course_project_2023_template.ipynb

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      "source": [
        "# Introduction to HLT Project (Template)\n",
        "\n",
        "- Student(s) Name(s):\n",
        "- Date:\n",
        "- Chosen Corpus:\n",
        "- Contributions (if group project):\n",
        "\n",
        "### Corpus information\n",
        "\n",
        "- Description of the chosen corpus:\n",
        "- Paper(s) and other published materials related to the corpus:\n",
        "- State-of-the-art performance (best published results) on this corpus:"
      ],
      "metadata": {
        "id": "ucyWlC5gbOyR"
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      "cell_type": "markdown",
      "source": [
        "---\n",
        "\n",
        "## 1. Setup"
      ],
      "metadata": {
        "id": "D5d-9uxrcDY-"
      }
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    {
      "cell_type": "code",
      "source": [
        "# Your code to install and import libraries etc. here"
      ],
      "metadata": {
        "id": "caHHQoqEcG1J"
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      "execution_count": null,
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      "source": [
        "---\n",
        "\n",
        "## 2. Data download and preprocessing\n",
        "\n",
        "### 2.1. Download the corpus"
      ],
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        "id": "ovUapilSb8iT"
      }
    },
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      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PDx40YyzbGPc"
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      "outputs": [],
      "source": [
        "# Your code to download the corpus here"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2.2. Preprocessing"
      ],
      "metadata": {
        "id": "cXb7CQNCbZOI"
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      "cell_type": "code",
      "source": [
        "# Your code for any necessary preprocessing here"
      ],
      "metadata": {
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      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "\n",
        "## 3. Machine learning model\n",
        "\n",
        "### 3.1. Model training"
      ],
      "metadata": {
        "id": "F1ntHh_JbrAg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Your code to train the machine learning model on the training set and evaluate the performance on the validation set here"
      ],
      "metadata": {
        "id": "Hs2Bf49zbn5C"
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      "execution_count": null,
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    {
      "cell_type": "markdown",
      "source": [
        "### 3.2 Hyperparameter optimization"
      ],
      "metadata": {
        "id": "nlO8RVuHcmAh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Your code for hyperparameter optimization here"
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      "metadata": {
        "id": "IzDrTDd0cWOG"
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    {
      "cell_type": "markdown",
      "source": [
        "### 3.3. Evaluation on test set"
      ],
      "metadata": {
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      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Your code to evaluate the final model on the test set here"
      ],
      "metadata": {
        "id": "BG7s-yr6crGF"
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      "execution_count": null,
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      "cell_type": "markdown",
      "source": [
        "---\n",
        "\n",
        "## 4. Results and summary\n",
        "\n",
        "### 4.1 Corpus insights\n",
        "\n",
        "(Briefly discuss what you learned about the corpus and its annotation)\n",
        "\n",
        "### 4.2 Results\n",
        "\n",
        "(Briefly summarize your results)\n",
        "\n",
        "### 4.3 Relation to state of the art\n",
        "\n",
        "(Compare your results to the state-of-the-art performance)\n",
        "\n",
        "---\n",
        "\n",
        "## 5. Bonus Task (optional)\n",
        "\n",
        "### 5.1. Annotating out-of-domain documents\n",
        "\n",
        "(Briefly describe the chosen out-of-domain documents)\n",
        "\n",
        "(Briefly describe the process of annotation)\n",
        "\n",
        "### 5.2 Conversion into dataset"
      ],
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    {
      "cell_type": "code",
      "source": [
        "# Your code to convert the annotations into a dataset here"
      ],
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        "id": "32DU04FndRdM"
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      "outputs": []
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    {
      "cell_type": "markdown",
      "source": [
        "### 5.3. Model evaluation on out-of-domain test set"
      ],
      "metadata": {
        "id": "4ghO4JemeFKK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Your code to evaluate the model on the out-of-domain test set here"
      ],
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        "id": "9tzYWQ_zeCYp"
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      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "markdown",
      "source": [
        "### 5.4 Bonus task results\n",
        "\n",
        "(Present the results of the evaluation on the out-of-domain test set)\n",
        "\n",
        "### 5.5. Annotated data"
      ],
      "metadata": {
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      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Include your annotated out-of-domain data here"
      ],
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        "id": "L2YJsiIGeYRe"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
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