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      "metadata": {
        "id": "view-in-github",
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        "<a href=\"https://colab.research.google.com/github/ThaDuyx/Classify/blob/dev/Cross_validation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "source": [
        "# Cross validation script\n"
      ],
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      "source": [
        "%%capture\n",
        "# install libraries\n",
        "!pip install datasets\n",
        "!pip install transformers\n",
        "!pip install evaluate\n",
        "!pip install accelerate -U"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# import libraries\n",
        "import numpy as np\n",
        "import evaluate\n",
        "from datasets import load_dataset\n",
        "from sklearn.model_selection import KFold\n",
        "from transformers import Trainer, TrainingArguments, AutoModelForAudioClassification, AutoFeatureExtractor, AutoConfig\n",
        "from huggingface_hub import notebook_login\n",
        "import torch"
      ],
      "metadata": {
        "id": "BKQ94s8Nes_T"
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      "execution_count": null,
      "outputs": []
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      "source": [
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      "cell_type": "code",
      "source": [
        "# setup distil-HuBERT model\n",
        "%%capture\n",
        "model_id = \"ntu-spml/distilhubert\"\n",
        "model_name = model_id.split(\"/\")[-1]\n",
        "datasetName = \"TheDuyx/augmented_bass_sounds\"\n",
        "datasetTag = \"augmented_bass_sounds\"\n",
        "pre_name=\"cross-bass10\""
      ],
      "metadata": {
        "id": "YO8mucmYgWC_"
      },
      "execution_count": null,
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    },
    {
      "cell_type": "code",
      "source": [
        "# load dataset\n",
        "%%capture\n",
        "dataset = load_dataset(datasetName)\n",
        "data = dataset['train']\n",
        "labels = np.array(data['label'])\n",
        "kf = KFold(n_splits=7, shuffle=True, random_state=42)"
      ],
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      "execution_count": null,
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    },
    {
      "cell_type": "code",
      "source": [
        "#def compute_metrics(pred):\n",
        "  #labels = pred.label_ids\n",
        "  #preds = pred.predictions.argmax(-1)\n",
        "  #accuracy = (preds == labels).astype(np.float32).mean().item()\n",
        "  #return {'accuracy': accuracy}\n",
        "\n",
        "\n",
        "def compute_metrics(eval_pred):\n",
        "    \"\"\"Computes accuracy on a batch of predictions\"\"\"\n",
        "    predictions = np.argmax(eval_pred.predictions, axis=1)\n",
        "    return metric.compute(predictions=predictions, references=eval_pred.label_ids)"
      ],
      "metadata": {
        "id": "5Ij_IE3_jDLW"
      },
      "execution_count": null,
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    },
    {
      "cell_type": "code",
      "source": [
        "results = []\n",
        "\n",
        "feature_extractor = AutoFeatureExtractor.from_pretrained(\n",
        "    model_id, do_normalize=True, return_attention_mask=True\n",
        ")\n",
        "id2label_fn = dataset[\"train\"].features[\"label\"].int2str\n",
        "id2label = {\n",
        "    str(i): id2label_fn(i)\n",
        "    for i in range(len(dataset[\"train\"].features[\"label\"].names))\n",
        "}\n",
        "label2id = {v: k for k, v in id2label.items()}\n",
        "num_labels = len(id2label)\n",
        "\n",
        "for fold, (train_index, val_index) in enumerate(kf.split(labels)):\n",
        "  train_dataset = data.select(train_index.tolist())\n",
        "  val_dataset = data.select(val_index.tolist())\n",
        "\n",
        "  model = AutoModelForAudioClassification.from_pretrained(\n",
        "      model_id,\n",
        "      num_labels=num_labels,\n",
        "      label2id=label2id,\n",
        "      id2label=id2label)\n",
        "\n",
        "  training_args = TrainingArguments(\n",
        "    output_dir=f'./results_fold3_{fold}',         # output directory\n",
        "    num_train_epochs=4,                           # total number of training epochs\n",
        "    per_device_train_batch_size=8,                # batch size for training\n",
        "    per_device_eval_batch_size=16,                # batch size for evaluation\n",
        "    warmup_steps=500,                             # number of warmup steps for learning rate scheduler\n",
        "    weight_decay=0.02,                            # strength of weight decay\n",
        "    learning_rate=5e-5,\n",
        "    gradient_accumulation_steps=4,\n",
        "    logging_dir=f'./logs_fold_{fold}',            # directory for storing logs\n",
        "    evaluation_strategy=\"epoch\",                  # evaluate every epoch\n",
        "    save_strategy=\"epoch\",                        # save checkpoints every epoch\n",
        "    fp16=True,\n",
        "    push_to_hub=True,\n",
        "  )\n",
        "\n",
        "  metric = evaluate.load(\"accuracy\")\n",
        "\n",
        "  # Initialize Trainer\n",
        "  trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=train_dataset,\n",
        "    eval_dataset=val_dataset,\n",
        "    tokenizer=feature_extractor,\n",
        "    compute_metrics=compute_metrics\n",
        "  )\n",
        "\n",
        "  trainer.train()\n",
        "  eval_result = trainer.evaluate()\n",
        "  results.append(eval_result)\n",
        "\n",
        "\n",
        "for fold, result in enumerate(results):\n",
        "  print(f\"Fold {fold}: {result}\")\n",
        "\n",
        "average_accuracy = np.mean([result['eval_accuracy'] for result in results])\n",
        "print(f\"Average accuracy: {average_accuracy}\")"
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          "text": [
            "Some weights of HubertForSequenceClassification were not initialized from the model checkpoint at ntu-spml/distilhubert and are newly initialized: ['classifier.bias', 'classifier.weight', 'encoder.pos_conv_embed.conv.parametrizations.weight.original0', 'encoder.pos_conv_embed.conv.parametrizations.weight.original1', 'projector.bias', 'projector.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1474: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/4.20k [00:00<?, ?B/s]"
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              "    <div>\n",
              "      \n",
              "      <progress value='3280' max='3280' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [3280/3280 36:21, Epoch 3/4]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
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              "      <td>0.021406</td>\n",
              "      <td>0.994970</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0.010181</td>\n",
              "      <td>0.998171</td>\n",
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              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.002300</td>\n",
              "      <td>0.002090</td>\n",
              "      <td>0.999543</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.000200</td>\n",
              "      <td>0.000070</td>\n",
              "      <td>1.000000</td>\n",
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              "  </tbody>\n",
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              "      \n",
              "      <progress value='274' max='274' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [274/274 01:02]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of HubertForSequenceClassification were not initialized from the model checkpoint at ntu-spml/distilhubert and are newly initialized: ['classifier.bias', 'classifier.weight', 'encoder.pos_conv_embed.conv.parametrizations.weight.original0', 'encoder.pos_conv_embed.conv.parametrizations.weight.original1', 'projector.bias', 'projector.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1474: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
            "  warnings.warn(\n"
          ]
        },
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          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
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              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='3280' max='3280' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [3280/3280 36:17, Epoch 3/4]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.997000</td>\n",
              "      <td>0.015262</td>\n",
              "      <td>0.996113</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.006700</td>\n",
              "      <td>0.004519</td>\n",
              "      <td>0.998857</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.001500</td>\n",
              "      <td>0.001812</td>\n",
              "      <td>0.999543</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.000100</td>\n",
              "      <td>0.002804</td>\n",
              "      <td>0.999543</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
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              "      \n",
              "      <progress value='274' max='274' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [274/274 01:01]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of HubertForSequenceClassification were not initialized from the model checkpoint at ntu-spml/distilhubert and are newly initialized: ['classifier.bias', 'classifier.weight', 'encoder.pos_conv_embed.conv.parametrizations.weight.original0', 'encoder.pos_conv_embed.conv.parametrizations.weight.original1', 'projector.bias', 'projector.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1474: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='3280' max='3280' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [3280/3280 36:15, Epoch 3/4]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>1.011200</td>\n",
              "      <td>0.048447</td>\n",
              "      <td>0.989941</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.011200</td>\n",
              "      <td>0.010349</td>\n",
              "      <td>0.997942</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.002400</td>\n",
              "      <td>0.003400</td>\n",
              "      <td>0.999314</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.000500</td>\n",
              "      <td>0.001335</td>\n",
              "      <td>0.999771</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
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              "    <div>\n",
              "      \n",
              "      <progress value='274' max='274' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [274/274 01:03]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of HubertForSequenceClassification were not initialized from the model checkpoint at ntu-spml/distilhubert and are newly initialized: ['classifier.bias', 'classifier.weight', 'encoder.pos_conv_embed.conv.parametrizations.weight.original0', 'encoder.pos_conv_embed.conv.parametrizations.weight.original1', 'projector.bias', 'projector.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1474: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='3280' max='3280' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [3280/3280 37:01, Epoch 3/4]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.995400</td>\n",
              "      <td>0.031935</td>\n",
              "      <td>0.989941</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.007300</td>\n",
              "      <td>0.006993</td>\n",
              "      <td>0.998628</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.005000</td>\n",
              "      <td>0.006150</td>\n",
              "      <td>0.998628</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.000400</td>\n",
              "      <td>0.005047</td>\n",
              "      <td>0.999314</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
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          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
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              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='274' max='274' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [274/274 01:03]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of HubertForSequenceClassification were not initialized from the model checkpoint at ntu-spml/distilhubert and are newly initialized: ['classifier.bias', 'classifier.weight', 'encoder.pos_conv_embed.conv.parametrizations.weight.original0', 'encoder.pos_conv_embed.conv.parametrizations.weight.original1', 'projector.bias', 'projector.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1474: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='3280' max='3280' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [3280/3280 37:03, Epoch 3/4]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.995400</td>\n",
              "      <td>0.020524</td>\n",
              "      <td>0.994970</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.008600</td>\n",
              "      <td>0.002063</td>\n",
              "      <td>0.999086</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.003100</td>\n",
              "      <td>0.002294</td>\n",
              "      <td>0.999086</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.000200</td>\n",
              "      <td>0.002059</td>\n",
              "      <td>0.999314</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
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          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='274' max='274' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [274/274 01:03]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of HubertForSequenceClassification were not initialized from the model checkpoint at ntu-spml/distilhubert and are newly initialized: ['classifier.bias', 'classifier.weight', 'encoder.pos_conv_embed.conv.parametrizations.weight.original0', 'encoder.pos_conv_embed.conv.parametrizations.weight.original1', 'projector.bias', 'projector.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1474: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='3280' max='3280' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [3280/3280 37:01, Epoch 3/4]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.994600</td>\n",
              "      <td>0.044686</td>\n",
              "      <td>0.989483</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.007000</td>\n",
              "      <td>0.009002</td>\n",
              "      <td>0.998400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.001600</td>\n",
              "      <td>0.004103</td>\n",
              "      <td>0.999314</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.000700</td>\n",
              "      <td>0.002131</td>\n",
              "      <td>0.999771</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
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          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='274' max='274' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [274/274 01:03]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of HubertForSequenceClassification were not initialized from the model checkpoint at ntu-spml/distilhubert and are newly initialized: ['classifier.bias', 'classifier.weight', 'encoder.pos_conv_embed.conv.parametrizations.weight.original0', 'encoder.pos_conv_embed.conv.parametrizations.weight.original1', 'projector.bias', 'projector.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1474: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
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              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='3280' max='3280' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [3280/3280 37:00, Epoch 3/4]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>1.029900</td>\n",
              "      <td>0.083326</td>\n",
              "      <td>0.979652</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.006800</td>\n",
              "      <td>0.001033</td>\n",
              "      <td>0.999771</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.002400</td>\n",
              "      <td>0.004468</td>\n",
              "      <td>0.998857</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.000200</td>\n",
              "      <td>0.001488</td>\n",
              "      <td>0.999543</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
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        },
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              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='274' max='274' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [274/274 01:03]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fold 0: {'eval_loss': 7.006908708717674e-05, 'eval_accuracy': 1.0, 'eval_runtime': 62.484, 'eval_samples_per_second': 70.002, 'eval_steps_per_second': 4.385, 'epoch': 3.998780859494057}\n",
            "Fold 1: {'eval_loss': 0.0028043121565133333, 'eval_accuracy': 0.9995427526291724, 'eval_runtime': 61.5453, 'eval_samples_per_second': 71.07, 'eval_steps_per_second': 4.452, 'epoch': 3.998780859494057}\n",
            "Fold 2: {'eval_loss': 0.0013350064400583506, 'eval_accuracy': 0.9997713763145862, 'eval_runtime': 64.186, 'eval_samples_per_second': 68.146, 'eval_steps_per_second': 4.269, 'epoch': 3.998780859494057}\n",
            "Fold 3: {'eval_loss': 0.005046503618359566, 'eval_accuracy': 0.9993141289437586, 'eval_runtime': 64.0579, 'eval_samples_per_second': 68.282, 'eval_steps_per_second': 4.277, 'epoch': 3.998780859494057}\n",
            "Fold 4: {'eval_loss': 0.0020590159110724926, 'eval_accuracy': 0.9993141289437586, 'eval_runtime': 63.799, 'eval_samples_per_second': 68.559, 'eval_steps_per_second': 4.295, 'epoch': 3.998780859494057}\n",
            "Fold 5: {'eval_loss': 0.002131472807377577, 'eval_accuracy': 0.9997713763145862, 'eval_runtime': 63.6922, 'eval_samples_per_second': 68.674, 'eval_steps_per_second': 4.302, 'epoch': 3.998780859494057}\n",
            "Fold 6: {'eval_loss': 0.0014875333290547132, 'eval_accuracy': 0.9995427526291724, 'eval_runtime': 63.5095, 'eval_samples_per_second': 68.872, 'eval_steps_per_second': 4.314, 'epoch': 3.998780859494057}\n",
            "Average accuracy: 0.9996080736821478\n"
          ]
        }
      ]
    }
  ]
}