Arabert.ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZK7AjxdBjW1Z"
      },
      "source": [
        "# Importing libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "UWG1FbUr7TRn",
        "outputId": "b3790558-6bbc-40ca-a298-f47a8f315e7d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n",
            "/content/drive/MyDrive/StanceCat-COV19\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "%cd /content/drive/MyDrive/StanceCat-COV19"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Imports"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cg_iBd0RjW1d",
        "outputId": "4f9147ae-9ad8-4727-e9ff-628845e94f80"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting emoji==1.4.2\n",
            "  Downloading emoji-1.4.2.tar.gz (184 kB)\n",
            "\u001b[K     |████████████████████████████████| 184 kB 16.6 MB/s \n",
            "\u001b[?25hBuilding wheels for collected packages: emoji\n",
            "  Building wheel for emoji (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for emoji: filename=emoji-1.4.2-py3-none-any.whl size=186469 sha256=fc6ccf0db1cafa24d038da994544538f758b3e184041c953c51fb0f8fac67590\n",
            "  Stored in directory: /root/.cache/pip/wheels/71/4d/3c/cada364d4ea0026deee7208dee1e61bcebd20aa2ae5dc154ba\n",
            "Successfully built emoji\n",
            "Installing collected packages: emoji\n",
            "Successfully installed emoji-1.4.2\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "import re\n",
        "import nltk\n",
        "from tqdm import tqdm\n",
        "from sklearn.metrics import f1_score\n",
        "nltk.download('punkt')\n",
        "from torch.optim import Adam\n",
        "import string\n",
        "\n",
        "!pip install emoji==1.4.2\n",
        "import emoji"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EfTzru_-jW1e"
      },
      "source": [
        "# Data PreProcessing"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Read and visualize data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "WrTbA7l-jW1f"
      },
      "outputs": [],
      "source": [
        "train = pd.read_csv('Dataset/train.csv')\n",
        "text_train, category_train, stance_train = train['text'], train['category'], train['stance']\n",
        "\n",
        "dev = pd.read_csv('Dataset/dev.csv')\n",
        "text_dev, category_dev, stance_dev = dev['text'], dev['category'], dev['stance']"
      ]
    },
    {
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              "      <td>بيل غيتس يتلقى لقاح #كوفيد19 من غير تصوير الاب...</td>\n",
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              "      <td>قولكن  رح يكونو اد المسؤولية ب لبنان لما يوصل ...</td>\n",
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              "      <th>7</th>\n",
              "      <td>من عوارض لقاح كورونا&lt;LF&gt;هو تهكير حسابك عتويتر&lt;...</td>\n",
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              "      <td>0</td>\n",
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              "      <th>8</th>\n",
              "      <td>هناك 1780 مليونيراً في لبنان. ماذا لو فُرضت ال...</td>\n",
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              "      <td>0</td>\n",
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            "text/plain": [
              "                                                text   category  stance\n",
              "0  بيل غيتس يتلقى لقاح #كوفيد19 من غير تصوير الاب...  celebrity       1\n",
              "1  وزير الصحة لحد اليوم وتحديدا هلأ بمؤتمروا الصح...  info_news       1\n",
              "2  قولكن  رح يكونو اد المسؤولية ب لبنان لما يوصل ...  info_news       1\n",
              "3  #تركيا.. وزير الصحة فخر الدين قوجة يتلقى أول ج...  celebrity       1\n",
              "4  وئام وهاب يشتم الدول الخليجية في كل طلة اعلامي...   personal       0\n",
              "5  لقاح #كورونا في أميركا.. قلق متزايد من \"التوزي...  info_news       0\n",
              "6  لبنان اشترى مليونان لقاح امريكي اذا شلنا يلي ع...  info_news       1\n",
              "7  من عوارض لقاح كورونا<LF>هو تهكير حسابك عتويتر<...   personal       0\n",
              "8  هناك 1780 مليونيراً في لبنان. ماذا لو فُرضت ال...  unrelated       0\n",
              "9  دعبول حضرتك منو انت وتطلب من قائد دولة إسلامية...  info_news       1"
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        "train.head(10)"
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              "      <td>عاجل 🔴 &lt;LF&gt;.&lt;LF&gt;&lt;LF&gt;.&lt;LF&gt;&lt;LF&gt;وزارة الصحة :&lt;LF&gt;...</td>\n",
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              "      <th>7</th>\n",
              "      <td>#فيديو | السفير الأميركي لدى السعودية بعد تلقي...</td>\n",
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              "      <td>تصريحات وبس الحكومة مع السيسي علي حسب اللقطة! ...</td>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e37c28ec-6e16-4d7a-8e68-acc67b47bb94');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                                text   category  stance\n",
              "0  #مريم_رجوي: <LF>حظر خامنئي المجرم شراء #لقاح_ك...  info_news       1\n",
              "1  #الصحة:<LF>•تم إعطاء 259.530 جرعة من لقاح #كور...       plan       1\n",
              "2  #خادم_الحرمين - حفظه الله - يتلقى الجرعة الأول...  celebrity       1\n",
              "3  #الصحه_العالميه: لقاحات #كورونا آمنة ولا خوف م...  info_news       1\n",
              "4  #وزيرة_الصحة \"#هالة_زايد\" تقول إنه يجرى مراجعة...  info_news       1\n",
              "5  2️⃣ وانتهى الفريق من الدراسات قبل السريرية ونش...  info_news       1\n",
              "6  عاجل 🔴 <LF>.<LF><LF>.<LF><LF>وزارة الصحة :<LF>...       plan       1\n",
              "7  #فيديو | السفير الأميركي لدى السعودية بعد تلقي...  info_news       1\n",
              "8  تصريحات وبس الحكومة مع السيسي علي حسب اللقطة! ...  info_news       0\n",
              "9  الاتحاد الاوروبي تفاوض لشراء لقاحات الكورونا م...  info_news       1"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dev.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5ZY3ifXHjW1h",
        "outputId": "0efa2e57-5c3e-4ea0-f92e-ffc9b33a6f51"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(6988,) (6988,) (6988,)\n",
            "(1000,) (1000,) (1000,)\n"
          ]
        }
      ],
      "source": [
        "text_train, category_train, stance_train = np.array(train['text']), np.array(train['category']), np.array(train['stance'])\n",
        "text_dev, category_dev, stance_dev = np.array(dev['text']), np.array(dev['category']), np.array(dev['stance'])\n",
        "\n",
        "print(text_train.shape, category_train.shape, stance_train.shape)\n",
        "print(text_dev.shape, category_dev.shape, stance_dev.shape)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rOUDf7cc9FvN"
      },
      "outputs": [],
      "source": [
        "# Clean/Normalize Arabic Text \n",
        "# taskeel, longation, ... etc\n",
        "def clean_str(text):\n",
        "    \n",
        "    #remove tashkeel\n",
        "    p_tashkeel = re.compile(r'[\\u0617-\\u061A\\u064B-\\u0652]')\n",
        "    text = re.sub(p_tashkeel,\"\", text)\n",
        "    \n",
        "    #remove longation\n",
        "    p_longation = re.compile(r'(.)\\1+')\n",
        "    subst = r\"\\1\\1\"\n",
        "    text = re.sub(p_longation, subst, text)\n",
        "    \n",
        "    text = text.replace('وو', 'و')\n",
        "    text = text.replace('يي', 'ي')\n",
        "    text = text.replace('اا', 'ا')\n",
        "    \n",
        "    #trim    \n",
        "    text = text.strip()\n",
        "\n",
        "    return text\n",
        "\n",
        "def PreProcessing(text, unk=''):\n",
        "\n",
        "  for i in range(len(text)):\n",
        "\n",
        "    # remove links\n",
        "    text[i] = re.sub(r'https?:\\/\\/.*[\\r\\n]*', '', text[i], flags=re.MULTILINE)\n",
        "\n",
        "    # limit repeated emojis to 2\n",
        "    prev_emoji = None\n",
        "    cnt = 0\n",
        "    remove_duplicate_emoji = []\n",
        "    for c in text[i]:\n",
        "        if c in emoji.UNICODE_EMOJI['en']:\n",
        "            if prev_emoji == c and cnt >=2:\n",
        "                continue\n",
        "            if prev_emoji != c:\n",
        "              cnt = 0\n",
        "            prev_emoji = c\n",
        "            cnt+=1\n",
        "        else:\n",
        "          prev_emoji = None\n",
        "          cnt = 0\n",
        "        remove_duplicate_emoji.append(c)\n",
        "    text[i] = \"\".join(remove_duplicate_emoji)\n",
        "\n",
        "    # seperate emojis\n",
        "    text[i] = ''.join((' '+c+' ') if c in emoji.UNICODE_EMOJI['en'] else c for c in text[i])\n",
        "\n",
        "    # Clean/Normalize Arabic Text\n",
        "    text[i] = clean_str(text[i]) \n",
        "\n",
        "    # remove multiple spaces\n",
        "    text[i] = re.sub(' +', ' ', text[i])\n",
        "\n",
        "  return text"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I-mWZQOI-JGB",
        "outputId": "1876e2bf-5ad4-4c1f-fd48-c9c5cbca15df"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "بيل غيتس يتلقى لقاح #كوفيد19 من غير تصوير الابرة و لا السيرنجة و لا الدواء و لابس بولو صيفي في عز الشتاء و يقول ان إحدى مزايا عمر ال 65 عامًا هي انه مؤهل للحصول على اللقاح ... يعنى ما كان يحتاج اللقاح لو كان عمره اصغر من 65 🤔 https://t.co/QQKFFUNwBn \n",
            " بيل غيتس يتلقى لقاح #كوفيد19 من غير تصوير الابرة و لا السيرنجة و لا الدواء و لابس بولو صيفي في عز الشتاء و يقول ان إحدى مزايا عمر ال 65 عاما هي انه مؤهل للحصول على اللقاح .. يعنى ما كان يحتاج اللقاح لو كان عمره اصغر من 65 🤔\n",
            "#مريم_رجوي: <LF>حظر خامنئي المجرم شراء #لقاح_كورونا يعد مجزرة متعمدة بحق الشعب الإيراني<LF><LF>نقل موقع مريم رجوي موقف رئيسة الجمهورية المنتخبة للمقاومة الإيرانية من تصريحات خامنئي المجرم حول حظر استيراد لقاح كورونا من الولايات المتحدة و بريطانيا و فرنسا. <LF>#اللقاح_حق_للناس https://t.co/AYXMbXjVKn \n",
            " #مريم_رجوي: <LF>حظر خامنئي المجرم شراء #لقاح_كورونا يعد مجزرة متعمدة بحق الشعب الإيراني<LF><LF>نقل موقع مريم رجوي موقف رئيسة الجمهورية المنتخبة للمقاومة الإيرانية من تصريحات خامنئي المجرم حول حظر استيراد لقاح كورونا من الولايات المتحدة و بريطانيا و فرنسا. <LF>#اللقاح_حق_للناس\n",
            "764\n"
          ]
        }
      ],
      "source": [
        "pre_text_train = PreProcessing(np.array(text_train))\n",
        "pre_text_dev = PreProcessing(np.array(text_dev))\n",
        "print(text_train[0], '\\n', pre_text_train[0])\n",
        "print(text_dev[0], '\\n', pre_text_dev[0])\n",
        "print(len(max(pre_text_train,key=len)))"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Save preprocessed data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Oui1fJOjBPsP"
      },
      "outputs": [],
      "source": [
        "with open('our_processed_train.txt','w', encoding='utf8') as f:\n",
        "\tfor i in pre_text_train:\n",
        "\t\tf.write('%s\\n'%i)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Building dictionaries to turn a category to number & vice versa"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YajWdDqojW1k",
        "outputId": "f2c709c9-2102-4fb0-806d-f713931c7936"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0 others\n",
            "1 rumors\n",
            "2 restrictions\n",
            "3 celebrity\n",
            "4 personal\n",
            "5 info_news\n",
            "6 requests\n",
            "7 advice\n",
            "8 unrelated\n",
            "9 plan\n",
            "3\n"
          ]
        }
      ],
      "source": [
        "category2id = {'others':0, 'rumors':1, 'restrictions':2, 'celebrity':3, 'personal':4, 'info_news':5, 'requests':6, 'advice':7, 'unrelated':8, 'plan':9}\n",
        "id2category = {id:word for (word, id) in category2id.items()}\n",
        "\n",
        "for id, word in id2category.items():\n",
        "  print(id, word)\n",
        "\n",
        "print(category2id['celebrity'])"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "wNLJr0azjW1l"
      },
      "source": [
        "# Model building & training"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BnzUMpPajW1l"
      },
      "source": [
        "### Ideas to try\n",
        "1) bi-directional\n",
        "2) pre-training\n",
        "3) multi-layers\n",
        "4) BERT\n",
        "5) transformers notebook\n",
        "6) packed_padded_sequences\n",
        "7) pre-trained embedding"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "-4d1daadjW1l"
      },
      "source": [
        "### Building the datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "v_PIOF-0jW1l"
      },
      "outputs": [],
      "source": [
        "from imblearn.over_sampling import RandomOverSampler\n",
        "from collections import Counter\n",
        "class Dataset(torch.utils.data.Dataset):\n",
        "\n",
        "  def __init__(self, x, y, oversample=False):\n",
        "\n",
        "    x = x.copy()\n",
        "    y = y.copy()\n",
        "\n",
        "    # apply arabert additional preprocessing & tokenize the input\n",
        "    x = [arabert_prep.preprocess(text) for text in x]\n",
        "    self.X = [tokenizer(text, padding='max_length', max_length = 512, truncation=True, return_tensors=\"pt\") for text in x]\n",
        "\n",
        "    self.Y = torch.tensor(y)\n",
        "\n",
        "    self.len = len(x)\n",
        "\n",
        "    print(sorted(Counter(self.Y.numpy()).items()))\n",
        "\n",
        "  def __len__(self):\n",
        "    return self.len\n",
        "\n",
        "  def __getitem__(self, idx):\n",
        "    return self.X[idx], self.Y[idx]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j41ekReTjW1m",
        "outputId": "0950a2f9-0da9-41e1-86df-40e932b67cdd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[(0, 438), (1, 1012), (2, 5538)]\n",
            "[(0, 167), (1, 79), (2, 18), (3, 975), (4, 1025), (5, 3616), (6, 112), (7, 67), (8, 323), (9, 606)]\n",
            "[(0, 70), (1, 126), (2, 804)]\n",
            "[(0, 17), (1, 15), (2, 2), (3, 145), (4, 128), (5, 545), (6, 20), (7, 10), (8, 36), (9, 82)]\n"
          ]
        }
      ],
      "source": [
        "stance_train_dataset = Dataset(pre_text_train, stance_train + 1, True)\n",
        "category_train_dataset = Dataset(pre_text_train, [category2id[category] for category in category_train], True)\n",
        "\n",
        "stance_dev_dataset = Dataset(pre_text_dev, stance_dev + 1)\n",
        "category_dev_dataset = Dataset(pre_text_dev, [category2id[category] for category in category_dev])"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### The arabert model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KNWkSYiTjW1m"
      },
      "outputs": [],
      "source": [
        "from transformers import BertModel\n",
        "class BertClassifier(nn.Module):\n",
        "\n",
        "    def __init__(self, output_dim, dropout=0.5):\n",
        "\n",
        "        super(BertClassifier, self).__init__()\n",
        "\n",
        "        self.bert = BertModel.from_pretrained('aubmindlab/bert-base-arabertv02-twitter')\n",
        "        self.dropout = nn.Dropout(dropout)\n",
        "        self.linear = nn.Linear(768, output_dim)\n",
        "        self.relu = nn.ReLU()\n",
        "\n",
        "    def forward(self, input_id, mask):\n",
        "\n",
        "        _, pooled_output = self.bert(input_ids=input_id, attention_mask=mask, return_dict=False)\n",
        "        dropout_output = self.dropout(pooled_output)\n",
        "        linear_output = self.linear(dropout_output)\n",
        "        final_layer = self.relu(linear_output)\n",
        "\n",
        "        return final_layer"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Use GPU if any"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Kny-X5tgjW1m"
      },
      "outputs": [],
      "source": [
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "vt4mx_qdjW1m"
      },
      "source": [
        "### Training function"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_551g6SEfIfx"
      },
      "outputs": [],
      "source": [
        "def train(model, train_dataset, train_dataloader, criterion, optimizer):\n",
        "  \n",
        "  model.train()\n",
        "\n",
        "  total_acc_train = 0\n",
        "  total_loss_train = 0\n",
        "\n",
        "  # for f1 score\n",
        "  y_true, y_pred = [], []\n",
        "\n",
        "  for train_input, train_label in tqdm(train_dataloader):\n",
        "\n",
        "    train_label = train_label.to(device)\n",
        "    mask = train_input['attention_mask'].to(device)\n",
        "    input_id = train_input['input_ids'].squeeze(1).to(device)\n",
        "\n",
        "    output = model(input_id, mask)\n",
        "\n",
        "    batch_loss = criterion(output, train_label.long())\n",
        "    total_loss_train += batch_loss.item()\n",
        "\n",
        "    acc = (output.argmax(dim=1) == train_label).sum().item()\n",
        "    total_acc_train += acc\n",
        "\n",
        "    y_true += train_label.tolist() \n",
        "    y_pred += output.argmax(dim=1).tolist()\n",
        "\n",
        "    model.zero_grad()\n",
        "    batch_loss.backward()\n",
        "    optimizer.step()\n",
        "\n",
        "  f1_macro_train = f1_score(y_true, y_pred, average='macro')\n",
        "\n",
        "  return total_loss_train/len(train_dataset), total_acc_train/len(train_dataset), f1_macro_train"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Evaluating function"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_Zg2G9hNDxh8"
      },
      "outputs": [],
      "source": [
        "def evaluate(model, test_dataset, test_dataloader, criterion):\n",
        "\n",
        "  model.eval()\n",
        "\n",
        "  total_acc_test = 0\n",
        "  total_loss_test = 0\n",
        "\n",
        "  # for f1 score\n",
        "  y_true, y_pred = [], []\n",
        "\n",
        "  with torch.no_grad():\n",
        "\n",
        "    for test_input, test_label in test_dataloader:\n",
        "\n",
        "      test_label = test_label.to(device)\n",
        "\n",
        "      # print(test_input['input_ids'].shape)\n",
        "      # print(\"attention_mask\",train_input['attention_mask'].shape)\n",
        "\n",
        "      mask = test_input['attention_mask'].to(device)\n",
        "      input_id = test_input['input_ids'].squeeze(1).to(device)\n",
        "\n",
        "      output = model(input_id, mask)\n",
        "\n",
        "      # print(output.shape)\n",
        "\n",
        "      batch_loss = criterion(output, test_label.long())\n",
        "      total_loss_test += batch_loss.item()\n",
        "\n",
        "      acc = (output.argmax(dim=1) == test_label).sum().item()\n",
        "      total_acc_test += acc\n",
        "\n",
        "      y_true += test_label.tolist() \n",
        "      y_pred += output.argmax(dim=1).tolist()\n",
        "\n",
        "  # print(len(y_true)-1, len(y_pred)-1)\n",
        "  f1_macro_test = f1_score(y_true, y_pred, average='macro')\n",
        "\n",
        "  return total_loss_test/len(test_dataset), total_acc_test/len(test_dataset), f1_macro_test"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Training and evaluating the model each epoch"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "umKC5RVvDzkE"
      },
      "outputs": [],
      "source": [
        "def train_evaluate(model, train_dataset, val_dataset, learning_rate, epochs, model_name):\n",
        "\n",
        "  # oversample\n",
        "  _, y_train = train_dataset[:]\n",
        "  class_sample_count = np.array([len(torch.where(y_train == t)[0]) for t in np.unique(y_train)])\n",
        "  print('counts', class_sample_count)\n",
        "\n",
        "  weight = 1.0 / class_sample_count\n",
        "  samples_weight = np.array([weight[t] for t in y_train])\n",
        "  samples_weight = torch.from_numpy(samples_weight)\n",
        "  print(samples_weight)\n",
        "\n",
        "  sampler = torch.utils.data.WeightedRandomSampler(samples_weight.type('torch.DoubleTensor'), len(samples_weight))\n",
        "\n",
        "  # dataloaders\n",
        "  train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=2, sampler=sampler)\n",
        "  val_dataloader = torch.utils.data.DataLoader(val_dataset, batch_size=2)\n",
        "\n",
        "  use_cuda = torch.cuda.is_available()\n",
        "  device = torch.device(\"cuda\" if use_cuda else \"cpu\")\n",
        "\n",
        "  criterion = nn.CrossEntropyLoss()\n",
        "  optimizer = Adam(model.parameters(), lr= learning_rate)\n",
        "\n",
        "  if use_cuda:\n",
        "    model = model.cuda()\n",
        "    criterion = criterion.cuda()\n",
        "\n",
        "  best_f1_macro = 0\n",
        "\n",
        "  for epoch_num in range(epochs):\n",
        "\n",
        "    loss_train, acc_train, f1_macro_train = train(model, train_dataset, train_dataloader, criterion, optimizer)\n",
        "    loss_val, acc_val, f1_macro_val = evaluate(model, val_dataset, val_dataloader, criterion)\n",
        "          \n",
        "    if f1_macro_val > best_f1_macro:\n",
        "      best_f1_macro = f1_macro_val\n",
        "    torch.save(model.state_dict(), f'Models/BERT/{model_name}/F1{f1_macro_val: .4f} Acc{acc_val: .4f}.pt')\n",
        "\n",
        "    print(\n",
        "        f'Epochs: {epoch_num + 1} \\\n",
        "        | Train Loss: {loss_train: .4f} \\\n",
        "        | Train Accuracy: {acc_train : .4f} \\\n",
        "        | Train F1_macro: {f1_macro_train: .4f} \\\n",
        "        | Val Loss: {loss_val: .4f} \\\n",
        "        | Val Accuracy: {acc_val: .4f} \\\n",
        "        | Val F1_macro: {f1_macro_val: .4f} \\\n",
        "        ')"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Training the stance classifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_Se-eux3fiMd",
        "outputId": "48d6a370-6253-424e-b8f1-b847bc2fab30"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at aubmindlab/bert-base-arabertv02-twitter were not used when initializing BertModel: ['cls.predictions.bias', 'cls.predictions.decoder.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.weight']\n",
            "- This IS expected if you are initializing BertModel 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 BertModel 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 BertModel were not initialized from the model checkpoint at aubmindlab/bert-base-arabertv02-twitter and are newly initialized: ['bert.pooler.dense.bias', 'bert.pooler.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"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "counts [ 438 1012 5538]\n",
            "tensor([0.0002, 0.0002, 0.0002,  ..., 0.0002, 0.0002, 0.0002],\n",
            "       dtype=torch.float64)\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 3494/3494 [12:55<00:00,  4.50it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epochs: 1         | Train Loss:  0.4485         | Train Accuracy:  0.5950         | Train F1_macro:  0.5965         | Val Loss:  0.3053         | Val Accuracy:  0.7560         | Val F1_macro:  0.5861         \n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 3494/3494 [12:55<00:00,  4.51it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epochs: 2         | Train Loss:  0.2996         | Train Accuracy:  0.7665         | Train F1_macro:  0.7667         | Val Loss:  0.2696         | Val Accuracy:  0.7840         | Val F1_macro:  0.6299         \n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 3494/3494 [12:55<00:00,  4.50it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epochs: 3         | Train Loss:  0.2374         | Train Accuracy:  0.8253         | Train F1_macro:  0.8258         | Val Loss:  0.2815         | Val Accuracy:  0.7960         | Val F1_macro:  0.6397         \n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 3494/3494 [12:56<00:00,  4.50it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epochs: 4         | Train Loss:  0.1952         | Train Accuracy:  0.8659         | Train F1_macro:  0.8655         | Val Loss:  0.2980         | Val Accuracy:  0.7960         | Val F1_macro:  0.6503         \n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 3494/3494 [12:56<00:00,  4.50it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epochs: 5         | Train Loss:  0.1633         | Train Accuracy:  0.8904         | Train F1_macro:  0.8898         | Val Loss:  0.2881         | Val Accuracy:  0.7980         | Val F1_macro:  0.6459         \n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 3494/3494 [12:55<00:00,  4.51it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epochs: 6         | Train Loss:  0.1412         | Train Accuracy:  0.9104         | Train F1_macro:  0.9099         | Val Loss:  0.2933         | Val Accuracy:  0.8030         | Val F1_macro:  0.6497         \n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 3494/3494 [12:55<00:00,  4.50it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epochs: 7         | Train Loss:  0.1148         | Train Accuracy:  0.9254         | Train F1_macro:  0.9247         | Val Loss:  0.2994         | Val Accuracy:  0.8070         | Val F1_macro:  0.6521         \n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 3494/3494 [12:56<00:00,  4.50it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epochs: 8         | Train Loss:  0.1039         | Train Accuracy:  0.9337         | Train F1_macro:  0.9331         | Val Loss:  0.2811         | Val Accuracy:  0.8170         | Val F1_macro:  0.6406         \n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 3494/3494 [12:56<00:00,  4.50it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epochs: 9         | Train Loss:  0.0949         | Train Accuracy:  0.9399         | Train F1_macro:  0.9402         | Val Loss:  0.2913         | Val Accuracy:  0.8210         | Val F1_macro:  0.6449         \n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 3494/3494 [12:56<00:00,  4.50it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epochs: 10         | Train Loss:  0.0796         | Train Accuracy:  0.9493         | Train F1_macro:  0.9493         | Val Loss:  0.3111         | Val Accuracy:  0.8160         | Val F1_macro:  0.6342         \n"
          ]
        }
      ],
      "source": [
        "EPOCHS = 10\n",
        "OUTPUT_DIM = 3\n",
        "LR = 1e-6\n",
        "\n",
        "model = BertClassifier(OUTPUT_DIM)\n",
        "              \n",
        "train_evaluate(model, stance_train_dataset, stance_dev_dataset, LR, EPOCHS, 'Stance')"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Training the category classifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gsZiircpVUpn",
        "outputId": "893f1ca3-0079-44c8-9f10-6b2ca4689805"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at aubmindlab/bert-base-arabertv02-twitter were not used when initializing BertModel: ['cls.predictions.bias', 'cls.predictions.decoder.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.weight']\n",
            "- This IS expected if you are initializing BertModel 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 BertModel 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 BertModel were not initialized from the model checkpoint at aubmindlab/bert-base-arabertv02-twitter and are newly initialized: ['bert.pooler.dense.bias', 'bert.pooler.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"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "counts [ 167   79   18  975 1025 3616  112   67  323  606]\n",
            "tensor([0.0010, 0.0003, 0.0003,  ..., 0.0003, 0.0003, 0.0003],\n",
            "       dtype=torch.float64)\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 3494/3494 [12:56<00:00,  4.50it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epochs: 1         | Train Loss:  1.0841         | Train Accuracy:  0.2497         | Train F1_macro:  0.2284         | Val Loss:  1.0489         | Val Accuracy:  0.2280         | Val F1_macro:  0.2009         \n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 3494/3494 [12:56<00:00,  4.50it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epochs: 2         | Train Loss:  0.8174         | Train Accuracy:  0.5268         | Train F1_macro:  0.5082         | Val Loss:  0.8198         | Val Accuracy:  0.3600         | Val F1_macro:  0.2971         \n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 3494/3494 [12:56<00:00,  4.50it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epochs: 3         | Train Loss:  0.6383         | Train Accuracy:  0.6234         | Train F1_macro:  0.6099         | Val Loss:  0.7425         | Val Accuracy:  0.3930         | Val F1_macro:  0.3356         \n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 3494/3494 [12:55<00:00,  4.50it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epochs: 4         | Train Loss:  0.5180         | Train Accuracy:  0.6896         | Train F1_macro:  0.6787         | Val Loss:  0.7184         | Val Accuracy:  0.4270         | Val F1_macro:  0.3474         \n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 3494/3494 [12:55<00:00,  4.51it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epochs: 5         | Train Loss:  0.4471         | Train Accuracy:  0.7265         | Train F1_macro:  0.7195         | Val Loss:  0.6439         | Val Accuracy:  0.5110         | Val F1_macro:  0.3783         \n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 3494/3494 [12:55<00:00,  4.51it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epochs: 6         | Train Loss:  0.3680         | Train Accuracy:  0.7881         | Train F1_macro:  0.7777         | Val Loss:  0.6895         | Val Accuracy:  0.4750         | Val F1_macro:  0.3611         \n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 3494/3494 [12:56<00:00,  4.50it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epochs: 7         | Train Loss:  0.3282         | Train Accuracy:  0.8024         | Train F1_macro:  0.7961         | Val Loss:  0.6666         | Val Accuracy:  0.4970         | Val F1_macro:  0.3743         \n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 3494/3494 [12:56<00:00,  4.50it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epochs: 8         | Train Loss:  0.2887         | Train Accuracy:  0.8280         | Train F1_macro:  0.8227         | Val Loss:  0.6343         | Val Accuracy:  0.5260         | Val F1_macro:  0.3820         \n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 3494/3494 [12:56<00:00,  4.50it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epochs: 9         | Train Loss:  0.2475         | Train Accuracy:  0.8487         | Train F1_macro:  0.8450         | Val Loss:  0.6447         | Val Accuracy:  0.5270         | Val F1_macro:  0.3891         \n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 3494/3494 [12:55<00:00,  4.50it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epochs: 10         | Train Loss:  0.2285         | Train Accuracy:  0.8605         | Train F1_macro:  0.8562         | Val Loss:  0.6288         | Val Accuracy:  0.5410         | Val F1_macro:  0.3924         \n"
          ]
        }
      ],
      "source": [
        "EPOCHS = 10\n",
        "OUTPUT_DIM = 10\n",
        "LR = 1e-6\n",
        "\n",
        "model = BertClassifier(OUTPUT_DIM)\n",
        "              \n",
        "train_evaluate(model, category_train_dataset, category_dev_dataset, LR, EPOCHS, 'Category')"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "Nsag0Z26v9sw"
      },
      "source": [
        "### Continue category training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nkmaJSAkwMaG",
        "outputId": "4370cb33-09d8-451f-ce86-4e77de7779fa"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "counts [ 167   79   18  975 1025 3616  112   67  323  606]\n",
            "tensor([0.0010, 0.0003, 0.0003,  ..., 0.0003, 0.0003, 0.0003],\n",
            "       dtype=torch.float64)\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 3494/3494 [13:59<00:00,  4.16it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epochs: 1         | Train Loss:  0.2046         | Train Accuracy:  0.8749         | Train F1_macro:  0.8716         | Val Loss:  0.6243         | Val Accuracy:  0.5750         | Val F1_macro:  0.3937         \n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 3494/3494 [14:03<00:00,  4.14it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epochs: 2         | Train Loss:  0.1945         | Train Accuracy:  0.8799         | Train F1_macro:  0.8745         | Val Loss:  0.6441         | Val Accuracy:  0.5340         | Val F1_macro:  0.3859         \n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 3494/3494 [14:02<00:00,  4.15it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epochs: 3         | Train Loss:  0.1656         | Train Accuracy:  0.9004         | Train F1_macro:  0.8953         | Val Loss:  0.6521         | Val Accuracy:  0.5440         | Val F1_macro:  0.3781         \n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 3494/3494 [14:02<00:00,  4.14it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epochs: 4         | Train Loss:  0.1623         | Train Accuracy:  0.8988         | Train F1_macro:  0.8961         | Val Loss:  0.6393         | Val Accuracy:  0.5540         | Val F1_macro:  0.3987         \n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 3494/3494 [14:03<00:00,  4.14it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epochs: 5         | Train Loss:  0.1466         | Train Accuracy:  0.9073         | Train F1_macro:  0.9061         | Val Loss:  0.6562         | Val Accuracy:  0.5630         | Val F1_macro:  0.3907         \n"
          ]
        }
      ],
      "source": [
        "EPOCHS = 5\n",
        "OUTPUT_DIM = 10\n",
        "LR = 1e-6\n",
        "\n",
        "train_evaluate(model, category_train_dataset, category_dev_dataset, LR, EPOCHS, 'Category')"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "vmcIeUdJPnA-"
      },
      "source": [
        "# Prediction for test set (no gold labels given)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Reading, visualizing and preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hah_UNw3PpnB",
        "outputId": "cc119c13-8d72-4aaa-9a94-07ee339ad2b0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2000\n"
          ]
        }
      ],
      "source": [
        "test = pd.read_csv('Dataset/test.csv')\n",
        "print(len(test))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "xng6tin3QB38",
        "outputId": "07ffdffa-1ec7-4bbe-b854-da2bf2a69e26"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-e2b91569-4307-43b2-b8a9-fa721eb6a109\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>بيل غيتس يعلن عن تلقيه الجرعة الأولى من اللقاح...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>محتاجين لقاح يقاوم الإفراط في التفكير</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>- إحنا نخلي لقاح فايزر الأمريكي &lt;LF&gt;للمسئولين ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>متحدث الصحة: أخذ لقاح كورونا ليس شرطاً للسماح ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>131,939 جرعة من لقاح #كوفيد19 تم تقديمها خلال ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5</td>\n",
              "      <td>أكثر من ١٠٠ مليون شخص حول العالم أخذوا لقاح ال...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>6</td>\n",
              "      <td>أمير #الرياض يتلقى الجرعة الأولى من #لقاح_كورو...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>7</td>\n",
              "      <td>@USER @USER لقاح مجاني https://t.co/chOtH9LG3V</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>8</td>\n",
              "      <td>لو سمعتوا كلام صديقي الطبيب العربي بعد حصوله ع...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>9</td>\n",
              "      <td>قال الرئیس أردوغان \"تركيا في المرتبة الثالثة ب...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "   id                                               text\n",
              "0   0  بيل غيتس يعلن عن تلقيه الجرعة الأولى من اللقاح...\n",
              "1   1              محتاجين لقاح يقاوم الإفراط في التفكير\n",
              "2   2  - إحنا نخلي لقاح فايزر الأمريكي <LF>للمسئولين ...\n",
              "3   3  متحدث الصحة: أخذ لقاح كورونا ليس شرطاً للسماح ...\n",
              "4   4  131,939 جرعة من لقاح #كوفيد19 تم تقديمها خلال ...\n",
              "5   5  أكثر من ١٠٠ مليون شخص حول العالم أخذوا لقاح ال...\n",
              "6   6  أمير #الرياض يتلقى الجرعة الأولى من #لقاح_كورو...\n",
              "7   7     @USER @USER لقاح مجاني https://t.co/chOtH9LG3V\n",
              "8   8  لو سمعتوا كلام صديقي الطبيب العربي بعد حصوله ع...\n",
              "9   9  قال الرئیس أردوغان \"تركيا في المرتبة الثالثة ب..."
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "test.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EKVArNq8QKlz",
        "outputId": "15f98321-a239-4b7a-9c80-93c969ef8589"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(2000,) (2000,)\n"
          ]
        }
      ],
      "source": [
        "id_test, text_test = test['id'], test['text']\n",
        "id_test, text_test = np.array(id_test), np.array(text_test)\n",
        "print(id_test.shape, text_test.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J4kxMicsQiUq",
        "outputId": "e2553f7f-0e7a-48ed-e4a3-956e0b5f82ae"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0 بيل غيتس يعلن عن تلقيه الجرعة الأولى من اللقاح ضد فيروس #كورونا<LF><LF>- قال بيل غيتس \"إحدى مزايا بلوغي عمر الـ 65 عامًا هي أنني مؤهل للحصول على لقاح COVID-19\" https://t.co/JGZhBysT8M\n",
            "1 محتاجين لقاح يقاوم الإفراط في التفكير\n",
            "2 - إحنا نخلي لقاح فايزر الأمريكي <LF>للمسئولين وكبار الشخصيات ورجال الأعمال<LF>عشان بيكسبوا كتير و هيقدروا يدفعوا كويس <LF>- ونخلي لقاح موديرنا الأمريكي<LF>للدكاترة و التمريض أهو رخيص شوية<LF>- ونخلي لقاح سينوفارم الصيني ولقاح سبوتنيك الروسي للشعب عشان اللقاح رخيص و الشعب بيستحمل 🤣 https://t.co/K25PLmhjxf\n",
            "3 متحدث الصحة: أخذ لقاح كورونا ليس شرطاً للسماح بالسفر للخارج  https://t.co/eMVUXxjftk @USER\n",
            "4 131,939 جرعة من لقاح #كوفيد19 تم تقديمها خلال الـ24 ساعة الماضية، ليبلغ عدد الجرعات الكلي 1,797,926 بمعدل توزيع 18.18 جرعة لكل 100 شخص.<LF><LF>#يدا_بيد_نتعافى https://t.co/aQd30YXVzo\n",
            "5 أكثر من ١٠٠ مليون شخص حول العالم أخذوا لقاح الكورونا https://t.co/Ve3ATxooBa\n",
            "6 أمير #الرياض يتلقى الجرعة الأولى من #لقاح_كورونا.<LF>-<LF> https://t.co/q6o6DCnuiZ\n",
            "7 @USER @USER لقاح مجاني https://t.co/chOtH9LG3V\n",
            "8 لو سمعتوا كلام صديقي الطبيب العربي بعد حصوله على الجرعة الثانية من لقاح كورونا واعجابه واشادته بهذا البلد قيادة وشعباً وممارسين صحيين لافتخرتم وتباهيتم ببلدكم <LF><LF>كم كان منبهراً بما شاهده من تنظيم وبما أُتخذ من خطوات<LF><LF>نسأل الله أن يديم علينا نعمه ويحفظ لنا قادتنا ووحدتنا 🇸🇦🇸🇦🇸🇦🇸🇦\n",
            "9 قال الرئیس أردوغان \"تركيا في المرتبة الثالثة بعد الولايات المتحدة والصين في عدد مشاريع تطوير لقاحات ضد فيروس كورونا، بحسب منظمة الصحة العالمية\".<LF>وأضاف أن 436 باحثا تركيا يشرفون على 17 مشروعا لتطوير لقاح مضاد لكورونا\n"
          ]
        }
      ],
      "source": [
        "for i in range(10):\n",
        "  print(id_test[i], text_test[i])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l_OoN0x_Q4PA",
        "outputId": "1205c58f-cba4-443c-ff1a-00999c676f39"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "بيل غيتس يعلن عن تلقيه الجرعة الأولى من اللقاح ضد فيروس #كورونا<LF><LF>- قال بيل غيتس \"إحدى مزايا بلوغي عمر الـ 65 عامًا هي أنني مؤهل للحصول على لقاح COVID-19\" https://t.co/JGZhBysT8M \n",
            " بيل غيتس يعلن عن تلقيه الجرعة الأولى من اللقاح ضد فيروس #كورونا<LF><LF>- قال بيل غيتس \"إحدى مزايا بلوغي عمر الـ 65 عاما هي أنني مؤهل للحصول على لقاح COVID-19\"\n",
            "332\n"
          ]
        }
      ],
      "source": [
        "pre_text_test = PreProcessing(np.array(text_test))\n",
        "print(text_test[0], '\\n', pre_text_test[0])\n",
        "print(len(max(pre_text_test,key=len)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cJT-sTlyVBBC",
        "outputId": "a9676518-9ece-4ce3-9c70-a6d4e738b611"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
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          ]
        }
      ],
      "source": [
        "test_dataset = Dataset(pre_text_test, id_test)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### predicting function"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5E6cp6QivnWf"
      },
      "outputs": [],
      "source": [
        "def predict(model, test_dataset):\n",
        "\n",
        "  model.eval()\n",
        "\n",
        "  test_dataloader = torch.utils.data.DataLoader(test_dataset, batch_size=2)\n",
        "\n",
        "  y_id, y_pred = [], []\n",
        "\n",
        "  with torch.no_grad():\n",
        "\n",
        "    for test_input, test_id in tqdm(test_dataloader):\n",
        "      \n",
        "      mask = test_input['attention_mask'].to(device)\n",
        "      input_id = test_input['input_ids'].squeeze(1).to(device)\n",
        "\n",
        "      output = model(input_id, mask)\n",
        "\n",
        "      y_id += test_id.tolist() \n",
        "      y_pred += output.argmax(dim=1).tolist()\n",
        "\n",
        "  return y_id, y_pred"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Loading the stance model and evaluating on dev set"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "96q-QuBY32CK",
        "outputId": "f741b4dd-9bec-4ac4-8898-9823115d1036"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at aubmindlab/bert-base-arabertv02-twitter were not used when initializing BertModel: ['cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.decoder.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.bias']\n",
            "- This IS expected if you are initializing BertModel 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 BertModel 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 BertModel were not initialized from the model checkpoint at aubmindlab/bert-base-arabertv02-twitter and are newly initialized: ['bert.pooler.dense.weight', 'bert.pooler.dense.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Val Loss:  0.2994     | Val Accuracy:  0.8070     | Val F1_macro:  0.6521     \n"
          ]
        }
      ],
      "source": [
        "EPOCHS = 10\n",
        "OUTPUT_DIM = 3\n",
        "stance_model = BertClassifier(OUTPUT_DIM) \n",
        "\n",
        "stance_model.to(device)\n",
        "stance_model.load_state_dict(torch.load('Models/BERT/Stance/Copy of F1 0.6521 Acc 0.8070.pt'))\n",
        "val_dataloader = torch.utils.data.DataLoader(stance_dev_dataset, batch_size=2)\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "\n",
        "loss_val, acc_val, f1_macro_val = evaluate(stance_model, stance_dev_dataset, val_dataloader, criterion)\n",
        "print(\n",
        "    f'Val Loss: {loss_val: .4f} \\\n",
        "    | Val Accuracy: {acc_val: .4f} \\\n",
        "    | Val F1_macro: {f1_macro_val: .4f} \\\n",
        "    ')"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Loading the category model and evaluating on dev set"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UKuJaTx7Idxs",
        "outputId": "44dec61e-8bea-4ee6-c122-88bb4614d1a1"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at aubmindlab/bert-base-arabertv02-twitter were not used when initializing BertModel: ['cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.decoder.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.bias']\n",
            "- This IS expected if you are initializing BertModel 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 BertModel 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 BertModel were not initialized from the model checkpoint at aubmindlab/bert-base-arabertv02-twitter and are newly initialized: ['bert.pooler.dense.weight', 'bert.pooler.dense.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Val Loss:  0.6393     | Val Accuracy:  0.5540     | Val F1_macro:  0.3987     \n"
          ]
        }
      ],
      "source": [
        "EPOCHS = 10\n",
        "OUTPUT_DIM = 10\n",
        "category_model = BertClassifier(OUTPUT_DIM) \n",
        "\n",
        "category_model.to(device)\n",
        "category_model.load_state_dict(torch.load('Models/BERT/Category/Copy of F1 0.3987 Acc 0.5540.pt'))\n",
        "val_dataloader = torch.utils.data.DataLoader(category_dev_dataset, batch_size=2)\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "\n",
        "loss_val, acc_val, f1_macro_val = evaluate(category_model, category_dev_dataset, val_dataloader, criterion)\n",
        "print(\n",
        "    f'Val Loss: {loss_val: .4f} \\\n",
        "    | Val Accuracy: {acc_val: .4f} \\\n",
        "    | Val F1_macro: {f1_macro_val: .4f} \\\n",
        "    ')"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Stance prediction"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VKE-BNd6bUm6",
        "outputId": "baee50ef-25dd-4aab-936e-5ad16ecce75c"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 1000/1000 [01:03<00:00, 15.68it/s]\n"
          ]
        }
      ],
      "source": [
        "stance_y_id, stance_y_pred = predict(stance_model, test_dataset)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bznXvnt_6q79"
      },
      "outputs": [],
      "source": [
        "stance_y_pred = np.array(stance_y_pred) - 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jGaGQ8YHiJrq",
        "outputId": "d981a968-1cc6-4450-dd9d-5fe6e7b2c57f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[(-1, 211), (0, 381), (1, 1408)]\n"
          ]
        }
      ],
      "source": [
        "print(sorted(Counter(stance_y_pred).items()))"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Category prediction"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w28ChDtHXrRp",
        "outputId": "ee494f4f-9c6f-4a77-db99-c7d49c8ace22"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 1000/1000 [01:04<00:00, 15.48it/s]\n"
          ]
        }
      ],
      "source": [
        "category_y_id, category_y_pred = predict(category_model, test_dataset)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tQiY03il7OR-"
      },
      "outputs": [],
      "source": [
        "category_y_pred = np.array([id2category[p] for p in category_y_pred])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CZj8SEX4igmB",
        "outputId": "7a836b9c-1cae-4561-b74a-44359f8e0ffa"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[('advice', 24), ('celebrity', 312), ('info_news', 557), ('others', 47), ('personal', 397), ('plan', 490), ('requests', 46), ('restrictions', 10), ('rumors', 44), ('unrelated', 73)]\n"
          ]
        }
      ],
      "source": [
        "print(sorted(Counter(category_y_pred).items()))"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Outputing predictions to csv file"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bOuhmcYA9HSi"
      },
      "outputs": [],
      "source": [
        "df = pd.DataFrame({\n",
        "    'id': category_y_id, \n",
        "    'category': category_y_pred,\n",
        "    'stance': stance_y_pred\n",
        "})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "lUBaysNZ9jHn",
        "outputId": "4e472263-46f2-4f07-8d83-f0f0fe4be93c"
      },
      "outputs": [
        {
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              "   id      category  stance\n",
              "0   0     celebrity       1\n",
              "1   1     unrelated       0\n",
              "2   2      personal       0\n",
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              "7   7      personal       1\n",
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