basic-kanarya.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eDQySpu7QwMB"
   },
   "source": [
    "# Hate Speech Detection in Turkish with LLM Kanarya"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2024-05-08T21:00:33.860237Z",
     "iopub.status.busy": "2024-05-08T21:00:33.859060Z",
     "iopub.status.idle": "2024-05-08T21:00:35.332803Z",
     "shell.execute_reply": "2024-05-08T21:00:35.331795Z",
     "shell.execute_reply.started": "2024-05-08T21:00:33.860186Z"
    },
    "executionInfo": {
     "elapsed": 28126,
     "status": "ok",
     "timestamp": 1715007981679,
     "user": {
      "displayName": "Bruce Wayne",
      "userId": "03236562819884458513"
     },
     "user_tz": -180
    },
    "id": "A_vNdve-Q_Sg",
    "outputId": "2e3f5ab3-8f01-4482-98af-4cd342885001"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/kaggle/input/hate-speech/Subtask_A/tr_gr_train.csv\n",
      "/kaggle/input/hate-speech/Subtask_A/refugee_train.csv\n",
      "/kaggle/input/hate-speech/Subtask_A/isr_pal_train.csv\n",
      "/kaggle/input/hate-speech/Subtask_A/.ipynb_checkpoints/isr_pal_train-checkpoint.csv\n",
      "/kaggle/input/hate-speech/Subtask_A/.ipynb_checkpoints/refugee_train-checkpoint.csv\n",
      "/kaggle/input/hate-speech/Subtask_A/.ipynb_checkpoints/tr_gr_train-checkpoint.csv\n",
      "/kaggle/input/hate-speech/Subtask_A/test/TaskA_withoutLabels.csv\n",
      "/kaggle/input/hate-speech/Subtask_A/test/TaskA_sampleSubmission.csv\n",
      "/kaggle/input/hate-speech/Subtask_A/test/.ipynb_checkpoints/TaskA_withoutLabels-checkpoint.csv\n",
      "/kaggle/input/hate-speech/Subtask_A/test/.ipynb_checkpoints/TaskA_sampleSubmission-checkpoint.csv\n",
      "/kaggle/input/adv-hate-spch/adv_df_refugee_train.csv\n",
      "/kaggle/input/adv-hate-spch/adv_df_tr_gr_train.csv\n",
      "/kaggle/input/adv-hate-spch/adv_df_isr_pal_train.csv\n"
     ]
    }
   ],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2024-05-08T21:00:35.335403Z",
     "iopub.status.busy": "2024-05-08T21:00:35.334830Z",
     "iopub.status.idle": "2024-05-08T21:05:58.917935Z",
     "shell.execute_reply": "2024-05-08T21:05:58.915781Z",
     "shell.execute_reply.started": "2024-05-08T21:00:35.335364Z"
    },
    "executionInfo": {
     "elapsed": 152680,
     "status": "ok",
     "timestamp": 1715008134355,
     "user": {
      "displayName": "Bruce Wayne",
      "userId": "03236562819884458513"
     },
     "user_tz": -180
    },
    "id": "zwVi3tv_QxmN",
    "outputId": "563b5db1-b116-4228-f3cf-b5fca6d848f4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "\u001b[?25hInstalling collected packages: triton, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, torch\n",
      "  Attempting uninstall: torch\n",
      "    Found existing installation: torch 2.1.2+cpu\n",
      "    Uninstalling torch-2.1.2+cpu:\n",
      "      Successfully uninstalled torch-2.1.2+cpu\n",
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "torchaudio 2.1.2+cpu requires torch==2.1.2, but you have torch 2.2.1 which is incompatible.\n",
      "torchtext 0.16.2+cpu requires torch==2.1.2, but you have torch 2.2.1 which is incompatible.\n",
      "torchvision 0.16.2+cpu requires torch==2.1.2, but you have torch 2.2.1 which is incompatible.\u001b[0m\u001b[31m\n",
      "\u001b[0mSuccessfully installed nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.19.3 nvidia-nvjitlink-cu12-12.4.127 nvidia-nvtx-cu12-12.1.105 torch-2.2.1 triton-2.2.0\n",
      "Collecting transformers==4.36.2\n",
      "  Downloading transformers-4.36.2-py3-none-any.whl.metadata (126 kB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m126.8/126.8 kB\u001b[0m \u001b[31m3.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n",
      "\u001b[?25hCollecting datasets==2.16.1\n",
      "  Downloading datasets-2.16.1-py3-none-any.whl.metadata (20 kB)\n",
      "Collecting accelerate==0.26.1\n",
      "  Downloading accelerate-0.26.1-py3-none-any.whl.metadata (18 kB)\n",
      "Collecting evaluate==0.4.1\n",
      "  Downloading evaluate-0.4.1-py3-none-any.whl.metadata (9.4 kB)\n",
      "Collecting bitsandbytes==0.42.0\n",
      "  Downloading bitsandbytes-0.42.0-py3-none-any.whl.metadata (9.9 kB)\n",
      "Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from transformers==4.36.2) (3.13.1)\n",
      "Requirement already satisfied: huggingface-hub<1.0,>=0.19.3 in /opt/conda/lib/python3.10/site-packages (from transformers==4.36.2) (0.22.2)\n",
      "Requirement already satisfied: numpy>=1.17 in /opt/conda/lib/python3.10/site-packages (from transformers==4.36.2) (1.26.4)\n",
      "Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from transformers==4.36.2) (21.3)\n",
      "Requirement already satisfied: pyyaml>=5.1 in /opt/conda/lib/python3.10/site-packages (from transformers==4.36.2) (6.0.1)\n",
      "Requirement already satisfied: regex!=2019.12.17 in /opt/conda/lib/python3.10/site-packages (from transformers==4.36.2) (2023.12.25)\n",
      "Requirement already satisfied: requests in /opt/conda/lib/python3.10/site-packages (from transformers==4.36.2) (2.31.0)\n",
      "Requirement already satisfied: tokenizers<0.19,>=0.14 in /opt/conda/lib/python3.10/site-packages (from transformers==4.36.2) (0.15.2)\n",
      "Requirement already satisfied: safetensors>=0.3.1 in /opt/conda/lib/python3.10/site-packages (from transformers==4.36.2) (0.4.3)\n",
      "Requirement already satisfied: tqdm>=4.27 in /opt/conda/lib/python3.10/site-packages (from transformers==4.36.2) (4.66.1)\n",
      "Requirement already satisfied: pyarrow>=8.0.0 in /opt/conda/lib/python3.10/site-packages (from datasets==2.16.1) (15.0.2)\n",
      "Requirement already satisfied: pyarrow-hotfix in /opt/conda/lib/python3.10/site-packages (from datasets==2.16.1) (0.6)\n",
      "Collecting dill<0.3.8,>=0.3.0 (from datasets==2.16.1)\n",
      "  Downloading dill-0.3.7-py3-none-any.whl.metadata (9.9 kB)\n",
      "Requirement already satisfied: pandas in /opt/conda/lib/python3.10/site-packages (from datasets==2.16.1) (2.2.2)\n",
      "Requirement already satisfied: xxhash in /opt/conda/lib/python3.10/site-packages (from datasets==2.16.1) (3.4.1)\n",
      "Requirement already satisfied: multiprocess in /opt/conda/lib/python3.10/site-packages (from datasets==2.16.1) (0.70.16)\n",
      "Collecting fsspec<=2023.10.0,>=2023.1.0 (from fsspec[http]<=2023.10.0,>=2023.1.0->datasets==2.16.1)\n",
      "  Downloading fsspec-2023.10.0-py3-none-any.whl.metadata (6.8 kB)\n",
      "Requirement already satisfied: aiohttp in /opt/conda/lib/python3.10/site-packages (from datasets==2.16.1) (3.9.1)\n",
      "Requirement already satisfied: psutil in /opt/conda/lib/python3.10/site-packages (from accelerate==0.26.1) (5.9.3)\n",
      "Requirement already satisfied: torch>=1.10.0 in /opt/conda/lib/python3.10/site-packages (from accelerate==0.26.1) (2.2.1)\n",
      "Collecting responses<0.19 (from evaluate==0.4.1)\n",
      "  Downloading responses-0.18.0-py3-none-any.whl.metadata (29 kB)\n",
      "Requirement already satisfied: scipy in /opt/conda/lib/python3.10/site-packages (from bitsandbytes==0.42.0) (1.11.4)\n",
      "Requirement already satisfied: attrs>=17.3.0 in /opt/conda/lib/python3.10/site-packages (from aiohttp->datasets==2.16.1) (23.2.0)\n",
      "Requirement already satisfied: multidict<7.0,>=4.5 in /opt/conda/lib/python3.10/site-packages (from aiohttp->datasets==2.16.1) (6.0.4)\n",
      "Requirement already satisfied: yarl<2.0,>=1.0 in /opt/conda/lib/python3.10/site-packages (from aiohttp->datasets==2.16.1) (1.9.3)\n",
      "Requirement already satisfied: frozenlist>=1.1.1 in /opt/conda/lib/python3.10/site-packages (from aiohttp->datasets==2.16.1) (1.4.1)\n",
      "Requirement already satisfied: aiosignal>=1.1.2 in /opt/conda/lib/python3.10/site-packages (from aiohttp->datasets==2.16.1) (1.3.1)\n",
      "Requirement already satisfied: async-timeout<5.0,>=4.0 in /opt/conda/lib/python3.10/site-packages (from aiohttp->datasets==2.16.1) (4.0.3)\n",
      "Requirement already satisfied: typing-extensions>=3.7.4.3 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub<1.0,>=0.19.3->transformers==4.36.2) (4.9.0)\n",
      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.10/site-packages (from packaging>=20.0->transformers==4.36.2) (3.1.1)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/lib/python3.10/site-packages (from requests->transformers==4.36.2) (3.3.2)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests->transformers==4.36.2) (3.6)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests->transformers==4.36.2) (1.26.18)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests->transformers==4.36.2) (2024.2.2)\n",
      "Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch>=1.10.0->accelerate==0.26.1) (1.12)\n",
      "Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch>=1.10.0->accelerate==0.26.1) (3.2.1)\n",
      "Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch>=1.10.0->accelerate==0.26.1) (3.1.2)\n",
      "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch>=1.10.0->accelerate==0.26.1) (12.1.105)\n",
      "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch>=1.10.0->accelerate==0.26.1) (12.1.105)\n",
      "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch>=1.10.0->accelerate==0.26.1) (12.1.105)\n",
      "Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /opt/conda/lib/python3.10/site-packages (from torch>=1.10.0->accelerate==0.26.1) (8.9.2.26)\n",
      "Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /opt/conda/lib/python3.10/site-packages (from torch>=1.10.0->accelerate==0.26.1) (12.1.3.1)\n",
      "Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /opt/conda/lib/python3.10/site-packages (from torch>=1.10.0->accelerate==0.26.1) (11.0.2.54)\n",
      "Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /opt/conda/lib/python3.10/site-packages (from torch>=1.10.0->accelerate==0.26.1) (10.3.2.106)\n",
      "Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /opt/conda/lib/python3.10/site-packages (from torch>=1.10.0->accelerate==0.26.1) (11.4.5.107)\n",
      "Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /opt/conda/lib/python3.10/site-packages (from torch>=1.10.0->accelerate==0.26.1) (12.1.0.106)\n",
      "Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /opt/conda/lib/python3.10/site-packages (from torch>=1.10.0->accelerate==0.26.1) (2.19.3)\n",
      "Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch>=1.10.0->accelerate==0.26.1) (12.1.105)\n",
      "Requirement already satisfied: triton==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torch>=1.10.0->accelerate==0.26.1) (2.2.0)\n",
      "Requirement already satisfied: nvidia-nvjitlink-cu12 in /opt/conda/lib/python3.10/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch>=1.10.0->accelerate==0.26.1) (12.4.127)\n",
      "INFO: pip is looking at multiple versions of multiprocess to determine which version is compatible with other requirements. This could take a while.\n",
      "Collecting multiprocess (from datasets==2.16.1)\n",
      "  Downloading multiprocess-0.70.15-py310-none-any.whl.metadata (7.2 kB)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in /opt/conda/lib/python3.10/site-packages (from pandas->datasets==2.16.1) (2.9.0.post0)\n",
      "Requirement already satisfied: pytz>=2020.1 in /opt/conda/lib/python3.10/site-packages (from pandas->datasets==2.16.1) (2023.3.post1)\n",
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      "Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch>=1.10.0->accelerate==0.26.1) (2.1.3)\n",
      "Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch>=1.10.0->accelerate==0.26.1) (1.3.0)\n",
      "Downloading transformers-4.36.2-py3-none-any.whl (8.2 MB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.2/8.2 MB\u001b[0m \u001b[31m61.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m00:01\u001b[0m\n",
      "\u001b[?25hDownloading datasets-2.16.1-py3-none-any.whl (507 kB)\n",
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      "\u001b[?25hDownloading accelerate-0.26.1-py3-none-any.whl (270 kB)\n",
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      "\u001b[?25hDownloading evaluate-0.4.1-py3-none-any.whl (84 kB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.1/84.1 kB\u001b[0m \u001b[31m4.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hDownloading bitsandbytes-0.42.0-py3-none-any.whl (105.0 MB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m105.0/105.0 MB\u001b[0m \u001b[31m13.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
      "\u001b[?25hDownloading dill-0.3.7-py3-none-any.whl (115 kB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.3/115.3 kB\u001b[0m \u001b[31m5.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hDownloading fsspec-2023.10.0-py3-none-any.whl (166 kB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m166.4/166.4 kB\u001b[0m \u001b[31m8.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hDownloading responses-0.18.0-py3-none-any.whl (38 kB)\n",
      "Downloading multiprocess-0.70.15-py310-none-any.whl (134 kB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m7.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hInstalling collected packages: fsspec, dill, responses, multiprocess, bitsandbytes, transformers, datasets, accelerate, evaluate\n",
      "  Attempting uninstall: fsspec\n",
      "    Found existing installation: fsspec 2024.2.0\n",
      "    Uninstalling fsspec-2024.2.0:\n",
      "      Successfully uninstalled fsspec-2024.2.0\n",
      "  Attempting uninstall: dill\n",
      "    Found existing installation: dill 0.3.8\n",
      "    Uninstalling dill-0.3.8:\n",
      "      Successfully uninstalled dill-0.3.8\n",
      "  Attempting uninstall: multiprocess\n",
      "    Found existing installation: multiprocess 0.70.16\n",
      "    Uninstalling multiprocess-0.70.16:\n",
      "      Successfully uninstalled multiprocess-0.70.16\n",
      "  Attempting uninstall: transformers\n",
      "    Found existing installation: transformers 4.39.3\n",
      "    Uninstalling transformers-4.39.3:\n",
      "      Successfully uninstalled transformers-4.39.3\n",
      "  Attempting uninstall: datasets\n",
      "    Found existing installation: datasets 2.18.0\n",
      "    Uninstalling datasets-2.18.0:\n",
      "      Successfully uninstalled datasets-2.18.0\n",
      "  Attempting uninstall: accelerate\n",
      "    Found existing installation: accelerate 0.29.3\n",
      "    Uninstalling accelerate-0.29.3:\n",
      "      Successfully uninstalled accelerate-0.29.3\n",
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "apache-beam 2.46.0 requires dill<0.3.2,>=0.3.1.1, but you have dill 0.3.7 which is incompatible.\n",
      "apache-beam 2.46.0 requires numpy<1.25.0,>=1.14.3, but you have numpy 1.26.4 which is incompatible.\n",
      "apache-beam 2.46.0 requires pyarrow<10.0.0,>=3.0.0, but you have pyarrow 15.0.2 which is incompatible.\n",
      "gcsfs 2024.2.0 requires fsspec==2024.2.0, but you have fsspec 2023.10.0 which is incompatible.\n",
      "pathos 0.3.2 requires dill>=0.3.8, but you have dill 0.3.7 which is incompatible.\n",
      "pathos 0.3.2 requires multiprocess>=0.70.16, but you have multiprocess 0.70.15 which is incompatible.\n",
      "s3fs 2024.2.0 requires fsspec==2024.2.0, but you have fsspec 2023.10.0 which is incompatible.\n",
      "torchaudio 2.1.2+cpu requires torch==2.1.2, but you have torch 2.2.1 which is incompatible.\n",
      "torchtext 0.16.2+cpu requires torch==2.1.2, but you have torch 2.2.1 which is incompatible.\n",
      "torchvision 0.16.2+cpu requires torch==2.1.2, but you have torch 2.2.1 which is incompatible.\u001b[0m\u001b[31m\n",
      "\u001b[0mSuccessfully installed accelerate-0.26.1 bitsandbytes-0.42.0 datasets-2.16.1 dill-0.3.7 evaluate-0.4.1 fsspec-2023.10.0 multiprocess-0.70.15 responses-0.18.0 transformers-4.36.2\n",
      "Collecting git+https://github.com/huggingface/trl@a3c5b7178ac4f65569975efadc97db2f3749c65e\n",
      "  Cloning https://github.com/huggingface/trl (to revision a3c5b7178ac4f65569975efadc97db2f3749c65e) to /tmp/pip-req-build-lq4qrbw6\n",
      "  Running command git clone --filter=blob:none --quiet https://github.com/huggingface/trl /tmp/pip-req-build-lq4qrbw6\n",
      "  Running command git rev-parse -q --verify 'sha^a3c5b7178ac4f65569975efadc97db2f3749c65e'\n",
      "  Running command git fetch -q https://github.com/huggingface/trl a3c5b7178ac4f65569975efadc97db2f3749c65e\n",
      "  Running command git checkout -q a3c5b7178ac4f65569975efadc97db2f3749c65e\n",
      "  Resolved https://github.com/huggingface/trl to commit a3c5b7178ac4f65569975efadc97db2f3749c65e\n",
      "  Installing build dependencies ... \u001b[?25ldone\n",
      "\u001b[?25h  Getting requirements to build wheel ... \u001b[?25ldone\n",
      "\u001b[?25h  Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n",
      "\u001b[?25hRequirement already satisfied: torch>=1.4.0 in /opt/conda/lib/python3.10/site-packages (from trl==0.7.11.dev0) (2.2.1)\n",
      "Requirement already satisfied: transformers>=4.31.0 in /opt/conda/lib/python3.10/site-packages (from trl==0.7.11.dev0) (4.36.2)\n",
      "Requirement already satisfied: numpy>=1.18.2 in /opt/conda/lib/python3.10/site-packages (from trl==0.7.11.dev0) (1.26.4)\n",
      "Requirement already satisfied: accelerate in /opt/conda/lib/python3.10/site-packages (from trl==0.7.11.dev0) (0.26.1)\n",
      "Requirement already satisfied: datasets in /opt/conda/lib/python3.10/site-packages (from trl==0.7.11.dev0) (2.16.1)\n",
      "Collecting tyro>=0.5.11 (from trl==0.7.11.dev0)\n",
      "  Downloading tyro-0.8.3-py3-none-any.whl.metadata (7.9 kB)\n",
      "Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch>=1.4.0->trl==0.7.11.dev0) (3.13.1)\n",
      "Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/lib/python3.10/site-packages (from torch>=1.4.0->trl==0.7.11.dev0) (4.9.0)\n",
      "Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch>=1.4.0->trl==0.7.11.dev0) (1.12)\n",
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      "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch>=1.4.0->trl==0.7.11.dev0) (12.1.105)\n",
      "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch>=1.4.0->trl==0.7.11.dev0) (12.1.105)\n",
      "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch>=1.4.0->trl==0.7.11.dev0) (12.1.105)\n",
      "Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /opt/conda/lib/python3.10/site-packages (from torch>=1.4.0->trl==0.7.11.dev0) (8.9.2.26)\n",
      "Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /opt/conda/lib/python3.10/site-packages (from torch>=1.4.0->trl==0.7.11.dev0) (12.1.3.1)\n",
      "Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /opt/conda/lib/python3.10/site-packages (from torch>=1.4.0->trl==0.7.11.dev0) (11.0.2.54)\n",
      "Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /opt/conda/lib/python3.10/site-packages (from torch>=1.4.0->trl==0.7.11.dev0) (10.3.2.106)\n",
      "Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /opt/conda/lib/python3.10/site-packages (from torch>=1.4.0->trl==0.7.11.dev0) (11.4.5.107)\n",
      "Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /opt/conda/lib/python3.10/site-packages (from torch>=1.4.0->trl==0.7.11.dev0) (12.1.0.106)\n",
      "Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /opt/conda/lib/python3.10/site-packages (from torch>=1.4.0->trl==0.7.11.dev0) (2.19.3)\n",
      "Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch>=1.4.0->trl==0.7.11.dev0) (12.1.105)\n",
      "Requirement already satisfied: triton==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torch>=1.4.0->trl==0.7.11.dev0) (2.2.0)\n",
      "Requirement already satisfied: nvidia-nvjitlink-cu12 in /opt/conda/lib/python3.10/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch>=1.4.0->trl==0.7.11.dev0) (12.4.127)\n",
      "Requirement already satisfied: huggingface-hub<1.0,>=0.19.3 in /opt/conda/lib/python3.10/site-packages (from transformers>=4.31.0->trl==0.7.11.dev0) (0.22.2)\n",
      "Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from transformers>=4.31.0->trl==0.7.11.dev0) (21.3)\n",
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      "Requirement already satisfied: regex!=2019.12.17 in /opt/conda/lib/python3.10/site-packages (from transformers>=4.31.0->trl==0.7.11.dev0) (2023.12.25)\n",
      "Requirement already satisfied: requests in /opt/conda/lib/python3.10/site-packages (from transformers>=4.31.0->trl==0.7.11.dev0) (2.31.0)\n",
      "Requirement already satisfied: tokenizers<0.19,>=0.14 in /opt/conda/lib/python3.10/site-packages (from transformers>=4.31.0->trl==0.7.11.dev0) (0.15.2)\n",
      "Requirement already satisfied: safetensors>=0.3.1 in /opt/conda/lib/python3.10/site-packages (from transformers>=4.31.0->trl==0.7.11.dev0) (0.4.3)\n",
      "Requirement already satisfied: tqdm>=4.27 in /opt/conda/lib/python3.10/site-packages (from transformers>=4.31.0->trl==0.7.11.dev0) (4.66.1)\n",
      "Requirement already satisfied: docstring-parser>=0.14.1 in /opt/conda/lib/python3.10/site-packages (from tyro>=0.5.11->trl==0.7.11.dev0) (0.15)\n",
      "Requirement already satisfied: rich>=11.1.0 in /opt/conda/lib/python3.10/site-packages (from tyro>=0.5.11->trl==0.7.11.dev0) (13.7.0)\n",
      "Collecting shtab>=1.5.6 (from tyro>=0.5.11->trl==0.7.11.dev0)\n",
      "  Downloading shtab-1.7.1-py3-none-any.whl.metadata (7.3 kB)\n",
      "Requirement already satisfied: psutil in /opt/conda/lib/python3.10/site-packages (from accelerate->trl==0.7.11.dev0) (5.9.3)\n",
      "Requirement already satisfied: pyarrow>=8.0.0 in /opt/conda/lib/python3.10/site-packages (from datasets->trl==0.7.11.dev0) (15.0.2)\n",
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      "Requirement already satisfied: pandas in /opt/conda/lib/python3.10/site-packages (from datasets->trl==0.7.11.dev0) (2.2.2)\n",
      "Requirement already satisfied: xxhash in /opt/conda/lib/python3.10/site-packages (from datasets->trl==0.7.11.dev0) (3.4.1)\n",
      "Requirement already satisfied: multiprocess in /opt/conda/lib/python3.10/site-packages (from datasets->trl==0.7.11.dev0) (0.70.15)\n",
      "Requirement already satisfied: aiohttp in /opt/conda/lib/python3.10/site-packages (from datasets->trl==0.7.11.dev0) (3.9.1)\n",
      "Requirement already satisfied: attrs>=17.3.0 in /opt/conda/lib/python3.10/site-packages (from aiohttp->datasets->trl==0.7.11.dev0) (23.2.0)\n",
      "Requirement already satisfied: multidict<7.0,>=4.5 in /opt/conda/lib/python3.10/site-packages (from aiohttp->datasets->trl==0.7.11.dev0) (6.0.4)\n",
      "Requirement already satisfied: yarl<2.0,>=1.0 in /opt/conda/lib/python3.10/site-packages (from aiohttp->datasets->trl==0.7.11.dev0) (1.9.3)\n",
      "Requirement already satisfied: frozenlist>=1.1.1 in /opt/conda/lib/python3.10/site-packages (from aiohttp->datasets->trl==0.7.11.dev0) (1.4.1)\n",
      "Requirement already satisfied: aiosignal>=1.1.2 in /opt/conda/lib/python3.10/site-packages (from aiohttp->datasets->trl==0.7.11.dev0) (1.3.1)\n",
      "Requirement already satisfied: async-timeout<5.0,>=4.0 in /opt/conda/lib/python3.10/site-packages (from aiohttp->datasets->trl==0.7.11.dev0) (4.0.3)\n",
      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.10/site-packages (from packaging>=20.0->transformers>=4.31.0->trl==0.7.11.dev0) (3.1.1)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/lib/python3.10/site-packages (from requests->transformers>=4.31.0->trl==0.7.11.dev0) (3.3.2)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests->transformers>=4.31.0->trl==0.7.11.dev0) (3.6)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests->transformers>=4.31.0->trl==0.7.11.dev0) (1.26.18)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests->transformers>=4.31.0->trl==0.7.11.dev0) (2024.2.2)\n",
      "Requirement already satisfied: markdown-it-py>=2.2.0 in /opt/conda/lib/python3.10/site-packages (from rich>=11.1.0->tyro>=0.5.11->trl==0.7.11.dev0) (3.0.0)\n",
      "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /opt/conda/lib/python3.10/site-packages (from rich>=11.1.0->tyro>=0.5.11->trl==0.7.11.dev0) (2.17.2)\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch>=1.4.0->trl==0.7.11.dev0) (2.1.3)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in /opt/conda/lib/python3.10/site-packages (from pandas->datasets->trl==0.7.11.dev0) (2.9.0.post0)\n",
      "Requirement already satisfied: pytz>=2020.1 in /opt/conda/lib/python3.10/site-packages (from pandas->datasets->trl==0.7.11.dev0) (2023.3.post1)\n",
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      "Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch>=1.4.0->trl==0.7.11.dev0) (1.3.0)\n",
      "Requirement already satisfied: mdurl~=0.1 in /opt/conda/lib/python3.10/site-packages (from markdown-it-py>=2.2.0->rich>=11.1.0->tyro>=0.5.11->trl==0.7.11.dev0) (0.1.2)\n",
      "Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.10/site-packages (from python-dateutil>=2.8.2->pandas->datasets->trl==0.7.11.dev0) (1.16.0)\n",
      "Downloading tyro-0.8.3-py3-none-any.whl (102 kB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m102.0/102.0 kB\u001b[0m \u001b[31m588.5 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m \u001b[36m0:00:01\u001b[0m\n",
      "\u001b[?25hDownloading shtab-1.7.1-py3-none-any.whl (14 kB)\n",
      "Building wheels for collected packages: trl\n",
      "  Building wheel for trl (pyproject.toml) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for trl: filename=trl-0.7.11.dev0-py3-none-any.whl size=151090 sha256=0483b37766fa6782323433ab4569e33cf8db418e2e3287a2e55994a1b48985f1\n",
      "  Stored in directory: /root/.cache/pip/wheels/69/fc/ec/e718a169cea1752bd9c3eb798e9d8a56686d0752c49f97e1c1\n",
      "Successfully built trl\n",
      "Installing collected packages: shtab, tyro, trl\n",
      "Successfully installed shtab-1.7.1 trl-0.7.11.dev0 tyro-0.8.3\n",
      "Collecting git+https://github.com/huggingface/peft@4a1559582281fc3c9283892caea8ccef1d6f5a4f\n",
      "  Cloning https://github.com/huggingface/peft (to revision 4a1559582281fc3c9283892caea8ccef1d6f5a4f) to /tmp/pip-req-build-r7nvaazv\n",
      "  Running command git clone --filter=blob:none --quiet https://github.com/huggingface/peft /tmp/pip-req-build-r7nvaazv\n",
      "  Running command git rev-parse -q --verify 'sha^4a1559582281fc3c9283892caea8ccef1d6f5a4f'\n",
      "  Running command git fetch -q https://github.com/huggingface/peft 4a1559582281fc3c9283892caea8ccef1d6f5a4f\n",
      "  Running command git checkout -q 4a1559582281fc3c9283892caea8ccef1d6f5a4f\n",
      "  Resolved https://github.com/huggingface/peft to commit 4a1559582281fc3c9283892caea8ccef1d6f5a4f\n",
      "  Installing build dependencies ... \u001b[?25ldone\n",
      "\u001b[?25h  Getting requirements to build wheel ... \u001b[?25ldone\n",
      "\u001b[?25h  Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n",
      "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /opt/conda/lib/python3.10/site-packages (from peft==0.7.2.dev0) (1.26.4)\n",
      "Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from peft==0.7.2.dev0) (21.3)\n",
      "Requirement already satisfied: psutil in /opt/conda/lib/python3.10/site-packages (from peft==0.7.2.dev0) (5.9.3)\n",
      "Requirement already satisfied: pyyaml in /opt/conda/lib/python3.10/site-packages (from peft==0.7.2.dev0) (6.0.1)\n",
      "Requirement already satisfied: torch>=1.13.0 in /opt/conda/lib/python3.10/site-packages (from peft==0.7.2.dev0) (2.2.1)\n",
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      "Requirement already satisfied: accelerate>=0.21.0 in /opt/conda/lib/python3.10/site-packages (from peft==0.7.2.dev0) (0.26.1)\n",
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      "Requirement already satisfied: huggingface-hub>=0.17.0 in /opt/conda/lib/python3.10/site-packages (from peft==0.7.2.dev0) (0.22.2)\n",
      "Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from huggingface-hub>=0.17.0->peft==0.7.2.dev0) (3.13.1)\n",
      "Requirement already satisfied: fsspec>=2023.5.0 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub>=0.17.0->peft==0.7.2.dev0) (2023.10.0)\n",
      "Requirement already satisfied: requests in /opt/conda/lib/python3.10/site-packages (from huggingface-hub>=0.17.0->peft==0.7.2.dev0) (2.31.0)\n",
      "Requirement already satisfied: typing-extensions>=3.7.4.3 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub>=0.17.0->peft==0.7.2.dev0) (4.9.0)\n",
      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.10/site-packages (from packaging>=20.0->peft==0.7.2.dev0) (3.1.1)\n",
      "Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch>=1.13.0->peft==0.7.2.dev0) (1.12)\n",
      "Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch>=1.13.0->peft==0.7.2.dev0) (3.2.1)\n",
      "Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch>=1.13.0->peft==0.7.2.dev0) (3.1.2)\n",
      "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch>=1.13.0->peft==0.7.2.dev0) (12.1.105)\n",
      "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch>=1.13.0->peft==0.7.2.dev0) (12.1.105)\n",
      "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch>=1.13.0->peft==0.7.2.dev0) (12.1.105)\n",
      "Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /opt/conda/lib/python3.10/site-packages (from torch>=1.13.0->peft==0.7.2.dev0) (8.9.2.26)\n",
      "Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /opt/conda/lib/python3.10/site-packages (from torch>=1.13.0->peft==0.7.2.dev0) (12.1.3.1)\n",
      "Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /opt/conda/lib/python3.10/site-packages (from torch>=1.13.0->peft==0.7.2.dev0) (11.0.2.54)\n",
      "Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /opt/conda/lib/python3.10/site-packages (from torch>=1.13.0->peft==0.7.2.dev0) (10.3.2.106)\n",
      "Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /opt/conda/lib/python3.10/site-packages (from torch>=1.13.0->peft==0.7.2.dev0) (11.4.5.107)\n",
      "Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /opt/conda/lib/python3.10/site-packages (from torch>=1.13.0->peft==0.7.2.dev0) (12.1.0.106)\n",
      "Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /opt/conda/lib/python3.10/site-packages (from torch>=1.13.0->peft==0.7.2.dev0) (2.19.3)\n",
      "Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch>=1.13.0->peft==0.7.2.dev0) (12.1.105)\n",
      "Requirement already satisfied: triton==2.2.0 in /opt/conda/lib/python3.10/site-packages (from torch>=1.13.0->peft==0.7.2.dev0) (2.2.0)\n",
      "Requirement already satisfied: nvidia-nvjitlink-cu12 in /opt/conda/lib/python3.10/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch>=1.13.0->peft==0.7.2.dev0) (12.4.127)\n",
      "Requirement already satisfied: regex!=2019.12.17 in /opt/conda/lib/python3.10/site-packages (from transformers->peft==0.7.2.dev0) (2023.12.25)\n",
      "Requirement already satisfied: tokenizers<0.19,>=0.14 in /opt/conda/lib/python3.10/site-packages (from transformers->peft==0.7.2.dev0) (0.15.2)\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch>=1.13.0->peft==0.7.2.dev0) (2.1.3)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/lib/python3.10/site-packages (from requests->huggingface-hub>=0.17.0->peft==0.7.2.dev0) (3.3.2)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests->huggingface-hub>=0.17.0->peft==0.7.2.dev0) (3.6)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests->huggingface-hub>=0.17.0->peft==0.7.2.dev0) (1.26.18)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests->huggingface-hub>=0.17.0->peft==0.7.2.dev0) (2024.2.2)\n",
      "Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch>=1.13.0->peft==0.7.2.dev0) (1.3.0)\n",
      "Building wheels for collected packages: peft\n",
      "  Building wheel for peft (pyproject.toml) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for peft: filename=peft-0.7.2.dev0-py3-none-any.whl size=183144 sha256=4a3978089c7fe74a261d04933335d5cc0d2372fb41dc6badec0dfdcda0b649ed\n",
      "  Stored in directory: /root/.cache/pip/wheels/94/88/30/67a50390d00daa42c311fb8b162fb3b320e9cdab6bd0bb8568\n",
      "Successfully built peft\n",
      "Installing collected packages: peft\n",
      "Successfully installed peft-0.7.2.dev0\n"
     ]
    }
   ],
   "source": [
    "# Install Pytorch & other libraries\n",
    "!pip install \"torch==2.2.1\" tensorboard\n",
    "\n",
    "# Install Hugging Face libraries\n",
    "!pip install  --upgrade \\\n",
    "  \"transformers==4.36.2\" \\\n",
    "  \"datasets==2.16.1\" \\\n",
    "  \"accelerate==0.26.1\" \\\n",
    "  \"evaluate==0.4.1\" \\\n",
    "  \"bitsandbytes==0.42.0\" \\\n",
    "  # \"trl==0.7.10\" # \\\n",
    "  # \"peft==0.7.1\" \\\n",
    "\n",
    "# install peft & trl from github\n",
    "!pip install git+https://github.com/huggingface/trl@a3c5b7178ac4f65569975efadc97db2f3749c65e --upgrade\n",
    "!pip install git+https://github.com/huggingface/peft@4a1559582281fc3c9283892caea8ccef1d6f5a4f --upgrade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2024-05-08T21:54:26.516076Z",
     "iopub.status.busy": "2024-05-08T21:54:26.515515Z",
     "iopub.status.idle": "2024-05-08T21:54:26.529820Z",
     "shell.execute_reply": "2024-05-08T21:54:26.528154Z",
     "shell.execute_reply.started": "2024-05-08T21:54:26.516040Z"
    },
    "executionInfo": {
     "elapsed": 16017,
     "status": "ok",
     "timestamp": 1715008601573,
     "user": {
      "displayName": "Bruce Wayne",
      "userId": "03236562819884458513"
     },
     "user_tz": -180
    },
    "id": "2AJLBFNLQ01N",
    "outputId": "1e2feb48-f712-4e48-a730-c423f4d0ac2a"
   },
   "outputs": [],
   "source": [
    "# import the required libraries\n",
    "import os\n",
    "import torch\n",
    "import string\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from pprint import pprint\n",
    "from random import randrange\n",
    "from functools import partial\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "import re\n",
    "\n",
    "from torch.utils.data import DataLoader\n",
    "import datasets # !pip install datasets\n",
    "from datasets import load_dataset, Dataset\n",
    "\n",
    "from transformers import AutoConfig\n",
    "from peft import LoraConfig, PeftModel, TaskType\n",
    "\n",
    "# tokenizers\n",
    "from transformers import (\n",
    "                          GPTJForSequenceClassification,\n",
    "                          AutoModelForSequenceClassification,\n",
    "                          AutoTokenizer,\n",
    "                          Trainer,\n",
    "                          DataCollatorWithPadding,\n",
    "                          AutoModelForCausalLM,\n",
    "                          BitsAndBytesConfig,\n",
    "                          DataCollatorForLanguageModeling,\n",
    "                          EarlyStoppingCallback,\n",
    "                          TrainingArguments,\n",
    "                          HfArgumentParser,\n",
    "                          pipeline,\n",
    "                          logging,\n",
    "                          set_seed\n",
    "                          )\n",
    "\n",
    "import bitsandbytes as bnb\n",
    "from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training, PeftModel, AutoPeftModelForCausalLM\n",
    "from trl import SFTTrainer\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import (accuracy_score,\n",
    "                             f1_score,\n",
    "                             classification_report,\n",
    "                             confusion_matrix,\n",
    "                             precision_recall_fscore_support,\n",
    "                             )\n",
    "\n",
    "import evaluate\n",
    "from evaluate import load\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-08T21:07:35.539348Z",
     "iopub.status.busy": "2024-05-08T21:07:35.538386Z",
     "iopub.status.idle": "2024-05-08T21:07:35.640913Z",
     "shell.execute_reply": "2024-05-08T21:07:35.639370Z",
     "shell.execute_reply.started": "2024-05-08T21:07:35.539302Z"
    },
    "executionInfo": {
     "elapsed": 23,
     "status": "ok",
     "timestamp": 1715008601573,
     "user": {
      "displayName": "Bruce Wayne",
      "userId": "03236562819884458513"
     },
     "user_tz": -180
    },
    "id": "4XZvGaBkQ03z"
   },
   "outputs": [],
   "source": [
    "df_tr_gr_train = pd.read_csv(\"/kaggle/input/adv-hate-spch/adv_df_tr_gr_train.csv\")\n",
    "df_refugee_train = pd.read_csv(\"/kaggle/input/adv-hate-spch/adv_df_refugee_train.csv\")\n",
    "df_isr_pal_train = pd.read_csv(\"/kaggle/input/adv-hate-spch/adv_df_isr_pal_train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "execution": {
     "iopub.execute_input": "2024-05-08T21:07:36.583830Z",
     "iopub.status.busy": "2024-05-08T21:07:36.583337Z",
     "iopub.status.idle": "2024-05-08T21:07:36.614603Z",
     "shell.execute_reply": "2024-05-08T21:07:36.612927Z",
     "shell.execute_reply.started": "2024-05-08T21:07:36.583790Z"
    },
    "executionInfo": {
     "elapsed": 22,
     "status": "ok",
     "timestamp": 1715008601573,
     "user": {
      "displayName": "Bruce Wayne",
      "userId": "03236562819884458513"
     },
     "user_tz": -180
    },
    "id": "3aCNsX5LRN2z",
    "outputId": "e6ffd807-df33-4b05-b9dd-ab5ac20bd746"
   },
   "outputs": [
    {
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       "      <th>1</th>\n",
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       "      <th>2</th>\n",
       "      <td>1503008593372749826</td>\n",
       "      <td>adalar silah savaş sebep tabi öncelik diplomas...</td>\n",
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       "      <th>4</th>\n",
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       "      <td>bugün yıl önce izmir o yunan işgal birlik ilk ...</td>\n",
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       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    id                                               text  \\\n",
       "0  1568524118357864451  yunan ülke işgal et namus tecavüz et o ağız si...   \n",
       "1  1527761356061134851  neslihan düşünce doğru konu yanlış yer ukrayna...   \n",
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       "\n",
       "   label  \n",
       "0      1  \n",
       "1      0  \n",
       "2      0  \n",
       "3      1  \n",
       "4      0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = pd.concat([df_tr_gr_train, df_isr_pal_train, df_refugee_train], ignore_index=True)\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2024-05-08T21:07:37.390163Z",
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     "shell.execute_reply.started": "2024-05-08T21:07:37.390131Z"
    },
    "executionInfo": {
     "elapsed": 22,
     "status": "ok",
     "timestamp": 1715008601574,
     "user": {
      "displayName": "Bruce Wayne",
      "userId": "03236562819884458513"
     },
     "user_tz": -180
    },
    "id": "wJAi5PbYRN48",
    "outputId": "a2c873f7-713a-4f29-85fa-65551131b2ba"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "yunan ülke işgal et namus tecavüz et o ağız sit deniz dök de osmanlı sene güç acı 1 \n",
      "\n",
      "incil ilk emir sev tevrat ilk emir yaşa kuran keri ilk emir sev yahudi yaşa müslüman oku 0 \n",
      "\n",
      "allah bela ver vuru filistin öl insan 1 \n",
      "\n",
      "ateşkes ardından gazze 0 \n",
      "\n",
      "eser şehit allah ne inan asker ne inan rahmet eyle el kol tut sağlam sudiye eğitim ver silah mermi ver o kullan ön saf din kardeş sonuç yap bu 0 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(df_train[\"text\"].iloc[0], df_train[\"label\"].iloc[0], \"\\n\")\n",
    "print(df_train[\"text\"].iloc[1000], df_train[\"label\"].iloc[1000], \"\\n\")\n",
    "print(df_train[\"text\"].iloc[2000], df_train[\"label\"].iloc[2000], \"\\n\")\n",
    "print(df_train[\"text\"].iloc[3000], df_train[\"label\"].iloc[3000] , \"\\n\")\n",
    "print(df_train[\"text\"].iloc[4000], df_train[\"label\"].iloc[4000], \"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2024-05-08T21:07:37.716817Z",
     "iopub.status.busy": "2024-05-08T21:07:37.716288Z",
     "iopub.status.idle": "2024-05-08T21:07:37.725861Z",
     "shell.execute_reply": "2024-05-08T21:07:37.724243Z",
     "shell.execute_reply.started": "2024-05-08T21:07:37.716776Z"
    },
    "executionInfo": {
     "elapsed": 470,
     "status": "ok",
     "timestamp": 1715008602024,
     "user": {
      "displayName": "Bruce Wayne",
      "userId": "03236562819884458513"
     },
     "user_tz": -180
    },
    "id": "sYJ3cnouRN76",
    "outputId": "f55c2bfe-fc33-4d6c-a277-8658f5bb1044"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "baş avrupa ülke ol üzere dünya antisemitizm art israil asker gayr insani hukuk dış davran sonuç sebep 0 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# emojiler\n",
    "print(df_train[\"text\"].iloc[1200], df_train[\"label\"].iloc[1200] , \"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "execution": {
     "iopub.execute_input": "2024-05-08T21:07:37.932198Z",
     "iopub.status.busy": "2024-05-08T21:07:37.931770Z",
     "iopub.status.idle": "2024-05-08T21:07:37.957559Z",
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     "shell.execute_reply.started": "2024-05-08T21:07:37.932167Z"
    },
    "executionInfo": {
     "elapsed": 23,
     "status": "ok",
     "timestamp": 1715008602025,
     "user": {
      "displayName": "Bruce Wayne",
      "userId": "03236562819884458513"
     },
     "user_tz": -180
    },
    "id": "KVChgr-yRRgG",
    "outputId": "23ccf720-ade1-4cde-9ff6-b84298dbc1b8"
   },
   "outputs": [
    {
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       "                       id                                               text  \\\n",
       "0     1568524118357864451  yunan ülke işgal et namus tecavüz et o ağız si...   \n",
       "1     1527761356061134851  neslihan düşünce doğru konu yanlış yer ukrayna...   \n",
       "2     1503008593372749826  adalar silah savaş sebep tabi öncelik diplomas...   \n",
       "3     1568255642095296513  murat izmir gavur de keşke yunan kazan de erme...   \n",
       "4     1525787544083910656  bugün yıl önce izmir o yunan işgal birlik ilk ...   \n",
       "...                   ...                                                ...   \n",
       "8460  1393434648647192577  sponsor şeytan ol katil siyonist işgal israil ...   \n",
       "8461  1515487032570789894           mübarek gün hürmet özgür ilk kıble allah   \n",
       "8462  1391751856884523008  bugün ibadethane sal yalnızca ibadet yerine ge...   \n",
       "8463  1514963851342663684  kurşun at işle ! çocuk korkut çalış kork ! kad...   \n",
       "8464  1392403022903488516  uyan ey müslüman ! ilk kıble mübarek mescid ak...   \n",
       "\n",
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      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "concated_dataset = pd.concat([df_tr_gr_train, df_refugee_train, df_isr_pal_train], ignore_index=True)\n",
    "concated_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "execution": {
     "iopub.execute_input": "2024-05-08T21:07:38.147583Z",
     "iopub.status.busy": "2024-05-08T21:07:38.147156Z",
     "iopub.status.idle": "2024-05-08T21:07:38.173627Z",
     "shell.execute_reply": "2024-05-08T21:07:38.172074Z",
     "shell.execute_reply.started": "2024-05-08T21:07:38.147549Z"
    },
    "executionInfo": {
     "elapsed": 22,
     "status": "ok",
     "timestamp": 1715008602025,
     "user": {
      "displayName": "Bruce Wayne",
      "userId": "03236562819884458513"
     },
     "user_tz": -180
    },
    "id": "pye6B0baRRim",
    "outputId": "85a82d1c-fbfe-4895-c5f0-58e29f855506"
   },
   "outputs": [
    {
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       "      <th>8459</th>\n",
       "      <td>1393654946936107008</td>\n",
       "      <td>uyan bedr aslan yad çağır kafkas kartallar söy...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8460</th>\n",
       "      <td>1393434648647192577</td>\n",
       "      <td>sponsor şeytan ol katil siyonist işgal israil ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8461</th>\n",
       "      <td>1515487032570789894</td>\n",
       "      <td>mübarek gün hürmet özgür ilk kıble allah</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8462</th>\n",
       "      <td>1391751856884523008</td>\n",
       "      <td>bugün ibadethane sal yalnızca ibadet yerine ge...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8463</th>\n",
       "      <td>1514963851342663684</td>\n",
       "      <td>kurşun at işle ! çocuk korkut çalış kork ! kad...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7738 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       id                                               text  \\\n",
       "0     1568524118357864451  yunan ülke işgal et namus tecavüz et o ağız si...   \n",
       "1     1527761356061134851  neslihan düşünce doğru konu yanlış yer ukrayna...   \n",
       "2     1503008593372749826  adalar silah savaş sebep tabi öncelik diplomas...   \n",
       "3     1568255642095296513  murat izmir gavur de keşke yunan kazan de erme...   \n",
       "4     1525787544083910656  bugün yıl önce izmir o yunan işgal birlik ilk ...   \n",
       "...                   ...                                                ...   \n",
       "8459  1393654946936107008  uyan bedr aslan yad çağır kafkas kartallar söy...   \n",
       "8460  1393434648647192577  sponsor şeytan ol katil siyonist işgal israil ...   \n",
       "8461  1515487032570789894           mübarek gün hürmet özgür ilk kıble allah   \n",
       "8462  1391751856884523008  bugün ibadethane sal yalnızca ibadet yerine ge...   \n",
       "8463  1514963851342663684  kurşun at işle ! çocuk korkut çalış kork ! kad...   \n",
       "\n",
       "      label  \n",
       "0         1  \n",
       "1         0  \n",
       "2         0  \n",
       "3         1  \n",
       "4         0  \n",
       "...     ...  \n",
       "8459      0  \n",
       "8460      1  \n",
       "8461      0  \n",
       "8462      0  \n",
       "8463      1  \n",
       "\n",
       "[7738 rows x 3 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "concated_dataset = concated_dataset.drop_duplicates('text')\n",
    "concated_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2024-05-08T21:07:38.860693Z",
     "iopub.status.busy": "2024-05-08T21:07:38.860139Z",
     "iopub.status.idle": "2024-05-08T21:07:38.896608Z",
     "shell.execute_reply": "2024-05-08T21:07:38.894554Z",
     "shell.execute_reply.started": "2024-05-08T21:07:38.860649Z"
    },
    "executionInfo": {
     "elapsed": 21,
     "status": "ok",
     "timestamp": 1715008602025,
     "user": {
      "displayName": "Bruce Wayne",
      "userId": "03236562819884458513"
     },
     "user_tz": -180
    },
    "id": "d6TEIcQBRRlI",
    "outputId": "b23c3bdb-854e-4fd3-fac3-245b40ed147a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 7738 entries, 0 to 8463\n",
      "Data columns (total 3 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   id      7738 non-null   int64 \n",
      " 1   text    7737 non-null   object\n",
      " 2   label   7738 non-null   int64 \n",
      "dtypes: int64(2), object(1)\n",
      "memory usage: 241.8+ KB\n"
     ]
    }
   ],
   "source": [
    "concated_dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2024-05-08T21:07:39.457359Z",
     "iopub.status.busy": "2024-05-08T21:07:39.456843Z",
     "iopub.status.idle": "2024-05-08T21:07:39.469398Z",
     "shell.execute_reply": "2024-05-08T21:07:39.467910Z",
     "shell.execute_reply.started": "2024-05-08T21:07:39.457294Z"
    },
    "executionInfo": {
     "elapsed": 19,
     "status": "ok",
     "timestamp": 1715008602026,
     "user": {
      "displayName": "Bruce Wayne",
      "userId": "03236562819884458513"
     },
     "user_tz": -180
    },
    "id": "9jJ9viwFRRoP",
    "outputId": "cc2796e5-9ea3-4755-eb2a-2e6b3c861f19"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "concated_dataset[\"text\"].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2024-05-08T21:07:39.906810Z",
     "iopub.status.busy": "2024-05-08T21:07:39.906140Z",
     "iopub.status.idle": "2024-05-08T21:07:39.918685Z",
     "shell.execute_reply": "2024-05-08T21:07:39.917219Z",
     "shell.execute_reply.started": "2024-05-08T21:07:39.906774Z"
    },
    "executionInfo": {
     "elapsed": 17,
     "status": "ok",
     "timestamp": 1715008602026,
     "user": {
      "displayName": "Bruce Wayne",
      "userId": "03236562819884458513"
     },
     "user_tz": -180
    },
    "id": "Lo_aBPbERxEs",
    "outputId": "3f590151-5274-4251-9f49-8123f44445c7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "empty_counts = concated_dataset[\"text\"].eq('').sum()\n",
    "print(empty_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "execution": {
     "iopub.execute_input": "2024-05-08T21:07:40.331613Z",
     "iopub.status.busy": "2024-05-08T21:07:40.331055Z",
     "iopub.status.idle": "2024-05-08T21:07:40.356164Z",
     "shell.execute_reply": "2024-05-08T21:07:40.354987Z",
     "shell.execute_reply.started": "2024-05-08T21:07:40.331555Z"
    },
    "executionInfo": {
     "elapsed": 16,
     "status": "ok",
     "timestamp": 1715008602026,
     "user": {
      "displayName": "Bruce Wayne",
      "userId": "03236562819884458513"
     },
     "user_tz": -180
    },
    "id": "4y9Tl14DRxGt",
    "outputId": "fd1cdbf1-bc6a-47a4-a38d-e145fad50d1f"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>text</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1568524118357864451</td>\n",
       "      <td>yunan ülke işgal et namus tecavüz et o ağız si...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1527761356061134851</td>\n",
       "      <td>neslihan düşünce doğru konu yanlış yer ukrayna...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1503008593372749826</td>\n",
       "      <td>adalar silah savaş sebep tabi öncelik diplomas...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1568255642095296513</td>\n",
       "      <td>murat izmir gavur de keşke yunan kazan de erme...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1525787544083910656</td>\n",
       "      <td>bugün yıl önce izmir o yunan işgal birlik ilk ...</td>\n",
       "      <td>0</td>\n",
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       "      <th>...</th>\n",
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       "      <th>8459</th>\n",
       "      <td>1393654946936107008</td>\n",
       "      <td>uyan bedr aslan yad çağır kafkas kartallar söy...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8460</th>\n",
       "      <td>1393434648647192577</td>\n",
       "      <td>sponsor şeytan ol katil siyonist işgal israil ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8461</th>\n",
       "      <td>1515487032570789894</td>\n",
       "      <td>mübarek gün hürmet özgür ilk kıble allah</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8462</th>\n",
       "      <td>1391751856884523008</td>\n",
       "      <td>bugün ibadethane sal yalnızca ibadet yerine ge...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8463</th>\n",
       "      <td>1514963851342663684</td>\n",
       "      <td>kurşun at işle ! çocuk korkut çalış kork ! kad...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7737 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       id                                               text  \\\n",
       "0     1568524118357864451  yunan ülke işgal et namus tecavüz et o ağız si...   \n",
       "1     1527761356061134851  neslihan düşünce doğru konu yanlış yer ukrayna...   \n",
       "2     1503008593372749826  adalar silah savaş sebep tabi öncelik diplomas...   \n",
       "3     1568255642095296513  murat izmir gavur de keşke yunan kazan de erme...   \n",
       "4     1525787544083910656  bugün yıl önce izmir o yunan işgal birlik ilk ...   \n",
       "...                   ...                                                ...   \n",
       "8459  1393654946936107008  uyan bedr aslan yad çağır kafkas kartallar söy...   \n",
       "8460  1393434648647192577  sponsor şeytan ol katil siyonist işgal israil ...   \n",
       "8461  1515487032570789894           mübarek gün hürmet özgür ilk kıble allah   \n",
       "8462  1391751856884523008  bugün ibadethane sal yalnızca ibadet yerine ge...   \n",
       "8463  1514963851342663684  kurşun at işle ! çocuk korkut çalış kork ! kad...   \n",
       "\n",
       "      label  \n",
       "0         1  \n",
       "1         0  \n",
       "2         0  \n",
       "3         1  \n",
       "4         0  \n",
       "...     ...  \n",
       "8459      0  \n",
       "8460      1  \n",
       "8461      0  \n",
       "8462      0  \n",
       "8463      1  \n",
       "\n",
       "[7737 rows x 3 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "concated_dataset = concated_dataset.dropna()\n",
    "concated_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2024-05-08T21:07:40.921353Z",
     "iopub.status.busy": "2024-05-08T21:07:40.920807Z",
     "iopub.status.idle": "2024-05-08T21:07:40.940575Z",
     "shell.execute_reply": "2024-05-08T21:07:40.938954Z",
     "shell.execute_reply.started": "2024-05-08T21:07:40.921311Z"
    },
    "executionInfo": {
     "elapsed": 14,
     "status": "ok",
     "timestamp": 1715008602026,
     "user": {
      "displayName": "Bruce Wayne",
      "userId": "03236562819884458513"
     },
     "user_tz": -180
    },
    "id": "BvjFcupMRRrM",
    "outputId": "2f67b522-e67a-4ef1-fb15-77e87475e8f3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 7737 entries, 0 to 8463\n",
      "Data columns (total 3 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   id      7737 non-null   int64 \n",
      " 1   text    7737 non-null   object\n",
      " 2   label   7737 non-null   int64 \n",
      "dtypes: int64(2), object(1)\n",
      "memory usage: 241.8+ KB\n"
     ]
    }
   ],
   "source": [
    "concated_dataset.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HHGrQP6PR3r6"
   },
   "source": [
    "### Test Train Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-08T21:07:42.154654Z",
     "iopub.status.busy": "2024-05-08T21:07:42.154189Z",
     "iopub.status.idle": "2024-05-08T21:07:42.232291Z",
     "shell.execute_reply": "2024-05-08T21:07:42.230938Z",
     "shell.execute_reply.started": "2024-05-08T21:07:42.154619Z"
    },
    "executionInfo": {
     "elapsed": 12,
     "status": "ok",
     "timestamp": 1715008602026,
     "user": {
      "displayName": "Bruce Wayne",
      "userId": "03236562819884458513"
     },
     "user_tz": -180
    },
    "id": "PldFbpZKR1FI"
   },
   "outputs": [],
   "source": [
    "concated_dataset.to_csv('hate_classification.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-08T21:07:42.631933Z",
     "iopub.status.busy": "2024-05-08T21:07:42.631431Z",
     "iopub.status.idle": "2024-05-08T21:07:42.638320Z",
     "shell.execute_reply": "2024-05-08T21:07:42.636692Z",
     "shell.execute_reply.started": "2024-05-08T21:07:42.631898Z"
    },
    "executionInfo": {
     "elapsed": 13,
     "status": "ok",
     "timestamp": 1715008602027,
     "user": {
      "displayName": "Bruce Wayne",
      "userId": "03236562819884458513"
     },
     "user_tz": -180
    },
    "id": "oHKOShmMR1Hd"
   },
   "outputs": [],
   "source": [
    "dataset_name = \"/kaggle/working/hate_classification.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 118,
     "referenced_widgets": [
      "615bc845353949c8b83aa58be5b7193d",
      "86fe5eaadb11402aab5cc00dd98512b2",
      "98fc29c6432046fab7031ffaf32d8ddb",
      "e6ad076b866c48a49d8e0d0a084acfb5",
      "5e78ace716764123be59ae2f5c606740",
      "32a9df4843314758b4991ca91b17db8d",
      "d3ca9e454d5e4a29b7016fcb8657d2e7",
      "693265a1b2514063a35f12266099673c",
      "c1d131f25f5f433aa5a1a67eb1fa312e",
      "56a9e8712d7b4c4384a6e8e88f0ea58b",
      "67121d1e87db4e2598f3bb0b327d1ca8"
     ]
    },
    "execution": {
     "iopub.execute_input": "2024-05-08T21:07:43.256657Z",
     "iopub.status.busy": "2024-05-08T21:07:43.254937Z",
     "iopub.status.idle": "2024-05-08T21:07:43.738653Z",
     "shell.execute_reply": "2024-05-08T21:07:43.737650Z",
     "shell.execute_reply.started": "2024-05-08T21:07:43.256581Z"
    },
    "executionInfo": {
     "elapsed": 1086,
     "status": "ok",
     "timestamp": 1715008603101,
     "user": {
      "displayName": "Bruce Wayne",
      "userId": "03236562819884458513"
     },
     "user_tz": -180
    },
    "id": "7nFp7Nm6R6gh",
    "outputId": "c42e9783-39f9-4872-bc82-da17fef42c3e"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "52ac40b083da4625be3290bc2b90a3a5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating train split: 0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/datasets/download/streaming_download_manager.py:778: FutureWarning: The 'verbose' keyword in pd.read_csv is deprecated and will be removed in a future version.\n",
      "  return pd.read_csv(xopen(filepath_or_buffer, \"rb\", download_config=download_config), **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['id', 'text', 'label'],\n",
       "    num_rows: 1000\n",
       "})"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "concated_dataset = load_dataset(\"csv\", data_files = dataset_name, split = \"train\", keep_default_na=False).shuffle().select(range(1000))\n",
    "concated_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2024-05-08T21:07:44.815183Z",
     "iopub.status.busy": "2024-05-08T21:07:44.814328Z",
     "iopub.status.idle": "2024-05-08T21:07:44.842303Z",
     "shell.execute_reply": "2024-05-08T21:07:44.841447Z",
     "shell.execute_reply.started": "2024-05-08T21:07:44.815135Z"
    },
    "executionInfo": {
     "elapsed": 7,
     "status": "ok",
     "timestamp": 1715008603102,
     "user": {
      "displayName": "Bruce Wayne",
      "userId": "03236562819884458513"
     },
     "user_tz": -180
    },
    "id": "PcvfQuWtR6i5",
    "outputId": "35682d0a-5246-4a30-9c65-43b4f72a9646"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['id', 'text', 'label'],\n",
       "        num_rows: 700\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['id', 'text', 'label'],\n",
       "        num_rows: 300\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "concated_dataset = concated_dataset.train_test_split(test_size=300/1000)\n",
    "concated_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-08T21:07:45.714905Z",
     "iopub.status.busy": "2024-05-08T21:07:45.714218Z",
     "iopub.status.idle": "2024-05-08T21:07:45.732717Z",
     "shell.execute_reply": "2024-05-08T21:07:45.731036Z",
     "shell.execute_reply.started": "2024-05-08T21:07:45.714871Z"
    },
    "executionInfo": {
     "elapsed": 2,
     "status": "ok",
     "timestamp": 1715008604723,
     "user": {
      "displayName": "Bruce Wayne",
      "userId": "03236562819884458513"
     },
     "user_tz": -180
    },
    "id": "AItjN6A1R6m1"
   },
   "outputs": [],
   "source": [
    "data = concated_dataset['train'].train_test_split(train_size=0.8, seed=42)\n",
    "\n",
    "data['val'] = data.pop(\"test\")\n",
    "\n",
    "data['test'] = concated_dataset['test']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2024-05-08T21:07:46.791827Z",
     "iopub.status.busy": "2024-05-08T21:07:46.791275Z",
     "iopub.status.idle": "2024-05-08T21:07:46.800439Z",
     "shell.execute_reply": "2024-05-08T21:07:46.798978Z",
     "shell.execute_reply.started": "2024-05-08T21:07:46.791787Z"
    },
    "executionInfo": {
     "elapsed": 543,
     "status": "ok",
     "timestamp": 1715008607026,
     "user": {
      "displayName": "Bruce Wayne",
      "userId": "03236562819884458513"
     },
     "user_tz": -180
    },
    "id": "txVHw4znR6pl",
    "outputId": "8234c3b8-3792-4ad2-bae2-9b321a7098e1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['id', 'text', 'label'],\n",
       "        num_rows: 560\n",
       "    })\n",
       "    val: Dataset({\n",
       "        features: ['id', 'text', 'label'],\n",
       "        num_rows: 140\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['id', 'text', 'label'],\n",
       "        num_rows: 300\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2024-05-08T21:07:48.981453Z",
     "iopub.status.busy": "2024-05-08T21:07:48.980533Z",
     "iopub.status.idle": "2024-05-08T21:07:49.035546Z",
     "shell.execute_reply": "2024-05-08T21:07:49.034238Z",
     "shell.execute_reply.started": "2024-05-08T21:07:48.981409Z"
    },
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1715008607454,
     "user": {
      "displayName": "Bruce Wayne",
      "userId": "03236562819884458513"
     },
     "user_tz": -180
    },
    "id": "TjMgcplbR-rb",
    "outputId": "456e75ed-dcf6-4fd3-fb34-0631a2a66130"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 560 entries, 0 to 559\n",
      "Data columns (total 3 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   id      560 non-null    int64 \n",
      " 1   text    560 non-null    object\n",
      " 2   label   560 non-null    int64 \n",
      "dtypes: int64(2), object(1)\n",
      "memory usage: 13.2+ KB\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 140 entries, 0 to 139\n",
      "Data columns (total 3 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   id      140 non-null    int64 \n",
      " 1   text    140 non-null    object\n",
      " 2   label   140 non-null    int64 \n",
      "dtypes: int64(2), object(1)\n",
      "memory usage: 3.4+ KB\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 300 entries, 0 to 299\n",
      "Data columns (total 3 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   id      300 non-null    int64 \n",
      " 1   text    300 non-null    object\n",
      " 2   label   300 non-null    int64 \n",
      "dtypes: int64(2), object(1)\n",
      "memory usage: 7.2+ KB\n"
     ]
    }
   ],
   "source": [
    "data['train'].to_pandas().info()\n",
    "data['val'].to_pandas().info()\n",
    "data['test'].to_pandas().info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uIuQ-VICSEWv"
   },
   "source": [
    "**As the classes are not balanced, we will compute the positive and negative weights and use them for loss calculation later:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2024-05-08T21:07:50.666782Z",
     "iopub.status.busy": "2024-05-08T21:07:50.665351Z",
     "iopub.status.idle": "2024-05-08T21:07:50.687383Z",
     "shell.execute_reply": "2024-05-08T21:07:50.685432Z",
     "shell.execute_reply.started": "2024-05-08T21:07:50.666732Z"
    },
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1715008607822,
     "user": {
      "displayName": "Bruce Wayne",
      "userId": "03236562819884458513"
     },
     "user_tz": -180
    },
    "id": "_R78YNz1R-tm",
    "outputId": "bc5b13b7-046e-4268-e2ab-584210cf2317"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "label\n",
       "0    385\n",
       "1    175\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['train'].to_pandas()[\"label\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2024-05-08T21:07:51.419842Z",
     "iopub.status.busy": "2024-05-08T21:07:51.418936Z",
     "iopub.status.idle": "2024-05-08T21:07:51.462324Z",
     "shell.execute_reply": "2024-05-08T21:07:51.460913Z",
     "shell.execute_reply.started": "2024-05-08T21:07:51.419613Z"
    },
    "executionInfo": {
     "elapsed": 6,
     "status": "ok",
     "timestamp": 1715008608489,
     "user": {
      "displayName": "Bruce Wayne",
      "userId": "03236562819884458513"
     },
     "user_tz": -180
    },
    "id": "QaK90q8YR-wV",
    "outputId": "efdad976-39a0-4e9a-c32d-69f3a6f03ad4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.6, 0.7272727272727273)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos_weights = len(data['train'].to_pandas()) / (2 * data['train'].to_pandas().label.value_counts()[1])\n",
    "neg_weights = len(data['train'].to_pandas()) / (2 * data['train'].to_pandas().label.value_counts()[0])\n",
    "\n",
    "pos_weights, neg_weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2024-05-08T21:07:57.919932Z",
     "iopub.status.busy": "2024-05-08T21:07:57.919344Z",
     "iopub.status.idle": "2024-05-08T21:07:57.933984Z",
     "shell.execute_reply": "2024-05-08T21:07:57.932368Z",
     "shell.execute_reply.started": "2024-05-08T21:07:57.919891Z"
    },
    "executionInfo": {
     "elapsed": 6,
     "status": "ok",
     "timestamp": 1715008608490,
     "user": {
      "displayName": "Bruce Wayne",
      "userId": "03236562819884458513"
     },
     "user_tz": -180
    },
    "id": "wJDrR0hDSIYn",
    "outputId": "1ba1d6de-9c0c-4a82-8653-d791666c566c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'id': 1414234688898666498,\n",
       " 'text': 'eser söz başkan sistem sonuç dolar gel günlük afgan mülteci sayı bini geç herşey hergün zam',\n",
       " 'label': 0}"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['train'][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cLuRISQrS40O"
   },
   "source": [
    "### Tokenizing and Loading the Kanarya Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-08T21:08:02.411671Z",
     "iopub.status.busy": "2024-05-08T21:08:02.411221Z",
     "iopub.status.idle": "2024-05-08T21:08:02.419281Z",
     "shell.execute_reply": "2024-05-08T21:08:02.416903Z",
     "shell.execute_reply.started": "2024-05-08T21:08:02.411635Z"
    },
    "executionInfo": {
     "elapsed": 6,
     "status": "ok",
     "timestamp": 1715008609719,
     "user": {
      "displayName": "Bruce Wayne",
      "userId": "03236562819884458513"
     },
     "user_tz": -180
    },
    "id": "xFud4N_wSIbN"
   },
   "outputs": [],
   "source": [
    "checkpoint = \"asafaya/kanarya-750m\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2024-05-08T21:08:08.371455Z",
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     "shell.execute_reply.started": "2024-05-08T21:08:08.371394Z"
    },
    "executionInfo": {
     "elapsed": 1992,
     "status": "ok",
     "timestamp": 1715008611707,
     "user": {
      "displayName": "Bruce Wayne",
      "userId": "03236562819884458513"
     },
     "user_tz": -180
    },
    "id": "avH2vUeASLgX",
    "outputId": "6f47f0b1-5b3b-4bcd-8b54-6baf45c7d2ef"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "51505015959c4dde9c5c9892a9327c67",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "tokenizer_config.json:   0%|          | 0.00/677 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "vocab.json:   0%|          | 0.00/589k [00:00<?, ?B/s]"
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     },
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    },
    {
     "data": {
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      },
      "text/plain": [
       "merges.txt:   0%|          | 0.00/370k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e3a48a326182409191383227a23bb8f7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "tokenizer.json:   0%|          | 0.00/1.52M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "version_major": 2,
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      },
      "text/plain": [
       "special_tokens_map.json:   0%|          | 0.00/435 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
    "tokenizer.pad_token_id = tokenizer.eos_token_id\n",
    "tokenizer.pad_token = tokenizer.eos_token"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-08T21:08:10.042871Z",
     "iopub.status.busy": "2024-05-08T21:08:10.042271Z",
     "iopub.status.idle": "2024-05-08T21:08:10.050248Z",
     "shell.execute_reply": "2024-05-08T21:08:10.048920Z",
     "shell.execute_reply.started": "2024-05-08T21:08:10.042824Z"
    },
    "executionInfo": {
     "elapsed": 13,
     "status": "ok",
     "timestamp": 1715008611708,
     "user": {
      "displayName": "Bruce Wayne",
      "userId": "03236562819884458513"
     },
     "user_tz": -180
    },
    "id": "DV2S5fPbSLiq"
   },
   "outputs": [],
   "source": [
    "def preprocess_function(examples):\n",
    "    return tokenizer(examples[\"text\"], padding=True, truncation=True, return_tensors='pt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
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     "height": 356,
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      "99047e1d12404654a0a96e3d7dd2a038",
      "a5a4f24e4f4545c0bbc55581929a6c14"
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    "execution": {
     "iopub.execute_input": "2024-05-08T21:08:10.052569Z",
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     "shell.execute_reply": "2024-05-08T21:08:10.464470Z",
     "shell.execute_reply.started": "2024-05-08T21:08:10.052538Z"
    },
    "executionInfo": {
     "elapsed": 13,
     "status": "ok",
     "timestamp": 1715008611708,
     "user": {
      "displayName": "Bruce Wayne",
      "userId": "03236562819884458513"
     },
     "user_tz": -180
    },
    "id": "brlhiCZNSLlm",
    "outputId": "f78baed9-408e-47b3-e733-3c5fe7496dc0"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "880ec7acbe0545fcb21d02e9c6e7d4e9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/560 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ee036dbbbffa45799050eb4c37a320de",
       "version_major": 2,
       "version_minor": 0
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      "text/plain": [
       "Map:   0%|          | 0/140 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e2052097eb55400cbf6cc399555ed7fa",
       "version_major": 2,
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      "text/plain": [
       "Map:   0%|          | 0/300 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['id', 'text', 'label', 'input_ids', 'attention_mask'],\n",
       "        num_rows: 560\n",
       "    })\n",
       "    val: Dataset({\n",
       "        features: ['id', 'text', 'label', 'input_ids', 'attention_mask'],\n",
       "        num_rows: 140\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['id', 'text', 'label', 'input_ids', 'attention_mask'],\n",
       "        num_rows: 300\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenized_data = data.map(preprocess_function, batched=True)\n",
    "tokenized_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
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    },
    "executionInfo": {
     "elapsed": 7,
     "status": "ok",
     "timestamp": 1715008611708,
     "user": {
      "displayName": "Bruce Wayne",
      "userId": "03236562819884458513"
     },
     "user_tz": -180
    },
    "id": "-mA4ENGZdrGC",
    "outputId": "28b24f86-1bab-4489-e531-4a697b9f5ca0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['label', 'input_ids', 'attention_mask'],\n",
       "        num_rows: 560\n",
       "    })\n",
       "    val: Dataset({\n",
       "        features: ['label', 'input_ids', 'attention_mask'],\n",
       "        num_rows: 140\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['label', 'input_ids', 'attention_mask'],\n",
       "        num_rows: 300\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenized_data = tokenized_data.remove_columns([\"id\", \"text\"])\n",
    "tokenized_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-06T21:40:27.109343Z",
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     },
     "user_tz": -180
    },
    "id": "75DtaGUPSQXC"
   },
   "outputs": [],
   "source": [
    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-08T21:08:17.691495Z",
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    "executionInfo": {
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      "userId": "03236562819884458513"
     },
     "user_tz": -180
    },
    "id": "OZduKvJ2TEQ9"
   },
   "outputs": [],
   "source": [
    "id2label = {1: \"HATE\", 0: \"NoN-HATE\"}\n",
    "label2id = {\"HATE\": 1, \"NoN-HATE\": 0}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
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    "executionInfo": {
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     "user_tz": -180
    },
    "id": "Bs_gJSpSTAbK",
    "outputId": "ff0e0fa9-8981-4147-fce9-43cbb4c5c041"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e19e6520f34740f2a9c65cadeb9a9ee6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "config.json:   0%|          | 0.00/967 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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      "text/plain": [
       "model.safetensors:   0%|          | 0.00/1.48G [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of GPTJForSequenceClassification were not initialized from the model checkpoint at asafaya/kanarya-750m and are newly initialized: ['score.weight']\n",
      "\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    }
   ],
   "source": [
    "model = AutoModelForSequenceClassification.from_pretrained(\n",
    "    checkpoint, num_labels=2, id2label=id2label, label2id=label2id\n",
    ")\n",
    "\n",
    "model.config.pad_token_id = model.config.eos_token_id"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8svz58n6ShOO"
   },
   "source": [
    "## Setup the trainer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZS0ozv5tSkS5"
   },
   "source": [
    "### Evaluation Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 49,
     "referenced_widgets": [
      "2fe7f651a9364b17b654512cd5e399e4",
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      "74dd9e654c674a4b8ea875283ccc575f",
      "b2b6a0475ec34a48b1546492ba2ef785",
      "d9b86a9b595f46159b04371f681124d6",
      "ed1207f0af7847419bc70cfa3bae49ca",
      "aad9d24f17f141718a44b3986377b9fb",
      "2429e7a72807429d969377c9719f7e6f",
      "23f46b2d3f274fe3a44df2803023d1c3",
      "1a7e1db31081499f8ea11f52688fd302"
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    "execution": {
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    },
    "executionInfo": {
     "elapsed": 1289,
     "status": "ok",
     "timestamp": 1715008620253,
     "user": {
      "displayName": "Bruce Wayne",
      "userId": "03236562819884458513"
     },
     "user_tz": -180
    },
    "id": "BHeAp6r_SQZI",
    "outputId": "e3efecce-a966-4555-a387-5f8bf6abf3f6"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2fd4c5c1732848cdb214f9827286821e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading builder script:   0%|          | 0.00/4.20k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "accuracy = evaluate.load(\"accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-06T21:40:36.126643Z",
     "iopub.status.busy": "2024-05-06T21:40:36.125237Z",
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     "shell.execute_reply": "2024-05-06T21:40:36.131641Z",
     "shell.execute_reply.started": "2024-05-06T21:40:36.126602Z"
    },
    "executionInfo": {
     "elapsed": 2,
     "status": "ok",
     "timestamp": 1715008620686,
     "user": {
      "displayName": "Bruce Wayne",
      "userId": "03236562819884458513"
     },
     "user_tz": -180
    },
    "id": "QYkiGeO5SQcN"
   },
   "outputs": [],
   "source": [
    "def compute_metrics(eval_pred):\n",
    "    predictions, labels = eval_pred\n",
    "\n",
    "    #print(eval_pred)\n",
    "    print(\"---------\")\n",
    "    #print(predictions)\n",
    "    print(\"---------\")\n",
    "\n",
    "    # Eğer predictions bir demetse, bir diziye dönüştür\n",
    "    if isinstance(predictions, tuple):\n",
    "        predictions = predictions[0]\n",
    "    # Use argmax to get the predicted class\n",
    "    predictions = np.argmax(predictions, axis=1)\n",
    "    # Compute and return the accuracy\n",
    "\n",
    "    print(\"---------\\n\\n\\n\")\n",
    "\n",
    "    return accuracy.compute(predictions=predictions, references=labels)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-06T21:40:37.456820Z",
     "iopub.status.busy": "2024-05-06T21:40:37.456033Z",
     "iopub.status.idle": "2024-05-06T21:40:37.463550Z",
     "shell.execute_reply": "2024-05-06T21:40:37.462384Z",
     "shell.execute_reply.started": "2024-05-06T21:40:37.456776Z"
    }
   },
   "outputs": [],
   "source": [
    "def compute_metrics(p):\n",
    "\n",
    "    predictions, labels = p\n",
    "    \n",
    "    predictions = predictions[0]\n",
    "    \n",
    "    predictions = np.argmax(predictions, axis=1)\n",
    "    precision, recall, f1, _ = precision_recall_fscore_support(labels, predictions, average='weighted')\n",
    "    acc = accuracy_score(labels, predictions)\n",
    "    return {'accuracy': acc, 'f1': f1, 'precision': precision, 'recall': recall}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9wgukmu0SyG-"
   },
   "source": [
    "## Custom Trainer for Weighted Loss\n",
    "\n",
    "We have an imbalanced distribution between positive and negative classes. We need to train our models with a weighted cross-entropy loss to account for that. The Trainer class doesn't support providing a custom loss as it expects to get the loss directly from the model's outputs.\n",
    "\n",
    "So, we need to define our custom WeightedCELossTrainer that overrides the compute_loss method to calculate the weighted cross-entropy loss based on the model's predictions and the input labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-06T21:40:38.460023Z",
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     "shell.execute_reply": "2024-05-06T21:40:38.468053Z",
     "shell.execute_reply.started": "2024-05-06T21:40:38.459941Z"
    },
    "executionInfo": {
     "elapsed": 2,
     "status": "ok",
     "timestamp": 1715008624507,
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      "displayName": "Bruce Wayne",
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     },
     "user_tz": -180
    },
    "id": "5e8ctfbPTOMk"
   },
   "outputs": [],
   "source": [
    "class WeightedCELossTrainer(Trainer):\n",
    "    def compute_loss(self, model, inputs, return_outputs=False):\n",
    "        labels = inputs.pop(\"labels\")\n",
    "        # Get model's predictions\n",
    "        outputs = model(**inputs)\n",
    "        logits = outputs.get(\"logits\")\n",
    "        # Compute custom loss\n",
    "        loss_fct = torch.nn.CrossEntropyLoss(weight=torch.tensor([neg_weights, pos_weights], device=model.device, dtype=logits.dtype))\n",
    "        loss = loss_fct(logits.view(-1, self.model.config.num_labels), labels.view(-1))\n",
    "        return (loss, outputs) if return_outputs else loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
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    "execution": {
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     "shell.execute_reply": "2024-05-06T22:41:51.983968Z",
     "shell.execute_reply.started": "2024-05-06T21:40:38.901737Z"
    },
    "id": "16FtyxbKSXZ8",
    "outputId": "a03b21b5-7ca1-4311-84da-2c42e2cfbb15"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Logging into wandb.ai. (Learn how to deploy a W&B server locally: https://wandb.me/wandb-server)\n",
      "\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n",
      "\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Paste an API key from your profile and hit enter, or press ctrl+c to quit:"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "  ········································\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "Tracking run with wandb version 0.16.6"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
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       "Run data is saved locally in <code>/kaggle/working/wandb/run-20240506_214051-85lj0zk1</code>"
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     "data": {
      "text/html": [
       "Syncing run <strong><a href='https://wandb.ai/erdemormann/huggingface/runs/85lj0zk1' target=\"_blank\">unique-vortex-55</a></strong> to <a href='https://wandb.ai/erdemormann/huggingface' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
      ],
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       " View project at <a href='https://wandb.ai/erdemormann/huggingface' target=\"_blank\">https://wandb.ai/erdemormann/huggingface</a>"
      ],
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      ]
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     "metadata": {},
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    },
    {
     "data": {
      "text/html": [
       " View run at <a href='https://wandb.ai/erdemormann/huggingface/runs/85lj0zk1' target=\"_blank\">https://wandb.ai/erdemormann/huggingface/runs/85lj0zk1</a>"
      ],
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       "<IPython.core.display.HTML object>"
      ]
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     "metadata": {},
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "You're using a GPT2TokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n",
      "\n",
      "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...\n",
      "\n",
      "/opt/conda/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
      "\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='70' max='70' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [70/70 59:45, Epoch 2/2]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Epoch</th>\n",
       "      <th>Training Loss</th>\n",
       "      <th>Validation Loss</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>F1</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>No log</td>\n",
       "      <td>0.693153</td>\n",
       "      <td>0.307143</td>\n",
       "      <td>0.144340</td>\n",
       "      <td>0.094337</td>\n",
       "      <td>0.307143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>No log</td>\n",
       "      <td>0.693132</td>\n",
       "      <td>0.692857</td>\n",
       "      <td>0.567149</td>\n",
       "      <td>0.480051</td>\n",
       "      <td>0.692857</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
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       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "\n",
      "/opt/conda/lib/python3.10/site-packages/torch/utils/checkpoint.py:460: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
      "\n",
      "  warnings.warn(\n",
      "\n",
      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "TrainOutput(global_step=70, training_loss=0.693188967023577, metrics={'train_runtime': 3672.5132, 'train_samples_per_second': 0.305, 'train_steps_per_second': 0.019, 'total_flos': 267957043200000.0, 'train_loss': 0.693188967023577, 'epoch': 2.0})"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_args = TrainingArguments(\n",
    "    output_dir=\"kanarya-lora-classification\",\n",
    "    learning_rate=2e-5,\n",
    "    lr_scheduler_type=\"constant\",\n",
    "    warmup_ratio= 0.1,\n",
    "    max_grad_norm= 0.3,\n",
    "    per_device_train_batch_size=16,\n",
    "    per_device_eval_batch_size=16,\n",
    "    num_train_epochs=2,\n",
    "    weight_decay=0.001,\n",
    "    evaluation_strategy=\"epoch\",\n",
    "    save_strategy=\"epoch\",\n",
    "    load_best_model_at_end=True,\n",
    "    gradient_checkpointing=True,\n",
    ")\n",
    "\n",
    "\"\"\"\n",
    "trainer = Trainer(\n",
    "    model=model,\n",
    "    args=training_args,\n",
    "    train_dataset=tokenized_data[\"train\"],\n",
    "    eval_dataset=tokenized_data[\"val\"],\n",
    "    tokenizer=tokenizer,\n",
    "    data_collator=data_collator,\n",
    "    compute_metrics=compute_metrics,\n",
    ")\n",
    "\n",
    "trainer.train()\n",
    "\"\"\"\n",
    "\n",
    "kanarya_trainer = WeightedCELossTrainer(\n",
    "    model=model,\n",
    "    args=training_args,\n",
    "    train_dataset=tokenized_data['train'],\n",
    "    eval_dataset=tokenized_data[\"val\"],\n",
    "    tokenizer=tokenizer,\n",
    "    data_collator=data_collator,\n",
    "    compute_metrics=compute_metrics\n",
    ")\n",
    "\n",
    "kanarya_trainer.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-06T22:41:51.994140Z",
     "iopub.status.busy": "2024-05-06T22:41:51.993682Z",
     "iopub.status.idle": "2024-05-06T22:43:02.306507Z",
     "shell.execute_reply": "2024-05-06T22:43:02.305260Z",
     "shell.execute_reply.started": "2024-05-06T22:41:51.994102Z"
    },
    "id": "MoPc2jdAXPNT"
   },
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'eval_loss': 0.6931318044662476,\n",
       " 'eval_accuracy': 0.6928571428571428,\n",
       " 'eval_f1': 0.5671488848704039,\n",
       " 'eval_precision': 0.4800510204081633,\n",
       " 'eval_recall': 0.6928571428571428,\n",
       " 'eval_runtime': 70.2382,\n",
       " 'eval_samples_per_second': 1.993,\n",
       " 'eval_steps_per_second': 0.128,\n",
       " 'epoch': 2.0}"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate the model\n",
    "results = kanarya_trainer.evaluate()\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-06T22:43:25.109645Z",
     "iopub.status.busy": "2024-05-06T22:43:25.109199Z",
     "iopub.status.idle": "2024-05-06T22:46:18.126784Z",
     "shell.execute_reply": "2024-05-06T22:46:18.125616Z",
     "shell.execute_reply.started": "2024-05-06T22:43:25.109614Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "# Predictions to get the confusion matrix\n",
    "predictions = kanarya_trainer.predict(tokenized_data['test'])\n",
    "preds = np.argmax(predictions.predictions[0], axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-06T22:47:32.354697Z",
     "iopub.status.busy": "2024-05-06T22:47:32.354208Z",
     "iopub.status.idle": "2024-05-06T22:47:32.369635Z",
     "shell.execute_reply": "2024-05-06T22:47:32.366692Z",
     "shell.execute_reply.started": "2024-05-06T22:47:32.354664Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
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      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-06T22:46:18.129037Z",
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     "iopub.status.idle": "2024-05-06T22:46:18.517585Z",
     "shell.execute_reply": "2024-05-06T22:46:18.516421Z",
     "shell.execute_reply.started": "2024-05-06T22:46:18.129008Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "label_map = {\n",
    "    'LABEL_0': 'Non-Hate',\n",
    "    'LABEL_1': 'Hate'\n",
    "}\n",
    "\n",
    "cm = confusion_matrix(predictions.label_ids, preds)\n",
    "\n",
    "# label_map to labels\n",
    "labels = [label_map[f'LABEL_{i}'] for i in range(len(label_map))]\n",
    "\n",
    "# Confusion Matrix\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=labels, yticklabels=labels)\n",
    "plt.xlabel('Predicted labels')\n",
    "plt.ylabel('True labels')\n",
    "plt.title('Confusion Matrix with Label Names')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Neden sadece 0 tahmini yapıyor eğitimsiz yapabiliyor 1 tahmini yapabiliyor. Eğitim az olduğu için mi böyle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Base Model Without Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-08T21:25:39.198563Z",
     "iopub.status.busy": "2024-05-08T21:25:39.197959Z",
     "iopub.status.idle": "2024-05-08T21:25:39.468076Z",
     "shell.execute_reply": "2024-05-08T21:25:39.466562Z",
     "shell.execute_reply.started": "2024-05-08T21:25:39.198512Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['id', 'text', 'label'],\n",
       "    num_rows: 1000\n",
       "})"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = load_dataset(\"csv\", data_files = dataset_name, split = \"train\", keep_default_na=False).shuffle().select(range(1000))\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "checkpoint = \"asafaya/kanarya-750m\"A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
    "tokenizer.pad_token_id = tokenizer.eos_token_id\n",
    "tokenizer.pad_token = tokenizer.eos_token"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-08T21:12:15.832924Z",
     "iopub.status.busy": "2024-05-08T21:12:15.832403Z",
     "iopub.status.idle": "2024-05-08T21:12:20.027118Z",
     "shell.execute_reply": "2024-05-08T21:12:20.025453Z",
     "shell.execute_reply.started": "2024-05-08T21:12:15.832866Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of GPTJForSequenceClassification were not initialized from the model checkpoint at asafaya/kanarya-750m and are newly initialized: ['score.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": [
    "straight_model = AutoModelForSequenceClassification.from_pretrained(\n",
    "    checkpoint, num_labels=2, id2label=id2label, label2id=label2id\n",
    ")\n",
    "\n",
    "straight_model.config.pad_token_id = model.config.eos_token_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-08T21:24:22.046216Z",
     "iopub.status.busy": "2024-05-08T21:24:22.044812Z",
     "iopub.status.idle": "2024-05-08T21:24:22.053906Z",
     "shell.execute_reply": "2024-05-08T21:24:22.052675Z",
     "shell.execute_reply.started": "2024-05-08T21:24:22.046155Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['id', 'text', 'label'],\n",
       "    num_rows: 1000\n",
       "})"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-08T21:26:37.402237Z",
     "iopub.status.busy": "2024-05-08T21:26:37.401796Z",
     "iopub.status.idle": "2024-05-08T21:26:37.530404Z",
     "shell.execute_reply": "2024-05-08T21:26:37.528407Z",
     "shell.execute_reply.started": "2024-05-08T21:26:37.402197Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000\n"
     ]
    }
   ],
   "source": [
    "texts = data[\"text\"][:] # [11:15]\n",
    "inputs = tokenizer(\n",
    "        texts,\n",
    "        truncation = True,\n",
    "        padding=True,\n",
    "        return_tensors=\"pt\"\n",
    "    )\n",
    "\n",
    "print(len(inputs[\"input_ids\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-08T21:26:37.565441Z",
     "iopub.status.busy": "2024-05-08T21:26:37.564196Z",
     "iopub.status.idle": "2024-05-08T21:40:11.414117Z",
     "shell.execute_reply": "2024-05-08T21:40:11.411710Z",
     "shell.execute_reply.started": "2024-05-08T21:26:37.565393Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[1.0068e-04, 6.1966e-05],\n",
      "        [8.2402e-05, 1.1153e-04],\n",
      "        [3.4514e-05, 1.1082e-04],\n",
      "        ...,\n",
      "        [1.4299e-04, 1.2338e-04],\n",
      "        [1.0987e-04, 1.0143e-04],\n",
      "        [5.3884e-05, 1.0006e-04]])\n"
     ]
    }
   ],
   "source": [
    "with torch.no_grad():\n",
    "    logits = straight_model(**inputs).logits\n",
    "    print(logits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-08T21:42:20.337804Z",
     "iopub.status.busy": "2024-05-08T21:42:20.337264Z",
     "iopub.status.idle": "2024-05-08T21:42:20.350066Z",
     "shell.execute_reply": "2024-05-08T21:42:20.348230Z",
     "shell.execute_reply.started": "2024-05-08T21:42:20.337769Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([8.2402e-05, 1.1153e-04])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logits[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-08T21:51:35.116248Z",
     "iopub.status.busy": "2024-05-08T21:51:35.115726Z",
     "iopub.status.idle": "2024-05-08T21:51:35.134361Z",
     "shell.execute_reply": "2024-05-08T21:51:35.133058Z",
     "shell.execute_reply.started": "2024-05-08T21:51:35.116213Z"
    }
   },
   "outputs": [],
   "source": [
    "y_pred = list()\n",
    "\n",
    "for i in range(1000):\n",
    "    predicted_class_id = logits[i].argmax().item()\n",
    "    y_pred.append(predicted_class_id)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-08T21:51:38.983226Z",
     "iopub.status.busy": "2024-05-08T21:51:38.982756Z",
     "iopub.status.idle": "2024-05-08T21:51:38.989941Z",
     "shell.execute_reply": "2024-05-08T21:51:38.988686Z",
     "shell.execute_reply.started": "2024-05-08T21:51:38.983193Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1]\n"
     ]
    }
   ],
   "source": [
    "print(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-08T21:51:41.688032Z",
     "iopub.status.busy": "2024-05-08T21:51:41.687476Z",
     "iopub.status.idle": "2024-05-08T21:51:41.698468Z",
     "shell.execute_reply": "2024-05-08T21:51:41.697131Z",
     "shell.execute_reply.started": "2024-05-08T21:51:41.687992Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'HATE'"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted_class_id = logits[1].argmax().item()\n",
    "model.config.id2label[predicted_class_id]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-08T21:52:13.513306Z",
     "iopub.status.busy": "2024-05-08T21:52:13.512747Z",
     "iopub.status.idle": "2024-05-08T21:52:13.532403Z",
     "shell.execute_reply": "2024-05-08T21:52:13.530712Z",
     "shell.execute_reply.started": "2024-05-08T21:52:13.513268Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "y_true = data[\"label\"]\n",
    "print(y_true)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-08T21:55:18.237127Z",
     "iopub.status.busy": "2024-05-08T21:55:18.236610Z",
     "iopub.status.idle": "2024-05-08T21:55:18.610291Z",
     "shell.execute_reply": "2024-05-08T21:55:18.608372Z",
     "shell.execute_reply.started": "2024-05-08T21:55:18.237092Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "label_map = {\n",
    "    'LABEL_0': 'Non-Hate',\n",
    "    'LABEL_1': 'Hate'\n",
    "}\n",
    "\n",
    "cm = confusion_matrix(y_true, y_pred)\n",
    "\n",
    "# label_map to labels\n",
    "labels = [label_map[f'LABEL_{i}'] for i in range(len(label_map))]\n",
    "\n",
    "# Confusion Matrix\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=labels, yticklabels=labels)\n",
    "plt.xlabel('Predicted labels')\n",
    "plt.ylabel('True labels')\n",
    "plt.title('Confusion Matrix with Label Names')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-08T21:56:12.291807Z",
     "iopub.status.busy": "2024-05-08T21:56:12.291216Z",
     "iopub.status.idle": "2024-05-08T21:56:12.307716Z",
     "shell.execute_reply": "2024-05-08T21:56:12.305971Z",
     "shell.execute_reply.started": "2024-05-08T21:56:12.291764Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.42936136696082683"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_true, y_pred, average='macro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-08T21:56:12.438171Z",
     "iopub.status.busy": "2024-05-08T21:56:12.437159Z",
     "iopub.status.idle": "2024-05-08T21:56:12.453301Z",
     "shell.execute_reply": "2024-05-08T21:56:12.451681Z",
     "shell.execute_reply.started": "2024-05-08T21:56:12.438104Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.432"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_true, y_pred, average='micro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "execution": {
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