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      "source": [
        "# Connect to Google Drive"
      ],
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      }
    },
    {
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      "execution_count": 1,
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "# Connect to google drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Import Libraries"
      ],
      "metadata": {
        "id": "FfT_Yae-X1DB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import torch\n",
        "from transformers import DistilBertTokenizer, DistilBertForSequenceClassification\n",
        "from sklearn.model_selection import train_test_split\n",
        "from torch.utils.data import DataLoader, TensorDataset\n",
        "from torch.optim import AdamW\n",
        "from tqdm import tqdm"
      ],
      "metadata": {
        "id": "wiAkqufRX0fH"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Read csv\n",
        "elon_tweets = pd.read_csv('/content/drive/MyDrive/elon_musk_tweets.csv')\n",
        "non_elon_tweets = pd.read_csv('/content/drive/MyDrive/Tweets.csv')\n",
        "\n",
        "elon_tweets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 843
        },
        "id": "VE8dG16AYAbp",
        "outputId": "ad34bdea-13fd-4717-a9e2-1487aeb8bcc6"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "                       id  user_name user_location           user_description  \\\n",
              "0     1544379368478212100  Elon Musk           NaN  Mars & Cars, Chips & Dips   \n",
              "1     1544377493263720450  Elon Musk           NaN  Mars & Cars, Chips & Dips   \n",
              "2     1544377130590552064  Elon Musk           NaN  Mars & Cars, Chips & Dips   \n",
              "3     1544375575724400645  Elon Musk           NaN  Mars & Cars, Chips & Dips   \n",
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              "5899  1665143503108677634  Elon Musk           NaN                        NaN   \n",
              "5900  1665139144425631747  Elon Musk           NaN                        NaN   \n",
              "5901  1665137204782419968  Elon Musk           NaN                        NaN   \n",
              "5902  1665131126900285445  Elon Musk           NaN                        NaN   \n",
              "5903  1665121551652474880  Elon Musk           NaN                        NaN   \n",
              "\n",
              "                   user_created  user_followers  user_friends  \\\n",
              "0     2009-06-02 20:12:29+00:00       101240855           115   \n",
              "1     2009-06-02 20:12:29+00:00       101240806           115   \n",
              "2     2009-06-02 20:12:29+00:00       101240806           115   \n",
              "3     2009-06-02 20:12:29+00:00       101240806           115   \n",
              "4     2009-06-02 20:12:29+00:00       101240806           115   \n",
              "...                         ...             ...           ...   \n",
              "5899  2009-06-02 20:12:29+00:00       143325985           330   \n",
              "5900  2009-06-02 20:12:29+00:00       143325985           330   \n",
              "5901  2009-06-02 20:12:29+00:00       143325985           330   \n",
              "5902  2009-06-02 20:12:29+00:00       143325985           330   \n",
              "5903  2009-06-02 20:12:29+00:00       143325985           330   \n",
              "\n",
              "      user_favourites  user_verified                       date  \\\n",
              "0               13503           True  2022-07-05 17:55:09+00:00   \n",
              "1               13503           True  2022-07-05 17:47:42+00:00   \n",
              "2               13503           True  2022-07-05 17:46:15+00:00   \n",
              "3               13503           True  2022-07-05 17:40:05+00:00   \n",
              "4               13503           True  2022-07-05 17:38:23+00:00   \n",
              "...               ...            ...                        ...   \n",
              "5899            25655          False  2023-06-03 23:48:42+00:00   \n",
              "5900            25655          False  2023-06-03 23:31:23+00:00   \n",
              "5901            25655          False  2023-06-03 23:23:41+00:00   \n",
              "5902            25655          False  2023-06-03 22:59:31+00:00   \n",
              "5903            25655          False  2023-06-03 22:21:29+00:00   \n",
              "\n",
              "                                                   text hashtags  \\\n",
              "0     @BillyM2k I find the gold toe sock – inevitabl...      NaN   \n",
              "1                    Sock Con, the conference for socks      NaN   \n",
              "2     Always something new for the magazine cover an...      NaN   \n",
              "3                      @ExplainThisBob This guy gets it      NaN   \n",
              "4     Sock tech is so advanced that you can get pret...      NaN   \n",
              "...                                                 ...      ...   \n",
              "5899                     @JonErlichman He’s not wrong …      NaN   \n",
              "5900  @alifarhat79 Guys, I think I maybe took too mu...      NaN   \n",
              "5901                                      @sriramk Cool      NaN   \n",
              "5902               @cb_doge Time to complete the circle      NaN   \n",
              "5903         @Jason Late stage civilization complacency      NaN   \n",
              "\n",
              "                  source  retweets  favorites  is_retweet  \n",
              "0     Twitter for iPhone       335       6542       False  \n",
              "1     Twitter for iPhone      1451      30753       False  \n",
              "2     Twitter for iPhone      1284      28610       False  \n",
              "3     Twitter for iPhone       131       3640       False  \n",
              "4     Twitter for iPhone      1191      23790       False  \n",
              "...                  ...       ...        ...         ...  \n",
              "5899  Twitter for iPhone       361       4791       False  \n",
              "5900  Twitter for iPhone      1609      61964       False  \n",
              "5901  Twitter for iPhone        46        879       False  \n",
              "5902  Twitter for iPhone       898      12467       False  \n",
              "5903  Twitter for iPhone      1997      38113       False  \n",
              "\n",
              "[5904 rows x 16 columns]"
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              "      <td>2009-06-02 20:12:29+00:00</td>\n",
              "      <td>101240806</td>\n",
              "      <td>115</td>\n",
              "      <td>13503</td>\n",
              "      <td>True</td>\n",
              "      <td>2022-07-05 17:38:23+00:00</td>\n",
              "      <td>Sock tech is so advanced that you can get pret...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Twitter for iPhone</td>\n",
              "      <td>1191</td>\n",
              "      <td>23790</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5899</th>\n",
              "      <td>1665143503108677634</td>\n",
              "      <td>Elon Musk</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2009-06-02 20:12:29+00:00</td>\n",
              "      <td>143325985</td>\n",
              "      <td>330</td>\n",
              "      <td>25655</td>\n",
              "      <td>False</td>\n",
              "      <td>2023-06-03 23:48:42+00:00</td>\n",
              "      <td>@JonErlichman He’s not wrong …</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Twitter for iPhone</td>\n",
              "      <td>361</td>\n",
              "      <td>4791</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5900</th>\n",
              "      <td>1665139144425631747</td>\n",
              "      <td>Elon Musk</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2009-06-02 20:12:29+00:00</td>\n",
              "      <td>143325985</td>\n",
              "      <td>330</td>\n",
              "      <td>25655</td>\n",
              "      <td>False</td>\n",
              "      <td>2023-06-03 23:31:23+00:00</td>\n",
              "      <td>@alifarhat79 Guys, I think I maybe took too mu...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Twitter for iPhone</td>\n",
              "      <td>1609</td>\n",
              "      <td>61964</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5901</th>\n",
              "      <td>1665137204782419968</td>\n",
              "      <td>Elon Musk</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2009-06-02 20:12:29+00:00</td>\n",
              "      <td>143325985</td>\n",
              "      <td>330</td>\n",
              "      <td>25655</td>\n",
              "      <td>False</td>\n",
              "      <td>2023-06-03 23:23:41+00:00</td>\n",
              "      <td>@sriramk Cool</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Twitter for iPhone</td>\n",
              "      <td>46</td>\n",
              "      <td>879</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5902</th>\n",
              "      <td>1665131126900285445</td>\n",
              "      <td>Elon Musk</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2009-06-02 20:12:29+00:00</td>\n",
              "      <td>143325985</td>\n",
              "      <td>330</td>\n",
              "      <td>25655</td>\n",
              "      <td>False</td>\n",
              "      <td>2023-06-03 22:59:31+00:00</td>\n",
              "      <td>@cb_doge Time to complete the circle</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Twitter for iPhone</td>\n",
              "      <td>898</td>\n",
              "      <td>12467</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5903</th>\n",
              "      <td>1665121551652474880</td>\n",
              "      <td>Elon Musk</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2009-06-02 20:12:29+00:00</td>\n",
              "      <td>143325985</td>\n",
              "      <td>330</td>\n",
              "      <td>25655</td>\n",
              "      <td>False</td>\n",
              "      <td>2023-06-03 22:21:29+00:00</td>\n",
              "      <td>@Jason Late stage civilization complacency</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Twitter for iPhone</td>\n",
              "      <td>1997</td>\n",
              "      <td>38113</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5904 rows × 16 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5455a4d6-ddbb-4edc-b901-6b4b7ada6208')\"\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "\n",
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              "    20% {\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "\n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('elon_tweets')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "elon_tweets",
              "summary": "{\n  \"name\": \"elon_tweets\",\n  \"rows\": 5904,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 32598337931898456,\n        \"min\": 1544316752657629189,\n        \"max\": 1668435272235720705,\n        \"num_unique_values\": 5904,\n        \"samples\": [\n          1661525947022180352,\n          1649039669190098947,\n          1607850458554449920\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"user_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Mr. Tweet\",\n          \"Elon Musk\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"user_location\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Boring\",\n          \"Twitter HQ\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"user_description\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Mars & Cars, Chips & Dips\",\n          \"Perfume Salesman\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"user_created\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"2009-06-02 20:12:29+00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"user_followers\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11862039,\n        \"min\": 101240806,\n        \"max\": 143325990,\n        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            }
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "# Drop off all columns except text from elon musk tweets. Delete row if retweet\n",
        "\n",
        "elon_tweets = elon_tweets[elon_tweets['is_retweet'] == False]\n",
        "elon_tweets = elon_tweets[['text']]\n",
        "\n",
        "elon_tweets"
      ],
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      "execution_count": 6,
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              "                                                   text\n",
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              "3                      @ExplainThisBob This guy gets it\n",
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              "...                                                 ...\n",
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              "5901                                      @sriramk Cool\n",
              "5902               @cb_doge Time to complete the circle\n",
              "5903         @Jason Late stage civilization complacency\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "elon_tweets",
              "summary": "{\n  \"name\": \"elon_tweets\",\n  \"rows\": 5904,\n  \"fields\": [\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5831,\n        \"samples\": [\n          \"The BBC interview last week was exceptional in illustrating why you cannot rely on the media for truth\",\n          \"@Teslaconomics Welcome back @jbstraubel!\",\n          \"@CorySteuben @Erdayastronaut @live_munro Interesting\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
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          "metadata": {},
          "execution_count": 6
        }
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    },
    {
      "cell_type": "code",
      "source": [
        "non_elon_tweets = non_elon_tweets[['text']]\n",
        "non_elon_tweets"
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      "metadata": {
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        "id": "aqnd6NMQYqbd",
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      "execution_count": 8,
      "outputs": [
        {
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              "                                                    text\n",
              "0                    I`d have responded, if I were going\n",
              "1          Sooo SAD I will miss you here in San Diego!!!\n",
              "2                              my boss is bullying me...\n",
              "3                         what interview! leave me alone\n",
              "4       Sons of ****, why couldn`t they put them on t...\n",
              "...                                                  ...\n",
              "27476   wish we could come see u on Denver  husband l...\n",
              "27477   I`ve wondered about rake to.  The client has ...\n",
              "27478   Yay good for both of you. Enjoy the break - y...\n",
              "27479                         But it was worth it  ****.\n",
              "27480     All this flirting going on - The ATG smiles...\n",
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              "      <th>0</th>\n",
              "      <td>I`d have responded, if I were going</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Sooo SAD I will miss you here in San Diego!!!</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>my boss is bullying me...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>what interview! leave me alone</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Sons of ****, why couldn`t they put them on t...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27476</th>\n",
              "      <td>wish we could come see u on Denver  husband l...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27477</th>\n",
              "      <td>I`ve wondered about rake to.  The client has ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27478</th>\n",
              "      <td>Yay good for both of you. Enjoy the break - y...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27479</th>\n",
              "      <td>But it was worth it  ****.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27480</th>\n",
              "      <td>All this flirting going on - The ATG smiles...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>27481 rows × 1 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f4021e34-82f7-4650-9bb2-6017378bd971')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
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              "        document.querySelector('#df-f4021e34-82f7-4650-9bb2-6017378bd971 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-f4021e34-82f7-4650-9bb2-6017378bd971');\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",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    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",
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              "    }\n",
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              "      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",
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              "        document.querySelector('#df-c4c86231-6605-4f75-ae73-99b670b0a687 button');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_03e8430c-6c80-42b3-9b67-9855963f7c32\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
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              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
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              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
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              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('non_elon_tweets')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "        document.querySelector('#id_03e8430c-6c80-42b3-9b67-9855963f7c32 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('non_elon_tweets');\n",
              "      }\n",
              "      })();\n",
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              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "non_elon_tweets",
              "summary": "{\n  \"name\": \"non_elon_tweets\",\n  \"rows\": 27481,\n  \"fields\": [\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 27480,\n        \"samples\": [\n          \" Enjoy! Family trumps everything\",\n          \" --of them kinda turns me off of it all.  And then I buy more of them and dig a deeper hole, etc. ;;\",\n          \"Clive it`s my birthday pat me  http://apps.facebook.com/dogbook/profile/view/6386106\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def load_and_preprocess_data(elon_file, non_elon_file):\n",
        "    elon_tweets = pd.read_csv(elon_file)\n",
        "    non_elon_tweets = pd.read_csv(non_elon_file)\n",
        "    non_elon_tweets = non_elon_tweets[['text']]\n",
        "    elon_tweets = elon_tweets[elon_tweets['is_retweet'] == False]\n",
        "    elon_tweets = elon_tweets[['text']]\n",
        "    # 'text' 列が存在することを確認し、存在しない場合は適切な列名に変更\n",
        "    text_column = 'text' if 'text' in elon_tweets.columns else elon_tweets.columns[0]\n",
        "\n",
        "    elon_tweets['label'] = 1\n",
        "    non_elon_tweets['label'] = 0\n",
        "\n",
        "    all_tweets = pd.concat([elon_tweets, non_elon_tweets], ignore_index=True)\n",
        "\n",
        "    # None値や空の文字列を除去\n",
        "    all_tweets = all_tweets.dropna(subset=[text_column])\n",
        "    all_tweets = all_tweets[all_tweets[text_column].astype(bool)]\n",
        "\n",
        "    # テキストを文字列に変換\n",
        "    texts = all_tweets[text_column].astype(str).tolist()\n",
        "    labels = all_tweets['label'].tolist()\n",
        "\n",
        "    return train_test_split(texts, labels, test_size=0.2, random_state=42)\n",
        "\n",
        "# データの読み込みと分割\n",
        "train_texts, test_texts, train_labels, test_labels = load_and_preprocess_data('/content/drive/MyDrive/elon_musk_tweets.csv', '/content/drive/MyDrive/Tweets.csv')"
      ],
      "metadata": {
        "id": "py1xhu7GYx8Y"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')\n",
        "model = DistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased', num_labels=2)\n",
        "\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "model.to(device)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n8B3YGtcZKno",
        "outputId": "ca1d834c-0364-4a9e-93da-e9d93667875e"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight', 'pre_classifier.bias', 'pre_classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DistilBertForSequenceClassification(\n",
              "  (distilbert): DistilBertModel(\n",
              "    (embeddings): Embeddings(\n",
              "      (word_embeddings): Embedding(30522, 768, padding_idx=0)\n",
              "      (position_embeddings): Embedding(512, 768)\n",
              "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "      (dropout): Dropout(p=0.1, inplace=False)\n",
              "    )\n",
              "    (transformer): Transformer(\n",
              "      (layer): ModuleList(\n",
              "        (0-5): 6 x TransformerBlock(\n",
              "          (attention): MultiHeadSelfAttention(\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "            (q_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (k_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (v_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "            (out_lin): Linear(in_features=768, out_features=768, bias=True)\n",
              "          )\n",
              "          (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "          (ffn): FFN(\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "            (lin1): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (lin2): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (activation): GELUActivation()\n",
              "          )\n",
              "          (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (pre_classifier): Linear(in_features=768, out_features=768, bias=True)\n",
              "  (classifier): Linear(in_features=768, out_features=2, bias=True)\n",
              "  (dropout): Dropout(p=0.2, inplace=False)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def preprocess_data(texts, labels):\n",
        "    encodings = tokenizer(texts, truncation=True, padding=True, max_length=128, return_tensors='pt')\n",
        "    dataset = TensorDataset(encodings['input_ids'], encodings['attention_mask'], torch.tensor(labels))\n",
        "    return dataset\n",
        "\n",
        "train_dataset = preprocess_data(train_texts, train_labels)\n",
        "test_dataset = preprocess_data(test_texts, test_labels)\n",
        "\n",
        "train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True)\n",
        "test_loader = DataLoader(test_dataset, batch_size=16)"
      ],
      "metadata": {
        "id": "HMnetkPMZZyN"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "optimizer = AdamW(model.parameters(), lr=5e-5)\n",
        "num_epochs = 3\n",
        "\n",
        "for epoch in range(num_epochs):\n",
        "    model.train()\n",
        "    total_loss = 0\n",
        "    for batch in tqdm(train_loader, desc=f'Epoch {epoch+1}/{num_epochs}'):\n",
        "        input_ids, attention_mask, labels = [b.to(device) for b in batch]\n",
        "        outputs = model(input_ids, attention_mask=attention_mask, labels=labels)\n",
        "        loss = outputs.loss\n",
        "        total_loss += loss.item()\n",
        "\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "    avg_loss = total_loss / len(train_loader)\n",
        "    print(f'Epoch {epoch+1}/{num_epochs} completed. Average loss: {avg_loss:.4f}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YIJem2_uac2p",
        "outputId": "3e15ed02-e42e-4a56-9e55-20438ae8645d"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch 1/3: 100%|██████████| 1670/1670 [04:16<00:00,  6.50it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/3 completed. Average loss: 0.0444\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch 2/3: 100%|██████████| 1670/1670 [04:15<00:00,  6.55it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 2/3 completed. Average loss: 0.0157\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch 3/3: 100%|██████████| 1670/1670 [04:15<00:00,  6.54it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 3/3 completed. Average loss: 0.0087\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.eval()\n",
        "correct = 0\n",
        "total = 0\n",
        "\n",
        "with torch.no_grad():\n",
        "    for batch in tqdm(test_loader, desc='Evaluating'):\n",
        "        input_ids, attention_mask, labels = [b.to(device) for b in batch]\n",
        "        outputs = model(input_ids, attention_mask=attention_mask)\n",
        "        _, predicted = torch.max(outputs.logits, 1)\n",
        "        total += labels.size(0)\n",
        "        correct += (predicted == labels).sum().item()\n",
        "\n",
        "accuracy = correct / total\n",
        "print(f'Test Accuracy: {accuracy:.2f}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YO88Wy9Uaicq",
        "outputId": "6ca5812d-fa21-47ac-f7f3-713f481c7be9"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Evaluating: 100%|██████████| 418/418 [00:17<00:00, 23.91it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Accuracy: 0.99\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def classify_tweet(text):\n",
        "    inputs = tokenizer(text, return_tensors='pt', truncation=True, padding=True, max_length=128).to(device)\n",
        "    with torch.no_grad():\n",
        "        outputs = model(**inputs)\n",
        "    probabilities = torch.softmax(outputs.logits, dim=1)\n",
        "    prediction = torch.argmax(probabilities, dim=1).item()\n",
        "    return \"Elon Musk\" if prediction == 1 else \"Not Elon Musk\"\n",
        "\n",
        "# 使用例\n",
        "new_tweet = \"I'm Elon\"\n",
        "result = classify_tweet(new_tweet)\n",
        "print(f\"The tweet '{new_tweet}' is classified as: {result}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UPWW-shsal2V",
        "outputId": "82ec7abb-d896-4601-8461-884a8fdb3fb9"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The tweet 'I'm Elon' is classified as: Not Elon Musk\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.save_pretrained('/content/drive/MyDrive/EMD')"
      ],
      "metadata": {
        "id": "_ZjJOIj8caI2"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer.save_pretrained('/content/drive/MyDrive/EMD')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UUxoItUce2VW",
        "outputId": "ce7bea54-090e-4888-dbc3-df47451ab21e"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('/content/drive/MyDrive/EMD/tokenizer_config.json',\n",
              " '/content/drive/MyDrive/EMD/special_tokens_map.json',\n",
              " '/content/drive/MyDrive/EMD/vocab.txt',\n",
              " '/content/drive/MyDrive/EMD/added_tokens.json')"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone https://huggingface.co/kix-intl/elon-musk-detector.git"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6jaCMh3mfLF4",
        "outputId": "ad1c6fa9-35dd-4ad4-e60e-d62d4833651d"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'elon-musk-detector'...\n",
            "fatal: could not read Username for 'https://huggingface.co': No such device or address\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "ed08792b62e14889b92ce01d10520ed4",
            "c0e08f3c449948e4971c9dc4934840c2",
            "b7ea807d74d841368a512deadbaeccb3",
            "632ed488a1a04fc2afe287fa5275c87a",
            "fe8e2d9c438d4d45bf5039db91b3bd33",
            "d148fb7b4d4b4571804e8e290fad547c",
            "141398f982974bbb85db2a555d4d007e",
            "8067924b93a049c3a33c2f196751d572",
            "b04ed66f9a4f41f3a2a07de004e8f4d9",
            "55e9bba010344ca4beb985df6e19fa0f",
            "d6b83cedf72b4b6f8064b99341f67a24",
            "504ebe991a2744129fe505e11eda37b4",
            "b558200eabf1452da063f6fd765407fb",
            "0ab6065d9f2b45879b71bfdd49a7b839",
            "048a8ad112794f628dfacaa6afc3392b",
            "9db360e78485441aaa8e1ded2e68dedd",
            "11ffd14bba034f50867e369fbf5daef1",
            "ff52a5a13235408a829a3d1f8774e3a6",
            "a40b0ea231da481099657870d5eee2c1",
            "3d0824795c76430285086b909b3f5338",
            "7beaed2d230d42e79106b3181d7774b1",
            "43dc5b885de04d70a6fb2ba162d1343b",
            "776ffb2a9e5644af8dfdea7d16f4ba2b",
            "f6a65b6db69246e389284d920ae95b53",
            "8d540e71071a48c09aa9649926409f7a",
            "7a70671da3b94fa2a5184a4f871ffca5",
            "7007fae84ff045bf907324783e43c76a",
            "d433c5972dd14c618cdb3c0f34389475",
            "f543f1f979ee4ae49ce089e338c75279",
            "738a1bcb6b3b41b8bc1f71733f4b791d",
            "eed4bf33ecb5450eb1b29b1b629b39db",
            "a85822ccb1c949e683e900b966025ad3"
          ]
        },
        "id": "8msqvr3RfqAN",
        "outputId": "ac700ed0-354f-4f33-dd32-1254a372ba2f"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ed08792b62e14889b92ce01d10520ed4"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone https://huggingface.co/kix-intl/elon-musk-detector.git"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bF4KlAUPf5cr",
        "outputId": "8cb2ca81-202b-4ba7-acda-c54712670109"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'elon-musk-detector'...\n",
            "remote: Enumerating objects: 3, done.\u001b[K\n",
            "remote: Total 3 (delta 0), reused 0 (delta 0), pack-reused 3 (from 1)\u001b[K\n",
            "Unpacking objects: 100% (3/3), 1.05 KiB | 1.05 MiB/s, done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd /content/elon-musk-detector"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YM1H1HBhgZAF",
        "outputId": "e04e4fa6-073f-48ab-b1cd-7fa15379d816"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/elon-musk-detector\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!git lfs install"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o7RwgBVNgceP",
        "outputId": "b9e3e396-3521-45ba-85c5-0793b354a7f7"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Updated git hooks.\n",
            "Git LFS initialized.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!huggingface-cli lfs-enable-largefiles ."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UaeOOTTSgg5C",
        "outputId": "aec2819e-634d-4232-b918-af718ed782f2"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Local repo set up for largefiles\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!mv /content/drive/MyDrive/EMD /content/elon-musk-detector/"
      ],
      "metadata": {
        "id": "qDKtmASagk3-"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!git config --global user.email \"koko8.dev@gmail.com\"\n",
        "!git config --global user.name \"kix-intl\""
      ],
      "metadata": {
        "id": "ALH3ER_vg43A"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!git add .\n",
        "!git commit -m \"add model\"\n",
        "!git push"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MmFAGauIgw8-",
        "outputId": "f5fee367-2c2d-4b9d-d037-7a43eb11da9a"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[main 3570286] add model\n",
            " 5 files changed, 30614 insertions(+)\n",
            " create mode 100644 config.json\n",
            " create mode 100644 model.safetensors\n",
            " create mode 100644 special_tokens_map.json\n",
            " create mode 100644 tokenizer_config.json\n",
            " create mode 100644 vocab.txt\n",
            "Uploading LFS objects: 100% (1/1), 272 MB | 11 MB/s, done.\n",
            "Enumerating objects: 8, done.\n",
            "Counting objects: 100% (8/8), done.\n",
            "Delta compression using up to 2 threads\n",
            "Compressing objects: 100% (7/7), done.\n",
            "Writing objects: 100% (7/7), 109.34 KiB | 5.47 MiB/s, done.\n",
            "Total 7 (delta 0), reused 0 (delta 0), pack-reused 0\n",
            "To https://huggingface.co/kix-intl/elon-musk-detector.git\n",
            "   c285fdd..3570286  main -> main\n"
          ]
        }
      ]
    }
  ]
}