialized from the model checkpoint at gpt2 and are newly initialized: ['h.0.attn.masked_bias', 'h.1.attn.masked_bias', 'h.2.attn.masked_bias', 'h.3.attn.masked_bias', 'h.4.attn.masked_bias', 'h.5.attn.masked_bias', 'h.6.attn.masked_bias', 'h.7.attn.masked_bias', 'h.8.attn.masked_bias', 'h.9.attn.masked_bias', 'h.10.attn.masked_bias', 'h.11.attn.masked_bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1042301.0, style=ProgressStyle(descript…"
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          },
          "metadata": {
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          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
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        },
        {
          "output_type": "stream",
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            "\n"
          ],
          "name": "stdout"
        },
        {
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1355256.0, style=ProgressStyle(descript…"
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          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8EI_VIkQslG5",
        "outputId": "c033db72-23e6-46e5-bd94-fa6218a25dd5"
      },
      "source": [
        "gpt2('Natural language processing is amazing!')"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'generated_text': \"Natural language processing is amazing! I hope you learned anything you can from this tutorial.\\n\\nThere's also a great link to the video which can be played in the new Youtube Video app, on iOS 9 you can also watch it here on iTunes\"}]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EzaECMR-tGlm"
      },
      "source": [
        "## Single Word Prediction "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q7WMUoPFsMx9"
      },
      "source": [
        "import torch\r\n",
        "from transformers import GPT2Tokenizer, GPT2LMHeadModel"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215,
          "referenced_widgets": [
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        },
        "id": "xZGGN9WisXFw",
        "outputId": "e3c81021-490c-4a12-919d-a1326d1d5bfb"
      },
      "source": [
        "tokeniser = GPT2Tokenizer.from_pretrained('gpt2')\r\n",
        "model = GPT2LMHeadModel.from_pretrained('gpt2')"
      ],
      "execution_count": null,
      "outputs": [
        {
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          "data": {
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        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
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          "output_type": "stream",
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            "\n"
          ],
          "name": "stdout"
        },
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            "\n"
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            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=548118077.0, style=ProgressStyle(descri…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6nMukKdysZXE",
        "outputId": "874b2cfa-ab9d-4e97-f8bb-7338dd3469a7"
      },
      "source": [
        "text = \"let us see how this turns\"\r\n",
        "indexed_tokens = tokeniser.encode(text)\r\n",
        "tokens_tensor = torch.tensor([indexed_tokens])\r\n",
        "model.eval()\r\n",
        "tokens_tensor = tokens_tensor.to('cuda')\r\n",
        "model.to('cuda')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GPT2LMHeadModel(\n",
              "  (transformer): GPT2Model(\n",
              "    (wte): Embedding(50257, 768)\n",
              "    (wpe): Embedding(1024, 768)\n",
              "    (drop): Dropout(p=0.1, inplace=False)\n",
              "    (h): ModuleList(\n",
              "      (0): Block(\n",
              "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): Attention(\n",
              "          (c_attn): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
              "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): MLP(\n",
              "          (c_fc): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (1): Block(\n",
              "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): Attention(\n",
              "          (c_attn): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
              "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): MLP(\n",
              "          (c_fc): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (2): Block(\n",
              "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): Attention(\n",
              "          (c_attn): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
              "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): MLP(\n",
              "          (c_fc): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (3): Block(\n",
              "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): Attention(\n",
              "          (c_attn): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
              "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): MLP(\n",
              "          (c_fc): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (4): Block(\n",
              "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): Attention(\n",
              "          (c_attn): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
              "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): MLP(\n",
              "          (c_fc): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (5): Block(\n",
              "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): Attention(\n",
              "          (c_attn): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
              "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): MLP(\n",
              "          (c_fc): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (6): Block(\n",
              "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): Attention(\n",
              "          (c_attn): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
              "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): MLP(\n",
              "          (c_fc): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (7): Block(\n",
              "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): Attention(\n",
              "          (c_attn): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
              "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): MLP(\n",
              "          (c_fc): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (8): Block(\n",
              "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): Attention(\n",
              "          (c_attn): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
              "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): MLP(\n",
              "          (c_fc): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (9): Block(\n",
              "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): Attention(\n",
              "          (c_attn): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
              "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): MLP(\n",
              "          (c_fc): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (10): Block(\n",
              "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): Attention(\n",
              "          (c_attn): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
              "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): MLP(\n",
              "          (c_fc): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (11): Block(\n",
              "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): Attention(\n",
              "          (c_attn): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
              "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): MLP(\n",
              "          (c_fc): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (ln_f): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "  )\n",
              "  (lm_head): Linear(in_features=768, out_features=50257, bias=False)\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CYOPv0BAshKd",
        "outputId": "1f3f6ab5-6e4c-4e9b-f4ea-b140753be062"
      },
      "source": [
        "with torch.no_grad():\r\n",
        "  outputs = model(tokens_tensor)\r\n",
        "  predictions = outputs[0]\r\n",
        "\r\n",
        "print(outputs[0].shape)\r\n",
        "\r\n",
        "predicted_index = torch.argmax(predictions[0, -1, :]).item()\r\n",
        "predicted_text = tokeniser.decode(indexed_tokens + [predicted_index])\r\n",
        "\r\n",
        "print(predicted_text)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "torch.Size([1, 6, 50257])\n",
            "let us see how this turns out\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bh3jcItctjeU"
      },
      "source": [
        "## Multi-word Generation "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DT8COhS1tKJt",
        "outputId": "ff06a3ee-5078-4152-e349-0075be7424f5"
      },
      "source": [
        "chars = 0\r\n",
        "text = \"i am very exited to present to you this\"\r\n",
        "while chars<50:\r\n",
        "  chars += 1\r\n",
        "  indexed_tokens = tokeniser.encode(text)\r\n",
        "  tokens_tensors = torch.tensor([indexed_tokens])\r\n",
        "  tokens_tensors = tokens_tensors.to('cuda')\r\n",
        "#  model = model.to('cuda')\r\n",
        "  with torch.no_grad():\r\n",
        "    outputs = model(tokens_tensors)\r\n",
        "    predictions = outputs[0]\r\n",
        "  predicted_index = torch.argmax(predictions[0,-1,:]).item()\r\n",
        "  text = tokeniser.decode(indexed_tokens + [predicted_index])\r\n",
        "\r\n",
        "print(text)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "i am very exited to present to you this new book. I am very excited to share with you the first chapter of the book, \"The Secret of the Soul.\"\n",
            "\n",
            "\n",
            "The Secret of the Soul is a book that I have been reading for over a year now. I have been\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gi4A5HKiuSTN"
      },
      "source": [
        "## Using Pytorch Libraries for GPT-2"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1N4nMDRctuHR",
        "outputId": "a0b991ec-d5ee-40bf-a5ea-b4c9bd1d280b"
      },
      "source": [
        "!git clone https://github.com/huggingface/pytorch-transformers.git"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'pytorch-transformers'...\n",
            "remote: Enumerating objects: 40, done.\u001b[K\n",
            "remote: Counting objects: 100% (40/40), done.\u001b[K\n",
            "remote: Compressing objects: 100% (31/31), done.\u001b[K\n",
            "remote: Total 62391 (delta 15), reused 9 (delta 3), pack-reused 62351\u001b[K\n",
            "Receiving objects: 100% (62391/62391), 47.56 MiB | 28.75 MiB/s, done.\n",
            "Resolving deltas: 100% (44164/44164), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kn6bPur0ubMQ",
        "outputId": "7191ebd5-7826-4351-b809-e000cc3d277b"
      },
      "source": [
        "!python pytorch-transformers/examples/text-generation/run_generation.py \\\r\n",
        "    --model_type=gpt2 \\\r\n",
        "    --length=100 \\\r\n",
        "    --model_name_or_path=gpt2 \\"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "02/13/2021 18:49:30 - WARNING - __main__ -   device: cuda, n_gpu: 1, 16-bits training: False\n",
            "02/13/2021 18:49:38 - INFO - __main__ -   Namespace(device=device(type='cuda'), fp16=False, k=0, length=100, model_name_or_path='gpt2', model_type='gpt2', n_gpu=1, no_cuda=False, num_return_sequences=1, p=0.9, padding_text='', prefix='', prompt='', repetition_penalty=1.0, seed=42, stop_token=None, temperature=1.0, xlm_language='')\n",
            "Model prompt >>> i am very exited to present to you this\n",
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
            "=== GENERATED SEQUENCE 1 ===\n",
            "2021-02-13 18:49:59.473178: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.10.1\n",
            "i am very exited to present to you this blend.\"\n",
            "\n",
            "\"It is really…tea,\" Angela corrected, brushing sheather lightly on her knee.\n",
            "\n",
            "\"Do you know who I am?\" She asked, smiling faintly at the blonde. \"Now I must say I've been close to Angela for a while now, and I think I owe her a lot for helping make the record story at your fingertips even better.\"\n",
            "\n",
            "\"I haven't,\" Angela shook her head. \"I still have hours left in me before all\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rP54zijKwhIp"
      },
      "source": [
        "# Text Summarisation "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 123
        },
        "id": "-0En7PBEufnG",
        "outputId": "0c765ce9-fabb-46f8-ac1f-a0a7aa2e6aa6"
      },
      "source": [
        "text = 'Shakespeare occupies a position unique in world literature. Other poets, such as Homer and Dante, and novelists, such as Leo Tolstoy and Charles Dickens, have transcended national barriers, but no writer’s living reputation can compare to that of Shakespeare, whose plays, written in the late 16th and early 17th centuries for a small repertory theatre, are now performed and read more often and in more countries than ever before. The prophecy of his great contemporary, the poet and dramatist Ben Jonson, that Shakespeare “was not of an age, but for all time,” has been fulfilled. It may be audacious even to attempt a definition of his greatness, but it is not so difficult to describe the gifts that enabled him to create imaginative visions of pathos and mirth that, whether read or witnessed in the theatre, fill the mind and linger there. He is a writer of great intellectual rapidity, perceptiveness, and poetic power. Other writers have had these qualities, but with Shakespeare the keenness of mind was applied not to abstruse or remote subjects but to human beings and their complete range of emotions and conflicts. Other writers have applied their keenness of mind in this way, but Shakespeare is astonishingly clever with words and images, so that his mental energy, when applied to intelligible human situations, finds full and memorable expression, convincing and imaginatively stimulating. As if this were not enough, the art form into which his creative energies went was not remote and bookish but involved the vivid stage impersonation of human beings, commanding sympathy and inviting vicarious participation. Thus, Shakespeare’s merits can survive translation into other languages and into cultures remote from that of Elizabethan England.'\r\n",
        "text"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Shakespeare occupies a position unique in world literature. Other poets, such as Homer and Dante, and novelists, such as Leo Tolstoy and Charles Dickens, have transcended national barriers, but no writer’s living reputation can compare to that of Shakespeare, whose plays, written in the late 16th and early 17th centuries for a small repertory theatre, are now performed and read more often and in more countries than ever before. The prophecy of his great contemporary, the poet and dramatist Ben Jonson, that Shakespeare “was not of an age, but for all time,” has been fulfilled. It may be audacious even to attempt a definition of his greatness, but it is not so difficult to describe the gifts that enabled him to create imaginative visions of pathos and mirth that, whether read or witnessed in the theatre, fill the mind and linger there. He is a writer of great intellectual rapidity, perceptiveness, and poetic power. Other writers have had these qualities, but with Shakespeare the keenness of mind was applied not to abstruse or remote subjects but to human beings and their complete range of emotions and conflicts. Other writers have applied their keenness of mind in this way, but Shakespeare is astonishingly clever with words and images, so that his mental energy, when applied to intelligible human situations, finds full and memorable expression, convincing and imaginatively stimulating. As if this were not enough, the art form into which his creative energies went was not remote and bookish but involved the vivid stage impersonation of human beings, commanding sympathy and inviting vicarious participation. Thus, Shakespeare’s merits can survive translation into other languages and into cultures remote from that of Elizabethan England.'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AiXpupfDw8Bk"
      },
      "source": [
        "summarizer = pipeline(\"summarization\")"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O-oNhM1ku6Kz",
        "outputId": "373d2e7d-8a42-4b6b-f877-b217edc73def"
      },
      "source": [
        "sum_op= (summarizer(text, max_length=50, min_length=30, do_sample=False))\r\n",
        "sum_op"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'summary_text': ' Shakespeare occupies a position unique in world literature . He is a writer of great intellectual rapidity, perceptiveness, and poetic power . Other writers have had these qualities, but with Shakespeare the keenness of mind was applied not to abstru'}]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215,
          "referenced_widgets": [
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            "dc2a5adcf92044cc9019fdee120a2f1b",
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            "4f9fa6cf19124f73bb310e0f0c188ffc",
            "249986db71764422a737e413f45ea8ae"
          ]
        },
        "id": "1zgPZZSAwmg_",
        "outputId": "18704093-08ad-460c-a9fd-ba08933272ac"
      },
      "source": [
        "from transformers import BartTokenizer, BartForConditionalGeneration, BartConfig\r\n",
        "\r\n",
        "model = BartForConditionalGeneration.from_pretrained('facebook/bart-large-cnn')\r\n",
        "tokenizer = BartTokenizer.from_pretrained('facebook/bart-large-cnn')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "10c89505027e44f7a42928eb8a609229",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1399.0, style=ProgressStyle(description…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d39366b18b1f4066993f18f4d76b7c9f",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1625270765.0, style=ProgressStyle(descr…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7f899e5b294d484390f8f6da4ef65a25",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=898823.0, style=ProgressStyle(descripti…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5df0eb0c05bd4848bd4e3b17884739f8",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=456318.0, style=ProgressStyle(descripti…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5z8nKe3EwrOL",
        "outputId": "84394db8-9b9c-482d-c732-4cee7851314c"
      },
      "source": [
        "inputs = tokenizer.batch_encode_plus([text], max_length=1024, return_tensors='pt')\r\n",
        "\r\n",
        "summary_ids = model.generate(inputs['input_ids'], num_beams=4, max_length=100, early_stopping=False)\r\n",
        "\r\n",
        "for ids in summary_ids:\r\n",
        "    short = tokenizer.decode(ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)\r\n",
        "\r\n",
        "    print(len(text), len(short))\r\n",
        "    print(short)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=True` to explicitly truncate examples to max length. Defaulting to 'longest_first' truncation strategy. If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy more precisely by providing a specific strategy to `truncation`.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1761 341\n",
            "Shakespeare occupies a position unique in world literature. He is a writer of great intellectual rapidity, perceptiveness, and poetic power. Other writers have had these qualities, but with Shakespeare the keenness of mind was applied not to abstruse or remote subjects but to human beings and their complete range of emotions and conflicts.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0BbwEqGvuqve"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}