AmazonML_mBERT.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "AmazonML2.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wu0cKY6Wl4GB",
        "outputId": "9bc143aa-fd8d-4a65-addb-0a4213b30426"
      },
      "source": [
        "!pip install transformers"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: transformers in /usr/local/lib/python3.7/dist-packages (4.9.1)\n",
            "Requirement already satisfied: huggingface-hub==0.0.12 in /usr/local/lib/python3.7/dist-packages (from transformers) (0.0.12)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers) (4.41.1)\n",
            "Requirement already satisfied: tokenizers<0.11,>=0.10.1 in /usr/local/lib/python3.7/dist-packages (from transformers) (0.10.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers) (2.23.0)\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: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.19.5)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.7/dist-packages (from transformers) (5.4.1)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers) (4.6.1)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from transformers) (21.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers) (3.0.12)\n",
            "Requirement already satisfied: sacremoses in /usr/local/lib/python3.7/dist-packages (from transformers) (0.0.45)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from huggingface-hub==0.0.12->transformers) (3.7.4.3)\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: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers) (3.5.0)\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: 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: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2021.5.30)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.15.0)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.0.1)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (7.1.2)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GHcEhhmUl8Ei"
      },
      "source": [
        "### Necessary packages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Oue26IXml7w_"
      },
      "source": [
        "import torch \n",
        "import torch.nn as nn\n",
        "from torch.utils.data import Dataset,DataLoader,SubsetRandomSampler\n",
        "import torch.optim as optim\n",
        "\n",
        "import os\n",
        "import copy\n",
        "from collections import defaultdict\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "from pylab import rcParams\n",
        "import csv\n",
        "import time\n",
        "from tqdm import tqdm\n",
        "import random\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.metrics import confusion_matrix,classification_report,accuracy_score\n",
        "from transformers import AutoTokenizer,AutoModel,AutoModelForSequenceClassification,AdamW,get_linear_schedule_with_warmup\n",
        "\n",
        "seed_val = 42 \n",
        "np.random.seed(seed_val)\n",
        "torch.manual_seed(seed_val)\n",
        "torch.cuda.manual_seed(seed_val)\n",
        "torch.cuda.manual_seed_all(seed_val)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QhDcvnU4JaoU",
        "outputId": "8d436d6f-c1f2-4c8f-bcef-74b15dbbdcb6"
      },
      "source": [
        "if torch.cuda.is_available():\n",
        "    device = torch.device('cuda')\n",
        "    print(\"Running on gpu\",torch.cuda.get_device_name(0))\n",
        "else:\n",
        "    device = 'cpu'\n",
        "    print('No GPU found Running on cpu')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Running on gpu Tesla P100-PCIE-16GB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5rIYey22l7tU",
        "outputId": "56b5d0a0-1bb2-48db-f6cc-f6e02766423f"
      },
      "source": [
        "from google.colab import drive \n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SVWO8IGIl7q5"
      },
      "source": [
        "dataset_dir = \"/content/drive/MyDrive/AmazonMLChallenge/dataset\"\n",
        "\n",
        "train_df = pd.read_csv(dataset_dir+\"/train.csv\",escapechar=\"\\\\\",quoting=csv.QUOTE_NONE)\n",
        "test_df = pd.read_csv(dataset_dir+\"/test.csv\",escapechar=\"\\\\\",quoting=csv.QUOTE_NONE)\n",
        "sample_df = pd.read_csv(dataset_dir+\"/sample_submission.csv\",escapechar=\"\\\\\",quoting=csv.QUOTE_NONE)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bA1OlKGbxtUD",
        "outputId": "b892951d-5ec1-425e-a65c-191e8d4e4138"
      },
      "source": [
        "train_df['TITLE'].isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "71"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Us7oVGbZx7a6",
        "outputId": "0d4dec00-7ff9-433b-ba96-e73e23f66a4b"
      },
      "source": [
        "len(train_df)-len(train_df.drop_duplicates())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "101494"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RWQ2zfmEyQ2U",
        "outputId": "906fa08f-6079-4150-997d-f2e9df45ddc8"
      },
      "source": [
        "train_df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2903024, 5)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lLWud-oKyTqM",
        "outputId": "61a73a83-9e28-4f61-e069-f41d73b7efa3"
      },
      "source": [
        "train_df = train_df.drop_duplicates()\n",
        "train_df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2801530, 5)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "quB5XEQvl7oz"
      },
      "source": [
        "train_df = train_df[train_df['TITLE'].notnull()]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jKUyDnA2l7Iu",
        "outputId": "297a0b22-9a00-44a7-f0ee-ea11a774a246"
      },
      "source": [
        "train_df['TITLE'].isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L5Id6SlFTuOj",
        "outputId": "22055e63-f86b-4209-e565-2eb5976e3cdf"
      },
      "source": [
        "le = LabelEncoder()\n",
        "train_df['BROWSE_NODE_ID'] = le.fit_transform(train_df['BROWSE_NODE_ID'])\n",
        "train_df['BROWSE_NODE_ID'].max()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9918"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-4SbM_wjxgK8"
      },
      "source": [
        "sentences = train_df['TITLE'].values \n",
        "labels = train_df['BROWSE_NODE_ID'].values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7GzsJdf1yhiw",
        "outputId": "d51744a9-bad6-4ed1-9232-784153626b65"
      },
      "source": [
        "print(sentences.shape,labels.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(2801467,) (2801467,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YMrrty_Oyi9c"
      },
      "source": [
        "train_sentences,val_sentences,train_labels,val_labels = train_test_split(sentences,labels,test_size = 0.1,random_state=seed_val)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mFqwJozMy5KS",
        "outputId": "b4d040ff-1fc8-49e5-b568-9754e804886e"
      },
      "source": [
        "print(f\"No. of training sentences {len(train_sentences)}\")\n",
        "print(f\"No. of validation sentences {len(val_sentences)}\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "No. of training sentences 2521320\n",
            "No. of validation sentences 280147\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M5reelgJzgsQ",
        "outputId": "2847bc3a-939e-4805-da24-9d2f476fbd99"
      },
      "source": [
        "train_df.memory_usage(deep= True)*(1e-6)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index               22.411736\n",
              "TITLE              410.878601\n",
              "DESCRIPTION       1593.625925\n",
              "BULLET_POINTS     1398.700015\n",
              "BRAND              183.974600\n",
              "BROWSE_NODE_ID      22.411736\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ur2v7Gg00WLo"
      },
      "source": [
        "indices , cnts = np.unique(labels,return_counts=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "l8LCKQdV136Y",
        "outputId": "e4a969db-9875-4e2b-b5d0-9c8d97aa8ffa"
      },
      "source": [
        "sns.countplot(y = cnts[ (cnts >=10) & (cnts <=100)] )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fcf99413950>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pknP3ajt3GpX"
      },
      "source": [
        "model_name = 'bert-base-multilingual-cased'\n",
        "max_input_length = 128\n",
        "batch_size = 64"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aZd6b3hl3Oea"
      },
      "source": [
        "### Data Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2i-ntoUF2I7X"
      },
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(model_name)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MCg52pkq3Km1",
        "outputId": "fb072914-24a8-4253-b9e5-c913ebbe020f"
      },
      "source": [
        "idx = 100000\n",
        "sample_text = sentences[idx]\n",
        "tokens =tokenizer.tokenize(sample_text)\n",
        "token_ids = tokenizer.convert_tokens_to_ids(tokens)\n",
        "print('Sample text {}'.format(sample_text))\n",
        "print('Tokens {}'.format(tokens))\n",
        "print('Token IDS {}'.format(token_ids))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Sample text BANBERRY DESIGNS Cat Picture - Kittens in a Basket Poster - Wall Decor for Kids Room - Black Framed Picture Artwork - 3D Holographic Wall Art (Renewed)\n",
            "Tokens ['BA', '##NB', '##ER', '##R', '##Y', 'DE', '##SI', '##G', '##NS', 'Cat', 'Picture', '-', 'Kit', '##tens', 'in', 'a', 'Basket', 'Post', '##er', '-', 'Wall', 'Dec', '##or', 'for', 'Kids', 'Room', '-', 'Black', 'Fram', '##ed', 'Picture', 'Art', '##work', '-', '3D', 'Ho', '##log', '##raphic', 'Wall', 'Art', '(', 'Rene', '##wed', ')']\n",
            "Token IDS [39999, 53244, 24093, 11273, 14703, 25067, 44802, 11447, 69196, 25059, 29582, 118, 45301, 58513, 10106, 169, 51976, 14539, 10165, 118, 19523, 46147, 10667, 10142, 28151, 26512, 118, 11750, 68063, 10336, 29582, 11397, 37382, 118, 17280, 20220, 18007, 48532, 19523, 11397, 113, 91790, 48854, 114]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qcgFU6Aj5hao",
        "outputId": "b29caa95-9229-4bcc-8879-f55ddb9614ed"
      },
      "source": [
        "tokenizer.sep_token,tokenizer.sep_token_id"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('[SEP]', 102)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H_qmWHfS6KW6",
        "outputId": "1e5bef35-28e2-46b7-b805-1901ae0596a4"
      },
      "source": [
        "tokenizer.cls_token,tokenizer.cls_token_id"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('[CLS]', 101)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MzvFCZNT6ONQ",
        "outputId": "9cb9a641-cffd-49e6-d10c-3ccc55ac02d0"
      },
      "source": [
        "tokenizer.pad_token,tokenizer.pad_token_id"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('[PAD]', 0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x_P7s9I96RQ_",
        "outputId": "430260ee-7b88-4400-901a-ff16c205e642"
      },
      "source": [
        "tokenizer.unk_token,tokenizer.unk_token_id"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('[UNK]', 100)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yGWRjGfb6ma4",
        "outputId": "ea9053d9-8cd1-40ad-a7f9-da48e08fd6a9"
      },
      "source": [
        "encoding = tokenizer.encode_plus(\n",
        "    sample_text,\n",
        "    max_length = max_input_length,\n",
        "    add_special_tokens = True,\n",
        "    pad_to_max_length=True,\n",
        "    return_attention_mask = True,\n",
        "    return_token_type_ids = False,\n",
        "    return_tensors = 'pt'\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=True` to explicitly truncate examples to max length. Defaulting to 'longest_first' truncation strategy. If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy more precisely by providing a specific strategy to `truncation`.\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2190: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  FutureWarning,\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gu9om6It7rtU",
        "outputId": "615dbb37-aced-455a-dda5-54b461da26b5"
      },
      "source": [
        "encoding"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': tensor([[  101, 39999, 53244, 24093, 11273, 14703, 25067, 44802, 11447, 69196,\n",
              "         25059, 29582,   118, 45301, 58513, 10106,   169, 51976, 14539, 10165,\n",
              "           118, 19523, 46147, 10667, 10142, 28151, 26512,   118, 11750, 68063,\n",
              "         10336, 29582, 11397, 37382,   118, 17280, 20220, 18007, 48532, 19523,\n",
              "         11397,   113, 91790, 48854,   114,   102,     0,     0,     0,     0,\n",
              "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
              "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
              "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
              "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
              "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
              "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
              "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
              "             0,     0,     0,     0,     0,     0,     0,     0]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,\n",
              "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "         0, 0, 0, 0, 0, 0, 0, 0]])}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IxXhFChq7suv",
        "outputId": "ec3f9765-f86c-4698-d615-9f5869ebc38d"
      },
      "source": [
        "encoding.keys()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['input_ids', 'attention_mask'])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xdvBrtuoRM8V",
        "outputId": "8037202b-506c-44c3-84fc-aa5283ae3a29"
      },
      "source": [
        "base_model = AutoModel.from_pretrained(model_name)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at bert-base-multilingual-cased were not used when initializing BertModel: ['cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.decoder.weight', 'cls.seq_relationship.weight', 'cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.bias']\n",
            "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rpiW_ZXORSIs",
        "outputId": "44736522-4fc1-46ec-a119-1c97188bc8c9"
      },
      "source": [
        "base_model(**encoding)['pooler_output']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              "         -6.1881e-01,  4.7095e-01, -1.7490e-01, -8.1941e-01, -1.8013e-01,\n",
              "          9.4538e-01,  1.7615e-01,  4.0728e-01,  3.0802e-01,  4.6182e-01,\n",
              "          2.8547e-01, -2.5268e-01,  2.6570e-01,  7.6445e-01,  1.1954e-01,\n",
              "          2.8118e-01,  2.9408e-01,  1.8466e-01, -1.9064e-01, -1.7126e-01,\n",
              "          9.9977e-01,  9.9976e-01,  2.0410e-01,  3.8496e-01, -1.7960e-01,\n",
              "         -3.3419e-01, -1.1375e-01,  3.8988e-01,  1.6993e-01,  3.4064e-01,\n",
              "         -1.6722e-01,  1.0988e-01, -3.2467e-01, -1.7432e-01, -1.0951e-01,\n",
              "         -1.2498e-01, -2.2251e-01,  1.2111e-01, -4.9070e-01,  3.9927e-01,\n",
              "          3.9976e-01,  7.9467e-02,  5.0262e-01,  1.4410e-01,  2.4170e-01,\n",
              "          9.2791e-04, -1.7968e-01,  3.9422e-01, -3.8138e-01, -2.7710e-01,\n",
              "         -2.7302e-01,  1.9866e-01, -9.9970e-01, -2.0178e-01, -1.2529e-01,\n",
              "         -2.2596e-01,  4.2483e-01,  1.5879e-01,  3.0664e-01, -3.7451e-01,\n",
              "         -2.1290e-01, -1.8121e-01,  2.8213e-01,  1.9080e-01,  2.5455e-01,\n",
              "         -2.8100e-01, -2.1505e-01,  3.3597e-01, -4.4709e-01,  9.3509e-02,\n",
              "         -2.6012e-01, -3.3194e-01, -6.3033e-01, -8.1921e-02, -3.7618e-01,\n",
              "          3.1011e-01, -3.4171e-01, -2.9633e-01,  2.6659e-01,  3.6808e-01,\n",
              "          2.2573e-01, -4.5782e-01,  3.9615e-01, -2.9358e-01,  1.8579e-01,\n",
              "          3.7096e-01,  3.6310e-01,  2.0527e-01, -1.5081e-01, -2.8271e-01,\n",
              "         -7.3023e-02, -1.9889e-01, -1.8293e-01,  4.2604e-01, -2.0414e-01,\n",
              "          2.7813e-01, -3.3783e-01,  1.3315e-01, -2.8312e-01,  1.4336e-01,\n",
              "          4.9208e-01,  3.4509e-01, -1.5483e-01,  3.7303e-01,  3.7251e-01,\n",
              "         -1.3209e-01,  3.7916e-01,  9.1304e-02, -1.7313e-01, -1.5685e-01,\n",
              "          9.9979e-01,  4.4335e-01,  1.9019e-01,  3.6279e-01, -1.4445e-01,\n",
              "          3.6601e-01,  1.7784e-01,  3.7684e-01, -3.3215e-01,  8.3262e-01,\n",
              "         -2.6622e-01,  3.1641e-01,  1.7257e-01,  5.1576e-01,  4.6803e-02,\n",
              "          2.5054e-01,  3.2290e-01,  7.5788e-01,  2.9854e-01,  2.2950e-01,\n",
              "          2.3500e-01,  2.4497e-01,  2.7758e-01,  2.6384e-01,  1.6615e-01,\n",
              "          4.3090e-01,  3.1220e-01, -1.8535e-01,  2.3240e-01, -1.7566e-01,\n",
              "         -2.8547e-01, -1.8211e-01, -1.3960e-01, -2.4390e-01,  1.3869e-01,\n",
              "         -1.9138e-01, -2.4036e-01, -2.0446e-01,  2.4514e-01, -3.0530e-01,\n",
              "          2.2790e-01, -1.7792e-01, -4.2000e-01,  5.7914e-01, -3.6687e-01,\n",
              "          2.6532e-01, -1.7646e-01,  5.9792e-02, -7.7039e-01,  6.9743e-02,\n",
              "         -1.5070e-01, -4.0666e-01, -2.7295e-01, -5.1093e-01,  2.3066e-01,\n",
              "          2.7569e-01, -6.8903e-02,  3.0470e-01, -2.9399e-01,  2.5747e-01,\n",
              "         -3.2078e-01, -2.1660e-01,  2.5739e-01, -9.9977e-01,  5.8933e-02,\n",
              "          2.6370e-01, -2.6231e-01,  1.7955e-01,  5.3310e-02,  1.9541e-01,\n",
              "          2.2689e-01, -4.4571e-01, -1.6301e-01, -2.6279e-01,  2.8013e-01,\n",
              "         -3.8523e-01,  3.1620e-02,  3.1581e-01, -3.9780e-01, -2.4709e-01,\n",
              "          1.1563e-02, -1.5850e-01,  3.2052e-01,  3.0278e-01, -1.4553e-01,\n",
              "          3.2182e-01, -2.5519e-01,  1.7120e-01, -1.6764e-01,  3.0437e-01,\n",
              "         -2.9663e-01, -2.1668e-01,  2.6822e-01, -3.4280e-01, -2.7483e-01,\n",
              "         -2.1463e-01,  1.1852e-01, -3.1846e-01,  2.0869e-01,  2.8265e-01,\n",
              "         -1.8713e-01,  2.3160e-01, -1.8654e-01,  2.8227e-01, -2.1581e-01,\n",
              "          2.5264e-01, -7.8039e-01, -4.9093e-01, -4.2850e-01, -2.7049e-01,\n",
              "          2.2581e-01,  4.0032e-01,  1.2927e-01,  2.2205e-01, -1.3598e-01,\n",
              "          2.4641e-01, -3.2547e-01,  1.2309e-01,  2.0697e-01, -5.7548e-02,\n",
              "          1.8304e-01, -2.0606e-01,  3.0494e-01, -3.7398e-01,  8.2780e-02,\n",
              "         -9.8834e-01, -2.0815e-01,  1.7693e-01,  2.1900e-01,  3.5717e-01,\n",
              "         -1.7051e-01, -2.6621e-01, -3.0386e-01, -2.8654e-01,  3.5121e-01,\n",
              "          1.8069e-01,  3.9309e-01,  1.1618e-02,  2.2608e-01, -1.8023e-01,\n",
              "         -2.2016e-01,  8.0214e-01, -3.1068e-01,  1.7759e-01,  4.3339e-01,\n",
              "          3.0960e-01,  8.6186e-01,  4.7097e-01,  2.8318e-01,  2.0643e-01,\n",
              "         -2.1800e-01,  3.5201e-01,  2.5075e-01]], grad_fn=<TanhBackward>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CbDzGp5o8e2v"
      },
      "source": [
        "## Choosing token length"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pl1hW5vd7z51"
      },
      "source": [
        "# token_lens = []\n",
        "# for txt in sentences:\n",
        "#     tokens = tokenizer.encode(txt,max_length=512)\n",
        "#     token_lens.append(len(tokens))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U-zSVzE18xzF"
      },
      "source": [
        "# sns.displot(token_lens)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PZqbay3_81xn"
      },
      "source": [
        "class AmazonDataset(Dataset):\n",
        "\n",
        "  def __init__(self, sentences, labels, tokenizer, max_length,with_labels=True):\n",
        "    self.sentences = sentences\n",
        "    self.labels = labels\n",
        "    self.tokenizer = tokenizer\n",
        "    self.max_length = max_length\n",
        "    self.with_labels = with_labels\n",
        "  \n",
        "  def __len__(self):\n",
        "    return len(self.sentences)\n",
        "  \n",
        "  def __getitem__(self, idx):\n",
        "    sentence = str(self.sentences[idx])\n",
        "    encoding = self.tokenizer.encode_plus(\n",
        "      sentence,\n",
        "      add_special_tokens=True,\n",
        "      max_length=self.max_length,\n",
        "      return_token_type_ids=False,\n",
        "      pad_to_max_length=True,\n",
        "      return_attention_mask=True,\n",
        "      return_tensors='pt',\n",
        "    )\n",
        "\n",
        "    if self.with_labels:\n",
        "        \n",
        "        label = self.labels[idx]\n",
        "\n",
        "        return {\n",
        "            'sentence': sentence,\n",
        "            'input_ids': encoding['input_ids'].flatten(),\n",
        "            'attention_mask': encoding['attention_mask'].flatten(),\n",
        "            'labels': torch.tensor(label, dtype=torch.long)\n",
        "        }\n",
        "    else:\n",
        "        return {\n",
        "            'sentence': sentence,\n",
        "            'input_ids': encoding['input_ids'].flatten(),\n",
        "            'attention_mask': encoding['attention_mask'].flatten(),\n",
        "        }"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P9NnNAOs_VXd"
      },
      "source": [
        "def create_data_loaders(sentences,labels,tokenizer,max_input_length,batch_size,with_labels):\n",
        "    ds = AmazonDataset(\n",
        "        sentences =sentences,\n",
        "        labels=labels,\n",
        "        tokenizer=tokenizer,\n",
        "        max_length=max_input_length,\n",
        "        with_labels = with_labels\n",
        "    )\n",
        "\n",
        "    return DataLoader(\n",
        "        ds,\n",
        "        batch_size=batch_size\n",
        "    )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hyta-on7CL5j"
      },
      "source": [
        "train_loader = create_data_loaders(\n",
        "    train_sentences,\n",
        "    train_labels,\n",
        "    tokenizer,\n",
        "    max_input_length=max_input_length,\n",
        "    batch_size=batch_size,\n",
        "    with_labels = True\n",
        ")\n",
        "\n",
        "val_loader = create_data_loaders(\n",
        "    val_sentences,\n",
        "    val_labels,\n",
        "    tokenizer,\n",
        "    max_input_length=max_input_length,\n",
        "    batch_size=batch_size,\n",
        "    with_labels = True\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7t9mcWUOId8b"
      },
      "source": [
        "class AmazonClassifier(nn.Module):\n",
        "\n",
        "  def __init__(self,base_model_name, n_classes):\n",
        "    super(AmazonClassifier, self).__init__()\n",
        "    self.base_model = AutoModel.from_pretrained(base_model_name)\n",
        "    self.drop = nn.Dropout(p=0.3)\n",
        "    self.out = nn.Linear(self.base_model.config.hidden_size, n_classes)\n",
        "  \n",
        "  def forward(self, input_ids, attention_mask):\n",
        "    pooled_output = self.base_model(\n",
        "      input_ids=input_ids,\n",
        "      attention_mask=attention_mask\n",
        "    )['pooler_output']\n",
        "    output = self.drop(pooled_output)\n",
        "    return self.out(output)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zXXmbmlAI6Bb",
        "outputId": "d1cb7735-ec71-4e20-ee4b-93da422e0caf"
      },
      "source": [
        "model = AmazonClassifier(base_model_name=model_name,n_classes=len(np.unique(labels)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at bert-base-multilingual-cased were not used when initializing BertModel: ['cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.decoder.weight', 'cls.seq_relationship.weight', 'cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.bias']\n",
            "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QspEPMeJeGOJ",
        "outputId": "d77213cd-0803-4a76-e3fd-655323c5bd41"
      },
      "source": [
        "model.load_state_dict(torch.load('/content/drive/MyDrive/AmazonMLChallenge/model_bert_2.pth'))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<All keys matched successfully>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pw0Pw3VcJD7c",
        "outputId": "0d8ae882-528d-421d-e679-c450a65e3891"
      },
      "source": [
        "model.to(device)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AmazonClassifier(\n",
              "  (base_model): BertModel(\n",
              "    (embeddings): BertEmbeddings(\n",
              "      (word_embeddings): Embedding(119547, 768, padding_idx=0)\n",
              "      (position_embeddings): Embedding(512, 768)\n",
              "      (token_type_embeddings): Embedding(2, 768)\n",
              "      (LayerNorm): LayerNorm((768,), eps=1e-12, 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-12, 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-12, 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-12, 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-12, 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-12, 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-12, 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-12, 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-12, 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-12, 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-12, 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-12, 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-12, 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-12, 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-12, 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-12, 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-12, 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-12, 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-12, 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-12, 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-12, 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-12, 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-12, 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-12, 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-12, 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",
              "  (drop): Dropout(p=0.3, inplace=False)\n",
              "  (out): Linear(in_features=768, out_features=9919, bias=True)\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R46XeyKCJHaA"
      },
      "source": [
        "num_epochs = 2\n",
        "\n",
        "optimizer = AdamW(model.parameters(), lr=2e-5, correct_bias=False)\n",
        "\n",
        "total_steps = len(train_loader) * num_epochs\n",
        "\n",
        "scheduler = get_linear_schedule_with_warmup(\n",
        "  optimizer,\n",
        "  num_warmup_steps=0,\n",
        "  num_training_steps=total_steps\n",
        ")\n",
        "\n",
        "loss_fn = nn.CrossEntropyLoss().to(device)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w12Iv6sxJSLG"
      },
      "source": [
        "def train_epoch(\n",
        "  model, \n",
        "  data_loader, \n",
        "  loss_fn, \n",
        "  optimizer, \n",
        "  device, \n",
        "  scheduler, \n",
        "  n_examples\n",
        "):\n",
        "  model = model.train()\n",
        "\n",
        "  losses = []\n",
        "  correct_predictions = 0\n",
        "  \n",
        "  for i,d in enumerate(data_loader):\n",
        "    if i%100 == 0:\n",
        "        print(f\"Processing batch {i+1}/{len(data_loader)}\")\n",
        "    input_ids = d[\"input_ids\"].to(device)\n",
        "    attention_mask = d[\"attention_mask\"].to(device)\n",
        "    labels = d[\"labels\"].to(device)\n",
        "\n",
        "    outputs = model(\n",
        "      input_ids=input_ids,\n",
        "      attention_mask=attention_mask\n",
        "    )\n",
        "\n",
        "    _, preds = torch.max(outputs, dim=1)\n",
        "    loss = loss_fn(outputs, labels)\n",
        "\n",
        "    correct_predictions += torch.sum(preds == labels)\n",
        "    # print(loss)\n",
        "    losses.append(loss.item())\n",
        "\n",
        "    loss.backward()\n",
        "    nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n",
        "    optimizer.step()\n",
        "    scheduler.step()\n",
        "    optimizer.zero_grad()\n",
        "\n",
        "  return correct_predictions.double() / n_examples, np.mean(losses)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3I1wdUl1JvLw"
      },
      "source": [
        "def eval_model(model, data_loader, loss_fn, device, n_examples):\n",
        "  model = model.eval()\n",
        "\n",
        "  losses = []\n",
        "  correct_predictions = 0\n",
        "\n",
        "  with torch.no_grad():\n",
        "    for i,d in enumerate(data_loader):\n",
        "      if i%100 == 0:\n",
        "          print(f\"Processing batch {i+1}/{len(data_loader)}\")\n",
        "      input_ids = d[\"input_ids\"].to(device)\n",
        "      attention_mask = d[\"attention_mask\"].to(device)\n",
        "      labels = d[\"labels\"].to(device)\n",
        "\n",
        "      outputs = model(\n",
        "        input_ids=input_ids,\n",
        "        attention_mask=attention_mask\n",
        "      )\n",
        "      _, preds = torch.max(outputs, dim=1)\n",
        "\n",
        "      loss = loss_fn(outputs, labels)\n",
        "\n",
        "      correct_predictions += torch.sum(preds == labels)\n",
        "      losses.append(loss.item())\n",
        "\n",
        "  return correct_predictions.double() / n_examples, np.mean(losses)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "BhZqTN1TJxl3",
        "outputId": "1ea87143-d9aa-48e0-cfc0-b8ebcab01bf9"
      },
      "source": [
        "%%time\n",
        "\n",
        "history = defaultdict(list)\n",
        "best_accuracy = 0\n",
        "\n",
        "for epoch in tqdm(range(num_epochs)):\n",
        "\n",
        "  print(f'Epoch {epoch + 1}/{num_epochs}')\n",
        "  print('-' * 10)\n",
        "\n",
        "  train_acc, train_loss = train_epoch(\n",
        "    model,\n",
        "    train_loader,    \n",
        "    loss_fn, \n",
        "    optimizer, \n",
        "    device, \n",
        "    scheduler, \n",
        "    len(train_labels)\n",
        "  )\n",
        "\n",
        "  print(f'Train loss {train_loss} accuracy {train_acc}')\n",
        "\n",
        "  val_acc, val_loss = eval_model(\n",
        "    model,\n",
        "    val_loader,\n",
        "    loss_fn, \n",
        "    device, \n",
        "    len(val_labels)\n",
        "  )\n",
        "\n",
        "  print(f'Val   loss {val_loss} accuracy {val_acc}')\n",
        "  print()\n",
        "\n",
        "  history['train_acc'].append(train_acc)\n",
        "  history['train_loss'].append(train_loss)\n",
        "  history['val_acc'].append(val_acc)\n",
        "  history['val_loss'].append(val_loss)\n",
        "\n",
        "  if val_acc > best_accuracy:\n",
        "    torch.save(model.state_dict(), 'best_model_state.bin')\n",
        "    best_accuracy = val_acc"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\r  0%|          | 0/2 [00:00<?, ?it/s]/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2190: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  FutureWarning,\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
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          ],
          "name": "stdout"
        },
        {
          "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-44-43b39aa91701>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_cell_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'time'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m''\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"\\nhistory = defaultdict(list)\\nbest_accuracy = 0\\n\\nfor epoch in tqdm(range(num_epochs)):\\n\\n  print(f'Epoch {epoch + 1}/{num_epochs}')\\n  print('-' * 10)\\n\\n  train_acc, train_loss = train_epoch(\\n    model,\\n    train_loader,    \\n    loss_fn, \\n    optimizer, \\n    device, \\n    scheduler, \\n    len(train_labels)\\n  )\\n\\n  print(f'Train loss {train_loss} accuracy {train_acc}')\\n\\n  val_acc, val_loss = eval_model(\\n    model,\\n    val_loader,\\n    loss_fn, \\n    device, \\n    len(val_labels)\\n  )\\n\\n  print(f'Val   loss {val_loss} accuracy {val_acc}')\\n  print()\\n\\n  history['train_acc'].append(train_acc)\\n  history['train_loss'].append(train_loss)\\n  history['val_acc'].append(val_acc)\\n  history['val_loss'].append(val_loss)\\n\\n  if val_acc > best_accuracy:\\n    torch.save(model.state_dict(), 'best_model_state.bin')\\n    best_accuracy = val_acc\"\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/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mrun_cell_magic\u001b[0;34m(self, magic_name, line, cell)\u001b[0m\n\u001b[1;32m   2115\u001b[0m             \u001b[0mmagic_arg_s\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvar_expand\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstack_depth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2116\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2117\u001b[0;31m                 \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmagic_arg_s\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcell\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   2118\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2119\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<decorator-gen-53>\u001b[0m in \u001b[0;36mtime\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/IPython/core/magic.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(f, *a, **k)\u001b[0m\n\u001b[1;32m    186\u001b[0m     \u001b[0;31m# but it's overkill for just that one bit of state.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    187\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mmagic_deco\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\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--> 188\u001b[0;31m         \u001b[0mcall\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\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    189\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    190\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\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/IPython/core/magics/execution.py\u001b[0m in \u001b[0;36mtime\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n\u001b[1;32m   1191\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1192\u001b[0m             \u001b[0mst\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclock2\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-> 1193\u001b[0;31m             \u001b[0mexec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mglob\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlocal_ns\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   1194\u001b[0m             \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclock2\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[1;32m   1195\u001b[0m             \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<timed exec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m<ipython-input-42-65c19ffcfc7d>\u001b[0m in \u001b[0;36mtrain_epoch\u001b[0;34m(model, data_loader, loss_fn, optimizer, device, scheduler, n_examples)\u001b[0m\n\u001b[1;32m     32\u001b[0m     \u001b[0mlosses\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\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[1;32m     33\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 34\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     35\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[0mmax_norm\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     36\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    253\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    254\u001b[0m                 inputs=inputs)\n\u001b[0;32m--> 255\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    256\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    257\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    147\u001b[0m     Variable._execution_engine.run_backward(\n\u001b[1;32m    148\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--> 149\u001b[0;31m         allow_unreachable=True, accumulate_grad=True)  # allow_unreachable flag\n\u001b[0m\u001b[1;32m    150\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    151\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wuJ1_SeMxiZZ",
        "outputId": "be91347a-16d4-4940-ca9e-6e698cfbc05c"
      },
      "source": [
        "val_acc, val_loss = eval_model(\n",
        "model,\n",
        "val_loader,\n",
        "loss_fn, \n",
        "device, \n",
        "len(val_labels)\n",
        ")\n",
        "\n",
        "print(f'Val   loss {val_loss} accuracy {val_acc}')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2190: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  FutureWarning,\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Processing batch 1/4378\n",
            "Processing batch 101/4378\n",
            "Processing batch 201/4378\n",
            "Processing batch 301/4378\n",
            "Processing batch 401/4378\n",
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            "Processing batch 1301/4378\n",
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            "Processing batch 1501/4378\n",
            "Processing batch 1601/4378\n",
            "Processing batch 1701/4378\n",
            "Processing batch 1801/4378\n",
            "Processing batch 1901/4378\n",
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            "Processing batch 2101/4378\n",
            "Processing batch 2201/4378\n",
            "Processing batch 2301/4378\n",
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            "Processing batch 2601/4378\n",
            "Processing batch 2701/4378\n",
            "Processing batch 2801/4378\n",
            "Processing batch 2901/4378\n",
            "Processing batch 3001/4378\n",
            "Processing batch 3101/4378\n",
            "Processing batch 3201/4378\n",
            "Processing batch 3301/4378\n",
            "Processing batch 3401/4378\n",
            "Processing batch 3501/4378\n",
            "Processing batch 3601/4378\n",
            "Processing batch 3701/4378\n",
            "Processing batch 3801/4378\n",
            "Processing batch 3901/4378\n",
            "Processing batch 4001/4378\n",
            "Processing batch 4101/4378\n",
            "Processing batch 4201/4378\n",
            "Processing batch 4301/4378\n",
            "Val   loss 1.2697275229012819 accuracy 0.7533687671115521\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "1ZiIvRraJ0eE",
        "outputId": "1d77c8b3-3e1a-44ce-e408-9798e7f591f7"
      },
      "source": [
        "plt.plot(history['train_acc'], label='train accuracy')\n",
        "plt.plot(history['val_acc'], label='validation accuracy')\n",
        "\n",
        "plt.title('Training history')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend()\n",
        "plt.ylim([0, 1]);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zcN3dAK2iurR"
      },
      "source": [
        "class AmazonDataset_test(Dataset):\n",
        "\n",
        "  def __init__(self,ids, sentences, tokenizer, max_length):\n",
        "    self.sentences = sentences\n",
        "    self.ids = ids\n",
        "    self.tokenizer = tokenizer\n",
        "    self.max_length = max_length\n",
        "  \n",
        "  def __len__(self):\n",
        "    return len(self.sentences)\n",
        "  \n",
        "  def __getitem__(self, idx):\n",
        "    sentence = str(self.sentences[idx])\n",
        "    encoding = self.tokenizer.encode_plus(\n",
        "      sentence,\n",
        "      add_special_tokens=True,\n",
        "      max_length=self.max_length,\n",
        "      return_token_type_ids=False,\n",
        "      pad_to_max_length=True,\n",
        "      return_attention_mask=True,\n",
        "      return_tensors='pt',\n",
        "    )\n",
        "\n",
        "\n",
        "    return {\n",
        "        'sentence': sentence,\n",
        "        'id':self.ids[idx],\n",
        "        'input_ids': encoding['input_ids'].flatten(),\n",
        "        'attention_mask': encoding['attention_mask'].flatten(),\n",
        "    }"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0QbZMCJRjUAh"
      },
      "source": [
        "ds_test = AmazonDataset_test(\n",
        "        sentences =test_df['TITLE'].values,\n",
        "        ids = test_df['PRODUCT_ID'].values,\n",
        "        tokenizer=tokenizer,\n",
        "        max_length=max_input_length,\n",
        "    )\n",
        "test_loader = DataLoader(\n",
        "        ds_test,\n",
        "        batch_size=batch_size\n",
        "    )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mcVYdi7sN1mj"
      },
      "source": [
        "def get_predictions(model, data_loader):\n",
        "  model = model.eval()\n",
        "  ids = []\n",
        "  sentences = []\n",
        "  predictions = []\n",
        "  prediction_probs = []\n",
        "  #real_values = []\n",
        "\n",
        "  with torch.no_grad():\n",
        "    for i,d in enumerate(data_loader):\n",
        "      if i%100 == 0:\n",
        "        print(f\"Processing batch {i+1}/{len(data_loader)}\")\n",
        "\n",
        "      sents = d[\"sentence\"]\n",
        "      input_ids = d[\"input_ids\"].to(device)\n",
        "      attention_mask = d[\"attention_mask\"].to(device)\n",
        "      id = d['id'].detach().cpu().numpy()\n",
        "    #   labels = d[\"labels\"].to(device)\n",
        "\n",
        "      outputs = model(\n",
        "        input_ids=input_ids,\n",
        "        attention_mask=attention_mask\n",
        "      )\n",
        "      _, preds = torch.max(outputs, dim=1)\n",
        "\n",
        "      probs = torch.nn.functional.softmax(outputs, dim=1)\n",
        "\n",
        "      sentences.extend(sents)\n",
        "      predictions.extend(preds)\n",
        "      prediction_probs.extend(probs)\n",
        "      ids.extend(id)\n",
        "    #   real_values.extend(labels)\n",
        "\n",
        "  predictions = torch.stack(predictions).cpu()\n",
        "  prediction_probs = torch.stack(prediction_probs).cpu()\n",
        "#   real_values = torch.stack(real_values).cpu()\n",
        "  return ids,sentences, predictions, prediction_probs,# real_values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EK_ppNJug__f",
        "outputId": "f2b910a9-8038-4030-86d6-3577c86e2935"
      },
      "source": [
        "ids,sents,predictions,prediction_probs = get_predictions(model,test_loader)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2190: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  FutureWarning,\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Processing batch 1/1731\n",
            "Processing batch 101/1731\n",
            "Processing batch 201/1731\n",
            "Processing batch 301/1731\n",
            "Processing batch 401/1731\n",
            "Processing batch 501/1731\n",
            "Processing batch 601/1731\n",
            "Processing batch 701/1731\n",
            "Processing batch 801/1731\n",
            "Processing batch 901/1731\n",
            "Processing batch 1001/1731\n",
            "Processing batch 1101/1731\n",
            "Processing batch 1201/1731\n",
            "Processing batch 1301/1731\n",
            "Processing batch 1401/1731\n",
            "Processing batch 1501/1731\n",
            "Processing batch 1601/1731\n",
            "Processing batch 1701/1731\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "FL_O00lnj-u-",
        "outputId": "5d38be97-13e2-493f-ac31-1fc8898ad1fe"
      },
      "source": [
        "df_sub = pd.DataFrame({\n",
        "    'PRODUCT_ID':ids,\n",
        "    'BROWSE_NODE_ID':le.inverse_transform(predictions)\n",
        "})\n",
        "df_sub"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PRODUCT_ID</th>\n",
              "      <th>BROWSE_NODE_ID</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1140</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>3329</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>4894</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>8915</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>110770</th>\n",
              "      <td>110771</td>\n",
              "      <td>4368</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>110771</th>\n",
              "      <td>110772</td>\n",
              "      <td>13568</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>110772</th>\n",
              "      <td>110773</td>\n",
              "      <td>13568</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>110773</th>\n",
              "      <td>110774</td>\n",
              "      <td>800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>110774</th>\n",
              "      <td>110775</td>\n",
              "      <td>3442</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>110775 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        PRODUCT_ID  BROWSE_NODE_ID\n",
              "0                1            1140\n",
              "1                2            3329\n",
              "2                3            4894\n",
              "3                4             125\n",
              "4                5            8915\n",
              "...            ...             ...\n",
              "110770      110771            4368\n",
              "110771      110772           13568\n",
              "110772      110773           13568\n",
              "110773      110774             800\n",
              "110774      110775            3442\n",
              "\n",
              "[110775 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kBQin7KEkQrH"
      },
      "source": [
        "df_sub.to_csv('/content/drive/MyDrive/AmazonMLChallenge/submission_bert3.csv',index=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TSXmC5Dkq1ru"
      },
      "source": [
        "torch.save(model.state_dict(),'/content/drive/MyDrive/AmazonMLChallenge/model_bert_3.pth')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P86Eznigrtwm"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}