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        "<a href=\"https://colab.research.google.com/github/eskblog/troll-bot/blob/main/trolls_tweet_generator.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
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        "!pip install -Uqq fastbook\n",
        "import fastbook\n",
        "#fastbook.setup_book()\n",
        "!pip install transformers\n",
        "import torch\n",
        "import transformers\n",
        "from transformers import pipeline\n",
        "\n",
        "!pip install sklearn"
      ],
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      "metadata": {
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        "\n",
        "\n",
        "# Import tweets and build training and test sets\n",
        "\n",
        "For this to work, download this zip file: https://www.kaggle.com/fivethirtyeight/russian-troll-tweets\n",
        "\n",
        "Next, extract it to a Google Drive directory and, mounting it on Google Colab paste the target directory address in the FOLDER_PATH below:\n",
        "\n",
        "(Thanks to the following article for guidance on re-training the GPT-2 model on new text: https://towardsdatascience.com/fine-tune-a-non-english-gpt-2-model-with-huggingface-9acc2dc7635b)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b6gwU6mzwq_N"
      },
      "source": [
        "import re\n",
        "from sklearn.model_selection import train_test_split\n",
        "import pandas as pd"
      ],
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 717
        },
        "id": "_tWSLGityZWz",
        "outputId": "a1112b1f-06c6-4328-f466-d5a96e09e594"
      },
      "source": [
        "FOLDER_PATH = \"/content/drive/MyDrive/Colab Notebooks/Personal projects/Trolls/Data\" #to be replaced by user with correct path where the CSV files are\n",
        "CSV_FILENAME = \"IRAhandle_tweets_1.csv\" #to save time on training, we use only the first of a number of CSV files. It has enough tweets to be useful\n",
        "\n",
        "MODEL_PATH = \"/content/drive/MyDrive/Colab Notebooks/Personal projects/Trolls/trolls-gpt2-model\" #this is where the GPT-2 model will be saved and loaded from\n",
        "\n",
        "tweets = pd.read_csv(FOLDER_PATH+\"/\"+CSV_FILENAME)\n",
        "tweets.head(2)\n",
        "eng_tweets = tweets[tweets.language == \"English\"] #filter to English tweets only\n",
        "eng_tweets.head()"
      ],
      "execution_count": 57,
      "outputs": [
        {
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              "      <th>harvested_date</th>\n",
              "      <th>following</th>\n",
              "      <th>followers</th>\n",
              "      <th>updates</th>\n",
              "      <th>post_type</th>\n",
              "      <th>account_type</th>\n",
              "      <th>new_june_2018</th>\n",
              "      <th>retweet</th>\n",
              "      <th>account_category</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>9.060000e+17</td>\n",
              "      <td>10_GOP</td>\n",
              "      <td>\"We have a sitting Democrat US Senator on trial for corruption and you've barely heard a peep from the mainstream media.\" ~ @nedryun https://t.co/gh6g0D1oiC</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>English</td>\n",
              "      <td>10/1/2017 19:58</td>\n",
              "      <td>10/1/2017 19:59</td>\n",
              "      <td>1052</td>\n",
              "      <td>9636</td>\n",
              "      <td>253</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Right</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>RightTroll</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>9.060000e+17</td>\n",
              "      <td>10_GOP</td>\n",
              "      <td>Marshawn Lynch arrives to game in anti-Trump shirt. Judging by his sagging pants the shirt should say Lynch vs. belt https://t.co/mLH1i30LZZ</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>English</td>\n",
              "      <td>10/1/2017 22:43</td>\n",
              "      <td>10/1/2017 22:43</td>\n",
              "      <td>1054</td>\n",
              "      <td>9637</td>\n",
              "      <td>254</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Right</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>RightTroll</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>9.060000e+17</td>\n",
              "      <td>10_GOP</td>\n",
              "      <td>Daughter of fallen Navy Sailor delivers powerful monologue on anthem protests, burns her NFL packers gear.  #BoycottNFL https://t.co/qDlFBGMeag</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>English</td>\n",
              "      <td>10/1/2017 22:50</td>\n",
              "      <td>10/1/2017 22:51</td>\n",
              "      <td>1054</td>\n",
              "      <td>9637</td>\n",
              "      <td>255</td>\n",
              "      <td>RETWEET</td>\n",
              "      <td>Right</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>RightTroll</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>9.060000e+17</td>\n",
              "      <td>10_GOP</td>\n",
              "      <td>JUST IN: President Trump dedicates Presidents Cup golf tournament trophy to the people of Florida, Texas and Puerto Rico. https://t.co/z9wVa4djAE</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>English</td>\n",
              "      <td>10/1/2017 23:52</td>\n",
              "      <td>10/1/2017 23:52</td>\n",
              "      <td>1062</td>\n",
              "      <td>9642</td>\n",
              "      <td>256</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Right</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>RightTroll</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>9.060000e+17</td>\n",
              "      <td>10_GOP</td>\n",
              "      <td>19,000 RESPECTING our National Anthem! #StandForOurAnthem🇺🇸 https://t.co/czutyGaMQV</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>English</td>\n",
              "      <td>10/1/2017 2:13</td>\n",
              "      <td>10/1/2017 2:13</td>\n",
              "      <td>1050</td>\n",
              "      <td>9645</td>\n",
              "      <td>246</td>\n",
              "      <td>RETWEET</td>\n",
              "      <td>Right</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>RightTroll</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   external_author_id  author  \\\n",
              "0        9.060000e+17  10_GOP   \n",
              "1        9.060000e+17  10_GOP   \n",
              "2        9.060000e+17  10_GOP   \n",
              "3        9.060000e+17  10_GOP   \n",
              "4        9.060000e+17  10_GOP   \n",
              "\n",
              "                                                                                                                                                        content  \\\n",
              "0  \"We have a sitting Democrat US Senator on trial for corruption and you've barely heard a peep from the mainstream media.\" ~ @nedryun https://t.co/gh6g0D1oiC   \n",
              "1                  Marshawn Lynch arrives to game in anti-Trump shirt. Judging by his sagging pants the shirt should say Lynch vs. belt https://t.co/mLH1i30LZZ   \n",
              "2               Daughter of fallen Navy Sailor delivers powerful monologue on anthem protests, burns her NFL packers gear.  #BoycottNFL https://t.co/qDlFBGMeag   \n",
              "3             JUST IN: President Trump dedicates Presidents Cup golf tournament trophy to the people of Florida, Texas and Puerto Rico. https://t.co/z9wVa4djAE   \n",
              "4                                                                           19,000 RESPECTING our National Anthem! #StandForOurAnthem🇺🇸 https://t.co/czutyGaMQV   \n",
              "\n",
              "    region language     publish_date   harvested_date  following  followers  \\\n",
              "0  Unknown  English  10/1/2017 19:58  10/1/2017 19:59       1052       9636   \n",
              "1  Unknown  English  10/1/2017 22:43  10/1/2017 22:43       1054       9637   \n",
              "2  Unknown  English  10/1/2017 22:50  10/1/2017 22:51       1054       9637   \n",
              "3  Unknown  English  10/1/2017 23:52  10/1/2017 23:52       1062       9642   \n",
              "4  Unknown  English   10/1/2017 2:13   10/1/2017 2:13       1050       9645   \n",
              "\n",
              "   updates post_type account_type  new_june_2018  retweet account_category  \n",
              "0      253       NaN        Right              0        0       RightTroll  \n",
              "1      254       NaN        Right              0        0       RightTroll  \n",
              "2      255   RETWEET        Right              0        1       RightTroll  \n",
              "3      256       NaN        Right              0        0       RightTroll  \n",
              "4      246   RETWEET        Right              0        1       RightTroll  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nbl-bCmbxXq9",
        "outputId": "88703dac-c597-414b-8739-6cca351a5847"
      },
      "source": [
        "train, test = train_test_split(eng_tweets[:10000], test_size = 0.15) #take a limited number of tweets to speed up processing\n",
        "\n",
        "def build_text_file(pd_column, dest_path):\n",
        "  \"\"\"Write the contents of a pandas column to a text file\"\"\"\n",
        "  f = open(dest_path, \"w\")\n",
        "  data = ''\n",
        "  for text in pd_column:\n",
        "    line = str(text).strip()\n",
        "    line = re.sub(r\"\\s\", \" \", line)\n",
        "    data += line + \" \" #note that we are not adding a line break...\n",
        "  print(data[:3000]) #for inspection\n",
        "  f.write(data)\n",
        "\n",
        "build_text_file(train['content'], \"traintext.txt\")\n",
        "build_text_file(test['content'], \"testtext.txt\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "#IStartCryingWhen I get slapped in the face and shoved into traffic. Photo: Yesterday, 32-year-old Philadelphia police officer Matthew Zagursky was caught on a video extorting... http://t.co/D6TZDE2UJZ #abi Grassroots Conservative Group Calls on Senate GOP to DITCH MITCH! https://t.co/HAfpo4Js59 #ABISS https://t.co/CitZRKcdFs BREAKING: Russia-backed #SyrianArmy advances on #Daesh-held #Palmyra, less than 20 km left https://t.co/qht9pujmvU #SyriaCrisis https://t.co/1ZDbtgfxY6 Me: let's make today a good day Also me: do you like my hoodie I got it for my birthday https://t.co/MKCeAXUt4N I'm a bad politician #SaidNoOneEver http://t.co/2uusTwBRBC #happyme I’m happiest when my music touches the lives of all people: from black 2 white, young 2 old, straight 2 gay https://t.co/L9slMMtcwE #Rihanna #star #awesome http://t.co/P03TqNJ4g7 #abi Mueller’s WITCH-HUNT Team Just Lost a Major Player! https://t.co/sxGjnUCTgL #ABISS https://t.co/Yg28RUl5Ns RT @smushinga: Lol funniest comment about #Desiigner freestyling https://t.co/XD0AArxZMr I’m just going to leave this right here. http://t.co/IUEKAtwnhB Good evening from the hollows of witching  hour �✨#art #blackart #3am https://t.co/hxMaQAVDvS Places for colored - turn left to Mexico #RejectedStatueOfLibertySigns Come on y'all, spread the word. http://t.co/3NzARL18U8 Want to see if a police officer has a history of misconduct?These states make it hard #PoliceBrutality #BlackTwitter http://t.co/O252j6gYYn '@SophieTSimmons @genesimmons @Reebok wonderful' Why do THESE Democrats Want a Lenient Sentence for CEO of “Male Escort” Service? https://t.co/i0CYsDp2os https://t.co/osIiNcNIZt Because Fuck You Citizen That's Why #Cops #PoliceState #Injustice #NoKnockRaids https://t.co/lVArZ47Slu '@deray @ShaunKing @ReverantRevan @trueblacknews Here is my opinion on #JeremyMcDowell's case http://t.co/SpmDeEkxzQ http://t.co/Spt4dL2DtE' #abi SHOCK VIDEO : Charlottesville Man Has Head Busted Open By ANTIFA https://t.co/4miXSbcxfG #ABISS https://t.co/TkTi79j7dq So, if someone shoots me he commits a crime, if a cop shoots me he prevents one. Double standarts! #MikeBrown #Fergusonremembers 'Mommy,am I gonna die':Mom of 5year-old Ohio girl accidently shot by cop trying to subdue dog says officer left scene http://t.co/qYtkKBqhGj #NowPlaying: Mighty Mi \"The Right Time (feat. Grand Puba & Chubb Rock) [DJ Yoda Remix]\" #rap #hiphop #music https://t.co/IiUgOiTGhI '@DamnRealPosts If I make you cry, I'm cry.' girls winning totally bae https://t.co/x2kls7Ar1g If people think Russia is so bad, then tell me why they just said their getting ready and preparing for North Korea with USA, so think twice #abi BREAKING : Unhinged Liberals Target this NEW State Flag For Destruction https://t.co/Jl1po6Shy5 #ABISS https://t.co/GaaQ1u1Kp7 The Lazy Way To Sell Your Music Online https://t.co/IEdLFo1dBy WHAT??? The NAACP Just Labeled this ENTIRE STATE “Racist” https://t.co/uDosYEPCih https://t.co/krvVeW7e9O Fidel Castro, Muhammad Ali, David Bowie, Prince, \n",
            "MOTHERING WHILE BLACK: @TheRevAl publicist #RachelNoerdlinger been target of NYC politicians for years #BlackTwitter https://t.co/OiNWTeKKEY #PoliceViolence claims piling up in courts across the #country #America #racism #PoliceBrutality #BlackLivesMatter http://t.co/bsYPA2m6W4 Black women  For real  #BlackLivesMatter #BlackTwitter #BlackPride #racism #racist #STOPRACISM2015 #blackandwhite http://t.co/11I9ThNxDd mah favs #asaprocky #ASAPSingAlong #TravisScott #rap #rapmonster #RAPMUSIC #we #blackpeople #blackstars #blc #blacktw http://t.co/veBlFjE6X6 I think that this 'war on #cops' rhetoric is just another way to protect #police from accountability.#PoliceBrutality http://t.co/a6dttlN3Wv She deserves it! https://t.co/ykezqvTBIu #IslamKills the ‘refugees’ don’t look like those in need, absolutely #StopIslam just like that https://t.co/Jl7TU0G29v Young Gawd got in trouble at school for basically being a smart ass. Told him no TV before or after baseball practice... Trump lancia super bomba contro Is https://t.co/AwjR7wFCa2 https://t.co/wRChGwLywk My boy Donald Glover is in the MCU. It's lit. #SpiderManHomecoming https://t.co/gSFOYZRKTk A long, romantic walk to the fridge to grab a beer #ThingsYouCantIgnore Dixon #police department unlawfully entering unlocked vehicles to prove a point. http://t.co/lw4eyIyMpb #DogThanking How to get out of bed? I dunno #KochFarms https://t.co/rjBvMvDyNO '@KimKardashian Ohhhh mah f*ckin GOOOOOD! Perfect YA' Check out all the different ways to make money with us! --> https://t.co/eA09BcswkU #Brussels but that dont say none of syrian refugees are terrorists #IslamKills Comedian Adam Carolla Testified Before Congress and NUKED College Snowflakes https://t.co/LkE5JtO39O https://t.co/GoiVMrpXFt Yeah, clothes nowadays seems a bit strange https://t.co/vIC1dcnH1n #ObservationsAtTheMarathon If I stop to play a hashtag game, I'll fall way behind Liberal College Moves 9/11 Memorial as So No One Gets “Triggered” https://t.co/0KzEX8XbgV https://t.co/6pKh6YdzKV My inspiration for the day. https://t.co/EVGAAfxQF1 STUDENT SENT HOME FOR WEARING #BLACKLIVESMATTER SHIRT WITH LIST OF #PEOPLE #KILLED BY #POLICE #BlackTwitter #Blc #BLM http://t.co/QtESjXyPNB Heres AbbyJohnson former planned parenthood executive. Now #prolife very informative on abortions and MIND BLOWING  https://t.co/n1u6XkpHwA '@MarthaStewart Wow, I think thats the point, when you can say you succeed.  I am so happy for you' Watch Diamond and Silk SHRED Obamacare! https://t.co/X5kF5HpUYH https://t.co/icH689R0nI '@menchithekid I get it yass  wish you good day bae' #IWishItWasIllegalTo fake orgasm NATO chief says post-#Brexit alliance \"more important than ever\": https://t.co/vh4HPE6SRC https://t.co/RHcAoOcTcB This is wrong!! #BanIslam https://t.co/od9tv6IYke By the way, this is the now infamous librarian dressed as Cat in the Hat. Oh my, this is some inapropriate stuff. https://t.co/wzcsoPHGb8 Experts: #TamirRice had hands in pockets when shot by #Cleveland police  https\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E0oRbqZWx_gm"
      },
      "source": [
        "train.head(2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 164,
          "referenced_widgets": [
            "ed58794658c041df87fdd005291f14dc",
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            "7d9c461f5ad64843bd0b5756980f8867",
            "ec3223232900470893313c9d269c665d",
            "260e11e628a041cbb41cc4a7c643b657",
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            "c7db8c9a556a4bad870954a6c83396ac",
            "8d96adbea0444478b3ed1d61e27114e4",
            "c1775c44c4f74742b17505697e21efc8",
            "859e091db88d4c6ba8d7688ac15b142c",
            "aacde4562d104328aac5276fb992d617",
            "e6d3db45e41544e6b79ff34d73ee0f25",
            "b6f625390bbe45ffbc3fb05d11b0d547",
            "88f770b0d76f4200a641e9d04bb3ecec",
            "e776e8bd741f4e1fab8924ef24c9d898",
            "d7e347e44c8541499a8372c9f341d84d"
          ]
        },
        "id": "kwIXGtGEyAj5",
        "outputId": "a814639b-2a54-418b-d1f9-381861250d26"
      },
      "source": [
        "#get GPT 2 tokenizer\n",
        "\n",
        "from transformers import GPT2Tokenizer, GPT2Model\n",
        "\n",
        "tokenizer = GPT2Tokenizer.from_pretrained(\"gpt2\")\n",
        "\n",
        "test_path = \"/content/testtext.txt\" #set the path for where the test and train strings will be saved\n",
        "train_path = \"/content/traintext.txt\""
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ed58794658c041df87fdd005291f14dc",
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1042301.0, 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": "66e6d31f548b416c831a8cb22980926e",
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=456318.0, style=ProgressStyle(descripti…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1355256.0, style=ProgressStyle(descript…"
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        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B3uKp3eCylll",
        "outputId": "617650b0-083a-48ec-fd45-3fd35ea9a1e8"
      },
      "source": [
        "#now we create a TextDataset\n",
        "from transformers import TextDataset, DataCollatorForLanguageModeling\n",
        "#note to self: may need to replace TextDataset due to update to library\n",
        "\n",
        "train_dataset = TextDataset(\n",
        "    tokenizer = tokenizer,\n",
        "    file_path = train_path,\n",
        "    block_size = 128)\n",
        "\n",
        "test_dataset = TextDataset(\n",
        "    tokenizer = tokenizer,\n",
        "    file_path = test_path,\n",
        "    block_size = 128)\n",
        "\n",
        "#this is somehow used to form a batch...\n",
        "data_collator = DataCollatorForLanguageModeling(\n",
        "        tokenizer=tokenizer, mlm=False)\n",
        "\n",
        "\n",
        "#so now we have converted our text files into training and testing data that can be read by the neural net (?)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/data/datasets/language_modeling.py:58: FutureWarning: This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets library. You can have a look at this example script for pointers: https://github.com/huggingface/transformers/blob/master/examples/pytorch/language-modeling/run_mlm.py\n",
            "  FutureWarning,\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 169,
          "referenced_widgets": [
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        "id": "OqyMyBZ70J85",
        "outputId": "e146d6ea-c628-41cb-966b-40b215c38594"
      },
      "source": [
        "#now we initialize a TRAINER\n",
        "from transformers import Trainer, TrainingArguments, AutoModelWithLMHead\n",
        "\n",
        "model = model = AutoModelWithLMHead.from_pretrained(\"gpt2\")\n",
        "\n",
        "#here we set the learning parameters\n",
        "training_args = TrainingArguments(\n",
        "    output_dir=\"./gpt2-trolls\", #The output directory\n",
        "    overwrite_output_dir=True, #overwrite the content of the output directory\n",
        "    num_train_epochs=6, # number of training epochs\n",
        "    per_device_train_batch_size=32, # batch size for training\n",
        "    per_device_eval_batch_size=64,  # batch size for evaluation\n",
        "    eval_steps = 400, # Number of update steps between two evaluations.\n",
        "    save_steps=800, # after # steps model is saved\n",
        "    warmup_steps=500,# number of warmup steps for learning rate scheduler\n",
        "    prediction_loss_only=True,\n",
        "    )\n",
        "\n",
        "#create the trainer\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args, #as you can see here we are passing the parameters creating above\n",
        "    data_collator=data_collator,\n",
        "    train_dataset=train_dataset,\n",
        "    eval_dataset=test_dataset,\n",
        "    #prediction_loss_only=True,\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/models/auto/modeling_auto.py:810: FutureWarning: The class `AutoModelWithLMHead` is deprecated and will be removed in a future version. Please use `AutoModelForCausalLM` for causal language models, `AutoModelForMaskedLM` for masked language models and `AutoModelForSeq2SeqLM` for encoder-decoder models.\n",
            "  FutureWarning,\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "16fbe77a159746c4980a729d161f55ca",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=665.0, style=ProgressStyle(description_…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "93ab6c8e9f954fde8d34e347aacd4479",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=548118077.0, style=ProgressStyle(descri…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "92Z2fZsvBtGE"
      },
      "source": [
        "nd"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9u1eeY8-5Vlg"
      },
      "source": [
        "# Now train the pre-trained model and then save the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "v3splqAo0KNb",
        "outputId": "491000d2-bf36-48e3-fa3c-92aa0f3bd488"
      },
      "source": [
        "trainer.train()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='396' max='396' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [396/396 07:54, Epoch 6/6]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=396, training_loss=4.187208079328441, metrics={'train_runtime': 475.9372, 'train_samples_per_second': 0.832, 'total_flos': 1201312040878080.0, 'epoch': 6.0, 'init_mem_cpu_alloc_delta': 2104594432, 'init_mem_gpu_alloc_delta': 511148032, 'init_mem_cpu_peaked_delta': 0, 'init_mem_gpu_peaked_delta': 0, 'train_mem_cpu_alloc_delta': 12132352, 'train_mem_gpu_alloc_delta': 1521358336, 'train_mem_cpu_peaked_delta': 0, 'train_mem_gpu_peaked_delta': 8584833024})"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YOBKkLAi236G"
      },
      "source": [
        "trainer.save_model(MODEL_PATH)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UbUWTdHZBxEk"
      },
      "source": [
        "# Build a pipeline with the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IVx8y69_7fZo"
      },
      "source": [
        "from transformers import pipeline\n",
        "trollsbot = pipeline('text-generation', model = MODEL_PATH, tokenizer = tokenizer, config={'max_length':800})\n"
      ],
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "id": "klHHKLX1F84w",
        "outputId": "71de3bdf-1bce-4300-d21d-3f84dc2b9bfa"
      },
      "source": [
        "result = trollsbot(\"SCIENCE is\")[0]['generated_text'] #testing it\n",
        "result"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "\"SCIENCE is #racism.' https://t.co/m9fRczTqSn #BlackLivesMatter #ABISS https://t.co/4oJ0R7qvJ3 #Stay\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ARXf7fp3-g1i"
      },
      "source": [
        "# Creating a Twitter bot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vTyNHfWY_k6-",
        "outputId": "9731f98f-b9b2-472e-f6ae-40baaa3bdde6"
      },
      "source": [
        "!pip install textwrap3"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting textwrap3\n",
            "  Downloading https://files.pythonhosted.org/packages/77/9c/a53e561d496ee5866bbeea4d3a850b3b545ed854f8a21007c1e0d872e94d/textwrap3-0.9.2-py2.py3-none-any.whl\n",
            "Installing collected packages: textwrap3\n",
            "Successfully installed textwrap3-0.9.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OlYpJyof9Qzo"
      },
      "source": [
        "#tweet screenshot generator\n",
        "\n",
        "from textwrap3 import wrap #to divide text into lines\n",
        "\n",
        "\n",
        "from PIL import Image, ImageDraw, ImageFont\n",
        "import requests\n",
        "import io\n",
        "\n",
        "def screenshot(textbody):\n",
        "  \"\"\"Draws screenshot of the generated Tweet\"\"\"\n",
        "\n",
        "  \n",
        "  img = Image.new('RGB', (810, 220), color = \"white\")\n",
        "\n",
        "  #username logo -- HACKER\n",
        "  logo_url = \"https://cdn.pixabay.com/photo/2017/11/27/07/50/putin-2980748_960_720.jpg\" #profile picture of the fake twitter user\n",
        "  logo = Image.open(requests.get(logo_url, stream=True).raw)\n",
        "  logo.thumbnail((63, 63)) #shrink logo\n",
        "  img.paste(logo, box = (20, 43)) #place logo\n",
        "\n",
        "  #create drawing instance\n",
        "  d = ImageDraw.Draw(img)\n",
        "\n",
        "  #set up fonts for the image\n",
        "  droidsans = requests.get('https://github.com/ProgrammingFonts/ProgrammingFonts/raw/master/Droid-Sans-Mono/droid-sans-mono-1.00/Droid%20Sans%20Mono.ttf', allow_redirects=True)\n",
        "\n",
        "  namefont = ImageFont.truetype(io.BytesIO(droidsans.content), size=29)\n",
        "  handlefont = ImageFont.truetype(io.BytesIO(droidsans.content), size=21)\n",
        "  textfont = ImageFont.truetype(io.BytesIO(droidsans.content), size=24)\n",
        "\n",
        "  #write name and username of account\n",
        "  d.text((100,42), \"Troll McDuck\", fill=(\"black\"), font=namefont)\n",
        "  d.text((310,48), \"@russkymir\", fill=(\"grey\"), font=handlefont)\n",
        "\n",
        "  #divide tweet into lines and print separately\n",
        "  textlines = wrap(textbody, width=40)\n",
        "  lineheight = 28\n",
        "  firstline_y=80\n",
        "  linecount = 0\n",
        "  for line in textlines:\n",
        "    d.text((100,firstline_y+linecount*lineheight), line, fill=(\"dimgray\"), font=textfont)\n",
        "    linecount += 1\n",
        "  \n",
        "  display(img)"
      ],
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 271
        },
        "id": "txa1lq3Y_2w-",
        "outputId": "befb4ede-d346-45d0-8f0c-d94ad18d9af2"
      },
      "source": [
        "\n",
        "def generate_tweet(start):\n",
        "  result = trollsbot(start)[0]['generated_text']\n",
        "  punctuation = \"!?.;\"\n",
        "  shortresult = \"\"\n",
        "  \n",
        "  #make sure end result only something with punctuation\n",
        "  for i in range(1, len(result)):\n",
        "    if result[-i] in punctuation:\n",
        "      shortresult = result[:-i+1]\n",
        "      break\n",
        "  if len(shortresult) != 0: #if a punctuation was found\n",
        "    return shortresult\n",
        "  else:\n",
        "    return generate_tweet(start) #call itself if no punctuation in this one\n",
        "\n",
        "\n",
        "#working troll bot\n",
        "start = input(\"Type start of tweet: \")\n",
        "shortresult = generate_tweet(start)\n",
        "screenshot(shortresult)"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Type start of tweet: Hillary should\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=810x220 at 0x7F335E1EFB10>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}