Project_Baseline-2.ipynb

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      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive', force_remount = True)"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SsJB5WW6d31Y"
      },
      "source": [
        "import os\n",
        "root_path = '/content/gdrive/MyDrive/Project/Dravidian-Offensive-Language-Identification'\n",
        "os.chdir(root_path)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cbNyW53id6NW",
        "outputId": "72615914-25c8-4bae-bd66-715232d14441"
      },
      "source": [
        "!pip install sentencepiece\n",
        "!pip install transformers\n",
        "!pip install demoji\n",
        "!pip install tweet-preprocessor"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting sentencepiece\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/f5/99/e0808cb947ba10f575839c43e8fafc9cc44e4a7a2c8f79c60db48220a577/sentencepiece-0.1.95-cp37-cp37m-manylinux2014_x86_64.whl (1.2MB)\n",
            "\u001b[K     |████████████████████████████████| 1.2MB 4.5MB/s \n",
            "\u001b[?25hInstalling collected packages: sentencepiece\n",
            "Successfully installed sentencepiece-0.1.95\n",
            "Collecting transformers\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/81/91/61d69d58a1af1bd81d9ca9d62c90a6de3ab80d77f27c5df65d9a2c1f5626/transformers-4.5.0-py3-none-any.whl (2.1MB)\n",
            "\u001b[K     |████████████████████████████████| 2.2MB 5.9MB/s \n",
            "\u001b[?25hRequirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from transformers) (3.8.1)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers) (4.41.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (2019.12.20)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from transformers) (20.9)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers) (3.0.12)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers) (2.23.0)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.19.5)\n",
            "Collecting sacremoses\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/08/cd/342e584ee544d044fb573ae697404ce22ede086c9e87ce5960772084cad0/sacremoses-0.0.44.tar.gz (862kB)\n",
            "\u001b[K     |████████████████████████████████| 870kB 34.9MB/s \n",
            "\u001b[?25hCollecting tokenizers<0.11,>=0.10.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ae/04/5b870f26a858552025a62f1649c20d29d2672c02ff3c3fb4c688ca46467a/tokenizers-0.10.2-cp37-cp37m-manylinux2010_x86_64.whl (3.3MB)\n",
            "\u001b[K     |████████████████████████████████| 3.3MB 33.7MB/s \n",
            "\u001b[?25hRequirement already satisfied: typing-extensions>=3.6.4; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->transformers) (3.7.4.3)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->transformers) (3.4.1)\n",
            "Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->transformers) (2.4.7)\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) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2020.12.5)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2.10)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.15.0)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (7.1.2)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.0.1)\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-0.0.44-cp37-none-any.whl size=886084 sha256=e5e4b988be1392f546bfd4fc2e00e2b7f461b9109792e811d6863cc955993671\n",
            "  Stored in directory: /root/.cache/pip/wheels/3e/fb/c0/13ab4d63d537658f448366744654323077c4d90069b6512f3c\n",
            "Successfully built sacremoses\n",
            "Installing collected packages: sacremoses, tokenizers, transformers\n",
            "Successfully installed sacremoses-0.0.44 tokenizers-0.10.2 transformers-4.5.0\n",
            "Collecting demoji\n",
            "  Downloading https://files.pythonhosted.org/packages/88/6a/34379abe01c9c36fe9fddc4181dd935332e7d0159ec3fae76f712e49bcea/demoji-0.4.0-py2.py3-none-any.whl\n",
            "Requirement already satisfied: requests<3.0.0 in /usr/local/lib/python3.7/dist-packages (from demoji) (2.23.0)\n",
            "Collecting colorama\n",
            "  Downloading https://files.pythonhosted.org/packages/44/98/5b86278fbbf250d239ae0ecb724f8572af1c91f4a11edf4d36a206189440/colorama-0.4.4-py2.py3-none-any.whl\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0->demoji) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0->demoji) (2020.12.5)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0->demoji) (2.10)\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<3.0.0->demoji) (1.24.3)\n",
            "Installing collected packages: colorama, demoji\n",
            "Successfully installed colorama-0.4.4 demoji-0.4.0\n",
            "Collecting tweet-preprocessor\n",
            "  Downloading https://files.pythonhosted.org/packages/17/9d/71bd016a9edcef8860c607e531f30bd09b13103c7951ae73dd2bf174163c/tweet_preprocessor-0.6.0-py3-none-any.whl\n",
            "Installing collected packages: tweet-preprocessor\n",
            "Successfully installed tweet-preprocessor-0.6.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o5EylLU_d_l2",
        "outputId": "cfc08783-1e0e-4a64-d9e6-3bfc88174f2d"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from transformers import AutoModel, AutoTokenizer\n",
        "import torch.nn as nn\n",
        "import torch\n",
        "import copy\n",
        "from transformers import BertModel, RobertaModel, BertTokenizer, RobertaTokenizer, AdamW, get_linear_schedule_with_warmup\n",
        "from torch.utils.data import TensorDataset, RandomSampler, SequentialSampler, random_split, DataLoader, IterableDataset, ConcatDataset\n",
        "import sklearn\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "from sklearn.model_selection import train_test_split, StratifiedKFold\n",
        "from sklearn.metrics import f1_score \n",
        "from tqdm import tqdm_notebook as tqdm\n",
        "import demoji \n",
        "import random\n",
        "demoji.download_codes() \n",
        "import preprocessor as p\n",
        "p.set_options(p.OPT.URL, p.OPT.MENTION, p.OPT.RESERVED)\n",
        "plt.rcParams['figure.figsize'] = [15, 8]\n",
        "plt.rcParams.update({'font.size': 8})\n",
        "RANDOM_SEED = 42"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading emoji data ...\n",
            "... OK (Got response in 0.16 seconds)\n",
            "Writing emoji data to /root/.demoji/codes.json ...\n",
            "... OK\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QYW98zgR4p7Z"
      },
      "source": [
        "import pdb"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d1e136K_fb2L",
        "outputId": "f9710c45-06d8-4099-f92a-d2847f67bf41"
      },
      "source": [
        "!git lfs install\n",
        "!git clone https://huggingface.co/xlm-roberta-base"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "git: 'lfs' is not a git command. See 'git --help'.\n",
            "\n",
            "The most similar command is\n",
            "\tlog\n",
            "Cloning into 'xlm-roberta-base'...\n",
            "remote: Enumerating objects: 24, done.\n",
            "remote: Counting objects: 100% (24/24), done.\n",
            "remote: Compressing objects: 100% (23/23), done.\n",
            "remote: Total 24 (delta 8), reused 0 (delta 0)\n",
            "Unpacking objects: 100% (24/24), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cnWY2xZyeHyk"
      },
      "source": [
        "# model_path = 'bert-base-multilingual-cased'\n",
        "# model_path = 'monsoon-nlp/tamillion'\n",
        "model_path = \"xlm-roberta-base\"\n",
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gMVQOJWSfsts"
      },
      "source": [
        "def random_seed(seed_value, use_cuda):\n",
        "    np.random.seed(seed_value)  \n",
        "    torch.manual_seed(seed_value)  \n",
        "    random.seed(seed_value)\n",
        "    if use_cuda:\n",
        "        torch.cuda.manual_seed(seed_value)\n",
        "        torch.cuda.manual_seed_all(seed_value)  \n",
        "        torch.backends.cudnn.deterministic = True\n",
        "        torch.backends.cudnn.benchmark = False\n",
        "random_seed(RANDOM_SEED, True)"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZQ2c4mObfy0C"
      },
      "source": [
        "class Dataset():\n",
        "    def __init__(self, train_data, val_data, label_data, batch_size = 32):\n",
        "        self.train_data = train_data\n",
        "        self.val_data = val_data\n",
        "        self.label_data = label_data\n",
        "        self.batch_size = batch_size\n",
        "\n",
        "        # self.label_dict = {'Not_offensive': 0,\n",
        "        #                     'Offensive_Targeted_Insult_Group': 3,\n",
        "        #                     'Offensive_Targeted_Insult_Individual': 5,\n",
        "        #                     'Offensive_Targeted_Insult_Other': 2,\n",
        "        #                     'Offensive_Untargetede': 4,\n",
        "        #                     'not-Tamil': 1}\n",
        "        self.label_dict = {'NOT': 0,\n",
        "                            'OFF': 1}\n",
        "                                    \n",
        "        self.count_dic = {}\n",
        "\n",
        "        self.train_inputs, self.train_labels = self.process_data(self.train_data)\n",
        "        self.val_inputs, self.val_labels = self.process_val_data(self.val_data,self.label_data)\n",
        "        count_dic = {}\n",
        "        for data in self.train_labels:\n",
        "            label = int(data)\n",
        "            count_dic[label] = count_dic.get(label, 0)+1\n",
        "        self.weights = torch.Tensor([len(self.train_labels)/count_dic[i] for i in range(2)]).to(device)\n",
        "        self.train_dataloader = self.get_dataloader(self.train_inputs, self.train_labels)\n",
        "        self.val_dataloader = self.get_dataloader(self.val_inputs, self. val_labels, train = False)\n",
        "\n",
        "    def tokenize(self, sentences, padding = True, max_len = 256):\n",
        "        # tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)\n",
        "        tokenizer = AutoTokenizer.from_pretrained(model_path)\n",
        "        input_ids, attention_masks = [], []\n",
        "        for sent in sentences:\n",
        "            encoded_dict = tokenizer.encode_plus(sent,\n",
        "                                                    add_special_tokens=True,\n",
        "                                                    max_length=max_len, \n",
        "                                                    padding='max_length', \n",
        "                                                    return_attention_mask = True,\n",
        "                                                    return_tensors = 'pt', \n",
        "                                                    truncation = True)\n",
        "            input_ids.append(encoded_dict['input_ids'])\n",
        "            attention_masks.append(encoded_dict['attention_mask'])\n",
        "        \n",
        "        input_ids = torch.cat(input_ids, dim=0)\n",
        "        attention_masks = torch.cat(attention_masks, dim=0)\n",
        "\n",
        "        return {'input_ids': input_ids, 'attention_masks': attention_masks}\n",
        "    \n",
        "    def process_data(self, data):\n",
        "        sentences, labels = [], []\n",
        "        print(len(data))\n",
        "        for line in data:\n",
        "            # pdb.set_trace()\n",
        "            sentence = line.strip().split('\\t')\n",
        "            # sentence = line.strip().split(',')\n",
        "            sentence = sentence[1:]\n",
        "            label = sentence.pop()\n",
        "            # if label == 'not-Kannada': print(sentence)\n",
        "            if label not in self.label_dict:\n",
        "                self.label_dict[label] = len(self.label_dict)\n",
        "            sentence = p.clean(' '.join(sentence)).replace('#','')\n",
        "            emoji_dict = demoji.findall(sentence)\n",
        "            if len(emoji_dict): \n",
        "                for emoji, text in emoji_dict.items():\n",
        "                    sentence = sentence.replace(emoji, ' '+text+' ')\n",
        "                    sentence = ' '.join(sentence.split())\n",
        "            sentences.append(sentence)\n",
        "            labels.append(self.label_dict[label])\n",
        "            # if label == 'not-Tamil': labels.append(1)\n",
        "            # else: labels.append(0)\n",
        "            self.count_dic[labels[-1]] = self.count_dic.get(labels[-1], 0) + 1\n",
        "        inputs = self.tokenize(sentences)\n",
        "\n",
        "        return inputs, torch.Tensor(labels)\n",
        "\n",
        "    def process_val_data(self, data, label_data):\n",
        "        sentences, labels = [], []\n",
        "        print(len(data))\n",
        "        for idx in range(len(data)):\n",
        "            # pdb.set_trace()\n",
        "            line = data[idx]\n",
        "            sentence = line.strip().split('\\t')\n",
        "            # sentence = line.strip().split(',')\n",
        "            sentence = sentence[1:]\n",
        "            label = label_data[idx].strip().split(',')\n",
        "            label = label[1:]\n",
        "            label = label[0]\n",
        "            # if label == 'not-Kannada': print(sentence)\n",
        "            if label not in self.label_dict:\n",
        "                self.label_dict[label] = len(self.label_dict)\n",
        "            sentence = p.clean(' '.join(sentence)).replace('#','')\n",
        "            emoji_dict = demoji.findall(sentence)\n",
        "            if len(emoji_dict): \n",
        "                for emoji, text in emoji_dict.items():\n",
        "                    sentence = sentence.replace(emoji, ' '+text+' ')\n",
        "                    sentence = ' '.join(sentence.split())\n",
        "            sentences.append(sentence)\n",
        "            labels.append(self.label_dict[label])\n",
        "            # if label == 'not-Tamil': labels.append(1)\n",
        "            # else: labels.append(0)\n",
        "            self.count_dic[labels[-1]] = self.count_dic.get(labels[-1], 0) + 1\n",
        "        inputs = self.tokenize(sentences)\n",
        "\n",
        "        return inputs, torch.Tensor(labels)\n",
        "    \n",
        "    def get_dataloader(self, inputs, labels, train = True):\n",
        "        data = TensorDataset(inputs['input_ids'], inputs['attention_masks'], labels)\n",
        "        if train:\n",
        "            sampler = RandomSampler(data)\n",
        "        else:\n",
        "            sampler = SequentialSampler(data)\n",
        "        return DataLoader(data, sampler=sampler, batch_size=self.batch_size)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pFzad1aMOo6b"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qZyhM7_3gAL9",
        "outputId": "16e07fcc-485c-4bba-f86c-014d04ce864f"
      },
      "source": [
        "!ls"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " Datasets    'RNN Models'\t\t     Transformers_Inference.ipynb\n",
            "'ML Models'   Transformer_Architecture.jpg   xlm-roberta-base\n",
            " README.md   'Transformer Models'\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QsdOYKOtf9SK"
      },
      "source": [
        "with open('/content/gdrive/MyDrive/Project/Dravidian-Offensive-Language-Identification/Datasets/Turkish/offenseval-tr-training-v1.tsv', 'r') as f:\n",
        "    train_data = f.readlines()\n",
        "with open('/content/gdrive/MyDrive/Project/Dravidian-Offensive-Language-Identification/Datasets/Turkish/offenseval-tr-testset-v1.tsv', 'r') as f:\n",
        "    val_data = f.readlines()\n",
        "with open('/content/gdrive/MyDrive/Project/Dravidian-Offensive-Language-Identification/Datasets/Turkish/offenseval-tr-labela-v1.tsv', 'r') as f:\n",
        "    label_data = f.readlines()    "
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V__Xb3n_PGH1",
        "outputId": "ce63a475-6166-4420-9da3-516df28041e5"
      },
      "source": [
        "len(train_data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "31756"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_f1_uehzPJpP",
        "outputId": "3a8b8b9f-c13d-4978-ebe5-b2e0013282e0"
      },
      "source": [
        "train_data[:10]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[\"20948\\t@USER en güzel uyuyan insan ödülü jeon jungkook'a gidiyor...\\tNOT\\n\",\n",
              " '10134\\t@USER Mekanı cennet olsun, saygılar sayın avukatımız,iyi günler dilerim\\tNOT\\n',\n",
              " '23457\\tKızlar aranızda kas yığını beylere düşenler olduğunu görüyorum yapmayın allah aşkına\\tNOT\\n',\n",
              " '18401\\tBiraz ders çalışayım. Tembellik ve uyku düşmanımızdır.\\tNOT\\n',\n",
              " '17525\\t@USER Trezeguet yerine El Sharawy daha iyi olmaz mı\\tNOT\\n',\n",
              " '11996\\t@USER Bence de olması gerekiyor. Hatta meslek lisesine ön hazırlık olarak ortaokulda bile çıraklık tarzında eğitimler verilip temeli hazırlanmalıdır. Lise çağına gelen bir çocuk için bir çok konuda vakit geçmiş oluyor. O nedenle de meslek liselerinde istenilen performans yakalanamıyor.\\tNOT\\n',\n",
              " '38452\\tMutlu gorunumlu ama daima mutsuz olanlar burdami? Mutlu olun olmaya calisin sizi mutlu edecek seyler yapin cok dusunurseniz mutsuz olursunuz hayati akisina birakin zaten olacak birsey olacaksa birgun elbet olur siz yinede mutlu olun.\\tNOT\\n',\n",
              " '12615\\t100 liraya tras oldum arkadaşım diyo ki ne kesiyo o kurban mı kesiyo agshsn\\tNOT\\n',\n",
              " '13520\\t@USER @USER @USER Reis bu ülkenin Devlet Başkanı ve Ak parti genel başkanı saygı duyacaksınız hergele\\tOFF\\n',\n",
              " '45562\\tVar olan, ancak düşünüldüğü kadarıyla vardır. Hannah Arendt\\tNOT\\n']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aUnOycLAPQZD",
        "outputId": "ed9e69dd-fd62-4ece-a4c1-82b386a82dd0"
      },
      "source": [
        "val_data[:10]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['41993\\t@USER Sayın başkanım bu şekilde devam inşallah👏\\n',\n",
              " '23000\\tHerkes gevşekliği kadar duyar kasıyor,hayat böyle!\\n',\n",
              " '42478\\tOlgun ilişkisi olan arkadaş size en güzel hediye .\\n',\n",
              " '21748\\t@USER @USER Burada atıp tutacağına o kötü koşullarda 3 kuruşa sen çalışsana yiyorsa.\\n',\n",
              " '13607\\t@USER İşte o onur dediğin sende yok sorun o işte\\n',\n",
              " '28439\\t@USER @USER Düşük satsa sahip hacimli projelerin hepsinde  farazi trend çizgilerinin hacimli kırılışı takip edilmeli. HOT yakın dönemde bunu yaptı ve ciddi bir momentum kazandı. Bu kırılım sonrası nereye gideceği tahmini ise tamamen kehanet olur. Tavsiyem bir sonraki kırılımı beklemeniz.\\n',\n",
              " '14664\\t@USER Ama alırken annem odaya girdi hay aq\\n',\n",
              " '22741\\t@USER yeniden yapılanma sezon içinde kadro dışı kararları ile olmaz. bu taraftara suçlu sunmaktır. siz değil,yönetim gerekçeleri açıklamalıdır.\\n',\n",
              " '40997\\t@USER Günaydın melis hanım. Hangi yaşta olursan ol arada bir delilik yapmak insana iyi geliyor.Bence\\n',\n",
              " '15724\\t@USER Tamamen senin şanssızlığın olmuş çoğu kişiye öyle denk gelip bir daha izlemiyolar\\n']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pj162lTrPTW3",
        "outputId": "081b7fa8-74f9-4af0-de0d-bcb38ab55dcf"
      },
      "source": [
        "label_data[:10]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['41993,NOT\\n',\n",
              " '23000,NOT\\n',\n",
              " '42478,NOT\\n',\n",
              " '21748,OFF\\n',\n",
              " '13607,OFF\\n',\n",
              " '28439,NOT\\n',\n",
              " '14664,OFF\\n',\n",
              " '22741,NOT\\n',\n",
              " '40997,NOT\\n',\n",
              " '15724,NOT\\n']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SAXCxy4EPhPV",
        "outputId": "424d6859-f6cd-46a1-d723-ee01e486adb9"
      },
      "source": [
        "len(label_data)==len(val_data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198,
          "referenced_widgets": [
            "b5015904a50e4d3c891abac40ee6f2a9",
            "ee07694b59d2488880ea3643da858ff4",
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            "c2795fd2706d4c3d851f0b9f13626932",
            "e909d9671c4f4f5ea3e0b279c29fb302",
            "297ced0e32864d0ab667c427a9f3f2b6",
            "fbb9850242f341ee8704ec9cf1773d4c",
            "2cb261eb0c3a4ceba7640574da62013a",
            "9deda276cd304e1e8d94c7fce14cf89f",
            "750e01323bb64f7388d77b67053184ea",
            "3f34cdc863b2472caf0d18e4ee000529",
            "f5e6a7e4e583447d8627c88fc13e68fd",
            "d55e9937c8294926b564b041275152c2",
            "a0b8216d56ae4ff6985307ff2763cc16",
            "1ade2d9172b04ae7b9edb7fdb2404ca2",
            "ee6d02c66fa849c881edf886719f8136",
            "c010f5009ca64fc5b3b8ccf68d1f11b7",
            "34363a5be73b4025998145b71197bbfd",
            "2156d006db5c470495aae41c06e6b22e",
            "89edb7a2a3114cbba341f13ab7119d5f",
            "2bbd7f7296f74e26a584beecc45f4091",
            "d935cc293c764f12bf59e37438398ca5",
            "2aeceb9dea4c4d809717c101bd1f12b9",
            "d488cba5f3884d0e9cfc6125410abb42"
          ]
        },
        "id": "vX9lipbhPCUP",
        "outputId": "e5a2871a-a169-43fa-df91-5cdf9d02ec54"
      },
      "source": [
        "data =Dataset(train_data, val_data, label_data)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "31756\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=512.0, style=ProgressStyle(description_…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9deda276cd304e1e8d94c7fce14cf89f",
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=5069051.0, style=ProgressStyle(descript…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c010f5009ca64fc5b3b8ccf68d1f11b7",
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=9096718.0, style=ProgressStyle(descript…"
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          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "3528\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-rn4csQxmaN1",
        "outputId": "99293327-9cb0-443f-ef06-9c163ca816fa"
      },
      "source": [
        "data.train_inputs"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'attention_masks': tensor([[1, 1, 1,  ..., 0, 0, 0],\n",
              "         [1, 1, 1,  ..., 0, 0, 0],\n",
              "         [1, 1, 1,  ..., 0, 0, 0],\n",
              "         ...,\n",
              "         [1, 1, 1,  ..., 0, 0, 0],\n",
              "         [1, 1, 1,  ..., 0, 0, 0],\n",
              "         [1, 1, 1,  ..., 0, 0, 0]]),\n",
              " 'input_ids': tensor([[     0,     22,  10577,  ...,      1,      1,      1],\n",
              "         [     0,  84096,  30514,  ...,      1,      1,      1],\n",
              "         [     0, 146000,    320,  ...,      1,      1,      1],\n",
              "         ...,\n",
              "         [     0,  58117,     25,  ...,      1,      1,      1],\n",
              "         [     0,      6,  23920,  ...,      1,      1,      1],\n",
              "         [     0,    799,  99671,  ...,      1,      1,      1]])}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "32OKxadygLVF"
      },
      "source": [
        "# Save and Load Functions\n",
        "def save_metrics(save_path, epochs, model, optimizer, F1):\n",
        "\n",
        "    state_dict = {'model_state_dict': model.state_dict(),\n",
        "                  'optimizer_state_dict': optimizer.state_dict(),\n",
        "                  'epochs': epochs+1,\n",
        "                  'F1': F1}\n",
        "    \n",
        "    torch.save(state_dict, save_path)\n",
        "    print(f'Model saved to ==> {save_path}')\n",
        "\n",
        "\n",
        "def load_metrics(load_path, model, optimizer):\n",
        "    try: \n",
        "        state_dict = torch.load(load_path, map_location=device)\n",
        "        model.load_state_dict(state_dict['model_state_dict'])\n",
        "        optimizer.load_state_dict(state_dict['optimizer_state_dict'])\n",
        "    except: \n",
        "        state_dict = {}\n",
        "\n",
        "    print(f'Model loaded from <== {load_path}')\n",
        "    \n",
        "    return state_dict.get('epochs', 0), state_dict.get('F1', 0)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SIdBaGmHg5WB"
      },
      "source": [
        "class Transform(torch.nn.Module):\n",
        "    def __init__(self, D_in):\n",
        "        super(Transform, self).__init__()\n",
        "        self.embeddings = AutoModel.from_pretrained(model_path)\n",
        "        self.dropout = nn.Dropout(0.3)\n",
        "        self.final = nn.Linear(D_in*2, 2, bias = True)\n",
        "\n",
        "    def forward(self, input_ids, mask):\n",
        "        outputs = self.embeddings(input_ids, mask)\n",
        "        out = outputs.last_hidden_state\n",
        "        mean_pooling = torch.mean(out, 1)\n",
        "        max_pooling, _ = torch.max(out, 1)\n",
        "\n",
        "        embed = torch.cat((mean_pooling, max_pooling), 1)\n",
        "        y_pred = self.final(self.dropout(embed))\n",
        "        return y_pred"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I4UHBPSDg60Z"
      },
      "source": [
        "def flat_accuracy(preds, labels):\n",
        "    pred_flat = np.argmax(preds, axis=1).flatten()\n",
        "    labels_flat = labels.flatten()\n",
        "    return np.sum(pred_flat == labels_flat) / len(labels_flat)\n",
        " \n",
        "def get_predicted(preds):\n",
        "    pred_flat = np.argmax(preds, axis=1).flatten()\n",
        "    return pred_flat\n",
        " \n",
        "def evaluate(test_dataloader, model, criterion, num_val_data):\n",
        "    model.eval()\n",
        "    avg_loss = 0.0\n",
        "    num_correct = 0\n",
        "    y_preds, y_test = np.array([]), np.array([])\n",
        "\n",
        "    for batch in tqdm(test_dataloader, total=len(test_dataloader)):\n",
        "        b_input_ids, b_input_mask, b_labels = batch[0].to(device), batch[1].to(device), batch[2].to(device).long()\n",
        "        with torch.no_grad():        \n",
        "            ypred = model(b_input_ids, b_input_mask)\n",
        "        loss = criterion(ypred, b_labels)\n",
        "        num_correct += (torch.argmax(ypred, axis=1) == b_labels).sum().item()\n",
        "        ypred = ypred.cpu().numpy()\n",
        "        label_ids = b_labels.to('cpu').numpy()\n",
        "        y_preds = np.hstack((y_preds, get_predicted(ypred)))\n",
        "        y_test = np.hstack((y_test, label_ids))\n",
        "        avg_loss += loss.item()\n",
        "        \n",
        "    \n",
        "    print('Avg-Loss: {:.8f}\\tValidation Accuracy: {:.8f}'.format(avg_loss / len(test_dataloader), num_correct / num_val_data))\n",
        "\n",
        "    weighted_f1 = f1_score(y_test, y_preds, average='macro')\n",
        "    return weighted_f1, y_preds, y_test\n",
        " \n",
        "def train(training_dataloader, validation_dataloader, model, filepath, weights = None, learning_rate = 3e-5, epochs = 25, print_every = 10):\n",
        "    total_steps = len(training_dataloader) * epochs\n",
        "    no_decay = ['bias', 'LayerNorm.weight', 'LayerNorm.bias']\n",
        "    optimizer_grouped_parameters = [\n",
        "        {'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], 'weight_decay': 0.01},\n",
        "        {'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}\n",
        "    ]\n",
        "    optimizer = AdamW(optimizer_grouped_parameters, lr=learning_rate, eps = 1e-8)\n",
        "    scheduler = get_linear_schedule_with_warmup(optimizer, \n",
        "                                                num_warmup_steps = 0, # Default value in run_glue.py\n",
        "                                                num_training_steps = total_steps)\n",
        "    \n",
        "    current_epoch, best_weighted_f1 = load_metrics(filepath, model, optimizer)\n",
        "    if weights == None:\n",
        "        criterion = nn.CrossEntropyLoss()\n",
        "    else:\n",
        "        criterion = nn.CrossEntropyLoss(weight=weights)\n",
        "    \n",
        "    for epoch_i in tqdm(range(current_epoch, epochs)):\n",
        "        model.train()\n",
        "        avg_loss = 0.0\n",
        "        for step, batch in tqdm(enumerate(training_dataloader), total=len(training_dataloader)):\n",
        "            b_input_ids, b_input_mask, b_labels = batch[0].to(device), batch[1].to(device), batch[2].to(device).long()\n",
        "            # pdb.set_trace()\n",
        "            outputs = model(b_input_ids, b_input_mask)\n",
        "            loss = criterion(outputs, b_labels)\n",
        " \n",
        "            # if step%print_every == 0:\n",
        "            #     print(loss.item())\n",
        " \n",
        "            optimizer.zero_grad()\n",
        "            loss.backward()\n",
        "            torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n",
        "            optimizer.step()\n",
        "            scheduler.step()\n",
        "            avg_loss += loss.item()\n",
        "        print('Epoch: {}\\tAvg-Loss: {:.8f}'.format(epoch_i, avg_loss / len(training_dataloader)))\n",
        " \n",
        "        print('### Validation Set Stats')\n",
        "        weighted_f1, ypred, ytest = evaluate(validation_dataloader, model, criterion, len(val_data))\n",
        "        print(\"  Weighted F1: {0:.2f}\".format(weighted_f1))\n",
        "        if weighted_f1 > best_weighted_f1:\n",
        "            best_weighted_f1 = weighted_f1\n",
        "            save_metrics(filepath, epoch_i, model, optimizer, weighted_f1)"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bXFNxR8iEVR1",
        "outputId": "6ddaea29-7082-4c7f-8ed0-2431fac941f8"
      },
      "source": [
        "len(val_data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3528"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bQF8G9CLg_QV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "7d9c1e3607d548afb69bbae1456b47dc",
            "624b39080407424ebd720d44801284fc",
            "d1f73b33a70d4194b9b8950ad96acfe8",
            "e95a233dfba74e5ca6485ca5e0cbf035",
            "474d2885096d4e0588652550f384f1f9",
            "7e7f731d54c8457baa4b891e8bbd6313",
            "ddc62706f2a64cd5b47846b1b9e1098d",
            "35105668489d463a9d0b34bffdfad10d"
          ]
        },
        "outputId": "f038e453-de2d-4c5d-8fb1-7fe64738f691"
      },
      "source": [
        "model = Transform(768).to(device)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7d9c1e3607d548afb69bbae1456b47dc",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1115590446.0, style=ProgressStyle(descr…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "88cmjkanh3Su",
        "outputId": "1db0121b-3e39-4773-aba6-29dabf3c2686"
      },
      "source": [
        "print(torch.__version__)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.8.1+cu101\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R3XKUVQAhAyS",
        "outputId": "f1a19ae6-5994-4ec6-f3d8-63b6e9dc4238"
      },
      "source": [
        "!pip install torch"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: torch in /usr/local/lib/python3.7/dist-packages (1.8.1+cu101)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from torch) (1.19.5)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch) (3.7.4.3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FM6Pfy8AilOQ"
      },
      "source": [
        "exit()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bbJKHYxWlFWF",
        "outputId": "cacbaed9-6eae-4914-a05e-96936d9f5502"
      },
      "source": [
        "optimizer = AdamW(model.parameters(), lr=2e-5, eps = 1e-8)\n",
        "no_decay = ['bias', 'LayerNorm.weight', 'LayerNorm.bias']\n",
        "optimizer_grouped_parameters = [\n",
        "        {'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], 'weight_decay': 0.01},\n",
        "        {'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}\n",
        "    ]\n",
        "optimizer = AdamW(optimizer_grouped_parameters, lr=3e-5, eps = 1e-8)\n",
        "load_metrics('/content/gdrive/MyDrive/Project/Dravidian-Offensive-Language-Identification/Datasets/Turkish/turkish01.pt', model, optimizer)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model loaded from <== /content/gdrive/MyDrive/Project/Dravidian-Offensive-Language-Identification/Datasets/Turkish/turkish01.pt\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2, 0.8754825682391689)"
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          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
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        },
        "id": "WYFlKVaLnjL8",
        "outputId": "f4efbbef-60c0-45f5-c870-bd3ea366e537"
      },
      "source": [
        "train(data.train_dataloader, data.val_dataloader, model, '/content/gdrive/MyDrive/Project/Dravidian-Offensive-Language-Identification/Datasets/Turkish/turkish04_macroF1.pt')"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model loaded from <== /content/gdrive/MyDrive/Project/Dravidian-Offensive-Language-Identification/Datasets/Turkish/turkish04_macroF1.pt\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:52: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "model_id": "3406324aedd545978ad7c13fce84d39d",
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          },
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        },
        {
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          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:55: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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              "model_id": "2d8a315d10e64be6b5ad07388385d97b",
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          },
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        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Epoch: 0\tAvg-Loss: 0.04203535\n",
            "### Validation Set Stats\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:16: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
            "  app.launch_new_instance()\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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          },
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        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Avg-Loss: 0.61291721\tValidation Accuracy: 0.87783447\n",
            "  Weighted F1: 0.80\n",
            "Model saved to ==> /content/gdrive/MyDrive/Project/Dravidian-Offensive-Language-Identification/Datasets/Turkish/turkish04_macroF1.pt\n"
          ],
          "name": "stdout"
        },
        {
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        },
        {
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          "text": [
            "\n",
            "Epoch: 1\tAvg-Loss: 0.04381318\n",
            "### Validation Set Stats\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
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          "text": [
            "\n",
            "Avg-Loss: 0.65701150\tValidation Accuracy: 0.87670068\n",
            "  Weighted F1: 0.80\n",
            "Model saved to ==> /content/gdrive/MyDrive/Project/Dravidian-Offensive-Language-Identification/Datasets/Turkish/turkish04_macroF1.pt\n"
          ],
          "name": "stdout"
        },
        {
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          "text": [
            "\n",
            "Epoch: 2\tAvg-Loss: 0.05483678\n",
            "### Validation Set Stats\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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          "text": [
            "\n",
            "Avg-Loss: 0.59791050\tValidation Accuracy: 0.86224490\n",
            "  Weighted F1: 0.78\n"
          ],
          "name": "stdout"
        },
        {
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          "text": [
            "\n",
            "Epoch: 3\tAvg-Loss: 0.05791055\n",
            "### Validation Set Stats\n"
          ],
          "name": "stdout"
        },
        {
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          "text": [
            "\n",
            "Avg-Loss: 0.75339498\tValidation Accuracy: 0.87244898\n",
            "  Weighted F1: 0.79\n"
          ],
          "name": "stdout"
        },
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            "\n",
            "Epoch: 4\tAvg-Loss: 0.06066962\n",
            "### Validation Set Stats\n"
          ],
          "name": "stdout"
        },
        {
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            "\n",
            "Avg-Loss: 0.69344776\tValidation Accuracy: 0.87386621\n",
            "  Weighted F1: 0.78\n"
          ],
          "name": "stdout"
        },
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            "Epoch: 5\tAvg-Loss: 0.02973967\n",
            "### Validation Set Stats\n"
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            "Avg-Loss: 0.78357799\tValidation Accuracy: 0.86876417\n",
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              "model_id": "aabf6ecd81bf4e25844278122ac99866",
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              "HBox(children=(FloatProgress(value=0.0, max=993.0), HTML(value='')))"
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          },
          "metadata": {
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        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Epoch: 6\tAvg-Loss: 0.03129435\n",
            "### Validation Set Stats\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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              "HBox(children=(FloatProgress(value=0.0, max=111.0), HTML(value='')))"
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          },
          "metadata": {
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          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Avg-Loss: 0.91678861\tValidation Accuracy: 0.87925170\n",
            "  Weighted F1: 0.79\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9eea983d2af845d7b122266b7002a84e",
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              "version_major": 2
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              "HBox(children=(FloatProgress(value=0.0, max=993.0), HTML(value='')))"
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          },
          "metadata": {
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          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Epoch: 7\tAvg-Loss: 0.03116830\n",
            "### Validation Set Stats\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "920f04e07002476dbb522330c00c24a4",
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              "HBox(children=(FloatProgress(value=0.0, max=111.0), HTML(value='')))"
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          },
          "metadata": {
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          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Avg-Loss: 0.95837651\tValidation Accuracy: 0.87159864\n",
            "  Weighted F1: 0.79\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "85dbb77dd83d471db7dfd4ec2f746ad3",
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              "HBox(children=(FloatProgress(value=0.0, max=993.0), HTML(value='')))"
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          },
          "metadata": {
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          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Epoch: 8\tAvg-Loss: 0.02983296\n",
            "### Validation Set Stats\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1a1f9665121a46ec9263d48784cd0a23",
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              "HBox(children=(FloatProgress(value=0.0, max=111.0), HTML(value='')))"
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          },
          "metadata": {
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        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Avg-Loss: 0.95956625\tValidation Accuracy: 0.87670068\n",
            "  Weighted F1: 0.78\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "89cf85584513445fbe1498980fcab9c3",
              "version_minor": 0,
              "version_major": 2
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            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=993.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-14-0de29f6e702d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_dataloader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mval_dataloader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'/content/gdrive/MyDrive/Project/Dravidian-Offensive-Language-Identification/Datasets/Turkish/turkish04_macroF1.pt'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-11-b266b3069d78>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(training_dataloader, validation_dataloader, model, filepath, weights, learning_rate, epochs, print_every)\u001b[0m\n\u001b[1;32m     63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     64\u001b[0m             \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzero_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 65\u001b[0;31m             \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     66\u001b[0m             \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclip_grad_norm_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m             \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/tensor.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m    243\u001b[0m                 \u001b[0mcreate_graph\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    244\u001b[0m                 inputs=inputs)\n\u001b[0;32m--> 245\u001b[0;31m         \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautograd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgradient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    246\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    247\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mregister_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/autograd/__init__.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m    145\u001b[0m     Variable._execution_engine.run_backward(\n\u001b[1;32m    146\u001b[0m         \u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad_tensors_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 147\u001b[0;31m         allow_unreachable=True, accumulate_grad=True)  # allow_unreachable flag\n\u001b[0m\u001b[1;32m    148\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6q6ST_FL669t"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2LMcPENf2aFz",
        "outputId": "27c1ce06-7783-4b84-d77c-95cfc3a031e9"
      },
      "source": [
        "model"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Transform(\n",
              "  (embeddings): XLMRobertaModel(\n",
              "    (embeddings): RobertaEmbeddings(\n",
              "      (word_embeddings): Embedding(250002, 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): RobertaEncoder(\n",
              "      (layer): ModuleList(\n",
              "        (0): RobertaLayer(\n",
              "          (attention): RobertaAttention(\n",
              "            (self): RobertaSelfAttention(\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): RobertaSelfOutput(\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): RobertaIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): RobertaOutput(\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): RobertaLayer(\n",
              "          (attention): RobertaAttention(\n",
              "            (self): RobertaSelfAttention(\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): RobertaSelfOutput(\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): RobertaIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): RobertaOutput(\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): RobertaLayer(\n",
              "          (attention): RobertaAttention(\n",
              "            (self): RobertaSelfAttention(\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): RobertaSelfOutput(\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): RobertaIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): RobertaOutput(\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): RobertaLayer(\n",
              "          (attention): RobertaAttention(\n",
              "            (self): RobertaSelfAttention(\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): RobertaSelfOutput(\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): RobertaIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): RobertaOutput(\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): RobertaLayer(\n",
              "          (attention): RobertaAttention(\n",
              "            (self): RobertaSelfAttention(\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): RobertaSelfOutput(\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): RobertaIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): RobertaOutput(\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): RobertaLayer(\n",
              "          (attention): RobertaAttention(\n",
              "            (self): RobertaSelfAttention(\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): RobertaSelfOutput(\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): RobertaIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): RobertaOutput(\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): RobertaLayer(\n",
              "          (attention): RobertaAttention(\n",
              "            (self): RobertaSelfAttention(\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): RobertaSelfOutput(\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): RobertaIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): RobertaOutput(\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): RobertaLayer(\n",
              "          (attention): RobertaAttention(\n",
              "            (self): RobertaSelfAttention(\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): RobertaSelfOutput(\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): RobertaIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): RobertaOutput(\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): RobertaLayer(\n",
              "          (attention): RobertaAttention(\n",
              "            (self): RobertaSelfAttention(\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): RobertaSelfOutput(\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): RobertaIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): RobertaOutput(\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): RobertaLayer(\n",
              "          (attention): RobertaAttention(\n",
              "            (self): RobertaSelfAttention(\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): RobertaSelfOutput(\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): RobertaIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): RobertaOutput(\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): RobertaLayer(\n",
              "          (attention): RobertaAttention(\n",
              "            (self): RobertaSelfAttention(\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): RobertaSelfOutput(\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): RobertaIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): RobertaOutput(\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): RobertaLayer(\n",
              "          (attention): RobertaAttention(\n",
              "            (self): RobertaSelfAttention(\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): RobertaSelfOutput(\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): RobertaIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): RobertaOutput(\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): RobertaPooler(\n",
              "      (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "      (activation): Tanh()\n",
              "    )\n",
              "  )\n",
              "  (dropout): Dropout(p=0.3, inplace=False)\n",
              "  (final): Linear(in_features=1536, out_features=2, bias=True)\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O_LazDPb2UIf"
      },
      "source": [
        "# load_metrics('detect_lang_tamil_mbert_1.pt', model, optimizer)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 171,
          "referenced_widgets": [
            "afe03446c0e7403dbfca985f0b7d1c01",
            "ec4d51b980634cc89040e5b726c16a40",
            "ae0bb727d50f4ff587be0fcba42e603c",
            "a1201ee1ab3841bcbc4cfe87a1e44db5",
            "cb86cb1db1464f0c8f4acfd70c54afdd",
            "ebc844aca7f147d28fc6366663b82f18",
            "dc1e3724bd584c159f12be39845019cf",
            "a3fbe16ee5c140c495f7d0c0a5767d39"
          ]
        },
        "id": "4UyZlBwm2Yct",
        "outputId": "249f4f3e-a7a9-4106-c456-bc8f343a23bc"
      },
      "source": [
        "weighted_f1, ypred, ytest = evaluate(data.val_dataloader, model, nn.CrossEntropyLoss(), len(val_data))\n",
        "print(weighted_f1)\n",
        "from sklearn.metrics import confusion_matrix\n",
        "array = confusion_matrix(ytest, ypred)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:16: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
            "  app.launch_new_instance()\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "afe03446c0e7403dbfca985f0b7d1c01",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=111.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Avg-Loss: 0.31872864\tValidation Accuracy: 0.87783447\n",
            "0.8040393790420306\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "-g6qfWVy2e3I",
        "outputId": "1b57986f-ed87-4f0f-cf73-5f2718d1592b"
      },
      "source": [
        "import seaborn as sn\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "df_cm = pd.DataFrame(array, range(2), range(2))\n",
        "# plt.figure(figsize=(10,7))\n",
        "sn.set(font_scale=1.4) # for label size\n",
        "sn.heatmap(df_cm, annot=True, annot_kws={\"size\": 16}) # font size\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zTP8wFu82rqO"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}