MuRIL_optuna.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/MD-Ryhan/Bert-Hyperparameter-Tuninug-with-Optuna/blob/main/MuRIL_optuna.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J-JmMSFS_Wh4",
        "outputId": "20b1018b-6a2c-4ba5-891f-716c6170f5bf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Installing collected packages: Mako, colorlog, cmaes, alembic, optuna\n",
            "Successfully installed Mako-1.2.4 alembic-1.12.0 cmaes-0.10.0 colorlog-6.7.0 optuna-3.3.0\n"
          ]
        }
      ],
      "source": [
        "pip install optuna"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5o9zZsF7JAGD",
        "outputId": "2dc04d8a-8831-45e8-ea00-b43ef4473fbf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
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            "Installing collected packages: xxhash, dill, multiprocess, datasets\n",
            "Successfully installed datasets-2.14.5 dill-0.3.7 multiprocess-0.70.15 xxhash-3.3.0\n"
          ]
        }
      ],
      "source": [
        "!pip install transformers==4.28.0\n",
        "\n",
        "!pip install datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "p-_7HAUttToM"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "import optuna\n",
        "import pandas as pd\n",
        "from datasets import Dataset, DatasetDict\n",
        "from transformers import AutoTokenizer, BertForSequenceClassification, AutoConfig, BertTokenizer, AutoModelForSequenceClassification ,TrainingArguments, Trainer\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GFrCdrV0piwY"
      },
      "outputs": [],
      "source": [
        "train = pd.read_excel(\"offtrain.xlsx\")\n",
        "validation = pd.read_excel(\"offval.xlsx\")\n",
        "test = pd.read_excel(\"offtest.xlsx\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BgwX-TP768IL"
      },
      "outputs": [],
      "source": [
        "from datasets import Dataset, DatasetDict\n",
        "import datasets\n",
        "\n",
        "train_df = pd.DataFrame({\n",
        "     \"text\" : train[\"text\"],\n",
        "     \"labels\" : train['label']\n",
        "\n",
        "})\n",
        "val_df = pd.DataFrame({\n",
        "     \"text\" : validation[\"text\"],\n",
        "     \"labels\" : validation['label']\n",
        "\n",
        "})\n",
        "\n",
        "test_df = pd.DataFrame({\n",
        "     \"text\" : test[\"text\"],\n",
        "     \"labels\" : test['label']\n",
        "\n",
        "})\n",
        "\n",
        "train_dataset = Dataset.from_dict(train_df)\n",
        "val_dataset = Dataset.from_dict(val_df)\n",
        "test_dataset = Dataset.from_dict(test_df)\n",
        "dataset = datasets.DatasetDict({\"train\":train_dataset,\"validation\":val_dataset,\"test\":test_dataset})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/518 [00:00<?, ?B/s]"
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              "Map:   0%|          | 0/5475 [00:00<?, ? examples/s]"
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      "source": [
        "from transformers import AutoTokenizer\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained('Hate-speech-CNERG/urdu-abusive-MuRIL')\n",
        "\n",
        "def encode_batch(batch):\n",
        "  \"\"\"Encodes a batch of input data using the model tokenizer.\"\"\"\n",
        "  return tokenizer(batch[\"text\"], max_length=80, truncation=True, padding=\"max_length\")\n",
        "\n",
        "# Encode the input data\n",
        "dataset = dataset.map(encode_batch, batched=True)\n",
        "# The transformers model expects the target class column to be named \"labels\"\n",
        "#dataset.rename_column_(\"label\", \"labels\")\n",
        "# Transform to pytorch tensors and only output the required columns\n",
        "dataset.set_format(type=\"torch\", columns=[\"input_ids\", \"attention_mask\", \"labels\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SBst8AnumIci"
      },
      "outputs": [],
      "source": [
        "# Define the hyperparameter search space\n",
        "import transformers\n",
        "SAVE_DIR = 'opt-test'\n",
        "def objective(trial):\n",
        "    # Define hyperparameters to search for\n",
        "    training_args = TrainingArguments(\n",
        "    output_dir=SAVE_DIR,\n",
        "    learning_rate = trial.suggest_float(\"learning_rate\", 0.99e-5, 2e-5,step=0.2525),\n",
        "    weight_decay = trial.suggest_float(\"weight_decay\", 0.001, 0.1,step=0.0198),\n",
        "    per_device_train_batch_size = 32,\n",
        "    per_device_eval_batch_size = 32,\n",
        "    num_train_epochs = trial.suggest_int('num_train_epochs', 3, 10),\n",
        "\n",
        "    )\n",
        "    #Set the default values for attention dropout, hidden dropout, and warmup\n",
        "    attention_dropout = trial.suggest_float(\"attention_dropout\", 0.03, 0.1, step=0.2425)\n",
        "    hidden_dropout = trial.suggest_float(\"hidden_dropout\", 0.03, 0.1, step=0.2425)\n",
        "    warmup_ratio = trial.suggest_float(\"warmup_ratio\", 0.04, 0.1, step=0.02)\n",
        "\n",
        "\n",
        "    # Load the XLM-RoBERTa tokenizer and model\n",
        "    tokenizer = AutoTokenizer.from_pretrained('Hate-speech-CNERG/urdu-abusive-MuRIL')\n",
        "    model = AutoModelForSequenceClassification.from_pretrained('Hate-speech-CNERG/urdu-abusive-MuRIL')\n",
        "\n",
        "    # Set the dropout rates and warmup ratio in the model configuration\n",
        "    model.config.attention_probs_dropout_prob = attention_dropout\n",
        "    model.config.hidden_dropout_prob = hidden_dropout\n",
        "    model.config.attention_probs_dropout_prob = attention_dropout\n",
        "    model.config.hidden_dropout_prob = hidden_dropout\n",
        "    model.config.warmup_ratio = warmup_ratio\n",
        "\n",
        "    trainer = Trainer(\n",
        "        model=model,\n",
        "        args=training_args,\n",
        "        train_dataset=dataset['train'],\n",
        "        eval_dataset=dataset['test']\n",
        "        )\n",
        "\n",
        "    trainer.train()\n",
        "    results = trainer.evaluate()\n",
        "    return results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WjBpQaBU4Cgk"
      },
      "outputs": [],
      "source": [
        "# Load the training, validation, and testing data\n",
        "train_dataset = dataset[\"train\"]\n",
        "val_dataset = dataset[\"validation\"]\n",
        "test_dataset = dataset[\"test\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IJkyrXzMBgLQ"
      },
      "outputs": [],
      "source": [
        "from transformers.optimization_tf import WarmUp"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xYZYDLIm4FrZ"
      },
      "outputs": [],
      "source": [
        "# Create the Optuna study and start the hyperparameter search\n",
        "study = optuna.create_study(direction=\"maximize\")\n",
        "study.optimize(objective, n_trials=5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lugvICHF4KU7"
      },
      "outputs": [],
      "source": [
        "# Print the best trial and its validation accuracy\n",
        "print('Best trial:')\n",
        "trial = study.best_trial\n",
        "print('  Value: {:.6f}'.format(trial.value))\n",
        "print('  Params: ')\n",
        "for key, value in trial.params.items():\n",
        "    print('    {}: {}'.format(key, value))\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the final model on the combined training and validation data using the best hyperparameters\n",
        "best_learning_rate = trial.params[\"learning_rate\"]\n",
        "best_num_hidden_layers = trial.params[\"num_hidden_layers\"]\n",
        "best_num_attention_heads = trial.params[\"num_attention_heads\"]\n",
        "best_dropout_rate = trial.params[\"dropout_rate\"]\n",
        "best_batch_size = trial.params[\"batch_size\"]\n",
        "best_warmup_ratio = trial.params[\"warmup_ratio\"]\n",
        "best_weight_decay = trial.params[\"weight_decay\"]\n",
        "best_num_epochs = trial.params[\"num_train_epochs\"]"
      ],
      "metadata": {
        "id": "krJ1PLvf8-0i"
      },
      "execution_count": null,
      "outputs": []
    }
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