code-all.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jM69npiLKlDM"
   },
   "source": [
    "**Question - 2**\n",
    "\n",
    "Used model is \"Distill Bert\". which is a model. The Model and Tokenizer from hugging face - \"distilbert-base-uncased\". First Creating the dataset by extracting data from youtube comments."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2hMnb7EvS25n"
   },
   "source": [
    "**Creating out custom dataset from youtube comments by using Google API**\n",
    "Using 3 categories \"Movies\", \"Technology\" and \"sports\" as lables 0,1 and 2 respectivly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "G8EKsFT6UgMm",
    "outputId": "b42c4ec4-6424-4361-adb3-8aed63080ca7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting langdetect\n",
      "  Downloading langdetect-1.0.9.tar.gz (981 kB)\n",
      "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/981.5 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r",
      "\u001b[2K     \u001b[91m━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m61.4/981.5 kB\u001b[0m \u001b[31m1.6 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r",
      "\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m972.8/981.5 kB\u001b[0m \u001b[31m15.1 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m981.5/981.5 kB\u001b[0m \u001b[31m12.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from langdetect) (1.16.0)\n",
      "Building wheels for collected packages: langdetect\n",
      "  Building wheel for langdetect (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for langdetect: filename=langdetect-1.0.9-py3-none-any.whl size=993227 sha256=6bbdddb2458face216d7a965320e4c6264d621db9c9688209d35798d4e51caa1\n",
      "  Stored in directory: /root/.cache/pip/wheels/95/03/7d/59ea870c70ce4e5a370638b5462a7711ab78fba2f655d05106\n",
      "Successfully built langdetect\n",
      "Installing collected packages: langdetect\n",
      "Successfully installed langdetect-1.0.9\n"
     ]
    }
   ],
   "source": [
    "!pip install langdetect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "HBLTNlZ9Uc0y"
   },
   "outputs": [],
   "source": [
    "import googleapiclient.discovery\n",
    "import pandas as pd\n",
    "from langdetect import detect\n",
    "from sklearn.model_selection import train_test_split\n",
    "import string\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import string\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "uzgpzU20Uwlt"
   },
   "outputs": [],
   "source": [
    "api_service_name = \"youtube\"\n",
    "api_version = \"v3\"\n",
    "DEVELOPER_KEY = \"Using personal Google API key\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "Vh9MSYMBUyXE"
   },
   "outputs": [],
   "source": [
    "youtube = googleapiclient.discovery.build(\n",
    "    api_service_name, api_version, developerKey=DEVELOPER_KEY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "plZ7H_VdU0iv"
   },
   "outputs": [],
   "source": [
    "def clean_text(text):\n",
    "    #Remove hyper links\n",
    "    text = re.sub(r'https?:\\/\\/\\S+', ' ', text)\n",
    "    #Remove @mentions\n",
    "    text = re.sub(r'@[A-Za-z0-9]+', ' ', text)\n",
    "    #Removing hashtags\n",
    "    text = re.sub(r'#[A-Za-z0-9]+', ' ', text)\n",
    "    #Remove anything that isn't a letter, number, or one of the punctuation marks listed\n",
    "    text = re.sub(r\"[^A-Za-z0-9#'?!,.]+\", ' ', text)\n",
    "    #The following regular expression are for removing apostrophes and expanding words\n",
    "    text = re.sub(r\"\\'s\", \" is\", text)\n",
    "    text = re.sub(r\"n\\'t\", \" not\", text)\n",
    "    text = re.sub(r\"\\'m\", \" am\", text)\n",
    "    text = re.sub(r\"\\'d\", \" would\", text)\n",
    "    text = re.sub(r\"won\\'t\", \"will not\", text)\n",
    "    text = re.sub(r\"can\\'t\", \"can not\", text)\n",
    "    text = re.sub(r\"\\'re\", \" are\", text)\n",
    "    text = re.sub(r\"\\'s\", \" is\", text)\n",
    "    text = re.sub(r\"\\'t\", \" not\", text)\n",
    "    text = re.sub(r\"\\'ll\", \" will\", text)\n",
    "    text = re.sub(r\"\\'ve\", \" have\", text)\n",
    "    #Removing other items\n",
    "    emoji_pattern = re.compile(\"[\"u\"\\U0001F600-\\U0001F64F\"  # emoticons\n",
    "                                  u\"\\U0001F300-\\U0001F5FF\"  # symbols & pictographs\n",
    "                                  u\"\\U0001F680-\\U0001F6FF\"  # transport & map symbols\n",
    "                                u\"\\U0001F1E0-\\U0001F1FF\"  # flags (iOS)\n",
    "                                \"]+\", flags=re.UNICODE)\n",
    "    emoji_pattern.sub(r'', text)\n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "90wUOZoHU0fO"
   },
   "outputs": [],
   "source": [
    "def get_data(id,cat):\n",
    "    request = youtube.commentThreads().list(\n",
    "        part=\"snippet\",\n",
    "        videoId=id,\n",
    "        maxResults=499)\n",
    "    response = request.execute()\n",
    "    comments = []\n",
    "    for item in response['items']:\n",
    "        comment = item['snippet']['topLevelComment']['snippet']\n",
    "        try:\n",
    "            if len(comment['textDisplay'])>30:\n",
    "                if detect(comment['textDisplay'])=='en':\n",
    "                    text = clean_text(comment['textDisplay'])\n",
    "                    comments.append([text, cat])\n",
    "        except:\n",
    "            continue\n",
    "    df = pd.DataFrame(comments, columns=['text', 'label'])\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "L7whxH8WU0dA"
   },
   "outputs": [],
   "source": [
    "cat=int(0)\n",
    "# movie - 1\n",
    "v_id=\"94TAFSMdkvk\"\n",
    "movie1_df = get_data(v_id,cat)\n",
    "\n",
    "# movie - 2\n",
    "v_id=\"3hqPFylPSns\"\n",
    "movie2_df = get_data(v_id,cat)\n",
    "\n",
    "# movie - 3\n",
    "v_id=\"9GEEMaVuFPE\"\n",
    "movie3_df = get_data(v_id,cat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "KqozHXu1U0ap",
    "outputId": "07cdd166-538d-4c91-d341-82b752358831"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                  text  label\n",
      "0    I remember in 2007 wondering whether Heath Led...      0\n",
      "1    Bro I cried so hard when the most intense part...      0\n",
      "2    Chills. Just utter chills, as the unplanned Ba...      0\n",
      "3    I hate all Dark Night content. Who tf wants an...      0\n",
      "4        Always makes me go cold, brilliant soundtrack      0\n",
      "..                                                 ...    ...\n",
      "208  I love this film, and I think every scene is b...      0\n",
      "209  MOST OF THE SCENES WERE TO DARK TO SEE WHAT WA...      0\n",
      "210              Groundhog Day with robots and aliens       0\n",
      "211          Man this movie is definitely my favorite.      0\n",
      "212  I would love a Prequal based around Rita and h...      0\n",
      "\n",
      "[213 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "movie_df = pd.concat([movie1_df, movie2_df, movie3_df], ignore_index=True)\n",
    "print(movie_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "hsbfTbyAU0YH"
   },
   "outputs": [],
   "source": [
    "mtrain, mtest = train_test_split(movie_df, test_size=0.25, random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "eXcYTIxYU0WD"
   },
   "outputs": [],
   "source": [
    "# technology - 1\n",
    "cat=int(1)\n",
    "# iPhone 15 Review: Welcome to the Club!\n",
    "v_id=\"0X0Jm8QValY\"\n",
    "technology1_df = get_data(v_id,cat)\n",
    "\n",
    "# technology - 2\n",
    "# Using Apple Vision Pro: What It’s Actually Like!\n",
    "v_id=\"dtp6b76pMak\"\n",
    "technology2_df = get_data(v_id,cat)\n",
    "\n",
    "# # technology - 3\n",
    "# # The NEW Asus ROG Zephyrus G16 and G14\n",
    "v_id=\"Qr0Xvw9cjCU\"\n",
    "technology3_df = get_data(v_id,cat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "RB62ylntU0Tt",
    "outputId": "69560ff5-99fc-46de-c315-13be0b0347c0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                  text  label\n",
      "0    you catch my attention. i wish i could grab so...      1\n",
      "1    Due to the pink color I will be using the 15 p...      1\n",
      "2     quot With the brightness of a box of crayons ...      1\n",
      "3    I still find it funny talking about refresh ra...      1\n",
      "4    My 13 is starting to crap out so idk if I wann...      1\n",
      "..                                                 ...    ...\n",
      "243  Boss, what do I think is the best performance ...      1\n",
      "244  If the 16 inch coming with that silver color a...      1\n",
      "245  g14 will work for a year and then it will brea...      1\n",
      "246  How is a 4090 115 watts compared to a 4090 175...      1\n",
      "247  I m on the 2020 G14 with the AMD Ryzen 9 4900H...      1\n",
      "\n",
      "[248 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "technology_df = pd.concat([technology1_df, technology2_df, technology3_df],ignore_index=True)#, technology4_df, technology5_df], ignore_index=True)\n",
    "print(technology_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "hsNjhpG4U0Ql"
   },
   "outputs": [],
   "source": [
    "ttrain, ttest = train_test_split(technology_df, test_size=0.25, random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "lLDJB-PlU0N6"
   },
   "outputs": [],
   "source": [
    "# Sports - 2\n",
    "cat=int(2)\n",
    "\n",
    "v_id=\"XxOh12Uhg08\"\n",
    "s1_df = get_data(v_id,cat)\n",
    "\n",
    "# 2 - 2\n",
    "v_id=\"s8WHY3Gusx0\"\n",
    "s2_df = get_data(v_id,cat)\n",
    "\n",
    "# 2 - 3\n",
    "v_id=\"qk7pZnxCX2o\"\n",
    "s3_df = get_data(v_id,cat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "cfgyXNf6U0LG",
    "outputId": "530410cb-af69-4432-e5fa-a9ca223408d7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                  text  label\n",
      "0                Horrible design and bad construction       2\n",
      "1    the most overengineered thing Ive ever seen! i...      2\n",
      "2     a href yes, accelerate to full speed in order...      2\n",
      "3    the thing looks ugly as hell, but to be fair, ...      2\n",
      "4        Everything I have seen . It is a hunk of junk      2\n",
      "..                                                 ...    ...\n",
      "188                                 a href where it is      2\n",
      "189  Idk how they put all these trail cams up on my...      2\n",
      "190  i have a HEARTATTACK when i saw this dog at a ...      2\n",
      "191  How i think i look like when try to drift usin...      2\n",
      "192  Whenever you see a shell car you know your abo...      2\n",
      "\n",
      "[193 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "s_df = pd.concat([s1_df, s2_df,s3_df], ignore_index=True)\n",
    "print(s_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "WG8kbGAIU0Ih"
   },
   "outputs": [],
   "source": [
    "strain, stest = train_test_split(s_df, test_size=0.25, random_state=42)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YlPw8hBnVwqx"
   },
   "source": [
    "Combining all dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "LI-zyuNoU0F1",
    "outputId": "a5a0f091-5a87-4b87-8d05-042ce5a8b11f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(489, 2)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = pd.concat([mtrain, ttrain, strain],ignore_index=True)\n",
    "train_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "b62Y7lWMUz6A",
    "outputId": "2caec209-a3b8-49c8-d99a-2b97634d2a24"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(165, 2)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df = pd.concat([mtest, ttest, stest],ignore_index=True)\n",
    "test_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "JujR1-9NVvwP"
   },
   "outputs": [],
   "source": [
    "train_df.to_csv(\"train.csv\", sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "7vikbJbQVvs9"
   },
   "outputs": [],
   "source": [
    "test_df.to_csv(\"test.csv\", sep=',')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FxBivyBUWAas"
   },
   "source": [
    "Now Finetuning the model and testing accuracy using the dataset we created"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1dgjKea19EFj",
    "outputId": "8d56d18b-f738-410f-c683-a5625c3fddff"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.40.0)\n",
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     ]
    }
   ],
   "source": [
    "!pip install transformers datasets torch pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "nfSI63SS9KGV"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from datasets import Dataset\n",
    "\n",
    "train_data = pd.read_csv('train.csv')\n",
    "train_texts = train_data['text'].tolist()\n",
    "train_labels = train_data['label'].tolist()\n",
    "test_data = pd.read_csv('test.csv')\n",
    "test_texts = test_data['text'].tolist()\n",
    "test_labels = test_data['label'].tolist()\n",
    "\n",
    "# Convert to our data to Hugging Face dataset format\n",
    "train_dataset = Dataset.from_dict({'text': train_texts, 'label': train_labels})\n",
    "test_dataset = Dataset.from_dict({'text': test_texts, 'label': test_labels})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 185,
     "referenced_widgets": [
      "b1320bb97f8147998f692720db989ee5",
      "b25a5e17a7c447d797c8b6535e532ec5",
      "5ae9561eb2154685a6391626abec2d95",
      "ba68f801ae614f309d1eb76ff660c9a2",
      "42984cce14014231a49ae3699fd69e08",
      "a286c0750d0543e5ab04ad3c3bd0e975",
      "f6c60262b64b47eebdc6987436699877",
      "b2dea6f20fd548ae8dd5ea67b82a6e4e",
      "365c7fc5ab9145178e866075be21cb29",
      "94c26687c702408699c4776d91533265",
      "01a31c2c0e0f46cdba7cefcfbad9821c",
      "a1af033a7b124118bcd50b6aaa8db47e",
      "4bb33bc07d374219a2f4a46a672acea8",
      "427c9f4950654a4d864b89e72b211d6d",
      "4f4cc720a25f4636996810736a3767bc",
      "89e660b509db425c93e9a21dc72a866d",
      "81bc831d716840da8e19f2a9bfb8ef69",
      "7af2227c888c4ff39120b1b56c3ab066",
      "e87844b25a6f431fb40282e4ec01c169",
      "226e4ad676994c97bbd00e17d3468c42",
      "7a1a920a292545a0be1b96ddb0132557",
      "286db40bcd724ea49ea444435f0556ee"
     ]
    },
    "id": "0Da6S83uAEy8",
    "outputId": "8b0a0d75-dccc-4d6b-aad2-7cf44d3bd0ed"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
      "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
      "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
      "You will be able to reuse this secret in all of your notebooks.\n",
      "Please note that authentication is recommended but still optional to access public models or datasets.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b1320bb97f8147998f692720db989ee5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/489 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a1af033a7b124118bcd50b6aaa8db47e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/165 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Loading the tokeniser and tokeninsing the data\n",
    "from transformers import DistilBertTokenizer\n",
    "\n",
    "tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')\n",
    "\n",
    "def tokenize_function(examples):\n",
    "    return tokenizer(examples['text'], padding=\"max_length\", truncation=True, max_length=512)\n",
    "\n",
    "tokenized_train = train_dataset.map(tokenize_function, batched=True)\n",
    "tokenized_test = test_dataset.map(tokenize_function, batched=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "iQyzOnuHAMHh",
    "outputId": "f2d08edd-a715-4ef4-a53e-058259e54c02"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight', 'pre_classifier.bias', 'pre_classifier.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    }
   ],
   "source": [
    "#Loading the model\n",
    "from transformers import DistilBertForSequenceClassification\n",
    "model = DistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased', num_labels=len(set(train_labels)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "EGAIIUxFAac_",
    "outputId": "5753cf00-3d54-4360-ce9f-1531adec9152"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: accelerate in /usr/local/lib/python3.10/dist-packages (0.29.3)\n",
      "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from accelerate) (1.25.2)\n",
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      "Requirement already satisfied: torch>=1.10.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (2.2.1+cu121)\n",
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      "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (12.1.105)\n",
      "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (12.1.105)\n",
      "Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (8.9.2.26)\n",
      "Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (12.1.3.1)\n",
      "Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (11.0.2.54)\n",
      "Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (10.3.2.106)\n",
      "Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (11.4.5.107)\n",
      "Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (12.1.0.106)\n",
      "Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2.19.3)\n",
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      "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate) (2.31.0)\n",
      "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate) (4.66.2)\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.10.0->accelerate) (2.1.5)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (3.3.2)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (3.7)\n",
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      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (2024.2.2)\n",
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     ]
    }
   ],
   "source": [
    "!pip install accelerate -U"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_cENU6_SB9dt",
    "outputId": "cf379222-255a-41e3-a1d2-c1f65e0f8b53"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-31-faaf2f42c7b8>:2: FutureWarning: load_metric is deprecated and will be removed in the next major version of datasets. Use 'evaluate.load' instead, from the new library 🤗 Evaluate: https://huggingface.co/docs/evaluate\n",
      "  accuracy_metric = load_metric(\"accuracy\")\n",
      "/usr/local/lib/python3.10/dist-packages/datasets/load.py:759: FutureWarning: The repository for accuracy contains custom code which must be executed to correctly load the metric. You can inspect the repository content at https://raw.githubusercontent.com/huggingface/datasets/2.19.0/metrics/accuracy/accuracy.py\n",
      "You can avoid this message in future by passing the argument `trust_remote_code=True`.\n",
      "Passing `trust_remote_code=True` will be mandatory to load this metric from the next major release of `datasets`.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "from datasets import load_metric\n",
    "accuracy_metric = load_metric(\"accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "id": "jt8ctqWIB6tR"
   },
   "outputs": [],
   "source": [
    "#Defining compueting metrices\n",
    "def compute_metrics(eval_pred):\n",
    "    logits, labels = eval_pred\n",
    "    predictions = logits.argmax(axis=-1)\n",
    "    return accuracy_metric.compute(predictions=predictions, references=labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "id": "n3QUfVoGAUyn"
   },
   "outputs": [],
   "source": [
    "from transformers import Trainer, TrainingArguments\n",
    "\n",
    "training_args = TrainingArguments(\n",
    "    output_dir='./results',\n",
    "    num_train_epochs=5,\n",
    "    # per_device_train_batch_size=8,\n",
    "    # logging_dir='./logs',\n",
    "    # logging_steps=1,  # Log every 10 steps\n",
    "    # evaluation_strategy='epoch',\n",
    "    # learning_rate= 5e-5,\n",
    "    # per_device_train_batch_size=4,  # Smaller batch size\n",
    "    # logging_steps=1,  # Log after every step\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 113
    },
    "id": "zZYg5IVcAWkC",
    "outputId": "34de85c9-299a-448b-90c2-cdfd5d026387"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='310' max='310' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [310/310 01:49, Epoch 5/5]\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>"
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       "      \n",
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       "      [21/21 00:02]\n",
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       "    "
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       "<IPython.core.display.HTML object>"
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     "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eval_loss': 0.6406182646751404, 'eval_accuracy': 0.8606060606060606, 'eval_runtime': 2.6534, 'eval_samples_per_second': 62.184, 'eval_steps_per_second': 7.914, 'epoch': 5.0}\n"
     ]
    }
   ],
   "source": [
    "trainer = Trainer(\n",
    "    model=model,\n",
    "    args=training_args,\n",
    "    train_dataset=tokenized_train,\n",
    "    eval_dataset=tokenized_test,\n",
    "    compute_metrics=compute_metrics\n",
    ")\n",
    "\n",
    "# Train the model\n",
    "trainer.train()\n",
    "\n",
    "evaluation_results = trainer.evaluate()\n",
    "print(evaluation_results)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-Q5gWoI7WdRL"
   },
   "source": [
    "Here is the accuracy of our model which is 86%. Distill-Bert model works with low number of parameters which makes it better for small and downstream instead of larger models like GPT, Mistral or Claude. For better accuracy than this we probabaly need more amount of data and pre-processing techniques for improving the quality of data. Although Youtube comments data does have a lot of outliers as well, so removing them will help in improving the model accuracy. Increasing amount of data is nessesary so that we can include more hyperparameters as well for fine tuning the model. And more."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "riJnletTHvah"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "gpuType": "T4",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
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