BERTopic_model_training.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "r2wqpqy-xFED"
   },
   "source": [
    "# BERTopic\n",
    "\n",
    "BERTopic is a formidable topic modelling model, it performs a lot better than the classic LDA model and has an exquisitly plesant feature of inferring the topic, again, another feature that LDA lacks\n",
    "\n",
    "This notebook is a support for the training process of the model that I will use in my TAP project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "QueK1IWyw3Q2",
    "outputId": "42fe7ed0-0395-4175-cb44-1d0b69f82238"
   },
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'google'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[1], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mgoogle\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcolab\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m drive\n\u001b[0;32m      3\u001b[0m \u001b[38;5;66;03m# Mount Google Drive\u001b[39;00m\n\u001b[0;32m      4\u001b[0m drive\u001b[38;5;241m.\u001b[39mmount(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m/content/drive\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'google'"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "\n",
    "# Mount Google Drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "gBAaevPuxgvW",
    "outputId": "4af0bf8e-3cdd-465a-fee7-30ac32e26122"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting bertopic\n",
      "  Downloading bertopic-0.15.0-py2.py3-none-any.whl (143 kB)\n",
      "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/143.4 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r",
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      "\u001b[?25hRequirement already satisfied: numpy>=1.20.0 in /usr/local/lib/python3.10/dist-packages (from bertopic) (1.23.5)\n",
      "Collecting hdbscan>=0.8.29 (from bertopic)\n",
      "  Downloading hdbscan-0.8.33.tar.gz (5.2 MB)\n",
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      "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
      "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
      "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
      "Collecting umap-learn>=0.5.0 (from bertopic)\n",
      "  Downloading umap-learn-0.5.3.tar.gz (88 kB)\n",
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      "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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      "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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      "\u001b[?25hCollecting huggingface-hub>=0.4.0 (from sentence-transformers>=0.4.1->bertopic)\n",
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      "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1 (from transformers<5.0.0,>=4.6.0->sentence-transformers>=0.4.1->bertopic)\n",
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      "Building wheels for collected packages: hdbscan, sentence-transformers, umap-learn, pynndescent\n",
      "  Building wheel for hdbscan (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for hdbscan: filename=hdbscan-0.8.33-cp310-cp310-linux_x86_64.whl size=3039156 sha256=cc1b3b00d665fdb25251c9320f0b428a79aee64edc1d64e598363fcf85916d89\n",
      "  Stored in directory: /root/.cache/pip/wheels/75/0b/3b/dc4f60b7cc455efaefb62883a7483e76f09d06ca81cf87d610\n",
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      "  Created wheel for umap-learn: filename=umap_learn-0.5.3-py3-none-any.whl size=82808 sha256=0d7d8731b2192c0528b34634a281026647faa242a110a0bbcbb29f1e5bbd83da\n",
      "  Stored in directory: /root/.cache/pip/wheels/a0/e8/c6/a37ea663620bd5200ea1ba0907ab3c217042c1d035ef606acc\n",
      "  Building wheel for pynndescent (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for pynndescent: filename=pynndescent-0.5.10-py3-none-any.whl size=55617 sha256=5761e5cea098742cb705d2192122bdb106d94771060ba6f899345567c761d6ba\n",
      "  Stored in directory: /root/.cache/pip/wheels/4a/38/5d/f60a40a66a9512b7e5e83517ebc2d1b42d857be97d135f1096\n",
      "Successfully built hdbscan sentence-transformers umap-learn pynndescent\n",
      "Installing collected packages: tokenizers, sentencepiece, safetensors, huggingface-hub, transformers, pynndescent, hdbscan, umap-learn, sentence-transformers, bertopic\n",
      "Successfully installed bertopic-0.15.0 hdbscan-0.8.33 huggingface-hub-0.16.4 pynndescent-0.5.10 safetensors-0.3.2 sentence-transformers-2.2.2 sentencepiece-0.1.99 tokenizers-0.13.3 transformers-4.31.0 umap-learn-0.5.3\n"
     ]
    }
   ],
   "source": [
    "!pip install -U bertopic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "Oxvo4l8SzeKd"
   },
   "outputs": [],
   "source": [
    "# Better process the dataset\n",
    "import re\n",
    "\n",
    "def remove_hyperlinks(text):\n",
    "    # Regular expression pattern to match URLs\n",
    "    url_pattern = r'http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\\\(\\\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+'\n",
    "\n",
    "    try:\n",
    "      # Replace URLs with an empty string\n",
    "      cleaned_text = re.sub(url_pattern, '', text)\n",
    "    except Esxception as e:\n",
    "      print(\"Parsing exception raised:\\t{}\".format(e))\n",
    "      return text\n",
    "\n",
    "    return cleaned_text\n",
    "\n",
    "input_file_path = '/content/drive/MyDrive/processed_mastodon_dataset_big.txt'    # Path to the input text file\n",
    "output_file_path = '/content/drive/MyDrive/parsed_processed_mastodon_dataset_big.txt'  # Path to the output file\n",
    "\n",
    "with open(input_file_path, 'r', encoding='utf-8') as input_file:\n",
    "    with open(output_file_path, 'w', encoding='utf-8') as output_file:\n",
    "        for line in input_file:\n",
    "            # Remove hyperlinks from the current line\n",
    "            processed_line = remove_hyperlinks(line)\n",
    "\n",
    "            # Write the processed line to the output file\n",
    "            output_file.write(processed_line)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "dDEQvV2qxod6",
    "outputId": "9a4915d3-3682-4e3f-acf9-a5fcb1221b1b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bundesregierung will WHO stärken #Gesundheitspolitik #Antwort #Bundestag Die Bundesregierung kündigt in einer Antwort auf AfD-Anfrage, die Weltgesundheitsorganisation stärken zu wollen.\n",
      "지난 8월 12일 방송 \"시청자가 먹은 음식 리뷰하기\"#ScienceFiction\n",
      "Hagi cookin' 😤\n",
      "황마법 불가능성 정리#ScienceFiction\n",
      "Visa Faces DOJ Scrutiny for How It Prices ‘Token’ TechnologyVisa Inc. is facing fresh scrutiny from the US Justice Department over how it charges merchants for technology it uses to protect cardholder information. #press\n",
      "String list length:\t220559\n"
     ]
    }
   ],
   "source": [
    "# Since the BERTopic object accepts a list of strings as an input, the following snippet transforms the dataset (which is in .txt format) into said string list\n",
    "docs = []\n",
    "\n",
    "with open('/content/drive/MyDrive/parsed_processed_mastodon_dataset_big.txt', 'r') as file:\n",
    "    for line in file:\n",
    "        docs.append(line.strip())\n",
    "\n",
    "# ~ head 5\n",
    "for i in range(5):\n",
    "  print(docs[i])\n",
    "\n",
    "# Show the list length, just to check\n",
    "print(\"String list length:\\t{}\".format(len(docs)))"
   ]
  },
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    {
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     "text": [
      "2023-08-17 19:07:54,094 - BERTopic - Transformed documents to Embeddings\n",
      "2023-08-17 19:50:32,688 - BERTopic - Reduced dimensionality\n",
      "2023-08-17 19:51:22,458 - BERTopic - Clustered reduced embeddings\n"
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       "      <td>2281</td>\n",
       "      <td>0_neigh_sayin_facepalm_orrrr</td>\n",
       "      <td>[neigh, sayin, facepalm, orrrr, checks, wins, ...</td>\n",
       "      <td>[Not helping, just sayin', He wins.]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1081</td>\n",
       "      <td>1_iel_んちゅ_ふじょ_อยท</td>\n",
       "      <td>[iel, んちゅ, ふじょ, อยท, peepoo, อยากทำให, งว, จได...</td>\n",
       "      <td>[ich🪚iel, ich🤳iel, เป้าหมายนึงที่อยากทำให้ได้ค...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>678</td>\n",
       "      <td>2_str_randint_今日も_一日一回クソガチャ</td>\n",
       "      <td>[str, randint, 今日も, 一日一回クソガチャ, 今日も一日, 今日は, 満腹度...</td>\n",
       "      <td>[リトくん​:happy_birth_day__i:​ドリアンの配信楽しみ！ #にじさんじ ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>386</td>\n",
       "      <td>3__interrobang_mark_lul_00台にまたくるかも_つかれたにい</td>\n",
       "      <td>[_interrobang_mark, lul, 00台にまたくるかも, つかれたにい, う...</td>\n",
       "      <td>[でもこう言う場合って大体遠いよね、、、うぐっ, 正直怒ってるのもおもろい​:lul:​から...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7662</th>\n",
       "      <td>7661</td>\n",
       "      <td>10</td>\n",
       "      <td>7661_欲しい_5000回くらいリロードしてたら通知api破壊して草なの_100rnされた...</td>\n",
       "      <td>[欲しい, 5000回くらいリロードしてたら通知api破壊して草なの, 100rnされたらミ...</td>\n",
       "      <td>[へそ注射​:daisukifes:​とゆか何をするにもおへそを介して行われる系の人体改造が...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7663</th>\n",
       "      <td>7662</td>\n",
       "      <td>10</td>\n",
       "      <td>7662_blindfully_developments_optima_multimedia</td>\n",
       "      <td>[blindfully, developments, optima, multimedia,...</td>\n",
       "      <td>[If anyone wants to keep up with the developme...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7664</th>\n",
       "      <td>7663</td>\n",
       "      <td>10</td>\n",
       "      <td>7663_oldtimer_classiccars_schwetzingen_mercede...</td>\n",
       "      <td>[oldtimer, classiccars, schwetzingen, mercedes...</td>\n",
       "      <td>[#Mercedes #MercedesBenz #Oldtimer #Classiccar...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7665</th>\n",
       "      <td>7664</td>\n",
       "      <td>10</td>\n",
       "      <td>7664_marianas_vaguealso_scumbut_scrub</td>\n",
       "      <td>[marianas, vaguealso, scumbut, scrub, 12thrits...</td>\n",
       "      <td>[@Alice Hey Slice, being that you're Miss Mann...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7666</th>\n",
       "      <td>7665</td>\n",
       "      <td>10</td>\n",
       "      <td>7665_launcher_neo_koler_textra</td>\n",
       "      <td>[launcher, neo, koler, textra, degooglisation,...</td>\n",
       "      <td>[Update #degooglisation :❌Textra ✅Simple SMS M...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>7667 rows × 5 columns</p>\n",
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      ],
      "text/plain": [
       "      Topic  Count                                               Name  \\\n",
       "0        -1  35615                                   -1_g0_s0_een_que   \n",
       "1         0   2281                       0_neigh_sayin_facepalm_orrrr   \n",
       "2         1   1081                                  1_iel_んちゅ_ふじょ_อยท   \n",
       "3         2    678                        2_str_randint_今日も_一日一回クソガチャ   \n",
       "4         3    386          3__interrobang_mark_lul_00台にまたくるかも_つかれたにい   \n",
       "...     ...    ...                                                ...   \n",
       "7662   7661     10  7661_欲しい_5000回くらいリロードしてたら通知api破壊して草なの_100rnされた...   \n",
       "7663   7662     10     7662_blindfully_developments_optima_multimedia   \n",
       "7664   7663     10  7663_oldtimer_classiccars_schwetzingen_mercede...   \n",
       "7665   7664     10              7664_marianas_vaguealso_scumbut_scrub   \n",
       "7666   7665     10                     7665_launcher_neo_koler_textra   \n",
       "\n",
       "                                         Representation  \\\n",
       "0     [g0, s0, een, que, de, por, para, se, en, prod...   \n",
       "1     [neigh, sayin, facepalm, orrrr, checks, wins, ...   \n",
       "2     [iel, んちゅ, ふじょ, อยท, peepoo, อยากทำให, งว, จได...   \n",
       "3     [str, randint, 今日も, 一日一回クソガチャ, 今日も一日, 今日は, 満腹度...   \n",
       "4     [_interrobang_mark, lul, 00台にまたくるかも, つかれたにい, う...   \n",
       "...                                                 ...   \n",
       "7662  [欲しい, 5000回くらいリロードしてたら通知api破壊して草なの, 100rnされたらミ...   \n",
       "7663  [blindfully, developments, optima, multimedia,...   \n",
       "7664  [oldtimer, classiccars, schwetzingen, mercedes...   \n",
       "7665  [marianas, vaguealso, scumbut, scrub, 12thrits...   \n",
       "7666  [launcher, neo, koler, textra, degooglisation,...   \n",
       "\n",
       "                                    Representative_Docs  \n",
       "0     [I often, way too often, run into people scare...  \n",
       "1                  [Not helping, just sayin', He wins.]  \n",
       "2     [ich🪚iel, ich🤳iel, เป้าหมายนึงที่อยากทำให้ได้ค...  \n",
       "3     [リトくん​:happy_birth_day__i:​ドリアンの配信楽しみ！ #にじさんじ ...  \n",
       "4     [でもこう言う場合って大体遠いよね、、、うぐっ, 正直怒ってるのもおもろい​:lul:​から...  \n",
       "...                                                 ...  \n",
       "7662  [へそ注射​:daisukifes:​とゆか何をするにもおへそを介して行われる系の人体改造が...  \n",
       "7663  [If anyone wants to keep up with the developme...  \n",
       "7664  [#Mercedes #MercedesBenz #Oldtimer #Classiccar...  \n",
       "7665  [@Alice Hey Slice, being that you're Miss Mann...  \n",
       "7666  [Update #degooglisation :❌Textra ✅Simple SMS M...  \n",
       "\n",
       "[7667 rows x 5 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This is the most computationally intensive portion of the notebook, the trainig phase\n",
    "# Without the hardware acceleration so kindly offered by google, it would've definitely\n",
    "# Taken MUCH longer to train this model\n",
    "from bertopic import BERTopic\n",
    "\n",
    "topic_model = BERTopic(embedding_model=\"all-MiniLM-L6-v2\", verbose=True)\n",
    "\n",
    "# topic_model.get_topic_info()\n",
    "topics, probs = topic_model.fit_transform(docs)\n",
    "topic_model.get_topic_info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "d5w_aHcp4POY"
   },
   "outputs": [],
   "source": [
    "# After the training is complete, an important step is to export the trined model\n",
    "# So that I may use it in the future\n",
    "\n",
    "embedding_model = \"sentence-transformers/all-MiniLM-L6-v2\"\n",
    "\n",
    "topic_model.save(\"/content/drive/MyDrive/\", serialization=\"pytorch\", save_ctfidf=True, save_embedding_model=embedding_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "b4KbyvolFjSs",
    "outputId": "3e3fdbaf-7353-49c6-fccc-9ceafa397516"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/content/drive/MyDrive/models/bertopic_mastodon_model.pkl']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Alternative save format\n",
    "import joblib\n",
    "\n",
    "model_filename = \"/content/drive/MyDrive/models/bertopic_mastodon_model.pkl\"\n",
    "joblib.dump(topic_model, model_filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lP9vdiDVGfrs"
   },
   "source": [
    "End of the model trainig phase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "uoQRHqsXGiEA"
   },
   "outputs": [],
   "source": []
  }
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