<commit_before>{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5wFF5JFyD2Ki"
      },
      "source": [
        "#### Copyright 2019 The TensorFlow Hub Authors.\n",
        "\n",
        "Licensed under the Apache License, Version 2.0 (the \"License\");"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Uf6NouXxDqGk"
      },
      "outputs": [],
      "source": [
        "# Copyright 2019 The TensorFlow Hub Authors. All Rights Reserved.\n",
        "#\n",
        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "#     http://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License.\n",
        "# =============================================================================="
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ORy-KvWXGXBo"
      },
      "source": [
        "# TF-Hub CORD-19 Swivel 임베딩 살펴보기\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MfBg1C5NB3X0"
      },
      "source": [
        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
        "  <td><a target=\"_blank\" href=\"https://www.tensorflow.org/hub/tutorials/cord_19_embeddings_keras\"><img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\">View on TensorFlow.org</a></td>\n",
        "  <td><a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/hub/blob/master/examples/colab/cord_19_embeddings_keras.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\">Run in Google Colab</a></td>\n",
        "  <td><a target=\"_blank\" href=\"https://github.com/tensorflow/hub/blob/master/examples/colab/cord_19_embeddings_keras.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\">View source on GitHub</a></td>\n",
        "  <td><a href=\"https://storage.googleapis.com/tensorflow_docs/hub/examples/colab/cord_19_embeddings_keras.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\">Download notebook</a></td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yI6Mh3-P0_Pk"
      },
      "source": [
        "TF-Hub(https://tfhub.dev/tensorflow/cord-19/swivel-128d/3)의 CORD-19 Swivel 텍스트 임베딩 모듈은 연구원들이 코로나바이러스감염증-19와 관련된 자연어 텍스트를 분석할 수 있도록 빌드되었습니다. 이러한 임베딩은 [CORD-19 데이터세트](https://pages.semanticscholar.org/coronavirus-research)에 있는 기사의 제목, 저자, 요약문, 본문 텍스트 및 참조 제목에 대해 훈련되었습니다.\n",
        "\n",
        "이 colab에서는 다음을 수행합니다.\n",
        "\n",
        "- 임베딩 공간에서 의미론적으로 유사한 단어를 분석합니다.\n",
        "- CORD-19 임베딩을 사용하여 SciCite 데이터세트에서 분류자를 훈련합니다.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gVWOrccw0_Pl"
      },
      "source": [
        "## Setup\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ym2nXOPuPV__"
      },
      "outputs": [],
      "source": [
        "import functools\n",
        "import itertools\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "\n",
        "import tensorflow as tf\n",
        "\n",
        "import tensorflow_datasets as tfds\n",
        "import tensorflow_hub as hub\n",
        "\n",<commit_msg>Translate cord_19_embeddings_keras.ipynb via GitLocalize<commit_after>{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5wFF5JFyD2Ki"
      },
      "source": [
        "#### Copyright 2019 The TensorFlow Hub Authors.\n",
        "\n",
        "Licensed under the Apache License, Version 2.0 (the \"License\");"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Uf6NouXxDqGk"
      },
      "outputs": [],
      "source": [
        "# Copyright 2019 The TensorFlow Hub Authors. All Rights Reserved.\n",
        "#\n",
        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "#     http://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License.\n",
        "# =============================================================================="
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ORy-KvWXGXBo"
      },
      "source": [
        "# TF-Hub CORD-19 Swivel 임베딩 살펴보기\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MfBg1C5NB3X0"
      },
      "source": [
        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
        "  <td><a target=\"_blank\" href=\"https://www.tensorflow.org/hub/tutorials/cord_19_embeddings_keras\"><img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\">View on TensorFlow.org</a></td>\n",
        "  <td><a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/hub/blob/master/examples/colab/cord_19_embeddings_keras.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\">Run in Google Colab</a></td>\n",
        "  <td><a target=\"_blank\" href=\"https://github.com/tensorflow/hub/blob/master/examples/colab/cord_19_embeddings_keras.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\">View source on GitHub</a></td>\n",
        "  <td><a href=\"https://storage.googleapis.com/tensorflow_docs/hub/examples/colab/cord_19_embeddings_keras.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\">Download notebook</a></td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yI6Mh3-P0_Pk"
      },
      "source": [
        "TF-Hub(https://tfhub.dev/tensorflow/cord-19/swivel-128d/3)의 CORD-19 Swivel 텍스트 임베딩 모듈은 연구원들이 코로나바이러스감염증-19와 관련된 자연어 텍스트를 분석할 수 있도록 빌드되었습니다. 이러한 임베딩은 [CORD-19 데이터세트](https://pages.semanticscholar.org/coronavirus-research)에 있는 기사의 제목, 저자, 요약문, 본문 텍스트 및 참조 제목에 대해 훈련되었습니다.\n",
        "\n",
        "이 colab에서는 다음을 수행합니다.\n",
        "\n",
        "- 임베딩 공간에서 의미론적으로 유사한 단어를 분석합니다.\n",
        "- CORD-19 임베딩을 사용하여 SciCite 데이터세트에서 분류자를 훈련합니다.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gVWOrccw0_Pl"
      },
      "source": [
        "## 설정\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ym2nXOPuPV__"
      },
      "outputs": [],
      "source": [
        "import functools\n",
        "import itertools\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "\n",
        "import tensorflow as tf\n",
        "\n",
        "import tensorflow_datasets as tfds\n",
        "import tensorflow_hub as hub\n",
        "\n",
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