moral_foundations.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WtDvAcL0AJDK",
        "outputId": "b11c8cc1-68ed-4992-8198-09fc28116950"
      },
      "outputs": [],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YAMynzDOZ6PG",
        "outputId": "b62c75c8-9b23-4872-88e0-bb0b9b663550"
      },
      "outputs": [],
      "source": [
        "!pip install evaluate\n",
        "!pip install accelerate -U\n",
        "!pip install --upgrade transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XfHNUBGqGoKV"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "import numpy as np\n",
        "import evaluate\n",
        "import torch\n",
        "from transformers import RobertaTokenizer, RobertaForSequenceClassification, Trainer, TrainingArguments, EarlyStoppingCallback, PretrainedConfig\n",
        "from transformers.integrations import TensorBoardCallback\n",
        "import matplotlib.pyplot as plt\n",
        "from torch.utils.tensorboard import SummaryWriter\n",
        "from torch.nn import BCEWithLogitsLoss"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "g6RjWGRlQ1GS"
      },
      "outputs": [],
      "source": [
        "file_path = '/content/drive/MyDrive/moral_foundations_mlc_hf/data/df_sentence_corpus.csv'\n",
        "\n",
        "df = pd.read_csv(file_path)\n",
        "\n",
        "sentences = df['sentence'].astype(str).tolist()\n",
        "labels = df[['care_virtue', 'care_vice', 'fairness_virtue', 'fairness_vice', 'loyalty_virtue',\n",
        "'loyalty_vice', 'authority_virtue', 'authority_vice', 'sanctity_virtue', 'sanctity_vice']].values\n",
        "\n",
        "label_names = ['care_virtue', 'care_vice', 'fairness_virtue', 'fairness_vice', 'loyalty_virtue',\n",
        "'loyalty_vice', 'authority_virtue', 'authority_vice', 'sanctity_virtue', 'sanctity_vice']\n",
        "\n",
        "train_texts, val_texts, train_labels, val_labels = train_test_split(\n",
        "    sentences, labels, test_size=0.2, random_state=666)"
      ]
    },
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        "outputId": "8bfc4476-796c-4f6f-cc20-f0ffd00670df"
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      "outputs": [],
      "source": [
        "tokenizer = RobertaTokenizer.from_pretrained('roberta-base')\n",
        "\n",
        "train_encodings = tokenizer(train_texts, truncation=True, padding=True)\n",
        "val_encodings = tokenizer(val_texts, truncation=True, padding=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fg6d0nj_Q1GZ"
      },
      "outputs": [],
      "source": [
        "class Dataset(torch.utils.data.Dataset):\n",
        "    def __init__(self, encodings, labels):\n",
        "        self.encodings = encodings\n",
        "        self.labels = labels\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}\n",
        "        item['labels'] = torch.tensor(self.labels[idx], dtype=torch.long)\n",
        "        return item\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.labels)\n",
        "\n",
        "train_dataset = Dataset(train_encodings, train_labels)\n",
        "val_dataset = Dataset(val_encodings, val_labels)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "colab": {
          "base_uri": "https://localhost:8080/",
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      "outputs": [],
      "source": [
        "model = RobertaForSequenceClassification.from_pretrained(\n",
        "    'roberta-base', num_labels=10, problem_type=\"multi_label_classification\")"
      ]
    },
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      "outputs": [],
      "source": [
        "training_args = TrainingArguments(\n",
        "    output_dir='/content/drive/MyDrive/moral_foundations_mlc_hf/results',\n",
        "    num_train_epochs=10,\n",
        "    per_device_train_batch_size=8,\n",
        "    per_device_eval_batch_size=8,\n",
        "    warmup_steps=500,\n",
        "    weight_decay=0.1,\n",
        "    logging_dir='/content/drive/MyDrive/moral_foundations_mlc_hf/logs',\n",
        "    logging_steps=100,\n",
        "    evaluation_strategy=\"steps\",\n",
        "    eval_steps=500,\n",
        "    save_steps=500,\n",
        "    load_best_model_at_end=True,\n",
        "    metric_for_best_model=\"eval_loss\",\n",
        "    greater_is_better=False,\n",
        "    fp16=True,\n",
        "    label_smoothing_factor=0.1,\n",
        ")\n",
        "\n",
        "early_stopping = EarlyStoppingCallback(early_stopping_patience=3)\n",
        "\n",
        "accuracy_metric = evaluate.load(\"accuracy\")\n",
        "f1_metric = evaluate.load(\"f1\")\n",
        "precision_metric = evaluate.load(\"precision\")\n",
        "recall_metric = evaluate.load(\"recall\")\n",
        "\n",
        "def compute_metrics(eval_pred):\n",
        "    logits, labels = eval_pred\n",
        "    predictions = torch.sigmoid(torch.tensor(logits)).numpy()\n",
        "    predictions = (predictions > 0.666).astype(int)\n",
        "\n",
        "    overall_accuracy = accuracy_metric.compute(predictions=predictions.flatten(), references=labels.flatten())[\"accuracy\"]\n",
        "    overall_f1 = f1_metric.compute(predictions=predictions.flatten(), references=labels.flatten(), average=\"weighted\")[\"f1\"]\n",
        "    overall_precision = precision_metric.compute(predictions=predictions.flatten(), references=labels.flatten(), average=\"weighted\")[\"precision\"]\n",
        "    overall_recall = recall_metric.compute(predictions=predictions.flatten(), references=labels.flatten(), average=\"weighted\")[\"recall\"]\n",
        "\n",
        "    per_class_metrics = {}\n",
        "    for i, label_name in enumerate(label_names):\n",
        "        class_preds = predictions[:, i]\n",
        "        class_labels = labels[:, i]\n",
        "\n",
        "        accuracy = accuracy_metric.compute(predictions=class_preds, references=class_labels)[\"accuracy\"]\n",
        "        f1 = f1_metric.compute(predictions=class_preds, references=class_labels, average=\"binary\")[\"f1\"]\n",
        "        precision = precision_metric.compute(predictions=class_preds, references=class_labels, average=\"binary\")[\"precision\"]\n",
        "        recall = recall_metric.compute(predictions=class_preds, references=class_labels, average=\"binary\")[\"recall\"]\n",
        "\n",
        "        per_class_metrics[label_name] = {\n",
        "            'accuracy': accuracy,\n",
        "            'precision': precision,\n",
        "            'recall': recall,\n",
        "            'f1': f1\n",
        "        }\n",
        "\n",
        "    metrics = {\n",
        "        'overall_accuracy': overall_accuracy,\n",
        "        'overall_precision': overall_precision,\n",
        "        'overall_recall': overall_recall,\n",
        "        'overall_f1': overall_f1,\n",
        "        'per_class_metrics': per_class_metrics\n",
        "    }\n",
        "\n",
        "    return metrics\n",
        "\n",
        "def multi_label_loss(outputs, labels):\n",
        "    loss_fct = BCEWithLogitsLoss()\n",
        "    return loss_fct(outputs.logits, labels.float())\n",
        "\n",
        "class PlottingCallback(TensorBoardCallback):\n",
        "    def __init__(self, tb_writer):\n",
        "        super().__init__()\n",
        "        self.tb_writer = tb_writer\n",
        "        self.train_losses = []\n",
        "        self.eval_losses = []\n",
        "        self.train_steps = []\n",
        "        self.eval_steps = []\n",
        "\n",
        "    def on_log(self, args, state, control, logs=None, **kwargs):\n",
        "        super().on_log(args, state, control, logs, **kwargs)\n",
        "        if \"loss\" in logs:\n",
        "            self.train_losses.append(logs[\"loss\"])\n",
        "            self.train_steps.append(state.global_step)\n",
        "        if \"eval_loss\" in logs:\n",
        "            self.eval_losses.append(logs[\"eval_loss\"])\n",
        "            self.eval_steps.append(state.global_step)\n",
        "\n",
        "        if 'eval_per_class_metrics' in logs:\n",
        "            for label, metrics in logs['eval_per_class_metrics'].items():\n",
        "                for metric_name, value in metrics.items():\n",
        "                    self.tb_writer.add_scalar(f'{label}/{metric_name}', value, state.global_step)\n",
        "\n",
        "    def on_train_end(self, args, state, control, **kwargs):\n",
        "        plt.figure(figsize=(10, 5))\n",
        "        plt.plot(self.train_steps, self.train_losses, label=\"Training Loss\")\n",
        "        plt.plot(self.eval_steps, self.eval_losses, label=\"Validation Loss\")\n",
        "        plt.xlabel(\"Steps\")\n",
        "        plt.ylabel(\"Loss\")\n",
        "        plt.title(\"Training and Validation Loss\")\n",
        "        plt.legend()\n",
        "        plt.savefig('/content/drive/MyDrive/moral_foundations_mlc_hf/plots/loss_plot.png')\n",
        "        plt.close()\n",
        "\n",
        "tb_writer = SummaryWriter('/content/drive/MyDrive/moral_foundations_mlc_hf/tensorboard_logs')\n",
        "\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=train_dataset,\n",
        "    eval_dataset=val_dataset,\n",
        "    compute_metrics=compute_metrics,\n",
        "    callbacks=[PlottingCallback(tb_writer), early_stopping],\n",
        ")\n",
        "\n",
        "def compute_loss(self, model, inputs, return_outputs=False):\n",
        "    labels = inputs.pop(\"labels\")\n",
        "    outputs = model(**inputs)\n",
        "    loss = multi_label_loss(outputs, labels)\n",
        "    return (loss, outputs) if return_outputs else loss\n",
        "\n",
        "trainer.compute_loss = compute_loss.__get__(trainer)\n",
        "\n",
        "class CustomConfig(PretrainedConfig):\n",
        "    model_type = \"roberta\"\n",
        "\n",
        "    def __init__(self, **kwargs):\n",
        "        super().__init__(**kwargs)\n",
        "        self.num_labels = len(label_names)\n",
        "        self.label_names = label_names\n",
        "\n",
        "config = CustomConfig.from_pretrained('roberta-base')\n",
        "config.label_names = label_names"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "EDmMVJfRQ1Gb",
        "outputId": "5c21f9ba-9dec-4dbf-e0ac-183a33605254"
      },
      "outputs": [],
      "source": [
        "trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Rm1E9anJCxMa"
      },
      "outputs": [],
      "source": [
        "evaluation = trainer.evaluate()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "iAE6JT3goJ4r",
        "outputId": "7628d4d6-02e7-4743-f0cc-b304cd0e754d"
      },
      "outputs": [],
      "source": [
        "print(\"Overall metrics:\")\n",
        "print(f\"Accuracy: {evaluation['eval_overall_accuracy']:.4f}\")\n",
        "print(f\"Precision: {evaluation['eval_overall_precision']:.4f}\")\n",
        "print(f\"Recall: {evaluation['eval_overall_recall']:.4f}\")\n",
        "print(f\"F1-score: {evaluation['eval_overall_f1']:.4f}\")\n",
        "\n",
        "print(\"\\nPer-class metrics:\")\n",
        "for label, metrics in evaluation['eval_per_class_metrics'].items():\n",
        "    print(f\"\\n{label}:\")\n",
        "    for metric_name, value in metrics.items():\n",
        "        print(f\"  {metric_name}: {value:.4f}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "21y_gfWsQ1Gc"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import json\n",
        "\n",
        "model_save_path = \"/content/drive/MyDrive/moral_foundations_mlc_hf/MoralFoundationsClassifier\"\n",
        "os.makedirs(model_save_path, exist_ok=True)\n",
        "model.save_pretrained(model_save_path)\n",
        "tokenizer.save_pretrained(model_save_path)\n",
        "config.save_pretrained(model_save_path)\n",
        "torch.save(model.state_dict(), os.path.join(model_save_path, \"pytorch_model.bin\"))\n",
        "\n",
        "with open(os.path.join(model_save_path, \"label_names.json\"), 'w') as f:\n",
        "    json.dump(label_names, f)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qHKvmOYLC2TY"
      },
      "outputs": [],
      "source": [
        "tb_writer.close()"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "machine_shape": "hm",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
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