Avicenna_AT.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ZeinabAghahadi/Syllogistic-Commonsense-Reasoning/blob/main/Avicenna_AT.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8OhO4xlwqExT"
      },
      "source": [
        "# Fine Tuning Roberta for Syllogistic NLI with Avicenna Dataset\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "97CEi-bdqExb"
      },
      "source": [
        "<a id='section01'></a>\n",
        "### Importing Libraries\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "979OUro5Eac3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2f9c2710-71c0-49e1-954f-592f6da2c896"
      },
      "source": [
        "!pip install transformers==3.0.2\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "import torch\n",
        "import seaborn as sns\n",
        "import transformers\n",
        "import json\n",
        "from tqdm import tqdm\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from transformers import RobertaModel, RobertaTokenizer\n",
        "import logging\n",
        "logging.basicConfig(level=logging.ERROR)\n",
        "from torch import cuda\n",
        "device = 'cuda' if cuda.is_available() else 'cpu'"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting transformers==3.0.2\n",
            "  Downloading transformers-3.0.2-py3-none-any.whl (769 kB)\n",
            "\u001b[K     |████████████████████████████████| 769 kB 30.8 MB/s \n",
            "\u001b[?25hRequirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers==3.0.2) (4.64.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers==3.0.2) (2.23.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from transformers==3.0.2) (1.21.6)\n",
            "Collecting tokenizers==0.8.1.rc1\n",
            "  Downloading tokenizers-0.8.1rc1-cp37-cp37m-manylinux1_x86_64.whl (3.0 MB)\n",
            "\u001b[K     |████████████████████████████████| 3.0 MB 7.4 MB/s \n",
            "\u001b[?25hRequirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers==3.0.2) (2022.6.2)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers==3.0.2) (3.7.1)\n",
            "Collecting sacremoses\n",
            "  Downloading sacremoses-0.0.53.tar.gz (880 kB)\n",
            "\u001b[K     |████████████████████████████████| 880 kB 50.4 MB/s \n",
            "\u001b[?25hCollecting sentencepiece!=0.1.92\n",
            "  Downloading sentencepiece-0.1.96-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.2 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.2 MB 54.2 MB/s \n",
            "\u001b[?25hRequirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from transformers==3.0.2) (21.3)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->transformers==3.0.2) (3.0.9)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers==3.0.2) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers==3.0.2) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers==3.0.2) (2022.6.15)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers==3.0.2) (2.10)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers==3.0.2) (1.15.0)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers==3.0.2) (7.1.2)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers==3.0.2) (1.1.0)\n",
            "Building wheels for collected packages: sacremoses\n",
            "  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sacremoses: filename=sacremoses-0.0.53-py3-none-any.whl size=895260 sha256=0caea7c462fa90d5f8485dd2d0354ce0f710890b40ed0d74cc40162bb98d624f\n",
            "  Stored in directory: /root/.cache/pip/wheels/87/39/dd/a83eeef36d0bf98e7a4d1933a4ad2d660295a40613079bafc9\n",
            "Successfully built sacremoses\n",
            "Installing collected packages: tokenizers, sentencepiece, sacremoses, transformers\n",
            "Successfully installed sacremoses-0.0.53 sentencepiece-0.1.96 tokenizers-0.8.1rc1 transformers-3.0.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c3Q9NDdmqEyo"
      },
      "source": [
        "<a id='section03'></a>\n",
        "### Reading the Avicenna dataset\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J3FzcAlgEac8"
      },
      "source": [
        "import pandas as pd\n",
        "Train_url=\"https://raw.githubusercontent.com/ZeinabAghahadi/Syllogistic-Commonsense-Reasoning/main/Data/RoBERTa/reg_adv_train.csv\"\n",
        "train = pd.read_csv(Train_url, encoding= 'unicode_escape')\n"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TFIoIjucGjJw",
        "outputId": "323cdac1-d678-4218-b4a5-e9580bec5eb8"
      },
      "source": [
        "train.shape\n"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5119, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "aTsOsl4MEadB",
        "outputId": "03cb9b2b-8db1-45cb-e10d-a45a440224ad"
      },
      "source": [
        "train.head()\n"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                            Premises  label\n",
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              "2  Insufficient income makes employees quit their...      0\n",
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              "4  the construction of dams severely affects rive...      1"
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    {
      "cell_type": "code",
      "metadata": {
        "id": "baSmeDdIEadM"
      },
      "source": [
        "new_df = train[['Premises', 'label']]\n"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nvXxpfNCGER2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
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        "outputId": "43ab5ed7-a89e-4add-f103-9dcb1e3c63d4"
      },
      "source": [
        "MAX_LEN = 256\n",
        "TRAIN_BATCH_SIZE = 8\n",
        "VALID_BATCH_SIZE = 4\n",
        "LEARNING_RATE = 1e-05\n",
        "tokenizer = RobertaTokenizer.from_pretrained('roberta-base', truncation=True, do_lower_case=True)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/899k [00:00<?, ?B/s]"
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3vWRDemOGxJD"
      },
      "source": [
        "class SyllogismData(Dataset):\n",
        "    def __init__(self, dataframe, tokenizer, max_len):\n",
        "        self.tokenizer = tokenizer\n",
        "        self.data = dataframe\n",
        "        self.text = dataframe.Premises\n",
        "        self.targets = self.data.label\n",
        "        self.max_len = max_len\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.text)\n",
        "\n",
        "    def __getitem__(self, index):\n",
        "        text = str(self.text[index])\n",
        "        text = \" \".join(text.split())\n",
        "\n",
        "        inputs = self.tokenizer.encode_plus(\n",
        "            text,\n",
        "            None,\n",
        "            add_special_tokens=True,\n",
        "            max_length=self.max_len,\n",
        "            pad_to_max_length=True,\n",
        "            return_token_type_ids=True\n",
        "        )\n",
        "        ids = inputs['input_ids']\n",
        "        mask = inputs['attention_mask']\n",
        "        token_type_ids = inputs[\"token_type_ids\"]\n",
        "\n",
        "\n",
        "        return {\n",
        "            'ids': torch.tensor(ids, dtype=torch.long),\n",
        "            'mask': torch.tensor(mask, dtype=torch.long),\n",
        "            'token_type_ids': torch.tensor(token_type_ids, dtype=torch.long),\n",
        "            'targets': torch.tensor(self.targets[index], dtype=torch.float)\n",
        "        }"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7Gpe9D1QHoCd",
        "outputId": "ecd96b85-3b4e-4c0c-aff3-ba9099d607b8"
      },
      "source": [
        "train_size = 1\n",
        "\n",
        "train_data=new_df.sample(frac=train_size,random_state=0)\n",
        "train_data = train_data.reset_index(drop=True)\n",
        "\n",
        "print(\"FULL train Dataset: {}\".format(new_df.shape))\n",
        "\n",
        "print(\"TRAIN Dataset: {}\".format(train_data.shape))\n",
        "\n",
        "training_set = SyllogismData(train_data, tokenizer, MAX_LEN)\n"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "FULL train Dataset: (5119, 2)\n",
            "TRAIN Dataset: (5119, 2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c1tInLk2Eadt"
      },
      "source": [
        "train_params = {'batch_size': TRAIN_BATCH_SIZE,\n",
        "                'shuffle': True,\n",
        "                'num_workers': 0\n",
        "                }\n",
        "\n",
        "\n",
        "training_loader = DataLoader(training_set, **train_params)\n"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yZk0A9K8qE0C"
      },
      "source": [
        "<a id='section04'></a>\n",
        "### Creating the Neural Network for Fine Tuning\n",
        "\n",
        "#### Neural Network\n",
        " - We will be creating a neural network with the `RobertaClass`. \n",
        " - This network will have the Roberta Language model followed by a `dropout` and finally a `Linear` layer to obtain the final outputs. \n",
        " - The data will be fed to the Roberta Language model as defined in the Avicenna dataset. \n",
        " - Final layer outputs is what will be compared to the `Syllogistic relation` to determine the accuracy of models prediction. \n",
        " \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HMqQTafXEaei"
      },
      "source": [
        "class RobertaClass(torch.nn.Module):\n",
        "    def __init__(self):\n",
        "        super(RobertaClass, self).__init__()\n",
        "        self.l1 = RobertaModel.from_pretrained(\"roberta-base\")\n",
        "        self.pre_classifier = torch.nn.Linear(768, 768)\n",
        "        self.dropout = torch.nn.Dropout(0.3)\n",
        "        self.classifier = torch.nn.Linear(768, 5)\n",
        "\n",
        "    def forward(self, input_ids, attention_mask, token_type_ids):\n",
        "        output_1 = self.l1(input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids)\n",
        "        hidden_state = output_1[0]\n",
        "        pooler = hidden_state[:, 0]\n",
        "        pooler = self.pre_classifier(pooler)\n",
        "        pooler = torch.nn.ReLU()(pooler)\n",
        "        pooler = self.dropout(pooler)\n",
        "        output = self.classifier(pooler)\n",
        "        return output"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
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        "id": "sZ55mIPZIkp_",
        "outputId": "4c043296-86a4-40bb-ed45-ad9846b3af28"
      },
      "source": [
        "model = RobertaClass()\n",
        "model.to(device)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/481 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "993bbc972fe641029051178c695d9407"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/501M [00:00<?, ?B/s]"
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0c6a7e89af0c49748739df1dd0f0ba80"
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          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RobertaClass(\n",
              "  (l1): RobertaModel(\n",
              "    (embeddings): RobertaEmbeddings(\n",
              "      (word_embeddings): Embedding(50265, 768, padding_idx=1)\n",
              "      (position_embeddings): Embedding(514, 768, padding_idx=1)\n",
              "      (token_type_embeddings): Embedding(1, 768)\n",
              "      (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "      (dropout): Dropout(p=0.1, inplace=False)\n",
              "    )\n",
              "    (encoder): BertEncoder(\n",
              "      (layer): ModuleList(\n",
              "        (0): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (1): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (2): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (3): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (4): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (5): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (6): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (7): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (8): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (9): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (10): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (11): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (pooler): BertPooler(\n",
              "      (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "      (activation): Tanh()\n",
              "    )\n",
              "  )\n",
              "  (pre_classifier): Linear(in_features=768, out_features=768, bias=True)\n",
              "  (dropout): Dropout(p=0.3, inplace=False)\n",
              "  (classifier): Linear(in_features=768, out_features=5, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gsRa7gY3qE0n"
      },
      "source": [
        "<a id='section05'></a>\n",
        "### Fine Tuning the Model\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XYZ7YuJ5InOS"
      },
      "source": [
        "loss_function = torch.nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.Adam(params =  model.parameters(), lr=LEARNING_RATE)"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yPhA2V3iIpzN"
      },
      "source": [
        "def calcuate_accuracy(preds, targets):\n",
        "    n_correct = (preds==targets).sum().item()\n",
        "    return n_correct"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mhqvtY2SIup7"
      },
      "source": [
        "def train(epoch):\n",
        "    tr_loss = 0\n",
        "    n_correct = 0\n",
        "    nb_tr_steps = 0\n",
        "    nb_tr_examples = 0\n",
        "    model.train()\n",
        "    for _,data in tqdm(enumerate(training_loader, 0)):\n",
        "        ids = data['ids'].to(device, dtype = torch.long)\n",
        "        mask = data['mask'].to(device, dtype = torch.long)\n",
        "        token_type_ids = data['token_type_ids'].to(device, dtype = torch.long)\n",
        "        targets = data['targets'].to(device, dtype = torch.long)\n",
        "\n",
        "        outputs = model(ids, mask, token_type_ids)\n",
        "        loss = loss_function(outputs, targets)\n",
        "        tr_loss += loss.item()\n",
        "        big_val, big_idx = torch.max(outputs.data, dim=1)\n",
        "        n_correct += calcuate_accuracy(big_idx, targets)\n",
        "\n",
        "        nb_tr_steps += 1\n",
        "        nb_tr_examples+=targets.size(0)\n",
        "        \n",
        "        if _%5000==0:\n",
        "            loss_step = tr_loss/nb_tr_steps\n",
        "            accu_step = (n_correct*100)/nb_tr_examples \n",
        "            print(f\"Training Loss per 5000 steps: {loss_step}\")\n",
        "            print(f\"Training Accuracy per 5000 steps: {accu_step}\")\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        # # When using GPU\n",
        "        optimizer.step()\n",
        "\n",
        "    print(f'The Total Accuracy for Epoch {epoch}: {(n_correct*100)/nb_tr_examples}')\n",
        "    epoch_loss = tr_loss/nb_tr_steps\n",
        "    epoch_accu = (n_correct*100)/nb_tr_examples\n",
        "    print(f\"Training Loss Epoch: {epoch_loss}\")\n",
        "    print(f\"Training Accuracy Epoch: {epoch_accu}\")\n",
        "\n",
        "    return "
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Afn7xaunJHnI",
        "outputId": "cc5b4f9c-ca43-4489-8daa-eacacbe099d5"
      },
      "source": [
        "EPOCHS = 10\n",
        "for epoch in range(EPOCHS):\n",
        "    train(epoch)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r0it [00:00, ?it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Loss per 5000 steps: 1.708037257194519\n",
            "Training Accuracy per 5000 steps: 0.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "640it [03:53,  2.74it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The Total Accuracy for Epoch 0: 68.21644852510256\n",
            "Training Loss Epoch: 0.6120537128066644\n",
            "Training Accuracy Epoch: 68.21644852510256\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r0it [00:00, ?it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Loss per 5000 steps: 0.3312811851501465\n",
            "Training Accuracy per 5000 steps: 75.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "640it [03:55,  2.72it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The Total Accuracy for Epoch 1: 87.8101191638992\n",
            "Training Loss Epoch: 0.28707254693144935\n",
            "Training Accuracy Epoch: 87.8101191638992\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r0it [00:00, ?it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Loss per 5000 steps: 0.08550095558166504\n",
            "Training Accuracy per 5000 steps: 100.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "640it [03:54,  2.73it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The Total Accuracy for Epoch 2: 94.4129712834538\n",
            "Training Loss Epoch: 0.1532793155463878\n",
            "Training Accuracy Epoch: 94.4129712834538\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r0it [00:00, ?it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Loss per 5000 steps: 0.108012855052948\n",
            "Training Accuracy per 5000 steps: 100.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "640it [03:55,  2.72it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The Total Accuracy for Epoch 3: 96.79624926743504\n",
            "Training Loss Epoch: 0.09261031845235265\n",
            "Training Accuracy Epoch: 96.79624926743504\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r0it [00:00, ?it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Loss per 5000 steps: 0.010424656793475151\n",
            "Training Accuracy per 5000 steps: 100.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "640it [03:54,  2.73it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The Total Accuracy for Epoch 4: 97.9683531939832\n",
            "Training Loss Epoch: 0.06504582521283737\n",
            "Training Accuracy Epoch: 97.9683531939832\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r0it [00:00, ?it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Loss per 5000 steps: 0.0051314448937773705\n",
            "Training Accuracy per 5000 steps: 100.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "640it [03:54,  2.73it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The Total Accuracy for Epoch 5: 98.96464153154913\n",
            "Training Loss Epoch: 0.03235902236738184\n",
            "Training Accuracy Epoch: 98.96464153154913\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r0it [00:00, ?it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Loss per 5000 steps: 0.0016921014757826924\n",
            "Training Accuracy per 5000 steps: 100.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "640it [03:54,  2.72it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The Total Accuracy for Epoch 6: 98.76929087712443\n",
            "Training Loss Epoch: 0.03588670645822276\n",
            "Training Accuracy Epoch: 98.76929087712443\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r0it [00:00, ?it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Loss per 5000 steps: 0.10505574941635132\n",
            "Training Accuracy per 5000 steps: 87.5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "640it [03:55,  2.72it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The Total Accuracy for Epoch 7: 98.94510646610667\n",
            "Training Loss Epoch: 0.0331467448568219\n",
            "Training Accuracy Epoch: 98.94510646610667\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r0it [00:00, ?it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Loss per 5000 steps: 0.013307084329426289\n",
            "Training Accuracy per 5000 steps: 100.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "640it [03:55,  2.72it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The Total Accuracy for Epoch 8: 99.33580777495605\n",
            "Training Loss Epoch: 0.020490384277627526\n",
            "Training Accuracy Epoch: 99.33580777495605\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r0it [00:00, ?it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Loss per 5000 steps: 0.07518977671861649\n",
            "Training Accuracy per 5000 steps: 100.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "640it [03:54,  2.72it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The Total Accuracy for Epoch 9: 99.14045712053135\n",
            "Training Loss Epoch: 0.027359391917798348\n",
            "Training Accuracy Epoch: 99.14045712053135\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vOcgTsovqE1A"
      },
      "source": [
        "<a id='section06'></a>\n",
        "### Validating the Model\n",
        "\n",
        "The unseen data is the 20% of Avicenna."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Test_url = 'https://raw.githubusercontent.com/ZeinabAghahadi/Syllogistic-Commonsense-Reasoning/main/Data/RoBERTa/reg_test.csv'\n",
        "test = pd.read_csv(Test_url, encoding= 'unicode_escape')\n",
        "test.head()\n",
        "test['label'].unique()\n",
        "test.describe()\n",
        "testnew_df = test[['Premises', 'label']]\n",
        "\n",
        "test_size = 1\n",
        "test_data=testnew_df\n",
        "\n",
        "print(\"FULL test Dataset: {}\".format(testnew_df.shape))\n",
        "print(\"TEST Dataset: {}\".format(test_data.shape))\n",
        "\n",
        "testing_set = SyllogismData(test_data, tokenizer, MAX_LEN)\n",
        "\n",
        "\n",
        "test_params = {'batch_size': VALID_BATCH_SIZE,\n",
        "                'shuffle': False,\n",
        "                'num_workers': 0\n",
        "                }\n",
        "testing_loader = DataLoader(testing_set, **test_params)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dalsaGHo4k9D",
        "outputId": "b6fd26e1-b185-4003-af2f-c141dfbc5271"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "FULL test Dataset: (1200, 2)\n",
            "TEST Dataset: (1200, 2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bFiNcy16JLwt"
      },
      "source": [
        "def valid(model, testing_loader):\n",
        "    model.eval()\n",
        "    n_correct = 0; n_wrong = 0; total = 0; tr_loss=0; nb_tr_steps=0; nb_tr_examples=0\n",
        "    with torch.no_grad():\n",
        "        for _, data in tqdm(enumerate(testing_loader, 0)):\n",
        "            ids = data['ids'].to(device, dtype = torch.long)\n",
        "            mask = data['mask'].to(device, dtype = torch.long)\n",
        "            token_type_ids = data['token_type_ids'].to(device, dtype=torch.long)\n",
        "            targets = data['targets'].to(device, dtype = torch.long)\n",
        "            outputs = model(ids, mask, token_type_ids).squeeze()\n",
        "            loss = loss_function(outputs, targets)\n",
        "            tr_loss += loss.item()\n",
        "            big_val, big_idx = torch.max(outputs.data, dim=1)\n",
        "            n_correct += calcuate_accuracy(big_idx, targets)\n",
        "            #print(big_idx)\n",
        "\n",
        "\n",
        "            nb_tr_steps += 1\n",
        "            nb_tr_examples+=targets.size(0)\n",
        "            \n",
        "            if _%5000==0:\n",
        "                loss_step = tr_loss/nb_tr_steps\n",
        "                accu_step = (n_correct*100)/nb_tr_examples\n",
        "    epoch_loss = tr_loss/nb_tr_steps\n",
        "    epoch_accu = (n_correct*100)/nb_tr_examples\n",
        "\n",
        "    return epoch_accu\n"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UcUylInzKdV-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ad0c26ab-30e2-454c-e4f9-81c113c1f27a"
      },
      "source": [
        "acc = valid(model, testing_loader)\n",
        "print(\"Accuracy on test data = %0.2f%%\" % acc)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "300it [00:19, 15.78it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy on test data = 88.17%\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    }
  ]
}