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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "## Install dependencies"
      ],
      "metadata": {
        "id": "0ART1R61lu1Y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/ZurichNLP/xstance.git"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l-Xuqd11bp7x",
        "outputId": "09b70ef3-72bd-46af-a167-3572e5b3090a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'xstance'...\n",
            "remote: Enumerating objects: 80, done.\u001b[K\n",
            "remote: Counting objects: 100% (80/80), done.\u001b[K\n",
            "remote: Compressing objects: 100% (57/57), done.\u001b[K\n",
            "remote: Total 80 (delta 36), reused 56 (delta 17), pack-reused 0\u001b[K\n",
            "Unpacking objects: 100% (80/80), 6.94 MiB | 9.42 MiB/s, done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip xstance/data/xstance-data-v1.0.zip -d xstance/data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LlWdkO44rFIp",
        "outputId": "fc4b155f-d592-45ca-957f-8282ca7cee07"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  xstance/data/xstance-data-v1.0.zip\n",
            "  inflating: xstance/data/valid.jsonl  \n",
            "  inflating: xstance/data/train.jsonl  \n",
            "  inflating: xstance/data/test.jsonl  \n",
            "  inflating: xstance/data/questions.it.jsonl  \n",
            "  inflating: xstance/data/questions.fr.jsonl  \n",
            "  inflating: xstance/data/questions.en.jsonl  \n",
            "  inflating: xstance/data/questions.de.jsonl  \n",
            "  inflating: xstance/data/LICENSE    \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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        },
        "id": "s69XTkyiamID",
        "outputId": "12343a26-01b8-4f08-fa97-17b5d60a5526"
      },
      "outputs": [
        {
          "output_type": "stream",
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          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting transformers==4.27.1\n",
            "  Downloading transformers-4.27.1-py3-none-any.whl (6.7 MB)\n",
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            "\u001b[?25hCollecting protobuf==3.20.0\n",
            "  Downloading protobuf-3.20.0-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.whl (1.0 MB)\n",
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            "\u001b[?25hCollecting datasets==2.10.1\n",
            "  Downloading datasets-2.10.1-py3-none-any.whl (469 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m469.0/469.0 KB\u001b[0m \u001b[31m47.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting evaluate==0.4.0\n",
            "  Downloading evaluate-0.4.0-py3-none-any.whl (81 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m81.4/81.4 KB\u001b[0m \u001b[31m12.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting sentencepiece==0.1.97\n",
            "  Downloading sentencepiece-0.1.97-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m84.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting jsonlines==3.1.0\n",
            "  Downloading jsonlines-3.1.0-py3-none-any.whl (8.6 kB)\n",
            "Requirement already satisfied: scikit-learn==1.2.2 in /usr/local/lib/python3.9/dist-packages (from -r requirements.txt (line 7)) (1.2.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.9/dist-packages (from transformers==4.27.1->-r requirements.txt (line 1)) (2.27.1)\n",
            "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1\n",
            "  Downloading tokenizers-0.13.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.6 MB)\n",
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            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.9/dist-packages (from transformers==4.27.1->-r requirements.txt (line 1)) (3.10.0)\n",
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            "Collecting dill<0.3.7,>=0.3.0\n",
            "  Downloading dill-0.3.6-py3-none-any.whl (110 kB)\n",
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            "\u001b[?25hCollecting multiprocess\n",
            "  Downloading multiprocess-0.70.14-py39-none-any.whl (132 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m132.9/132.9 KB\u001b[0m \u001b[31m16.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting aiohttp\n",
            "  Downloading aiohttp-3.8.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.0 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.0/1.0 MB\u001b[0m \u001b[31m58.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: pyarrow>=6.0.0 in /usr/local/lib/python3.9/dist-packages (from datasets==2.10.1->-r requirements.txt (line 3)) (9.0.0)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.9/dist-packages (from datasets==2.10.1->-r requirements.txt (line 3)) (1.4.4)\n",
            "Collecting xxhash\n",
            "  Downloading xxhash-3.2.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (212 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m212.2/212.2 KB\u001b[0m \u001b[31m20.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "Requirement already satisfied: fsspec[http]>=2021.11.1 in /usr/local/lib/python3.9/dist-packages (from datasets==2.10.1->-r requirements.txt (line 3)) (2023.3.0)\n",
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            "  Downloading aiosignal-1.3.1-py3-none-any.whl (7.6 kB)\n",
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            "Collecting frozenlist>=1.1.1\n",
            "  Downloading frozenlist-1.3.3-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (158 kB)\n",
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            "\u001b[?25hCollecting yarl<2.0,>=1.0\n",
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            "Collecting multidict<7.0,>=4.5\n",
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            "Installing collected packages: tokenizers, sentencepiece, xxhash, protobuf, multidict, jsonlines, frozenlist, dill, async-timeout, yarl, responses, multiprocess, aiosignal, transformers, aiohttp, datasets, evaluate\n",
            "  Attempting uninstall: protobuf\n",
            "    Found existing installation: protobuf 3.19.6\n",
            "    Uninstalling protobuf-3.19.6:\n",
            "      Successfully uninstalled protobuf-3.19.6\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "tensorflow 2.11.0 requires protobuf<3.20,>=3.9.2, but you have protobuf 3.20.0 which is incompatible.\n",
            "googleapis-common-protos 1.58.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.0 which is incompatible.\n",
            "google-cloud-translate 3.8.4 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.0 which is incompatible.\n",
            "google-cloud-language 2.6.1 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.0 which is incompatible.\n",
            "google-cloud-firestore 2.7.3 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.0 which is incompatible.\n",
            "google-cloud-datastore 2.11.1 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.0 which is incompatible.\n",
            "google-cloud-bigquery 3.4.2 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.0 which is incompatible.\n",
            "google-cloud-bigquery-storage 2.19.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.0 which is incompatible.\n",
            "google-api-core 2.11.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed aiohttp-3.8.4 aiosignal-1.3.1 async-timeout-4.0.2 datasets-2.10.1 dill-0.3.6 evaluate-0.4.0 frozenlist-1.3.3 jsonlines-3.1.0 multidict-6.0.4 multiprocess-0.70.14 protobuf-3.20.0 responses-0.18.0 sentencepiece-0.1.97 tokenizers-0.13.2 transformers-4.27.1 xxhash-3.2.0 yarl-1.8.2\n"
          ]
        }
      ],
      "source": [
        "!pip install -r requirements.txt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import shutil\n",
        "from pathlib import Path\n",
        "\n",
        "import evaluate\n",
        "import jsonlines\n",
        "import numpy as np\n",
        "import transformers\n",
        "from datasets import load_dataset, disable_caching\n",
        "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
        "from transformers import DataCollatorWithPadding\n",
        "from transformers import TrainingArguments, Trainer"
      ],
      "metadata": {
        "id": "cv_GbucgbVKG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load model and pre-process data"
      ],
      "metadata": {
        "id": "VLc-XwDGl4sL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_name = \"ZurichNLP/swissbert\""
      ],
      "metadata": {
        "id": "QBEayEeXcoTi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_dir = Path(\"finetuned_models\") / model_name\n",
        "model_dir.mkdir(parents=True, exist_ok=True)"
      ],
      "metadata": {
        "id": "SU_bypercaQ3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = load_dataset(\"x_stance\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233,
          "referenced_widgets": [
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        },
        "id": "esrm0WyUck1R",
        "outputId": "af9f1e21-50f2-42ad-a293-e1e16c844587"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/4.32k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "70942aefa75d496aa6a3624df092b1e1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading metadata:   0%|          | 0.00/2.19k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3e3f2103790d48408133610b1f14b15d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/7.02k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0d64d8be54cd4be69e3920cb68b684eb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset x_stance/default to /root/.cache/huggingface/datasets/x_stance/default/0.1.0/a5dbbc65742772e61dfd52276a9de65f4789c429bf30d549e983c8c65b72347c...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/6.41M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0f6a4130ca94475eba1a96f7c1d9b630"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/45640 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8c880732f64f4e05b701baf4786cb82f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split:   0%|          | 0/17705 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "957cc5082c29455396da2492dd80f3be"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation split:   0%|          | 0/3926 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "903c97e2425f4e498714be9bf66c1016"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset x_stance downloaded and prepared to /root/.cache/huggingface/datasets/x_stance/default/0.1.0/a5dbbc65742772e61dfd52276a9de65f4789c429bf30d549e983c8c65b72347c. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fe8576260fcf4bf88a8798c2edd097e7"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Print an example\n",
        "dataset[\"validation\"][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uoAKIqlnc30H",
        "outputId": "7f6b23e7-e49c-44e2-874e-3092c784d27f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'question': 'Sollen Ausländer/-innen, die seit mindestens zehn Jahren in der Schweiz leben, das Stimm- und Wahlrecht auf Gemeindeebene erhalten?',\n",
              " 'id': 17188,\n",
              " 'question_id': 3427,\n",
              " 'language': 'de',\n",
              " 'comment': 'Ich bin finde das geht zu wenig weit. Alle Menschen die hier leben sollen das Recht auf Mitsprache haben.',\n",
              " 'label': 'FAVOR',\n",
              " 'numerical_label': 100,\n",
              " 'author': '89900aa69be7',\n",
              " 'topic': 'Immigration'}"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Enumerate all possible labels\n",
        "set(dataset[\"validation\"][\"label\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6uBtUF-6c8Rg",
        "outputId": "5b6dbe1b-277d-4822-c00c-f68a4223acd7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'AGAINST', 'FAVOR'}"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "id2label = {0: \"AGAINST\", 1: \"FAVOR\"}\n",
        "label2id = {\"AGAINST\": 0, \"FAVOR\": 1}"
      ],
      "metadata": {
        "id": "XmzoZY4-dAqQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(model_name)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "dc6341eda1ef401a95f20d40927be0ae",
            "966d4beea129452bb5bb9393831fc8df",
            "632a4f84237a4a659be1968606253801",
            "50e93d2923724341936777deaecaa071",
            "9b40557f8731438e9cf0aae9db414826",
            "7de87c5151e34258b79d625f24c8544e",
            "b943f7fdfea3478d8bfaddf049652292",
            "274074cd09f34755b1f8542af8f43f0d",
            "1f366d914a6f4d54968927fbb9fa99e3",
            "a62899d4a28f453c860a72b602e75b04",
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            "deacc69b52904a3ba0b5c6cbbec70ac3",
            "7806bcb8a2444acca9e28d1586af54bf",
            "4cb4ecc95467418682c4c43dc4383abd",
            "0b38d2af13574ef49ff6772555f3e42f",
            "a4ea4f699a924e03ab4ebaf401a61f67",
            "98e85bec51a84e5fab21fe14c47a6910",
            "13e9a151ca04489892c912751e824243",
            "8111686e705a46508532f910ef0450e1",
            "e113d144c0ac4bf188324f6204da974a",
            "5366ae6b64c14807b5edf9816669b2e7",
            "2ed275e5a9a84e148c3eee1e407c9e60",
            "92674451959946ee9472ca0896fd17b6",
            "b4595ae8293c41e2b04cfa6f25cc4004",
            "eceabaea52f446faa4ef7655d56a9a32",
            "ab95d6e08afc421484aeae2de8b5f9ec",
            "bd56df2afda94661a2540e3f5c70d416",
            "924d8148e7c64bc196a2b45d660a8613",
            "2d7bb257081d451d80b9e1196b55e953",
            "aae2813c79f847a8946124892741c34e",
            "f12b1fd14d044e908e8270a2cd89f8be",
            "557878a8ba10469eb5e4a927a1346320",
            "0e597a6b2e3c4a2d947b85a60d99b9a7"
          ]
        },
        "id": "vvy_JKU_dDjs",
        "outputId": "e5a18c80-7b0c-4152-bac2-07ae6ab3b7f8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/403 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "dc6341eda1ef401a95f20d40927be0ae"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)tencepiece.bpe.model:   0%|          | 0.00/1.16M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "deacc69b52904a3ba0b5c6cbbec70ac3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/1.18k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "92674451959946ee9472ca0896fd17b6"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "adapter_map = {\n",
        "    \"de\": 0,\n",
        "    \"fr\": 1,\n",
        "    \"it\": 2,\n",
        "}\n",
        "\n",
        "def preprocess(examples):\n",
        "    examples[\"label\"] = [label2id[label] for label in examples[\"label\"]]\n",
        "    examples[\"lang_ids\"] = [adapter_map[language] for language in examples[\"language\"]]\n",
        "    examples = tokenizer(\n",
        "        examples[\"question\"],\n",
        "        examples[\"comment\"],\n",
        "        padding=\"max_length\",\n",
        "        truncation=True,\n",
        "        max_length=256,\n",
        "    )\n",
        "    return examples"
      ],
      "metadata": {
        "id": "5SDvE_ixdKJJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "preprocessed_dataset = dataset.map(preprocess, batched=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17,
          "referenced_widgets": [
            "9ce3394ef740448ea104482b6357851b",
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            "ee66b2c434b741b19c4e0840ecf2626f",
            "0b58a732b3604411999710f2ff4f54ff",
            "06f95509bd944f73ae6a3bc548cfb680",
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            "351706b0fe854686b7de4d1aa6bd8179",
            "9a12394e195e4cfda60d5cb3fbfbfae9",
            "4b52b0c188b24ef19db038d17ca7f002",
            "bb02909b7f8b4af987d8269b1034887e",
            "3a8423f5c9e84599a1fe15b563aba1b7",
            "716beac407c94af486a457f4f95d39bf",
            "cc1dc47dfba448d38ae786c009d44ae4"
          ]
        },
        "id": "ga-QQlbyd3X2",
        "outputId": "f0b1f8b0-4ddc-4f5e-f41d-07c7c43ae7c2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/45640 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9ce3394ef740448ea104482b6357851b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/17705 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8e57ddfba69447ecb04fbda0ba92ad3f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/3926 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "50b305a0faf84aa4b4b3483be35816ee"
            }
          },
          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Print a pre-processed example\n",
        "preprocessed_dataset[\"validation\"][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NUN_YzNheHqx",
        "outputId": "ddc609e3-0453-4076-a2e0-1f79f7c4eb94"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "{'question': 'Sollen Ausländer/-innen, die seit mindestens zehn Jahren in der Schweiz leben, das Stimm- und Wahlrecht auf Gemeindeebene erhalten?',\n",
              " 'id': 17188,\n",
              " 'question_id': 3427,\n",
              " 'language': 'de',\n",
              " 'comment': 'Ich bin finde das geht zu wenig weit. Alle Menschen die hier leben sollen das Recht auf Mitsprache haben.',\n",
              " 'label': 1,\n",
              " 'numerical_label': 100,\n",
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            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset = preprocessed_dataset[\"train\"].shuffle(seed=42)"
      ],
      "metadata": {
        "id": "bH49ZbE_d6IW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = AutoModelForSequenceClassification.from_pretrained(\n",
        "    model_name,\n",
        "    num_labels=2,\n",
        "    id2label=id2label,\n",
        "    label2id=label2id,\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 192,
          "referenced_widgets": [
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            "4156864f74b642a78448e57095f72331",
            "9610b54e267c4242abaf091c7e8bb1a7",
            "96eb7b25e1fd4e7cac4c138e0efd4160",
            "0954195fe8684bdeb69e3a906f7bc6fb",
            "ddbc0fe3950c45a280523f02c742c4f2",
            "2a9a2b34834e4cd89f997586fcfdb3d7",
            "8237467facff441fa23045a6b29e5bc3",
            "1d7541851a07400f8c18b59941cb21c2",
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            "76d9530a3d3146afa730c171f4f6aadc"
          ]
        },
        "id": "f5cOlXCcd_DU",
        "outputId": "83b8e67e-1923-41b5-8075-f56dd31bfe44"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/854 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3d092887e90542848ea2d16487307727"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/612M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2a9a2b34834e4cd89f997586fcfdb3d7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at ZurichNLP/swissbert were not used when initializing XmodForSequenceClassification: ['lm_head.dense.weight', 'lm_head.bias', 'lm_head.layer_norm.bias', 'lm_head.layer_norm.weight', 'lm_head.dense.bias']\n",
            "- This IS expected if you are initializing XmodForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing XmodForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of XmodForSequenceClassification were not initialized from the model checkpoint at ZurichNLP/swissbert and are newly initialized: ['classifier.out_proj.weight', 'classifier.out_proj.bias', 'classifier.dense.bias', 'classifier.dense.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Freeze language adapters\n",
        "for layer in model.roberta.encoder.layer:\n",
        "    if layer.output.adapter_layer_norm is not None:\n",
        "        for parameter in layer.output.adapter_layer_norm.parameters():\n",
        "            parameter.requires_grad = False\n",
        "    for parameter in layer.output.adapter_modules.parameters():\n",
        "        parameter.requires_grad = False"
      ],
      "metadata": {
        "id": "iIxs1irFePqc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Fine-tune the model"
      ],
      "metadata": {
        "id": "dfX9zITsmMRR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy = evaluate.load(\"accuracy\")\n",
        "\n",
        "def compute_metrics(eval_pred):\n",
        "    predictions, labels = eval_pred\n",
        "    predictions = np.argmax(predictions, axis=1)\n",
        "    return accuracy.compute(predictions=predictions, references=labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
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            "38d89680829a4015a39356bb05dad9f4"
          ]
        },
        "id": "xvKoVSOzecYA",
        "outputId": "6271e0b3-2776-4a5f-cf58-461d268a0083"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/4.20k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "25ed4bf11fcb4714b5c44a26a85a7b44"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"
      ],
      "metadata": {
        "id": "-xp8lqX8egCw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "training_args = TrainingArguments(\n",
        "    output_dir=model_dir,\n",
        "    learning_rate=1e-5,\n",
        "    per_device_train_batch_size=16,\n",
        "    per_device_eval_batch_size=16,\n",
        "    num_train_epochs=1,  # Set to 3 to reproduce results in paper\n",
        "    evaluation_strategy=\"epoch\",\n",
        "    save_strategy=\"epoch\",\n",
        "    load_best_model_at_end=True,\n",
        "    seed=553589,\n",
        ")\n",
        "\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=train_dataset,\n",
        "    eval_dataset=preprocessed_dataset[\"validation\"],\n",
        "    tokenizer=tokenizer,\n",
        "    data_collator=data_collator,\n",
        "    compute_metrics=compute_metrics,\n",
        ")"
      ],
      "metadata": {
        "id": "QOkAshO8ehsv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "zCeSH6iLfrC8",
        "outputId": "e09c6d6f-e231-4c6c-ffaa-3d02ce0f7bba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='2853' max='2853' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [2853/2853 36:27, Epoch 1/1]\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>1</td>\n",
              "      <td>0.504100</td>\n",
              "      <td>0.500317</td>\n",
              "      <td>0.755476</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=2853, training_loss=0.5488554014893862, metrics={'train_runtime': 2188.6097, 'train_samples_per_second': 20.853, 'train_steps_per_second': 1.304, 'total_flos': 7992796563333120.0, 'train_loss': 0.5488554014893862, 'epoch': 1.0})"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Evaluate on the test sets"
      ],
      "metadata": {
        "id": "cAs21V4JmQaY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "output = trainer.predict(preprocessed_dataset[\"test\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "asHprbbdfyS_",
        "outputId": "93f7bf11-c0d3-4be7-961d-be0911d2e1d6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predicted_label_ids = np.argmax(output.predictions, axis=1)\n",
        "predicted_labels = [id2label[label_id] for label_id in predicted_label_ids.tolist()]"
      ],
      "metadata": {
        "id": "QTUGPhWKf5bn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "out_path = model_dir / \"predictions.jsonl\"\n",
        "with jsonlines.open(out_path, \"w\") as f:\n",
        "    for label in predicted_labels:\n",
        "        f.write({\"label\": label})"
      ],
      "metadata": {
        "id": "RKdpfBJYf8vp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!python xstance/evaluate.py --gold xstance/data/test.jsonl --pred {out_path.resolve()}"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cf_rOeNCf-3c",
        "outputId": "e539871c-cdff-4244-b2ef-1e46867a2261"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "new_comments_defr\n",
            "DE 76.34064932765187\n",
            "FR 77.09480122324159\n",
            "\n",
            "new_questions_defr\n",
            "DE 72.91853784248686\n",
            "FR 76.23040472108377\n",
            "\n",
            "new_topics_defr\n",
            "DE 72.513362814061\n",
            "FR 74.34102696068727\n",
            "\n",
            "new_comments_it\n",
            "IT 71.63860405499884\n",
            "\n"
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "CKaQUB3drOR4"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}