training.ipynb

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          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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        "id": "ynScY97qQZsM",
        "outputId": "1e13708e-4b9c-4c8d-b196-f98c6eefedab"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content/drive/My Drive/akinrobotics\n"
          ]
        }
      ],
      "source": [
        "%cd /content/drive/My Drive/akinrobotics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "qldj4CVVQZw5",
        "outputId": "37e5e148-33cb-4100-faa7-6ebceb585c72"
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      "outputs": [
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          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Package wordnet is already up-to-date!\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "import re # regular expression libary.\n",
        "import nltk # Natural Language toolkit\n",
        "nltk.download(\"stopwords\")  #downloading stopwords\n",
        "nltk.download('punkt')\n",
        "from nltk import word_tokenize,sent_tokenize\n",
        "nltk.download('wordnet')\n",
        "import nltk as nlp\n",
        "from nltk.corpus import stopwords\n",
        "\n",
        "import joblib\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from wordcloud import WordCloud\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import confusion_matrix,accuracy_score,classification_report,precision_score\n",
        "from xgboost import XGBClassifier\n",
        "from lightgbm import LGBMClassifier\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zossJflNQZzN"
      },
      "outputs": [],
      "source": [
        "data = pd.read_csv(\"7allV03.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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        "id": "-hOi1N3MTGra",
        "outputId": "33dd777e-7563-4c93-d939-f8f43618c4d0"
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      "outputs": [
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            "text/plain": [
              "        category                                               text\n",
              "0       siyaset    3 milyon ile ön seçim vaadi mhp nin 10 olağan...\n",
              "1       siyaset    mesut_yılmaz yüce_divan da ceza alabilirdi pr...\n",
              "2       siyaset    disko lar kaldırılıyor başbakan_yardımcısı ar...\n",
              "3       siyaset    sarıgül anayasa_mahkemesi ne gidiyor mustafa_...\n",
              "4       siyaset    erdoğan idamın bir haklılık sebebi var demek ...\n",
              "...          ...                                                ...\n",
              "4895  teknoloji    iphone lara geri dönüyor ios 6 sürümüyle tele...\n",
              "4896  teknoloji    muslukta devrim sadece elimizi yıkadığımız mu...\n",
              "4897  teknoloji    halka iyi anlatılmalı bilgi_teknolojileri ile...\n",
              "4898  teknoloji    çöpe gidiyorlar apple 775 bin uygulamayla app...\n",
              "4899  teknoloji    google bu kez edward_gorey dedi ! google bu k...\n",
              "\n",
              "[4900 rows x 2 columns]"
            ]
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          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
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      "source": [
        "data['category'] = data['category'].replace({'dunya': 0, 'ekonomi': 1, 'kultur': 2, 'saglik': 3, 'siyaset': 4, 'spor': 5, 'teknoloji': 6})"
      ]
    },
    {
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        "df_copy = data"
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      "metadata": {
        "id": "dtRJXPD-Qy9W"
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        "text_list=[]\n",
        "\n",
        "for text in df_copy.text:\n",
        "    text = text.lower()\n",
        "    text = re.sub(\"[^abcçdefgğhıijklmnoöprsştuüvyz]\",\" \",text)\n",
        "    text=nltk.word_tokenize(text)\n",
        "    text =[word for word in text if not word in set(stopwords.words(\"turkish\"))]\n",
        "    lemma=nlp.WordNetLemmatizer()\n",
        "    text=[lemma.lemmatize(word) for word in text]\n",
        "    text=\" \".join(text)\n",
        "    text_list.append(text)"
      ]
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              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4895</th>\n",
              "      <td>6</td>\n",
              "      <td>iphone lara geri dönüyor ios 6 sürümüyle tele...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4896</th>\n",
              "      <td>6</td>\n",
              "      <td>muslukta devrim sadece elimizi yıkadığımız mu...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4897</th>\n",
              "      <td>6</td>\n",
              "      <td>halka iyi anlatılmalı bilgi_teknolojileri ile...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4898</th>\n",
              "      <td>6</td>\n",
              "      <td>çöpe gidiyorlar apple 775 bin uygulamayla app...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4899</th>\n",
              "      <td>6</td>\n",
              "      <td>google bu kez edward_gorey dedi ! google bu k...</td>\n",
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              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
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              "</div>\n",
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            ],
            "text/plain": [
              "      category                                               text\n",
              "0            4   3 milyon ile ön seçim vaadi mhp nin 10 olağan...\n",
              "1            4   mesut_yılmaz yüce_divan da ceza alabilirdi pr...\n",
              "2            4   disko lar kaldırılıyor başbakan_yardımcısı ar...\n",
              "3            4   sarıgül anayasa_mahkemesi ne gidiyor mustafa_...\n",
              "4            4   erdoğan idamın bir haklılık sebebi var demek ...\n",
              "...        ...                                                ...\n",
              "4895         6   iphone lara geri dönüyor ios 6 sürümüyle tele...\n",
              "4896         6   muslukta devrim sadece elimizi yıkadığımız mu...\n",
              "4897         6   halka iyi anlatılmalı bilgi_teknolojileri ile...\n",
              "4898         6   çöpe gidiyorlar apple 775 bin uygulamayla app...\n",
              "4899         6   google bu kez edward_gorey dedi ! google bu k...\n",
              "\n",
              "[4900 rows x 2 columns]"
            ]
          },
          "execution_count": 63,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_copy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "t-2A_sWlQ0yn"
      },
      "outputs": [],
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "\n",
        "max_features=500\n",
        "\n",
        "count_vectorizer=CountVectorizer(max_features=max_features)\n",
        "\n",
        "sparce_matrix=count_vectorizer.fit_transform(text_list).toarray()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-e3WG-iEQ03O",
        "outputId": "ee88c2cb-b47a-4a1a-d855-81fd3ed0ec0e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(4900, 500)"
            ]
          },
          "execution_count": 65,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sparce_matrix.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D3tULGQZQ05b",
        "outputId": "e9c0750c-1c6b-41c5-9dd9-95a9def065e7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n",
              "       [1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n",
              "       [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],\n",
              "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
              "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
              "       [0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n",
              "       [0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
              "       [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
              "       [1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
              "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0]])"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sparce_matrix[0:10,0:20]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "g6LKuwY3Q0-C"
      },
      "outputs": [],
      "source": [
        "from sklearn import preprocessing\n",
        "le = preprocessing.LabelEncoder()\n",
        "\n",
        "labels=le.fit_transform(df_copy.category)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ugu5K0C_eDBF"
      },
      "outputs": [],
      "source": [
        "X = sparce_matrix\n",
        "y = labels"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PHy164VQQ1AR"
      },
      "outputs": [],
      "source": [
        "data=pd.DataFrame(count_vectorizer.get_feature_names_out(),columns=[\"Words\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "YZCEuL8-cz-3",
        "outputId": "309d0145-99c0-465f-e21a-c0b0f0563b85"
      },
      "outputs": [
        {
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              "        document.querySelector('#df-2b00a438-e31a-424b-9dc7-34948e04b4fa button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-2b00a438-e31a-424b-9dc7-34948e04b4fa');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-f59921d3-8c59-40f7-819d-d639da95f687\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f59921d3-8c59-40f7-819d-d639da95f687')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f59921d3-8c59-40f7-819d-d639da95f687 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "      category                                               text\n",
              "0            4   3 milyon ile ön seçim vaadi mhp nin 10 olağan...\n",
              "1            4   mesut_yılmaz yüce_divan da ceza alabilirdi pr...\n",
              "2            4   disko lar kaldırılıyor başbakan_yardımcısı ar...\n",
              "3            4   sarıgül anayasa_mahkemesi ne gidiyor mustafa_...\n",
              "4            4   erdoğan idamın bir haklılık sebebi var demek ...\n",
              "...        ...                                                ...\n",
              "4895         6   iphone lara geri dönüyor ios 6 sürümüyle tele...\n",
              "4896         6   muslukta devrim sadece elimizi yıkadığımız mu...\n",
              "4897         6   halka iyi anlatılmalı bilgi_teknolojileri ile...\n",
              "4898         6   çöpe gidiyorlar apple 775 bin uygulamayla app...\n",
              "4899         6   google bu kez edward_gorey dedi ! google bu k...\n",
              "\n",
              "[4900 rows x 2 columns]"
            ]
          },
          "execution_count": 49,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_copy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0JGjdh74RC5F",
        "outputId": "34d30434-d245-409d-fd56-af907a81d430"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "x_train (3920, 500)\n",
            "x_test (980, 500)\n",
            "y_train (3920,)\n",
            "y_test (980,)\n"
          ]
        }
      ],
      "source": [
        "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=42)\n",
        "print(\"x_train\",X_train.shape)\n",
        "print(\"x_test\",X_test.shape)\n",
        "print(\"y_train\",y_train.shape)\n",
        "print(\"y_test\",y_test.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LNvuK5slUPIv",
        "outputId": "08451d57-8996-4188-a14a-66c79a33b36c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Doğruluk Skoru: 0.8295918367346938\n"
          ]
        }
      ],
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "\n",
        "\n",
        "model = LogisticRegression()\n",
        "\n",
        "\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "\n",
        "y_pred = model.predict(X_test)\n",
        "joblib.dump(model, 'logistic_regression.joblib')\n",
        "\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(\"Doğruluk Skoru:\", accuracy)\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zmS_MDLPd1Xo"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}