Working_With_Reddit.ipynb

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    "colab": {
      "name": "Working With Reddit.ipynb",
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      "name": "python3",
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        "outputId": "4c8de718-5d16-4a89-d201-650e0b2273f6"
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      "source": [
        "!pip install praw"
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          "text": [
            "Collecting praw\n",
            "  Downloading praw-7.5.0-py3-none-any.whl (176 kB)\n",
            "\u001b[K     |████████████████████████████████| 176 kB 7.6 MB/s \n",
            "\u001b[?25hCollecting prawcore<3,>=2.1\n",
            "  Downloading prawcore-2.3.0-py3-none-any.whl (16 kB)\n",
            "Collecting update-checker>=0.18\n",
            "  Downloading update_checker-0.18.0-py3-none-any.whl (7.0 kB)\n",
            "Collecting websocket-client>=0.54.0\n",
            "  Downloading websocket_client-1.2.3-py3-none-any.whl (53 kB)\n",
            "\u001b[K     |████████████████████████████████| 53 kB 1.4 MB/s \n",
            "\u001b[?25hRequirement already satisfied: requests<3.0,>=2.6.0 in /usr/local/lib/python3.7/dist-packages (from prawcore<3,>=2.1->praw) (2.23.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3.0,>=2.6.0->prawcore<3,>=2.1->praw) (2021.10.8)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3.0,>=2.6.0->prawcore<3,>=2.1->praw) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3.0,>=2.6.0->prawcore<3,>=2.1->praw) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3.0,>=2.6.0->prawcore<3,>=2.1->praw) (3.0.4)\n",
            "Installing collected packages: websocket-client, update-checker, prawcore, praw\n",
            "Successfully installed praw-7.5.0 prawcore-2.3.0 update-checker-0.18.0 websocket-client-1.2.3\n"
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      "cell_type": "code",
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        "id": "YJExtMdb7DMK"
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      "source": [
        "import praw\n",
        "import pandas as pd\n",
        "import datetime as dt "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RHF9t1aVbKyG"
      },
      "source": [
        "https://www.reddit.com/prefs/apps"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M9kDVv0JbEKk"
      },
      "source": [
        "#Authorized Reddit Instance\n",
        "reddit = praw.Reddit(client_id ='**', # PUT YOUR OWN ID\n",
        "                     client_secret ='***', # PUT YOUR OWN SECRET KEY\n",
        "                     user_agent ='Accordi___**',\n",
        "                     username = '',\n",
        "                     password = '',\n",
        "                     check_for_async=False) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "id": "xy2KIqIYb9oo"
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      "source": [
        ""
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dZtrcrcEuFHP"
      },
      "source": [
        "subreddit1 = reddit.subreddit('minecraft')\n",
        "python_subreddit = subreddit1.hot(limit=10000)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yAOIlQG77gYU"
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      "source": [
        "dict = { \"title\":[],\n",
        "                \"subreddit\":[],\n",
        "                \"score\":[], \n",
        "                \"id\":[], \n",
        "                \"url\":[], \n",
        "                \"comms_num\": [], \n",
        "                \"created\": [], \n",
        "                \"body\":[]}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sOnl_Xo27ga6"
      },
      "source": [
        "for submission in python_subreddit:\n",
        "    dict[\"title\"].append(submission.title)\n",
        "    dict['subreddit'].append(submission.subreddit)\n",
        "    dict[\"score\"].append(submission.score)\n",
        "    dict[\"id\"].append(submission.id)\n",
        "    dict[\"url\"].append(submission.url)\n",
        "    dict[\"comms_num\"].append(submission.num_comments)\n",
        "    dict[\"created\"].append(submission.created)\n",
        "    dict[\"body\"].append(submission.selftext)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lmqW16ig8kY8"
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      "source": [
        "mine=pd.DataFrame(dict)"
      ],
      "execution_count": null,
      "outputs": []
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              "      <th></th>\n",
              "      <th>title</th>\n",
              "      <th>subreddit</th>\n",
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              "      <th>0</th>\n",
              "      <td>Minecraft Java Edition 1.18.1 has been released!</td>\n",
              "      <td>Minecraft</td>\n",
              "      <td>2196</td>\n",
              "      <td>rd5pjr</td>\n",
              "      <td>https://www.reddit.com/r/Minecraft/comments/rd...</td>\n",
              "      <td>311</td>\n",
              "      <td>1.639132e+09</td>\n",
              "      <td>We’re now releasing Minecraft: Java Edition 1....</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Minecraft Bi-Weekly Build Challenge #139: Ski ...</td>\n",
              "      <td>Minecraft</td>\n",
              "      <td>251</td>\n",
              "      <td>rjyz6b</td>\n",
              "      <td>https://www.reddit.com/r/Minecraft/comments/rj...</td>\n",
              "      <td>18</td>\n",
              "      <td>1.639928e+09</td>\n",
              "      <td>#**[LAST WEEK'S THEME: WATERMILL](https://www....</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>I found this cool visual trick with spectator ...</td>\n",
              "      <td>Minecraft</td>\n",
              "      <td>9434</td>\n",
              "      <td>rsfsjl</td>\n",
              "      <td>https://v.redd.it/ty4ulk6mnr881</td>\n",
              "      <td>87</td>\n",
              "      <td>1.640908e+09</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Why did all my villagers disappear!??</td>\n",
              "      <td>Minecraft</td>\n",
              "      <td>15430</td>\n",
              "      <td>rs8h55</td>\n",
              "      <td>https://i.redd.it/lyb7g10b3q881.jpg</td>\n",
              "      <td>1171</td>\n",
              "      <td>1.640889e+09</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>I made Minecraft parkour that builds itself</td>\n",
              "      <td>Minecraft</td>\n",
              "      <td>1558</td>\n",
              "      <td>rsgir7</td>\n",
              "      <td>https://v.redd.it/szmmrgqxtr881</td>\n",
              "      <td>55</td>\n",
              "      <td>1.640910e+09</td>\n",
              "      <td></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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>858</th>\n",
              "      <td>Why does this keep happening?? Dirt keeps form...</td>\n",
              "      <td>Minecraft</td>\n",
              "      <td>28</td>\n",
              "      <td>rrfy0g</td>\n",
              "      <td>https://i.redd.it/50srz6776j881.jpg</td>\n",
              "      <td>46</td>\n",
              "      <td>1.640805e+09</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>859</th>\n",
              "      <td>What should I build on this platform in the ci...</td>\n",
              "      <td>Minecraft</td>\n",
              "      <td>7</td>\n",
              "      <td>rrpgak</td>\n",
              "      <td>https://i.redd.it/ssyh1vfp7l881.png</td>\n",
              "      <td>6</td>\n",
              "      <td>1.640830e+09</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>860</th>\n",
              "      <td>Minecraft won’t install even though I’m connec...</td>\n",
              "      <td>Minecraft</td>\n",
              "      <td>4</td>\n",
              "      <td>rrt17d</td>\n",
              "      <td>https://i.redd.it/glx8h4t03m881.jpg</td>\n",
              "      <td>4</td>\n",
              "      <td>1.640840e+09</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>861</th>\n",
              "      <td>Finding end stronghold</td>\n",
              "      <td>Minecraft</td>\n",
              "      <td>1</td>\n",
              "      <td>rs0p4y</td>\n",
              "      <td>https://www.reddit.com/r/Minecraft/comments/rs...</td>\n",
              "      <td>6</td>\n",
              "      <td>1.640867e+09</td>\n",
              "      <td>So today a friend and I were looking for the e...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>862</th>\n",
              "      <td>I found it</td>\n",
              "      <td>Minecraft</td>\n",
              "      <td>13</td>\n",
              "      <td>rrkt4y</td>\n",
              "      <td>https://i.redd.it/a1lzquad6k881.jpg</td>\n",
              "      <td>1</td>\n",
              "      <td>1.640817e+09</td>\n",
              "      <td></td>\n",
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            "text/plain": [
              "                                                 title  ...                                               body\n",
              "0     Minecraft Java Edition 1.18.1 has been released!  ...  We’re now releasing Minecraft: Java Edition 1....\n",
              "1    Minecraft Bi-Weekly Build Challenge #139: Ski ...  ...  #**[LAST WEEK'S THEME: WATERMILL](https://www....\n",
              "2    I found this cool visual trick with spectator ...  ...                                                   \n",
              "3                Why did all my villagers disappear!??  ...                                                   \n",
              "4          I made Minecraft parkour that builds itself  ...                                                   \n",
              "..                                                 ...  ...                                                ...\n",
              "858  Why does this keep happening?? Dirt keeps form...  ...                                                   \n",
              "859  What should I build on this platform in the ci...  ...                                                   \n",
              "860  Minecraft won’t install even though I’m connec...  ...                                                   \n",
              "861                             Finding end stronghold  ...  So today a friend and I were looking for the e...\n",
              "862                                         I found it  ...                                                   \n",
              "\n",
              "[863 rows x 8 columns]"
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      "source": [
        "mine.to_csv(\"mine_reddit_31DEC2021.csv\", index=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
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      },
      "source": [
        "mine=pd.read_csv(\"nlp_reddit2.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
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              "      <th>subreddit</th>\n",
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              "      <th>0</th>\n",
              "      <td>This subreddit is about the PSYCHOLOGICAL TECH...</td>\n",
              "      <td>NLP</td>\n",
              "      <td>59</td>\n",
              "      <td>lfhuhu</td>\n",
              "      <td>https://www.reddit.com/r/NLP/comments/lfhuhu/t...</td>\n",
              "      <td>15</td>\n",
              "      <td>1.612808e+09</td>\n",
              "      <td>Are you interested in Natural Language Process...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Self-Promotion Thread | October — December 2021</td>\n",
              "      <td>NLP</td>\n",
              "      <td>3</td>\n",
              "      <td>pz3cv2</td>\n",
              "      <td>https://www.reddit.com/r/NLP/comments/pz3cv2/s...</td>\n",
              "      <td>2</td>\n",
              "      <td>1.633075e+09</td>\n",
              "      <td>This post is for any and all self-promotional ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>[Anchoring] Examples of « Feeling Powerful » m...</td>\n",
              "      <td>NLP</td>\n",
              "      <td>3</td>\n",
              "      <td>rqpslg</td>\n",
              "      <td>https://www.reddit.com/r/NLP/comments/rqpslg/a...</td>\n",
              "      <td>3</td>\n",
              "      <td>1.640726e+09</td>\n",
              "      <td>In the process of anchoring, it can be hard fo...</td>\n",
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              "                                                 title  ...                                               body\n",
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              "1      Self-Promotion Thread | October — December 2021  ...  This post is for any and all self-promotional ...\n",
              "2    [Anchoring] Examples of « Feeling Powerful » m...  ...  In the process of anchoring, it can be hard fo...\n",
              "3    Just sold 7/8 of my NLP and personal developme...  ...  Have you had a similar experience?\\n\\nI know e...\n",
              "4                                     Just for fun....  ...                                                NaN\n",
              "..                                                 ...  ...                                                ...\n",
              "974  HOW TO LET GO OF A TOXIC PERSON | SITUATION | ...  ...                                                NaN\n",
              "975  HOW TO BECOME MORE PERSUASIVE | INFLUENTIAL | ...  ...                                                NaN\n",
              "976                Looking for a Discord server on NLP  ...  Does anyone know of a NLP focused discord server?\n",
              "977  WHAT INSPIRES US? | MIRROR NEURONS | PATTERN &...  ...                                                NaN\n",
              "978  MIRROR TECHNIQUE | HOW TO BREAK SUBSTANCE ABUS...  ...                                                NaN\n",
              "\n",
              "[979 rows x 8 columns]"
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              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b9f604ea-096e-48a3-923e-9fbd517c7029 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-b9f604ea-096e-48a3-923e-9fbd517c7029');\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",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                                 title  ...                                               body\n",
              "0    This subreddit is about the PSYCHOLOGICAL TECH...  ...  Are you interested in Natural Language Process...\n",
              "1      Self-Promotion Thread | October — December 2021  ...  This post is for any and all self-promotional ...\n",
              "2    [Anchoring] Examples of « Feeling Powerful » m...  ...  In the process of anchoring, it can be hard fo...\n",
              "3    Just sold 7/8 of my NLP and personal developme...  ...  Have you had a similar experience?\\n\\nI know e...\n",
              "5        Best youtube channels to learn NLP Techniques  ...  Hi everyone!\\n\\n&#x200B;\\n\\nIm interessted in ...\n",
              "..                                                 ...  ...                                                ...\n",
              "967             HOW TO DEAL WITH A BULLY | NLP REFRAME  ...  \\*\\*\\*Scroll to Bottom for Video\\*\\*\\*\\n\\n**HO...\n",
              "968                            NLP Anchoring and drugs  ...  *First and foremost, I would like to state tha...\n",
              "972                 Help with embedded commands please  ...  I know the theory of how they work. I tried it...\n",
              "973                                    NLP in New York  ...  I’m fascinated with NLP and am looking for a q...\n",
              "976                Looking for a Discord server on NLP  ...  Does anyone know of a NLP focused discord server?\n",
              "\n",
              "[444 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install -U sentence-transformers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cEakGJoJm2OV",
        "outputId": "28fb2be4-b382-4d38-cbf8-1d3a678d84e5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting sentence-transformers\n",
            "  Downloading sentence-transformers-2.1.0.tar.gz (78 kB)\n",
            "\u001b[K     |████████████████████████████████| 78 kB 4.5 MB/s \n",
            "\u001b[?25hCollecting transformers<5.0.0,>=4.6.0\n",
            "  Downloading transformers-4.15.0-py3-none-any.whl (3.4 MB)\n",
            "\u001b[K     |████████████████████████████████| 3.4 MB 16.0 MB/s \n",
            "\u001b[?25hCollecting tokenizers>=0.10.3\n",
            "  Downloading tokenizers-0.11.1-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (6.8 MB)\n",
            "\u001b[K     |████████████████████████████████| 6.8 MB 51.3 MB/s \n",
            "\u001b[?25hRequirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from sentence-transformers) (4.62.3)\n",
            "Requirement already satisfied: torch>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from sentence-transformers) (1.10.0+cu111)\n",
            "Requirement already satisfied: torchvision in /usr/local/lib/python3.7/dist-packages (from sentence-transformers) (0.11.1+cu111)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from sentence-transformers) (1.19.5)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from sentence-transformers) (1.0.1)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from sentence-transformers) (1.4.1)\n",
            "Requirement already satisfied: nltk in /usr/local/lib/python3.7/dist-packages (from sentence-transformers) (3.2.5)\n",
            "Collecting sentencepiece\n",
            "  Downloading sentencepiece-0.1.96-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.2 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.2 MB 55.0 MB/s \n",
            "\u001b[?25hCollecting huggingface-hub\n",
            "  Downloading huggingface_hub-0.2.1-py3-none-any.whl (61 kB)\n",
            "\u001b[K     |████████████████████████████████| 61 kB 581 kB/s \n",
            "\u001b[?25hRequirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch>=1.6.0->sentence-transformers) (3.10.0.2)\n",
            "Collecting pyyaml>=5.1\n",
            "  Downloading PyYAML-6.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (596 kB)\n",
            "\u001b[K     |████████████████████████████████| 596 kB 74.3 MB/s \n",
            "\u001b[?25hCollecting sacremoses\n",
            "  Downloading sacremoses-0.0.46-py3-none-any.whl (895 kB)\n",
            "\u001b[K     |████████████████████████████████| 895 kB 65.0 MB/s \n",
            "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers) (2.23.0)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers) (4.8.2)\n",
            "Collecting tokenizers>=0.10.3\n",
            "  Downloading tokenizers-0.10.3-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (3.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 3.3 MB 42.2 MB/s \n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers) (3.4.0)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers) (2019.12.20)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers) (21.3)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->transformers<5.0.0,>=4.6.0->sentence-transformers) (3.0.6)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers<5.0.0,>=4.6.0->sentence-transformers) (3.6.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from nltk->sentence-transformers) (1.15.0)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers<5.0.0,>=4.6.0->sentence-transformers) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers<5.0.0,>=4.6.0->sentence-transformers) (2021.10.8)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers<5.0.0,>=4.6.0->sentence-transformers) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers<5.0.0,>=4.6.0->sentence-transformers) (3.0.4)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers<5.0.0,>=4.6.0->sentence-transformers) (1.1.0)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers<5.0.0,>=4.6.0->sentence-transformers) (7.1.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->sentence-transformers) (3.0.0)\n",
            "Requirement already satisfied: pillow!=8.3.0,>=5.3.0 in /usr/local/lib/python3.7/dist-packages (from torchvision->sentence-transformers) (7.1.2)\n",
            "Building wheels for collected packages: sentence-transformers\n",
            "  Building wheel for sentence-transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sentence-transformers: filename=sentence_transformers-2.1.0-py3-none-any.whl size=121000 sha256=925731c8014245ddf12473b5596b8bc66499de1d1ae0ad646a28992a69c51cbf\n",
            "  Stored in directory: /root/.cache/pip/wheels/90/f0/bb/ed1add84da70092ea526466eadc2bfb197c4bcb8d4fa5f7bad\n",
            "Successfully built sentence-transformers\n",
            "Installing collected packages: pyyaml, tokenizers, sacremoses, huggingface-hub, transformers, sentencepiece, sentence-transformers\n",
            "  Attempting uninstall: pyyaml\n",
            "    Found existing installation: PyYAML 3.13\n",
            "    Uninstalling PyYAML-3.13:\n",
            "      Successfully uninstalled PyYAML-3.13\n",
            "Successfully installed huggingface-hub-0.2.1 pyyaml-6.0 sacremoses-0.0.46 sentence-transformers-2.1.0 sentencepiece-0.1.96 tokenizers-0.10.3 transformers-4.15.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sentence_transformers import SentenceTransformer\n",
        "model = SentenceTransformer('paraphrase-MiniLM-L6-v2')\n",
        "\n",
        "#Our sentences we like to encode\n",
        "sentences = ['This framework generates embeddings for each input sentence',\n",
        "    'Sentences are passed as a list of string.',\n",
        "    'The quick brown fox jumps over the lazy dog.', \n",
        "    'I ate the cake bla bla']\n",
        "\n",
        "#Sentences are encoded by calling model.encode()\n",
        "embeddings = model.encode(sentences)\n",
        "\n",
        "#Print the embeddings\n",
        "for sentence, embedding in zip(sentences, embeddings):\n",
        "    print(\"Sentence:\", sentence)\n",
        "    print(\"Embedding:\", embedding)\n",
        "    print(\"\")"
      ],
      "metadata": {
        "id": "faZWeMNTmqWD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "embeddings.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7XTI3IU30s-F",
        "outputId": "cd90d960-8eb6-4444-8988-f2b47319f36d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4, 384)"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "emds=model.encode(list(mine3.body))"
      ],
      "metadata": {
        "id": "jGjenqlYoTJH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "emds.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X6vnC3rVoXoh",
        "outputId": "1f71dd0c-a055-4f92-e2b7-2dfefb299b27"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(444, 384)"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mine3[mine3.score<20].score.hist(bins=8, figsize =(20,10))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        },
        "id": "e-8t_lxqm0Ma",
        "outputId": "70cc6be3-211a-400d-ee5f-c17453fdeab4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5c1febdc90>"
            ]
          },
          "metadata": {},
          "execution_count": 83
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y= mine2.score>3"
      ],
      "metadata": {
        "id": "ngF5HavwoA22"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sum(y), len(y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I2fLZHIrpWuH",
        "outputId": "b10700d8-81ea-4d5a-94c7-fcc173d710ea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(221, 444)"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X=emds"
      ],
      "metadata": {
        "id": "PhU8JhRrpXUx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC\n",
        "cls= SVC()\n",
        "cls.fit(X,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0bMW9JQUpc22",
        "outputId": "68d82d25-2cb5-4c8f-c6ab-d13be7723d43"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC()"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cls.score(X,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8Xt451wDpq65",
        "outputId": "a5499ea9-4f86-4e6f-de84-696ff7a83e0c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9099099099099099"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fIsyL7C1psYK",
        "outputId": "6c40b6d7-049e-44e6-9ca8-33e9f2a7196d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(444, 384)"
            ]
          },
          "metadata": {},
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train= X[:300]\n",
        "y_train=y[:300]\n",
        "X_test= X[300:]\n",
        "y_test=y[300:]"
      ],
      "metadata": {
        "id": "3TjP5YKFpurg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cls= SVC()\n",
        "cls.fit(X_train,y_train)\n",
        "cls.score(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bPJqozMKp2Nu",
        "outputId": "aad02e1d-252c-42dd-83c6-b70954ce3c6e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9166666666666666"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cls.score(X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7xl0cqxuqQMg",
        "outputId": "33555357-a3c0-4813-8e7c-1b2de766c34d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.4652777777777778"
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sum(y_test), len(y_test), sum(y_test) / len(y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O7v_2MetqEy_",
        "outputId": "849ab462-3b22-44c4-cece-efe10ff2c979"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(67, 144, 0.4652777777777778)"
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install gradio"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "21fYlapTrRXx",
        "outputId": "2eda763a-b96a-4142-ee48-a8e645be58ba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting gradio\n",
            "  Downloading gradio-2.6.4-py3-none-any.whl (900 kB)\n",
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            "Building wheels for collected packages: ffmpy, flask-cachebuster\n",
            "  Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for ffmpy: filename=ffmpy-0.3.0-py3-none-any.whl size=4710 sha256=a2cf857cf9b2942b6ff9c03ce6e303bb238f11d122ff482de428b895b9666fa5\n",
            "  Stored in directory: /root/.cache/pip/wheels/13/e4/6c/e8059816e86796a597c6e6b0d4c880630f51a1fcfa0befd5e6\n",
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            "Successfully built ffmpy flask-cachebuster\n",
            "Installing collected packages: pynacl, monotonic, cryptography, bcrypt, backoff, pydub, pycryptodome, paramiko, markdown2, Flask-Login, Flask-Cors, flask-cachebuster, ffmpy, analytics-python, gradio\n",
            "Successfully installed Flask-Cors-3.0.10 Flask-Login-0.5.0 analytics-python-1.4.0 backoff-1.10.0 bcrypt-3.2.0 cryptography-36.0.1 ffmpy-0.3.0 flask-cachebuster-1.0.0 gradio-2.6.4 markdown2-2.4.2 monotonic-1.6 paramiko-2.9.1 pycryptodome-3.12.0 pydub-0.25.1 pynacl-1.4.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def detect(text):\n",
        "  return cls.predict([model.encode(text)])[0]"
      ],
      "metadata": {
        "id": "wKAOReKVwnZB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "detect(\"bla bla \")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RGBCU8xL34qJ",
        "outputId": "5cb21345-46e6-424d-995c-ba60cf878e81"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import gradio as gr\n",
        "iface = gr.Interface(fn=detect, inputs=\"text\", outputs=\"text\").launch()"
      ],
      "metadata": {
        "id": "sBq9PeZpqJWz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVR"
      ],
      "metadata": {
        "id": "Q8lGnNbxxlDQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y= mine2.score\n",
        "cls= SVR()\n",
        "cls.fit(X,y)"
      ],
      "metadata": {
        "id": "wu7CNDh4r7Rm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y"
      ],
      "metadata": {
        "id": "zcV4IfbMxgqq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def detect(text):\n",
        "  return cls.predict([model.encode(text)])[0]"
      ],
      "metadata": {
        "id": "0bMWzLS1xg9I"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "detect(\"nlp stuies are very important\")"
      ],
      "metadata": {
        "id": "4KemVLhxxiNY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import gradio as gr\n",
        "  # Implement image segmentation model here...\n",
        "  # Return segmented image\n",
        "\n",
        "iface = gr.Interface(fn=detect, inputs=\"text\", outputs=\"number\").launch()"
      ],
      "metadata": {
        "id": "5YDV81yRxZQ_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "_AHscYkAxd4Z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#kmeans(X, 7)"
      ],
      "metadata": {
        "id": "53l6HGVy4t1f"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}