cs772-project.ipynb

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  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/utsav-desai/CS772.git\n",
        "!pip install accelerate -U datasets\n",
        "!pip install transformers[torch]S"
      ],
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        "colab": {
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        },
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      },
      "execution_count": 1,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "fatal: destination path 'CS772' already exists and is not an empty directory.\n",
            "Requirement already satisfied: accelerate in /usr/local/lib/python3.10/dist-packages (0.30.0)\n",
            "Requirement already satisfied: datasets in /usr/local/lib/python3.10/dist-packages (2.19.0)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from accelerate) (1.25.2)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (24.0)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from accelerate) (5.9.5)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from accelerate) (6.0.1)\n",
            "Requirement already satisfied: torch>=1.10.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (2.2.1+cu121)\n",
            "Requirement already satisfied: huggingface-hub in /usr/local/lib/python3.10/dist-packages (from accelerate) (0.23.0)\n",
            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from accelerate) (0.4.3)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from datasets) (3.14.0)\n",
            "Requirement already satisfied: pyarrow>=12.0.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (14.0.2)\n",
            "Requirement already satisfied: pyarrow-hotfix in /usr/local/lib/python3.10/dist-packages (from datasets) (0.6)\n",
            "Requirement already satisfied: dill<0.3.9,>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (0.3.8)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets) (2.0.3)\n",
            "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (2.31.0)\n",
            "Requirement already satisfied: tqdm>=4.62.1 in /usr/local/lib/python3.10/dist-packages (from datasets) (4.66.2)\n",
            "Requirement already satisfied: xxhash in /usr/local/lib/python3.10/dist-packages (from datasets) (3.4.1)\n",
            "Requirement already satisfied: multiprocess in /usr/local/lib/python3.10/dist-packages (from datasets) (0.70.16)\n",
            "Requirement already satisfied: fsspec[http]<=2024.3.1,>=2023.1.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (2023.6.0)\n",
            "Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from datasets) (3.9.5)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.3.1)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (23.2.0)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.4.1)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (6.0.5)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.9.4)\n",
            "Requirement already satisfied: async-timeout<5.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (4.0.3)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate) (4.11.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (2024.2.2)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.3)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.1.3)\n",
            "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (8.9.2.26)\n",
            "Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (12.1.3.1)\n",
            "Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (11.0.2.54)\n",
            "Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (10.3.2.106)\n",
            "Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (11.4.5.107)\n",
            "Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (12.1.0.106)\n",
            "Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2.19.3)\n",
            "Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (12.1.105)\n",
            "Requirement already satisfied: triton==2.2.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2.2.0)\n",
            "Requirement already satisfied: nvidia-nvjitlink-cu12 in /usr/local/lib/python3.10/dist-packages (from nvidia-cusolver-cu12==11.4.5.107->torch>=1.10.0->accelerate) (12.4.127)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2023.4)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2024.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.16.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.10.0->accelerate) (2.1.5)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.10.0->accelerate) (1.3.0)\n",
            "\u001b[31mERROR: Invalid requirement: 'transformers[torch]S'\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!nvidia-smi"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-28T19:34:47.753305Z",
          "iopub.execute_input": "2024-04-28T19:34:47.753608Z",
          "iopub.status.idle": "2024-04-28T19:34:48.789958Z",
          "shell.execute_reply.started": "2024-04-28T19:34:47.753582Z",
          "shell.execute_reply": "2024-04-28T19:34:48.789048Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IbKS3q35t79i",
        "outputId": "8f5cdf04-90fb-44f0-b816-1ca81e94c5eb"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sun May  5 09:27:57 2024       \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   CUDA Version: 12.2     |\n",
            "|-----------------------------------------+----------------------+----------------------+\n",
            "| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                                         |                      |               MIG M. |\n",
            "|=========================================+======================+======================|\n",
            "|   0  Tesla T4                       Off | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   64C    P8              10W /  70W |      0MiB / 15360MiB |      0%      Default |\n",
            "|                                         |                      |                  N/A |\n",
            "+-----------------------------------------+----------------------+----------------------+\n",
            "                                                                                         \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| Processes:                                                                            |\n",
            "|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n",
            "|        ID   ID                                                             Usage      |\n",
            "|=======================================================================================|\n",
            "|  No running processes found                                                           |\n",
            "+---------------------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Import Libraries"
      ],
      "metadata": {
        "id": "cW1aCY3gzvYe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "os.environ[\"WANDB_DISABLED\"] = \"True\"\n",
        "import numpy as np  # linear algebra\n",
        "import pandas as pd  # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "import torch\n",
        "import os\n",
        "from sklearn.model_selection import train_test_split\n",
        "from datasets import Dataset, load_metric"
      ],
      "metadata": {
        "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
        "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
        "execution": {
          "iopub.status.busy": "2024-04-28T19:34:48.792097Z",
          "iopub.execute_input": "2024-04-28T19:34:48.792804Z",
          "iopub.status.idle": "2024-04-28T19:34:54.011023Z",
          "shell.execute_reply.started": "2024-04-28T19:34:48.792766Z",
          "shell.execute_reply": "2024-04-28T19:34:54.010004Z"
        },
        "trusted": true,
        "id": "uiPL_RR6t79k"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install spacy_cleaner"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-28T19:34:54.012025Z",
          "iopub.execute_input": "2024-04-28T19:34:54.012429Z",
          "iopub.status.idle": "2024-04-28T19:35:10.727184Z",
          "shell.execute_reply.started": "2024-04-28T19:34:54.012402Z",
          "shell.execute_reply": "2024-04-28T19:35:10.726008Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NVBPHjjgt79k",
        "outputId": "16232bc6-84a4-4245-8839-d9ea38e6555a"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: spacy_cleaner in /usr/local/lib/python3.10/dist-packages (3.2.1)\n",
            "Requirement already satisfied: spacy<3.8.0,>=3.7.2 in /usr/local/lib/python3.10/dist-packages (from spacy_cleaner) (3.7.4)\n",
            "Requirement already satisfied: spacy-lookups-data<1.1.0,>=1.0.5 in /usr/local/lib/python3.10/dist-packages (from spacy_cleaner) (1.0.5)\n",
            "Requirement already satisfied: tqdm<4.67.0,>=4.66.1 in /usr/local/lib/python3.10/dist-packages (from spacy_cleaner) (4.66.2)\n",
            "Requirement already satisfied: types-tqdm<5.0.0.0,>=4.66.0.5 in /usr/local/lib/python3.10/dist-packages (from spacy_cleaner) (4.66.0.20240417)\n",
            "Requirement already satisfied: spacy-legacy<3.1.0,>=3.0.11 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->spacy_cleaner) (3.0.12)\n",
            "Requirement already satisfied: spacy-loggers<2.0.0,>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->spacy_cleaner) (1.0.5)\n",
            "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->spacy_cleaner) (1.0.10)\n",
            "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->spacy_cleaner) (2.0.8)\n",
            "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->spacy_cleaner) (3.0.9)\n",
            "Requirement already satisfied: thinc<8.3.0,>=8.2.2 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->spacy_cleaner) (8.2.3)\n",
            "Requirement already satisfied: wasabi<1.2.0,>=0.9.1 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->spacy_cleaner) (1.1.2)\n",
            "Requirement already satisfied: srsly<3.0.0,>=2.4.3 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->spacy_cleaner) (2.4.8)\n",
            "Requirement already satisfied: catalogue<2.1.0,>=2.0.6 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->spacy_cleaner) (2.0.10)\n",
            "Requirement already satisfied: weasel<0.4.0,>=0.1.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->spacy_cleaner) (0.3.4)\n",
            "Collecting typer<0.10.0,>=0.3.0 (from spacy<3.8.0,>=3.7.2->spacy_cleaner)\n",
            "  Downloading typer-0.9.4-py3-none-any.whl (45 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m46.0/46.0 kB\u001b[0m \u001b[31m1.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: smart-open<7.0.0,>=5.2.1 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->spacy_cleaner) (6.4.0)\n",
            "Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->spacy_cleaner) (2.31.0)\n",
            "Requirement already satisfied: pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->spacy_cleaner) (2.7.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->spacy_cleaner) (3.1.3)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->spacy_cleaner) (67.7.2)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->spacy_cleaner) (24.0)\n",
            "Requirement already satisfied: langcodes<4.0.0,>=3.2.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->spacy_cleaner) (3.4.0)\n",
            "Requirement already satisfied: numpy>=1.19.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->spacy_cleaner) (1.25.2)\n",
            "Requirement already satisfied: language-data>=1.2 in /usr/local/lib/python3.10/dist-packages (from langcodes<4.0.0,>=3.2.0->spacy<3.8.0,>=3.7.2->spacy_cleaner) (1.2.0)\n",
            "Requirement already satisfied: annotated-types>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4->spacy<3.8.0,>=3.7.2->spacy_cleaner) (0.6.0)\n",
            "Requirement already satisfied: pydantic-core==2.18.2 in /usr/local/lib/python3.10/dist-packages (from pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4->spacy<3.8.0,>=3.7.2->spacy_cleaner) (2.18.2)\n",
            "Requirement already satisfied: typing-extensions>=4.6.1 in /usr/local/lib/python3.10/dist-packages (from pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4->spacy<3.8.0,>=3.7.2->spacy_cleaner) (4.11.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.8.0,>=3.7.2->spacy_cleaner) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.8.0,>=3.7.2->spacy_cleaner) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.8.0,>=3.7.2->spacy_cleaner) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.8.0,>=3.7.2->spacy_cleaner) (2024.2.2)\n",
            "Requirement already satisfied: blis<0.8.0,>=0.7.8 in /usr/local/lib/python3.10/dist-packages (from thinc<8.3.0,>=8.2.2->spacy<3.8.0,>=3.7.2->spacy_cleaner) (0.7.11)\n",
            "Requirement already satisfied: confection<1.0.0,>=0.0.1 in /usr/local/lib/python3.10/dist-packages (from thinc<8.3.0,>=8.2.2->spacy<3.8.0,>=3.7.2->spacy_cleaner) (0.1.4)\n",
            "Requirement already satisfied: click<9.0.0,>=7.1.1 in /usr/local/lib/python3.10/dist-packages (from typer<0.10.0,>=0.3.0->spacy<3.8.0,>=3.7.2->spacy_cleaner) (8.1.7)\n",
            "Requirement already satisfied: cloudpathlib<0.17.0,>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from weasel<0.4.0,>=0.1.0->spacy<3.8.0,>=3.7.2->spacy_cleaner) (0.16.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->spacy<3.8.0,>=3.7.2->spacy_cleaner) (2.1.5)\n",
            "Requirement already satisfied: marisa-trie>=0.7.7 in /usr/local/lib/python3.10/dist-packages (from language-data>=1.2->langcodes<4.0.0,>=3.2.0->spacy<3.8.0,>=3.7.2->spacy_cleaner) (1.1.0)\n",
            "Installing collected packages: typer\n",
            "  Attempting uninstall: typer\n",
            "    Found existing installation: typer 0.12.3\n",
            "    Uninstalling typer-0.12.3:\n",
            "      Successfully uninstalled typer-0.12.3\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",
            "fastapi-cli 0.0.2 requires typer>=0.12.3, but you have typer 0.9.4 which is incompatible.\n",
            "gradio 4.29.0 requires typer<1.0,>=0.12; sys_platform != \"emscripten\", but you have typer 0.9.4 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed typer-0.9.4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import spacy, re\n",
        "import spacy_cleaner\n",
        "from spacy_cleaner.processing import removers, mutators"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-28T19:35:10.729723Z",
          "iopub.execute_input": "2024-04-28T19:35:10.730036Z",
          "iopub.status.idle": "2024-04-28T19:35:14.086953Z",
          "shell.execute_reply.started": "2024-04-28T19:35:10.730009Z",
          "shell.execute_reply": "2024-04-28T19:35:14.085909Z"
        },
        "trusted": true,
        "id": "Fc_q4sett79l"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "train = pd.read_csv(\"./CS772/Project/data/train.csv\")\n",
        "test = pd.read_csv(\"./CS772/Project/data/test.csv\")"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-28T19:35:14.088466Z",
          "iopub.execute_input": "2024-04-28T19:35:14.088883Z",
          "iopub.status.idle": "2024-04-28T19:35:14.156037Z",
          "shell.execute_reply.started": "2024-04-28T19:35:14.088858Z",
          "shell.execute_reply": "2024-04-28T19:35:14.15515Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rf2Un7jIt79l",
        "outputId": "da8b276a-5c42-45c0-ea27-e7c2bf3f77fe"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 35 ms, sys: 12 ms, total: 47 ms\n",
            "Wall time: 49.7 ms\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train.head(10)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-28T19:35:14.157173Z",
          "iopub.execute_input": "2024-04-28T19:35:14.157462Z",
          "iopub.status.idle": "2024-04-28T19:35:14.174547Z",
          "shell.execute_reply.started": "2024-04-28T19:35:14.157438Z",
          "shell.execute_reply": "2024-04-28T19:35:14.173831Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "QfJVhNWGt79l",
        "outputId": "518b5478-f1db-44e8-a745-457a125fad4d"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              "\n",
              "   target  \n",
              "0       1  \n",
              "1       1  \n",
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              "                                                    [key], {});\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "train",
              "summary": "{\n  \"name\": \"train\",\n  \"rows\": 7613,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3137,\n        \"min\": 1,\n        \"max\": 10873,\n        \"num_unique_values\": 7613,\n        \"samples\": [\n          3796,\n          3185,\n          7769\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"keyword\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 221,\n        \"samples\": [\n          \"injury\",\n          \"nuclear%20reactor\",\n          \"engulfed\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"location\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3341,\n        \"samples\": [\n          \"Oklahoma\",\n          \"Starling City\",\n          \"Trinidad and Tobago\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 7503,\n        \"samples\": [\n          \"Three Homes Demolished in Unrecognized Arab Village - International Middle East Media Center http://t.co/ik8m4Yi9T4\",\n          \"Reid Lake fire prompts campground evacuation order http://t.co/jBODKM6rBU\",\n          \"FAAN orders evacuation of abandoned aircraft at MMA http://t.co/dEvYbnVXGQ via @todayng\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"target\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
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    {
      "cell_type": "code",
      "source": [
        "len(train)"
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      "outputs": [
        {
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          "data": {
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              "7613"
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          "metadata": {},
          "execution_count": 8
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      "cell_type": "code",
      "source": [
        "max(train[\"id\"])"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10873"
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          "metadata": {},
          "execution_count": 9
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      "cell_type": "code",
      "source": [
        "train[\"keyword\"].value_counts()"
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        "execution": {
          "iopub.status.busy": "2024-04-28T19:35:14.194082Z",
          "iopub.execute_input": "2024-04-28T19:35:14.19438Z",
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          "shell.execute_reply": "2024-04-28T19:35:14.208871Z"
        },
        "trusted": true,
        "colab": {
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        "id": "7s3NX3o5t79m",
        "outputId": "ba27744f-6c59-452a-85c8-7edceef352d4"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "keyword\n",
              "fatalities               45\n",
              "deluge                   42\n",
              "armageddon               42\n",
              "sinking                  41\n",
              "damage                   41\n",
              "                         ..\n",
              "forest%20fire            19\n",
              "epicentre                12\n",
              "threat                   11\n",
              "inundation               10\n",
              "radiation%20emergency     9\n",
              "Name: count, Length: 221, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train[\"location\"].value_counts()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-28T19:35:14.213388Z",
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        "outputId": "f1c56c2d-659f-4481-afc8-891a0132b70b"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "location\n",
              "USA                    104\n",
              "New York                71\n",
              "United States           50\n",
              "London                  45\n",
              "Canada                  29\n",
              "                      ... \n",
              "MontrÌ©al, QuÌ©bec       1\n",
              "Montreal                 1\n",
              "ÌÏT: 6.4682,3.18287      1\n",
              "Live4Heed??              1\n",
              "Lincoln                  1\n",
              "Name: count, Length: 3341, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train = train[train.columns[3:]]  # Removed Other columns\n",
        "test = test[test.columns[3:]]"
      ],
      "metadata": {
        "execution": {
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        "trusted": true,
        "id": "y3P9-wsdt79m"
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      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train[\"text\"] = train[\"text\"].str.lower()\n",
        "test[\"text\"] = test[\"text\"].str.lower()"
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      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-28T19:35:14.237547Z",
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      "execution_count": 13,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "test.head(5)"
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        "execution": {
          "iopub.status.busy": "2024-04-28T19:35:14.254572Z",
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          "height": 206
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        "id": "d-OOsbOst79n",
        "outputId": "72438dee-bfcf-424b-e8b3-48262ac037b3"
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      "execution_count": 14,
      "outputs": [
        {
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              "                                                text\n",
              "0                 just happened a terrible car crash\n",
              "1  heard about #earthquake is different cities, s...\n",
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              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-410d5dc2-ba74-4521-8abc-b5e633d4dbd1 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-410d5dc2-ba74-4521-8abc-b5e633d4dbd1');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-78aa25d1-c873-48f9-9df6-37934c35754b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-78aa25d1-c873-48f9-9df6-37934c35754b')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-78aa25d1-c873-48f9-9df6-37934c35754b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "test",
              "summary": "{\n  \"name\": \"test\",\n  \"rows\": 3263,\n  \"fields\": [\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3243,\n        \"samples\": [\n          \"latest: usa: huge sinkhole swallows up brooklyn intersection http://t.co/vspkhg3nzy\",\n          \"i liked a @youtube video http://t.co/a5ytaw9vih s.o.s. rona guide - the red whirlwind\",\n          \"hitchbot travels europe and greeted with open arms. gets destroyed after two weeks in america. there's a lesson to be learned here.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train.head(5)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-28T19:35:14.26799Z",
          "iopub.execute_input": "2024-04-28T19:35:14.268268Z",
          "iopub.status.idle": "2024-04-28T19:35:14.281573Z",
          "shell.execute_reply.started": "2024-04-28T19:35:14.268245Z",
          "shell.execute_reply": "2024-04-28T19:35:14.280621Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "gIgEVIpLt79n",
        "outputId": "167f9789-5b8b-4977-c2c1-f28d822e58ee"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                text  target\n",
              "0  our deeds are the reason of this #earthquake m...       1\n",
              "1             forest fire near la ronge sask. canada       1\n",
              "2  all residents asked to 'shelter in place' are ...       1\n",
              "3  13,000 people receive #wildfires evacuation or...       1\n",
              "4  just got sent this photo from ruby #alaska as ...       1"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-803c9c30-4b02-4d67-8fae-fc2cb32c6e0d\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>text</th>\n",
              "      <th>target</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>our deeds are the reason of this #earthquake m...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>forest fire near la ronge sask. canada</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>all residents asked to 'shelter in place' are ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>13,000 people receive #wildfires evacuation or...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>just got sent this photo from ruby #alaska as ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-803c9c30-4b02-4d67-8fae-fc2cb32c6e0d')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-803c9c30-4b02-4d67-8fae-fc2cb32c6e0d button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-803c9c30-4b02-4d67-8fae-fc2cb32c6e0d');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-f7181c59-1505-4831-ae0e-140cc31b3c6a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f7181c59-1505-4831-ae0e-140cc31b3c6a')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f7181c59-1505-4831-ae0e-140cc31b3c6a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "train",
              "summary": "{\n  \"name\": \"train\",\n  \"rows\": 7613,\n  \"fields\": [\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 7502,\n        \"samples\": [\n          \"three homes demolished in unrecognized arab village - international middle east media center http://t.co/ik8m4yi9t4\",\n          \"reid lake fire prompts campground evacuation order http://t.co/jbodkm6rbu\",\n          \"faan orders evacuation of abandoned aircraft at mma http://t.co/devybnvxgq via @todayng\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"target\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Setting up the pipeline to clean the data using spacy\n",
        "nlp = spacy.load(\"en_core_web_sm\")\n",
        "nlp"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-28T19:35:14.282884Z",
          "iopub.execute_input": "2024-04-28T19:35:14.283308Z",
          "iopub.status.idle": "2024-04-28T19:35:15.690218Z",
          "shell.execute_reply.started": "2024-04-28T19:35:14.283272Z",
          "shell.execute_reply": "2024-04-28T19:35:15.689255Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yuGWaEPnt79n",
        "outputId": "3cca53f9-efb7-4ac4-9c71-5f87e46fb6ae"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<spacy.lang.en.English at 0x7aaaa2598eb0>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pipeline = spacy_cleaner.Cleaner(\n",
        "    nlp,  # pre-built language model\n",
        "    removers.remove_email_token,\n",
        "    removers.remove_punctuation_token,\n",
        "    removers.remove_stopword_token,\n",
        "    removers.remove_url_token,\n",
        "    removers.remove_number_token,\n",
        "    mutators.mutate_lemma_token,\n",
        ")"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-28T19:35:15.691416Z",
          "iopub.execute_input": "2024-04-28T19:35:15.691702Z",
          "iopub.status.idle": "2024-04-28T19:35:15.69645Z",
          "shell.execute_reply.started": "2024-04-28T19:35:15.691678Z",
          "shell.execute_reply": "2024-04-28T19:35:15.695552Z"
        },
        "trusted": true,
        "id": "R99ml216t79n"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train.head(5)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-28T19:35:15.697587Z",
          "iopub.execute_input": "2024-04-28T19:35:15.697882Z",
          "iopub.status.idle": "2024-04-28T19:35:15.712502Z",
          "shell.execute_reply.started": "2024-04-28T19:35:15.697851Z",
          "shell.execute_reply": "2024-04-28T19:35:15.7114Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "wV5cTv_Ot79n",
        "outputId": "77bd32f3-9d69-444c-de0f-f10658fbdc07"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                text  target\n",
              "0  our deeds are the reason of this #earthquake m...       1\n",
              "1             forest fire near la ronge sask. canada       1\n",
              "2  all residents asked to 'shelter in place' are ...       1\n",
              "3  13,000 people receive #wildfires evacuation or...       1\n",
              "4  just got sent this photo from ruby #alaska as ...       1"
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              "        const element = document.querySelector('#df-200d9018-8499-4e5f-9432-02a40c5338bd');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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              "\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "        document.querySelector('#df-9530ffa1-5782-44ed-92c7-3d7794bbe1c8 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "train",
              "summary": "{\n  \"name\": \"train\",\n  \"rows\": 7613,\n  \"fields\": [\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 7502,\n        \"samples\": [\n          \"three homes demolished in unrecognized arab village - international middle east media center http://t.co/ik8m4yi9t4\",\n          \"reid lake fire prompts campground evacuation order http://t.co/jbodkm6rbu\",\n          \"faan orders evacuation of abandoned aircraft at mma http://t.co/devybnvxgq via @todayng\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"target\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "train[\"text\"] = pipeline.clean(train[\"text\"])"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-28T19:35:15.713616Z",
          "iopub.execute_input": "2024-04-28T19:35:15.713964Z",
          "iopub.status.idle": "2024-04-28T19:35:35.526093Z",
          "shell.execute_reply.started": "2024-04-28T19:35:15.713931Z",
          "shell.execute_reply": "2024-04-28T19:35:35.5251Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z8VNuveVt79o",
        "outputId": "c7ea40d4-ecbc-476e-ff4e-c7a7dd7830ed"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Cleaning Progress: 100%|██████████| 7613/7613 [00:35<00:00, 217.21it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 25.7 s, sys: 234 ms, total: 25.9 s\n",
            "Wall time: 35.1 s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train.head(5)  # Now we got the clean texts"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-28T19:35:35.527357Z",
          "iopub.execute_input": "2024-04-28T19:35:35.52765Z",
          "iopub.status.idle": "2024-04-28T19:35:35.539002Z",
          "shell.execute_reply.started": "2024-04-28T19:35:35.527624Z",
          "shell.execute_reply": "2024-04-28T19:35:35.538083Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "JmnYjkGht79o",
        "outputId": "c9455a33-931f-49db-d1b3-d1009f120ced"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                text  target\n",
              "0               deed reason earthquake allah forgive       1\n",
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              "2  resident ask shelter place notify officer evac...       1\n",
              "3  people receive wildfire evacuation order calif...       1\n",
              "4  get send photo ruby alaska smoke wildfire pour...       1"
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              "      const buttonEl =\n",
              "        document.querySelector('#df-7ed97641-fb54-4d22-9963-1a5d0cd0aea1 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-7ed97641-fb54-4d22-9963-1a5d0cd0aea1');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-11eddd48-ff38-4e62-b8b3-91ea9c35972b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-11eddd48-ff38-4e62-b8b3-91ea9c35972b')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-11eddd48-ff38-4e62-b8b3-91ea9c35972b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "train",
              "summary": "{\n  \"name\": \"train\",\n  \"rows\": 7613,\n  \"fields\": [\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6907,\n        \"samples\": [\n          \"new weapon cause un imaginable destruction\",\n          \"come idea fragrance concept bath bomb call blood enemy bathe\",\n          \"taiwan grace expect large rock tree mud unstable and/or saturate land slide hazardous hilly mountain area\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"target\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train[\"text\"][100]"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-28T19:35:35.540217Z",
          "iopub.execute_input": "2024-04-28T19:35:35.540588Z",
          "iopub.status.idle": "2024-04-28T19:35:35.548549Z",
          "shell.execute_reply.started": "2024-04-28T19:35:35.540558Z",
          "shell.execute_reply": "2024-04-28T19:35:35.547665Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "C6QArszTt79o",
        "outputId": "9ed9c0a0-c751-458e-edaa-b342e80561e7"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'.@norwaymfa bahrain police previously die road accident kill explosion'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train[\"text\"][102]"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-28T19:35:35.549757Z",
          "iopub.execute_input": "2024-04-28T19:35:35.550001Z",
          "iopub.status.idle": "2024-04-28T19:35:35.560356Z",
          "shell.execute_reply.started": "2024-04-28T19:35:35.549978Z",
          "shell.execute_reply": "2024-04-28T19:35:35.559525Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "Jl7-wl09t79o",
        "outputId": "d6d8b7a2-7309-4e65-a95a-998148be5ae3"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'@aftershock_delo scuf ps live game cya'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## there were tags(usually a name , which doesnot have any effect in the output) in the sentences and some unwanted symbols which accompanies\n",
        "\n",
        "eg .ir icemoon aftershock dubstep trapmusic dnb edm dance ices\\x89 (I have seen this,but forget the which line it is ,😂)"
      ],
      "metadata": {
        "id": "ypeXchZgt79o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# let us remove the tags and unwanted symbols to make it more clean\n",
        "\n",
        "SymbolRemover = lambda x: re.sub(\"[#=><\\/.]\", \"\", x)\n",
        "\n",
        "TagRemover = lambda word: re.sub(\"@\\w+\", \"\", word)\n",
        "\n",
        "train[\"text\"] = train[\"text\"].apply(SymbolRemover)\n",
        "\n",
        "train[\"text\"] = train[\"text\"].apply(TagRemover)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-28T19:35:35.561463Z",
          "iopub.execute_input": "2024-04-28T19:35:35.561927Z",
          "iopub.status.idle": "2024-04-28T19:35:35.600642Z",
          "shell.execute_reply.started": "2024-04-28T19:35:35.561902Z",
          "shell.execute_reply": "2024-04-28T19:35:35.599946Z"
        },
        "trusted": true,
        "id": "cYLIRGrht79q"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train[\"text\"][102]"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-28T19:35:35.6016Z",
          "iopub.execute_input": "2024-04-28T19:35:35.601851Z",
          "iopub.status.idle": "2024-04-28T19:35:35.607163Z",
          "shell.execute_reply.started": "2024-04-28T19:35:35.601829Z",
          "shell.execute_reply": "2024-04-28T19:35:35.606265Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "UimvTIZlt79q",
        "outputId": "0493eb80-4cf0-4b62-bc63-f24e1d1fcc80"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "' scuf ps live game cya'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train[\"text\"][100]"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-28T19:35:35.608299Z",
          "iopub.execute_input": "2024-04-28T19:35:35.608852Z",
          "iopub.status.idle": "2024-04-28T19:35:35.61646Z",
          "shell.execute_reply.started": "2024-04-28T19:35:35.608821Z",
          "shell.execute_reply": "2024-04-28T19:35:35.615766Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "0eFt9gkft79q",
        "outputId": "49144577-f05d-45e5-df5f-df3981a4344d"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "' bahrain police previously die road accident kill explosion'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!ls"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-28T19:35:35.61758Z",
          "iopub.execute_input": "2024-04-28T19:35:35.617908Z",
          "iopub.status.idle": "2024-04-28T19:35:36.580124Z",
          "shell.execute_reply.started": "2024-04-28T19:35:35.617885Z",
          "shell.execute_reply": "2024-04-28T19:35:36.578857Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DFkvlx65t79q",
        "outputId": "3572657f-ff1e-4620-ac1e-3da9d260b0cf"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CS772  distilbert  flagged  my_model_weights  sample_data\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<div style=\"background-image: url('https://media.istockphoto.com/id/1341408852/video/colored-smoke-on-a-dark-background-blue-and-red-light-with-smoke.jpg?s=640x640&k=20&c=v2DQUY8IVbli_6FH_9KAs6YWRXlDdYiBJHfp7JFh7NY='); background-size: cover; background-position: center; border-radius: 10px; padding: 20px; color: #ffffff;\">\n",
        "    <h3>Using the pre-build model from Hugging face(Transformers) for Classification</h3>\n",
        "</div>"
      ],
      "metadata": {
        "id": "mYKSoatxt79q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's write the logic for the Model in pytorch\n",
        "from transformers import (\n",
        "    AutoTokenizer,\n",
        "    DataCollator,\n",
        "    DataCollatorWithPadding,\n",
        "    AutoModelForSequenceClassification,\n",
        "    Trainer,\n",
        "    TrainingArguments,\n",
        ")\n"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-28T19:35:36.581844Z",
          "iopub.execute_input": "2024-04-28T19:35:36.582253Z",
          "iopub.status.idle": "2024-04-28T19:35:52.553943Z",
          "shell.execute_reply.started": "2024-04-28T19:35:36.582213Z",
          "shell.execute_reply": "2024-04-28T19:35:52.55302Z"
        },
        "trusted": true,
        "id": "y5lRuyK_t79r"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<div style=\"background-image: url('https://media.istockphoto.com/id/1341408852/video/colored-smoke-on-a-dark-background-blue-and-red-light-with-smoke.jpg?s=640x640&k=20&c=v2DQUY8IVbli_6FH_9KAs6YWRXlDdYiBJHfp7JFh7NY='); background-size: cover; background-position: center; border-radius: 10px; padding: 20px; color: #ffffff;\">\n",
        "    <h2>Using the \"distilbert/distilbert-base-uncased\" from transformers</h2>\n",
        "    <a href=https://huggingface.co/transformers/v3.0.2/model_doc/auto.html#>Click to Refer for further<a>\n",
        "</div>"
      ],
      "metadata": {
        "id": "F7PjlDGJt79r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = \"distilbert/distilbert-base-uncased\"\n",
        "learning_rate = 2e-5\n",
        "epochs = 16"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-28T19:35:52.555009Z",
          "iopub.execute_input": "2024-04-28T19:35:52.55565Z",
          "iopub.status.idle": "2024-04-28T19:35:52.56146Z",
          "shell.execute_reply.started": "2024-04-28T19:35:52.555621Z",
          "shell.execute_reply": "2024-04-28T19:35:52.560606Z"
        },
        "trusted": true,
        "id": "AWSxMG_Pt79r"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_split, valid_split = train_test_split(\n",
        "    train, test_size=0.2\n",
        ")  # sppliting the data for train and valid"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-28T19:35:52.569335Z",
          "iopub.execute_input": "2024-04-28T19:35:52.569635Z",
          "iopub.status.idle": "2024-04-28T19:35:52.600943Z",
          "shell.execute_reply.started": "2024-04-28T19:35:52.56961Z",
          "shell.execute_reply": "2024-04-28T19:35:52.600163Z"
        },
        "trusted": true,
        "id": "Bh4tw4Ftt79r"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(\n",
        "    model\n",
        ")  # Maping the TOkenizer for the model classification\n",
        "train_ds = Dataset.from_pandas(train_split)\n",
        "valid_ds = Dataset.from_pandas(valid_split)\n",
        "test_ds = Dataset.from_pandas(test)\n",
        "\n",
        "\n",
        "def process_token(example, tokenizer=tokenizer):\n",
        "    return tokenizer(example[\"text\"])\n",
        "\n",
        "\n",
        "tokenized_train = train_ds.map(process_token)\n",
        "tokenized_valid = valid_ds.map(process_token)\n",
        "tokenized_test = test_ds.map(process_token)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-28T19:35:52.601987Z",
          "iopub.execute_input": "2024-04-28T19:35:52.602289Z",
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          "shell.execute_reply": "2024-04-28T19:35:55.745146Z"
        },
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272,
          "referenced_widgets": [
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            "a69eec54d7834689ae4080b1081990e9",
            "be30c34775bc4452b1e306e2871eefb0",
            "c5b51a65795e45d3a62bfc3904b56246",
            "8a7f892161c74fd093f92a307b903b26"
          ]
        },
        "id": "nxiP0U8lt79r",
        "outputId": "ca0c5d0f-ab44-4a60-9c44-c68046d565f6"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/6090 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "dc602fc49b264d319e23bb77b0f2d0fb"
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        },
        {
          "output_type": "display_data",
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            "text/plain": [
              "Map:   0%|          | 0/1523 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "d41da7e176d54ae3956459f387046739"
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/3263 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "23bb25c66bf34914b6ecdeffe4d73af5"
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_train[0]"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-28T19:35:55.747267Z",
          "iopub.execute_input": "2024-04-28T19:35:55.747548Z",
          "iopub.status.idle": "2024-04-28T19:35:55.754024Z",
          "shell.execute_reply.started": "2024-04-28T19:35:55.747523Z",
          "shell.execute_reply": "2024-04-28T19:35:55.753176Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GgFUi_pkt79s",
        "outputId": "44665dac-bc1a-4ee6-cdb5-7c93e7dee2e9"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'text': 'common running injury avoid',\n",
              " 'target': 0,\n",
              " '__index_level_0__': 4593,\n",
              " 'input_ids': [101, 2691, 2770, 4544, 4468, 102],\n",
              " 'attention_mask': [1, 1, 1, 1, 1, 1]}"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_test[0]"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-28T19:35:55.755313Z",
          "iopub.execute_input": "2024-04-28T19:35:55.755922Z",
          "iopub.status.idle": "2024-04-28T19:35:55.765388Z",
          "shell.execute_reply.started": "2024-04-28T19:35:55.755888Z",
          "shell.execute_reply": "2024-04-28T19:35:55.76451Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n9Sm5BODt79s",
        "outputId": "b0bc0759-fcb1-41ff-d002-763039f368e8"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'text': 'just happened a terrible car crash',\n",
              " 'input_ids': [101, 2074, 3047, 1037, 6659, 2482, 5823, 102],\n",
              " 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1]}"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "columns_to_remove = [\"__index_level_0__\"]\n",
        "train_dataset = tokenized_train.remove_columns(columns_to_remove)\n",
        "valid_dataset = tokenized_valid.remove_columns(columns_to_remove)\n",
        "test_dataset = tokenized_test"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-28T19:35:55.845961Z",
          "iopub.execute_input": "2024-04-28T19:35:55.847202Z",
          "iopub.status.idle": "2024-04-28T19:35:55.858739Z",
          "shell.execute_reply.started": "2024-04-28T19:35:55.847157Z",
          "shell.execute_reply": "2024-04-28T19:35:55.857941Z"
        },
        "trusted": true,
        "id": "E6yJbPWPt79s"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset[0]"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-28T19:35:55.859609Z",
          "iopub.execute_input": "2024-04-28T19:35:55.859836Z",
          "iopub.status.idle": "2024-04-28T19:35:55.872515Z",
          "shell.execute_reply.started": "2024-04-28T19:35:55.859816Z",
          "shell.execute_reply": "2024-04-28T19:35:55.871685Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XB5EJeUat79s",
        "outputId": "0399094c-ed97-4d62-a9b9-7f55981b214c"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'text': 'common running injury avoid',\n",
              " 'target': 0,\n",
              " 'input_ids': [101, 2691, 2770, 4544, 4468, 102],\n",
              " 'attention_mask': [1, 1, 1, 1, 1, 1]}"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "valid_dataset[1]"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-28T19:35:55.873641Z",
          "iopub.execute_input": "2024-04-28T19:35:55.874272Z",
          "iopub.status.idle": "2024-04-28T19:35:55.883849Z",
          "shell.execute_reply.started": "2024-04-28T19:35:55.874242Z",
          "shell.execute_reply": "2024-04-28T19:35:55.883014Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "waq6Kjl1t79s",
        "outputId": "e06f4cda-221f-4d69-d020-4c9cd7de9398"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'text': 'new summer long thin body bag hip word skirt blue m',\n",
              " 'target': 0,\n",
              " 'input_ids': [101,\n",
              "  2047,\n",
              "  2621,\n",
              "  2146,\n",
              "  4857,\n",
              "  2303,\n",
              "  4524,\n",
              "  5099,\n",
              "  2773,\n",
              "  9764,\n",
              "  2630,\n",
              "  1049,\n",
              "  102],\n",
              " 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset = train_dataset.rename_column(\"target\", \"label\")\n",
        "valid_dataset = valid_dataset.rename_column(\"target\", \"label\")"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-28T19:35:55.884953Z",
          "iopub.execute_input": "2024-04-28T19:35:55.885363Z",
          "iopub.status.idle": "2024-04-28T19:35:55.895895Z",
          "shell.execute_reply.started": "2024-04-28T19:35:55.885331Z",
          "shell.execute_reply": "2024-04-28T19:35:55.89516Z"
        },
        "trusted": true,
        "id": "cDUOvSnxt79t"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset[0]"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-28T19:35:55.89696Z",
          "iopub.execute_input": "2024-04-28T19:35:55.89724Z",
          "iopub.status.idle": "2024-04-28T19:35:55.908545Z",
          "shell.execute_reply.started": "2024-04-28T19:35:55.897217Z",
          "shell.execute_reply": "2024-04-28T19:35:55.907799Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EbMArJPEt79t",
        "outputId": "c92dc81f-fe3d-4b4a-937e-bb4314fb4fda"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'text': 'common running injury avoid',\n",
              " 'label': 0,\n",
              " 'input_ids': [101, 2691, 2770, 4544, 4468, 102],\n",
              " 'attention_mask': [1, 1, 1, 1, 1, 1]}"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_dataset[0]"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-28T19:35:55.909521Z",
          "iopub.execute_input": "2024-04-28T19:35:55.910022Z",
          "iopub.status.idle": "2024-04-28T19:35:55.91927Z",
          "shell.execute_reply.started": "2024-04-28T19:35:55.909991Z",
          "shell.execute_reply": "2024-04-28T19:35:55.918473Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b0ZFXDpzt790",
        "outputId": "68065488-943d-4b71-80b6-eac8a5be95d1"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'text': 'just happened a terrible car crash',\n",
              " 'input_ids': [101, 2074, 3047, 1037, 6659, 2482, 5823, 102],\n",
              " 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1]}"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def compute_metrics(eval_pred):\n",
        "    load_acc = load_metric(\"accuracy\")\n",
        "    load_f1 = load_metric(\"f1\")\n",
        "    logits, labels = eval_pred\n",
        "    predictions = np.argmax(logits, axis=-1)\n",
        "    acc = load_acc.compute(predictions=predictions, references=labels)[\"accuracy\"]\n",
        "    f1 = load_f1.compute(predictions=predictions, references=labels)[\"f1\"]\n",
        "    print(f\"Accuracy: {acc} \\nF1: {f1}\")\n",
        "    return {\"acc\": acc, \"f1\": f1}"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-28T19:35:55.920218Z",
          "iopub.execute_input": "2024-04-28T19:35:55.920516Z",
          "iopub.status.idle": "2024-04-28T19:35:55.928824Z",
          "shell.execute_reply.started": "2024-04-28T19:35:55.920483Z",
          "shell.execute_reply": "2024-04-28T19:35:55.927936Z"
        },
        "trusted": true,
        "id": "Dm2BWVfGt790"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# train_dataset"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-28T19:35:55.930008Z",
          "iopub.execute_input": "2024-04-28T19:35:55.930355Z",
          "iopub.status.idle": "2024-04-28T19:35:55.93823Z",
          "shell.execute_reply.started": "2024-04-28T19:35:55.930324Z",
          "shell.execute_reply": "2024-04-28T19:35:55.937452Z"
        },
        "trusted": true,
        "id": "klsDQc5st790"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<h2>Training the DistiltBert model</h2>"
      ],
      "metadata": {
        "id": "zrj1p5OKt790"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = AutoModelForSequenceClassification.from_pretrained(model)\n",
        "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)\n",
        "training_args = TrainingArguments(\n",
        "    learning_rate=learning_rate,\n",
        "    num_train_epochs=epochs,\n",
        "    per_device_train_batch_size=16,\n",
        "    weight_decay=0.01,\n",
        "    output_dir=\"distilbert\",\n",
        ")\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=train_dataset,\n",
        "    eval_dataset=valid_dataset,\n",
        "    data_collator=data_collator,\n",
        "    compute_metrics=compute_metrics,\n",
        ")"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-28T19:35:55.939437Z",
          "iopub.execute_input": "2024-04-28T19:35:55.939826Z",
          "iopub.status.idle": "2024-04-28T19:35:58.338906Z",
          "shell.execute_reply.started": "2024-04-28T19:35:55.939798Z",
          "shell.execute_reply": "2024-04-28T19:35:58.338119Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V6Mk2rW6t790",
        "outputId": "ade2985f-c658-4e94-af1c-3082debd1cc5"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert/distilbert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight', 'pre_classifier.bias', 'pre_classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "Using the `WANDB_DISABLED` environment variable is deprecated and will be removed in v5. Use the --report_to flag to control the integrations used for logging result (for instance --report_to none).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# trainer.args"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-28T19:35:58.340259Z",
          "iopub.execute_input": "2024-04-28T19:35:58.340615Z",
          "iopub.status.idle": "2024-04-28T19:35:58.344905Z",
          "shell.execute_reply.started": "2024-04-28T19:35:58.340581Z",
          "shell.execute_reply": "2024-04-28T19:35:58.343948Z"
        },
        "trusted": true,
        "id": "B_zclLxMt791"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-28T19:35:58.34592Z",
          "iopub.execute_input": "2024-04-28T19:35:58.346188Z",
          "iopub.status.idle": "2024-04-28T19:38:56.988579Z",
          "shell.execute_reply.started": "2024-04-28T19:35:58.346165Z",
          "shell.execute_reply": "2024-04-28T19:38:56.987776Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 486
        },
        "id": "rWgEiz9Xt791",
        "outputId": "fa40a40a-74ce-4524-af1e-a1d60985c085"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='6096' max='6096' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [6096/6096 06:37, Epoch 16/16]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>0.440000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>0.321800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1500</td>\n",
              "      <td>0.232300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2000</td>\n",
              "      <td>0.144900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2500</td>\n",
              "      <td>0.113800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3000</td>\n",
              "      <td>0.082300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3500</td>\n",
              "      <td>0.062300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4000</td>\n",
              "      <td>0.059000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4500</td>\n",
              "      <td>0.051000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5000</td>\n",
              "      <td>0.042100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5500</td>\n",
              "      <td>0.038100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6000</td>\n",
              "      <td>0.035200</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=6096, training_loss=0.13367325407782876, metrics={'train_runtime': 397.8111, 'train_samples_per_second': 244.94, 'train_steps_per_second': 15.324, 'total_flos': 592011293215392.0, 'train_loss': 0.13367325407782876, 'epoch': 16.0})"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.state.log_history"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vTTMQh0CHe5s",
        "outputId": "8feed0c7-cce9-42a9-e704-f558d8f67086"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'loss': 0.44,\n",
              "  'grad_norm': 4.655557155609131,\n",
              "  'learning_rate': 1.835958005249344e-05,\n",
              "  'epoch': 1.3123359580052494,\n",
              "  'step': 500},\n",
              " {'loss': 0.3218,\n",
              "  'grad_norm': 13.00670051574707,\n",
              "  'learning_rate': 1.671916010498688e-05,\n",
              "  'epoch': 2.6246719160104988,\n",
              "  'step': 1000},\n",
              " {'loss': 0.2323,\n",
              "  'grad_norm': 2.329192876815796,\n",
              "  'learning_rate': 1.5078740157480316e-05,\n",
              "  'epoch': 3.937007874015748,\n",
              "  'step': 1500},\n",
              " {'loss': 0.1449,\n",
              "  'grad_norm': 3.7008907794952393,\n",
              "  'learning_rate': 1.3438320209973755e-05,\n",
              "  'epoch': 5.2493438320209975,\n",
              "  'step': 2000},\n",
              " {'loss': 0.1138,\n",
              "  'grad_norm': 8.784838676452637,\n",
              "  'learning_rate': 1.1797900262467192e-05,\n",
              "  'epoch': 6.561679790026247,\n",
              "  'step': 2500},\n",
              " {'loss': 0.0823,\n",
              "  'grad_norm': 0.9720982313156128,\n",
              "  'learning_rate': 1.015748031496063e-05,\n",
              "  'epoch': 7.874015748031496,\n",
              "  'step': 3000},\n",
              " {'loss': 0.0623,\n",
              "  'grad_norm': 2.4014062881469727,\n",
              "  'learning_rate': 8.517060367454069e-06,\n",
              "  'epoch': 9.186351706036746,\n",
              "  'step': 3500},\n",
              " {'loss': 0.059,\n",
              "  'grad_norm': 0.07448063790798187,\n",
              "  'learning_rate': 6.876640419947507e-06,\n",
              "  'epoch': 10.498687664041995,\n",
              "  'step': 4000},\n",
              " {'loss': 0.051,\n",
              "  'grad_norm': 67.16661834716797,\n",
              "  'learning_rate': 5.236220472440945e-06,\n",
              "  'epoch': 11.811023622047244,\n",
              "  'step': 4500},\n",
              " {'loss': 0.0421,\n",
              "  'grad_norm': 2.105804204940796,\n",
              "  'learning_rate': 3.595800524934384e-06,\n",
              "  'epoch': 13.123359580052494,\n",
              "  'step': 5000},\n",
              " {'loss': 0.0381,\n",
              "  'grad_norm': 0.07422575354576111,\n",
              "  'learning_rate': 1.9553805774278216e-06,\n",
              "  'epoch': 14.435695538057743,\n",
              "  'step': 5500},\n",
              " {'loss': 0.0352,\n",
              "  'grad_norm': 0.012910156510770321,\n",
              "  'learning_rate': 3.149606299212599e-07,\n",
              "  'epoch': 15.748031496062993,\n",
              "  'step': 6000},\n",
              " {'train_runtime': 397.8111,\n",
              "  'train_samples_per_second': 244.94,\n",
              "  'train_steps_per_second': 15.324,\n",
              "  'total_flos': 592011293215392.0,\n",
              "  'train_loss': 0.13367325407782876,\n",
              "  'epoch': 16.0,\n",
              "  'step': 6096}]"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "# Plot training and validation loss\n",
        "train_loss = [x[\"loss\"] for x in trainer.state.log_history[:-1]]\n",
        "steps = [x[\"step\"] for x in trainer.state.log_history[:-1]]\n",
        "\n",
        "plt.plot(steps,train_loss, label='Training Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Loss')\n",
        "plt.title('Training and Validation Loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "QoonJ8IfGwJ9",
        "outputId": "d9d96780-2455-4133-cfd2-edae74070ec8"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<div style=\"background-image: url('https://media.istockphoto.com/id/1341408852/video/colored-smoke-on-a-dark-background-blue-and-red-light-with-smoke.jpg?s=640x640&k=20&c=v2DQUY8IVbli_6FH_9KAs6YWRXlDdYiBJHfp7JFh7NY='); background-size: cover; background-position: center; border-radius: 10px; padding: 20px; color: #ffffff;\">\n",
        "    <h2>Getting the Predictions</h2>\n",
        "</div>"
      ],
      "metadata": {
        "id": "4A3p1N8wt791"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-28T19:39:01.688686Z",
          "iopub.execute_input": "2024-04-28T19:39:01.689572Z",
          "iopub.status.idle": "2024-04-28T19:39:01.695133Z",
          "shell.execute_reply.started": "2024-04-28T19:39:01.689536Z",
          "shell.execute_reply": "2024-04-28T19:39:01.694178Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3BSDlUr8t791",
        "outputId": "1cb0d59e-1c35-41be-df15-a6d8316d6e37"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['text', 'label', 'input_ids', 'attention_mask'],\n",
              "    num_rows: 6090\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_dataset"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-28T19:39:03.062587Z",
          "iopub.execute_input": "2024-04-28T19:39:03.062938Z",
          "iopub.status.idle": "2024-04-28T19:39:03.069085Z",
          "shell.execute_reply.started": "2024-04-28T19:39:03.062911Z",
          "shell.execute_reply": "2024-04-28T19:39:03.068292Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "13DUWk3Ht791",
        "outputId": "b27ead1b-ab28-4418-e7bc-4007e33b15f7"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['text', 'input_ids', 'attention_mask'],\n",
              "    num_rows: 3263\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = trainer.predict(test_dataset=train_dataset, ignore_keys=[\"label\"])"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-28T19:39:05.789813Z",
          "iopub.execute_input": "2024-04-28T19:39:05.790581Z",
          "iopub.status.idle": "2024-04-28T19:39:20.508297Z",
          "shell.execute_reply.started": "2024-04-28T19:39:05.79055Z",
          "shell.execute_reply": "2024-04-28T19:39:20.507222Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309,
          "referenced_widgets": [
            "f4217b958b0843ea830faac439e6b960",
            "5882b4d8fa974dddae8678869c08dc5a",
            "23b7b6db5e9d432c96c414835c3f370e",
            "27636a737ab74f529d65226c900d23f6",
            "1c53aca632f246dfbf2dbfdd791b380c",
            "e93bae75704e4af3b701e6986dac2ebf",
            "46e0e3d7ecdd41878ee86012185c9448",
            "5bd50a9c149f4ac987fcb8024238686c",
            "1b29d2dc635c48a4b0857dfe86c0b646",
            "86e54a2c67444699b3855b289aa47b69",
            "780085d0550a46ae82f539edea4466c0",
            "ca65031daf6d4e6ca250ed4c6ac3ad06",
            "f01750ba2e14450bb672b9809aaf7cae",
            "0ec3b62188024b539680f4ea401c507a",
            "0ce421a23fd546a198be66a2da214e7c",
            "5146f64395e84875bfbe1c37976b0140",
            "318b042b13a549dd81a22af6159f339b",
            "6cf2b8cb10714ae3adcb7314b06c4b50",
            "9f61ad92edb24ac19462aa371a108057",
            "d9d08bb5b01341bbb1fe51fec7bf98ba",
            "f93109a7c1c743b9865b0a08e36599e2",
            "ea3db797d0444d048edad53e03cf540f"
          ]
        },
        "id": "iTxVJCptt791",
        "outputId": "3ea7fa9b-69ed-49dc-8cca-9f5d96403900"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-39-a673c1bbe437>:2: FutureWarning: load_metric is deprecated and will be removed in the next major version of datasets. Use 'evaluate.load' instead, from the new library 🤗 Evaluate: https://huggingface.co/docs/evaluate\n",
            "  load_acc = load_metric(\"accuracy\")\n",
            "/usr/local/lib/python3.10/dist-packages/datasets/load.py:759: FutureWarning: The repository for accuracy contains custom code which must be executed to correctly load the metric. You can inspect the repository content at https://raw.githubusercontent.com/huggingface/datasets/2.19.0/metrics/accuracy/accuracy.py\n",
            "You can avoid this message in future by passing the argument `trust_remote_code=True`.\n",
            "Passing `trust_remote_code=True` will be mandatory to load this metric from the next major release of `datasets`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/1.65k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f4217b958b0843ea830faac439e6b960"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/datasets/load.py:759: FutureWarning: The repository for f1 contains custom code which must be executed to correctly load the metric. You can inspect the repository content at https://raw.githubusercontent.com/huggingface/datasets/2.19.0/metrics/f1/f1.py\n",
            "You can avoid this message in future by passing the argument `trust_remote_code=True`.\n",
            "Passing `trust_remote_code=True` will be mandatory to load this metric from the next major release of `datasets`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/2.32k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ca65031daf6d4e6ca250ed4c6ac3ad06"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.986863711001642 \n",
            "F1: 0.9847735059002665\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions.predictions[0]"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-28T19:23:41.378282Z",
          "iopub.execute_input": "2024-04-28T19:23:41.378692Z",
          "iopub.status.idle": "2024-04-28T19:23:41.385549Z",
          "shell.execute_reply.started": "2024-04-28T19:23:41.378664Z",
          "shell.execute_reply": "2024-04-28T19:23:41.384634Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s9YjNmNWt792",
        "outputId": "6d9ddf4a-f02d-4aaf-f8f2-217a4fb5e170"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 4.3180733, -4.596767 ], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions_Strength_Class = np.argmax(\n",
        "    predictions.predictions, axis=1\n",
        ")  # Return the indexof the max value classtrain_dataset"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-28T19:23:50.117752Z",
          "iopub.execute_input": "2024-04-28T19:23:50.11847Z",
          "iopub.status.idle": "2024-04-28T19:23:50.123063Z",
          "shell.execute_reply.started": "2024-04-28T19:23:50.118434Z",
          "shell.execute_reply": "2024-04-28T19:23:50.122014Z"
        },
        "trusted": true,
        "id": "y-zgFsJLt792"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictions_Strength_Class  # Shows the label of our texts/tweets"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-28T19:23:53.725766Z",
          "iopub.execute_input": "2024-04-28T19:23:53.726168Z",
          "iopub.status.idle": "2024-04-28T19:23:53.732877Z",
          "shell.execute_reply.started": "2024-04-28T19:23:53.726136Z",
          "shell.execute_reply": "2024-04-28T19:23:53.731911Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0cywiB6dt792",
        "outputId": "4665cc11-c1ce-4394-b022-50a324a71835"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 0, ..., 1, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<div style=\"background-image: url('https://media.istockphoto.com/id/1341408852/video/colored-smoke-on-a-dark-background-blue-and-red-light-with-smoke.jpg?s=640x640&k=20&c=v2DQUY8IVbli_6FH_9KAs6YWRXlDdYiBJHfp7JFh7NY='); background-size: cover; background-position: center; border-radius: 10px; padding: 20px; color: #ffffff;\">\n",
        "    <h2>Making the Submission File</h2>\n",
        "</div>"
      ],
      "metadata": {
        "id": "BxozLcmst792"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!du -h ./my_model_weights/"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V5Q9P0A6CZ2q",
        "outputId": "3ca26023-2e5d-4309-d144-29a15eff88f8"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "256M\t./my_model_weights/\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.save_pretrained(\"my_model_weights\")"
      ],
      "metadata": {
        "id": "dnwC1OJeCHdm"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "# Importing the model weights in a new model instance\n",
        "new_model = AutoModelForSequenceClassification.from_pretrained(\n",
        "    \"my_model_weights\"\n",
        ")\n"
      ],
      "metadata": {
        "id": "uhdAwedGBIki"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install gradio"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8Le6NmcaCuA5",
        "outputId": "8332b475-98b4-4207-cab5-d7698bb616d2"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "  Using cached typer-0.12.3-py3-none-any.whl (47 kB)\n",
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            "Requirement already satisfied: uvloop!=0.15.0,!=0.15.1,>=0.14.0 in /usr/local/lib/python3.10/dist-packages (from uvicorn>=0.14.0->gradio) (0.19.0)\n",
            "Requirement already satisfied: watchfiles>=0.13 in /usr/local/lib/python3.10/dist-packages (from uvicorn>=0.14.0->gradio) (0.21.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.19.3->gradio) (3.3.2)\n",
            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich>=10.11.0->typer<1.0,>=0.12->gradio) (0.1.2)\n",
            "Installing collected packages: typer\n",
            "  Attempting uninstall: typer\n",
            "    Found existing installation: typer 0.9.4\n",
            "    Uninstalling typer-0.9.4:\n",
            "      Successfully uninstalled typer-0.9.4\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",
            "spacy 3.7.4 requires typer<0.10.0,>=0.3.0, but you have typer 0.12.3 which is incompatible.\n",
            "weasel 0.3.4 requires typer<0.10.0,>=0.3.0, but you have typer 0.12.3 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed typer-0.12.3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt: using the weights saved in the above cell, make a gradio UI for testing this model. The input should be a text sentence and output is the model output.\n",
        "\n",
        "import gradio as gr\n",
        "\n",
        "def classify_text(text):\n",
        "    # Preprocess the text\n",
        "    text = text.lower()\n",
        "    text = re.sub(\"[#=><\\/.]\", \"\", text)\n",
        "    text = re.sub(\"@\\w+\", \"\", text)\n",
        "\n",
        "    # Tokenize the text\n",
        "    tokenized_text = tokenizer(text)\n",
        "\n",
        "    # Convert the tokenized text to a tensor\n",
        "    input_ids = torch.tensor(tokenized_text[\"input_ids\"]).unsqueeze(0)\n",
        "    attention_mask = torch.tensor(tokenized_text[\"attention_mask\"]).unsqueeze(0)\n",
        "\n",
        "    # Load the model\n",
        "    model = AutoModelForSequenceClassification.from_pretrained(\"my_model_weights\")\n",
        "\n",
        "    # Make predictions\n",
        "    outputs = model(input_ids, attention_mask=attention_mask)\n",
        "    predictions = torch.argmax(outputs.logits, dim=-1)\n",
        "\n",
        "    # Return the predictions\n",
        "    return predictions.item()\n",
        "\n",
        "demo = gr.Interface(\n",
        "    fn=classify_text,\n",
        "    inputs=gr.Textbox(label=\"Enter a text sentence here\"),\n",
        "    outputs=\"label\",\n",
        "    examples=[\n",
        "        \"This is a disaster!\",\n",
        "        \"Earthquake is expected in china !\",\n",
        "        \"I'm feeling happy.\",\n",
        "    ],\n",
        ")\n",
        "\n",
        "demo.launch()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 626
        },
        "id": "sWWdUZcSBMCx",
        "outputId": "389f197a-c7e9-45ad-bf8f-52064aa0b440"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Setting queue=True in a Colab notebook requires sharing enabled. Setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n",
            "\n",
            "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
            "Running on public URL: https://1d70afc8e6414ee0c7.gradio.live\n",
            "\n",
            "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<div><iframe src=\"https://1d70afc8e6414ee0c7.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": []
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "submission[\"target\"] = predictions_Strength_Class\n",
        "submission = submission.set_index(\"id\", drop=True)\n",
        "submission.to_csv(\"/kaggle/working/submission.csv\")"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-10T14:33:34.226102Z",
          "iopub.execute_input": "2024-04-10T14:33:34.226479Z",
          "iopub.status.idle": "2024-04-10T14:33:34.249539Z",
          "shell.execute_reply.started": "2024-04-10T14:33:34.226447Z",
          "shell.execute_reply": "2024-04-10T14:33:34.248813Z"
        },
        "trusted": true,
        "id": "R57kwYsat792"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "submission.head(5)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-04-10T14:33:35.327848Z",
          "iopub.execute_input": "2024-04-10T14:33:35.328827Z",
          "iopub.status.idle": "2024-04-10T14:33:35.33894Z",
          "shell.execute_reply.started": "2024-04-10T14:33:35.328793Z",
          "shell.execute_reply": "2024-04-10T14:33:35.337953Z"
        },
        "trusted": true,
        "id": "D1FqlodPt793"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = trainer.predict(test_dataset=test_dataset)"
      ],
      "metadata": {
        "id": "xA9Bpmtmt793"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}