Few_Shot_Arabic_Sentiment_Analysis_using_SetFit.ipynb

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        "from datasets import load_dataset\n",
        "from datasets.arrow_dataset import Dataset\n",
        "from sentence_transformers.losses import CosineSimilarityLoss\n",
        "\n",
        "from setfit import SetFitModel, SetFitTrainer\n",
        "\n",
        "from typing import Dict"
      ]
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        "dataset = load_dataset(\"ar_res_reviews\", split=\"train\")\n",
        "dataset = dataset.rename_column(\"polarity\", \"label\")\n",
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    {
      "cell_type": "code",
      "source": [
        "def train_eval_test_split(dataset : Dataset) -> Dict[str, Dataset]:\n",
        "    split_datasets = dict()\n",
        "    train_eval_test = dataset.train_test_split(test_size=0.4, stratify_by_column=\"label\", seed=10)\n",
        "    split_datasets[\"train\"] = train_eval_test[\"train\"]\n",
        "    eval_test = train_eval_test[\"test\"].train_test_split(test_size=0.5, stratify_by_column=\"label\", seed=10)\n",
        "    split_datasets[\"test\"] = eval_test[\"test\"]\n",
        "    return split_datasets"
      ],
      "metadata": {
        "id": "wED0H54rHMHp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# we are using the same split as the previous notebooks to compare the performance of the same test set\n",
        "split_datasets = train_eval_test_split(dataset)"
      ],
      "metadata": {
        "id": "SfzBChq4ILLO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# We do not need all the training samples, we are just going to select few samples from each class\n",
        "\n",
        "examples_per_class = 64\n",
        "for seed in range(1000):\n",
        "    split: dict = split_datasets[\"train\"].train_test_split(train_size=examples_per_class*2, shuffle=True, seed=seed) \n",
        "    train_ds = split[\"train\"]\n",
        "    test_ds = split_datasets[\"test\"]\n",
        "    if sum(train_ds[\"label\"]) == int(0.7*2*examples_per_class):\n",
        "        print(\"seed:\", seed)\n",
        "        break\n",
        "print(train_ds[\"label\"])\n",
        "print(test_ds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7VtiY44wsl2h",
        "outputId": "769428d0-a2d0-4805-acfa-497b7833a3f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "seed: 6\n",
            "[1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0]\n",
            "Dataset({\n",
            "    features: ['label', 'text'],\n",
            "    num_rows: 1673\n",
            "})\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load SetFit model from Hub\n",
        "model = SetFitModel.from_pretrained(\"sentence-transformers/paraphrase-multilingual-mpnet-base-v2\")\n",
        "\n",
        "# Create trainer\n",
        "trainer = SetFitTrainer(\n",
        "    model=model,\n",
        "    train_dataset=train_ds,\n",
        "    eval_dataset=test_ds,\n",
        "    loss_class=CosineSimilarityLoss,\n",
        "    batch_size=16,\n",
        "    num_iterations=40, # Number of text pairs to generate for contrastive learning\n",
        "    num_epochs=4 # Number of epochs to use for contrastive learning\n",
        ")"
      ],
      "metadata": {
        "id": "YFAMCm0ssnWL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Train and evaluate!\n",
        "trainer.train()\n",
        "metrics = trainer.evaluate()\n",
        "print(metrics)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334,
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        },
        "id": "Wnu8YZhlRFFm",
        "outputId": "582c5849-a950-4a10-acfb-3d2c1a6ef4ce"
      },
      "execution_count": null,
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "***** Running training *****\n",
            "  Num examples = 10240\n",
            "  Num epochs = 4\n",
            "  Total optimization steps = 2560\n",
            "  Total train batch size = 16\n"
          ]
        },
        {
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              "model_id": "31a0d65c027e4ffdb2ca7aab7f87dc38",
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            "text/plain": [
              "Epoch:   0%|          | 0/4 [00:00<?, ?it/s]"
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              "Iteration:   0%|          | 0/2560 [00:00<?, ?it/s]"
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              "Iteration:   0%|          | 0/2560 [00:00<?, ?it/s]"
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        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "***** Running evaluation *****\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/4.20k [00:00<?, ?B/s]"
            ],
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          "name": "stdout",
          "text": [
            "{'accuracy': 0.8123132098027496}\n"
          ]
        }
      ]
    }
  ]
}