Summarize-Pegasus.ipynb

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            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
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    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/srulikbd/Deep-Learning/blob/master/Summarize-Pegasus.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CQkIx5hP41zU",
        "outputId": "a60d65b8-2270-47eb-aa91-b23df3674929"
      },
      "source": [
        "!pip install -U transformers\n",
        "!pip install sentencepiece"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting transformers\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/50/0c/7d5950fcd80b029be0a8891727ba21e0cd27692c407c51261c3c921f6da3/transformers-4.1.1-py3-none-any.whl (1.5MB)\n",
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            "\u001b[?25hRequirement already satisfied, skipping upgrade: dataclasses; python_version < \"3.7\" in /usr/local/lib/python3.6/dist-packages (from transformers) (0.8)\n",
            "Collecting tokenizers==0.9.4\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/0f/1c/e789a8b12e28be5bc1ce2156cf87cb522b379be9cadc7ad8091a4cc107c4/tokenizers-0.9.4-cp36-cp36m-manylinux2010_x86_64.whl (2.9MB)\n",
            "\u001b[K     |████████████████████████████████| 2.9MB 52.3MB/s \n",
            "\u001b[?25hCollecting sacremoses\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/7d/34/09d19aff26edcc8eb2a01bed8e98f13a1537005d31e95233fd48216eed10/sacremoses-0.0.43.tar.gz (883kB)\n",
            "\u001b[K     |████████████████████████████████| 890kB 41.6MB/s \n",
            "\u001b[?25hRequirement already satisfied, skipping upgrade: requests in /usr/local/lib/python3.6/dist-packages (from transformers) (2.23.0)\n",
            "Requirement already satisfied, skipping upgrade: filelock in /usr/local/lib/python3.6/dist-packages (from transformers) (3.0.12)\n",
            "Requirement already satisfied, skipping upgrade: packaging in /usr/local/lib/python3.6/dist-packages (from transformers) (20.8)\n",
            "Requirement already satisfied, skipping upgrade: regex!=2019.12.17 in /usr/local/lib/python3.6/dist-packages (from transformers) (2019.12.20)\n",
            "Requirement already satisfied, skipping upgrade: numpy in /usr/local/lib/python3.6/dist-packages (from transformers) (1.19.4)\n",
            "Requirement already satisfied, skipping upgrade: tqdm>=4.27 in /usr/local/lib/python3.6/dist-packages (from transformers) (4.41.1)\n",
            "Requirement already satisfied, skipping upgrade: six in /usr/local/lib/python3.6/dist-packages (from sacremoses->transformers) (1.15.0)\n",
            "Requirement already satisfied, skipping upgrade: click in /usr/local/lib/python3.6/dist-packages (from sacremoses->transformers) (7.1.2)\n",
            "Requirement already satisfied, skipping upgrade: joblib in /usr/local/lib/python3.6/dist-packages (from sacremoses->transformers) (1.0.0)\n",
            "Requirement already satisfied, skipping upgrade: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->transformers) (2.10)\n",
            "Requirement already satisfied, skipping upgrade: 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, skipping upgrade: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->transformers) (2020.12.5)\n",
            "Requirement already satisfied, skipping upgrade: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->transformers) (3.0.4)\n",
            "Requirement already satisfied, skipping upgrade: 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=361cf5648055b96b7ae627c670d56597bece896bfcc57b24ee696abb973a8df2\n",
            "  Stored in directory: /root/.cache/pip/wheels/29/3c/fd/7ce5c3f0666dab31a50123635e6fb5e19ceb42ce38d4e58f45\n",
            "Successfully built sacremoses\n",
            "Installing collected packages: tokenizers, sacremoses, transformers\n",
            "Successfully installed sacremoses-0.0.43 tokenizers-0.9.4 transformers-4.1.1\n",
            "Collecting sentencepiece\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/e5/2d/6d4ca4bef9a67070fa1cac508606328329152b1df10bdf31fb6e4e727894/sentencepiece-0.1.94-cp36-cp36m-manylinux2014_x86_64.whl (1.1MB)\n",
            "\u001b[K     |████████████████████████████████| 1.1MB 13.5MB/s \n",
            "\u001b[?25hInstalling collected packages: sentencepiece\n",
            "Successfully installed sentencepiece-0.1.94\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 165,
          "referenced_widgets": [
            "0021de91fffd43fb936d451b26212553",
            "4ad915a3d80c45b2ba69d9ab1104b4a0",
            "448bd6fd874e41f58104c60241421f96",
            "827f47567713435dbdc5e823affb7bb1",
            "ac6f6ef3de784fdb9f1838ae8b632f44",
            "7df16df897724a7c97305b913c569f8f",
            "771afe0810dc45bc86961930cf7d991a",
            "58cc19b9de324af882a0eca089561e55",
            "7da39267b52948ce902ec211fa27e11b",
            "f0c0a284d6434df4850e43fc2d3915bd",
            "a960aef0ad6e458d9493e590b5b5c1be",
            "1ed094ceb6e34f02a27caa12a65ff11d",
            "6cd8dadf1bb442478094fcad8c1a855c",
            "d88ac234fa0044f39acb199ff68376df",
            "d58fb7e629ca460a8a908caecbad1405",
            "4de52ebf4ddd424eb8d28c41898b38a2",
            "353f7bfcecf649e3bf97a306ba21b607",
            "cec0b1af59304b3181d38179f01fc385",
            "091894c44095473f92adc83092ba7422",
            "0ce27752e6114124ad3de25954b01dde",
            "be174f1fee68459b9297de3cd7251d05",
            "d4738f64b2b54edaa4da929ab1291edc",
            "e610c05d88b44ea68d9183387530ddeb",
            "55e137b95c8e4a7ab8b403e87916f388"
          ]
        },
        "id": "jGtUoOtX5lfm",
        "outputId": "746cc0e3-7934-483c-ce15-b682e750368f"
      },
      "source": [
        "from transformers import PegasusTokenizer\n",
        "tokenizer = PegasusTokenizer.from_pretrained('google/pegasus-large')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0021de91fffd43fb936d451b26212553",
              "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": "7da39267b52948ce902ec211fa27e11b",
              "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": "353f7bfcecf649e3bf97a306ba21b607",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=88.0, style=ProgressStyle(description_w…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7SIB6HBpRAJW",
        "outputId": "6d95e3a8-ecf8-4157-9842-d6bf34177a41"
      },
      "source": [
        "from transformers import PegasusForConditionalGeneration, PegasusTokenizer\r\n",
        "import torch\r\n",
        "src_text = [\r\n",
        "\"\"\"The present invention relates to a method, system and UE for state transition. The method includes: after a transition from the CELL-PCH state to the CELL-FACH state, a UE receives the scheduling control channel continuously, or receives simultaneously the scheduling control channel according to the receiving time corresponding to the DRX mechanism when the UE is in the CELL-FACH state and the receiving time corresponding to the DRX mechanism when the UE is in the CELL-PCH state; after obtaining an indication of normal reception, the UE receives the scheduling control channel according to the receiving time corresponding to the DRX mechanism when the UE is in the CELL-FACH state; the radio network controller has be informed that the UE has transited from the CELL-PCH state to the CELL-FACH state simultaneously when the UE obtains the indication of normal reception, so as to avoid data loss in a certain period after the state transition of the UE.\"\"\"]\r\n",
        "model_name = 'google/pegasus-big_patent'\r\n",
        "torch_device = 'cuda' if torch.cuda.is_available() else 'cpu'\r\n",
        "tokenizer = PegasusTokenizer.from_pretrained(model_name)\r\n",
        "model = PegasusForConditionalGeneration.from_pretrained(model_name).to(torch_device)\r\n",
        "batch = tokenizer.prepare_seq2seq_batch(src_text, truncation=True, padding='longest', return_tensors=\"pt\").to(torch_device)\r\n",
        "translated = model.generate(**batch)\r\n",
        "tgt_text = tokenizer.batch_decode(translated, skip_special_tokens=True)\r\n",
        "print(tgt_text[0])"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Some weights of PegasusForConditionalGeneration were not initialized from the model checkpoint at google/pegasus-big_patent and are newly initialized: ['lm_head.weight']\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": "stream",
          "text": [
            "The present invention relates to a method, system and UE for state transition. The method includes: after a transition from the CDMA-CDMA state to the CDMA-CDMA state, a UE receives the scheduling control channel continuously, or receives simultaneously the scheduling control channel according to the receiving time corresponding to the NodeD mechanism when the UE is in the CDMA-CDMA state and the receiving time corresponding to the NodeD mechanism when the UE is in the CDMA-CDMA state.\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}