applicationjinan (1).ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QSSzNwnHPNoy",
        "outputId": "61b3cc6c-0542-4305-d04c-040b212d4948"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting datasets\n",
            "  Downloading datasets-2.4.0-py3-none-any.whl (365 kB)\n",
            "\u001b[K     |████████████████████████████████| 365 kB 30.7 MB/s \n",
            "\u001b[?25hRequirement already satisfied: multiprocess in /usr/local/lib/python3.7/dist-packages (from datasets) (0.70.13)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from datasets) (21.3)\n",
            "Requirement already satisfied: tqdm>=4.62.1 in /usr/local/lib/python3.7/dist-packages (from datasets) (4.64.0)\n",
            "Requirement already satisfied: pyarrow>=6.0.0 in /usr/local/lib/python3.7/dist-packages (from datasets) (6.0.1)\n",
            "Requirement already satisfied: dill<0.3.6 in /usr/local/lib/python3.7/dist-packages (from datasets) (0.3.5.1)\n",
            "Collecting responses<0.19\n",
            "  Downloading responses-0.18.0-py3-none-any.whl (38 kB)\n",
            "Collecting xxhash\n",
            "  Downloading xxhash-3.0.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (212 kB)\n",
            "\u001b[K     |████████████████████████████████| 212 kB 73.6 MB/s \n",
            "\u001b[?25hRequirement already satisfied: aiohttp in /usr/local/lib/python3.7/dist-packages (from datasets) (3.8.1)\n",
            "Collecting fsspec[http]>=2021.11.1\n",
            "  Downloading fsspec-2022.7.1-py3-none-any.whl (141 kB)\n",
            "\u001b[K     |████████████████████████████████| 141 kB 69.2 MB/s \n",
            "\u001b[?25hRequirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from datasets) (4.12.0)\n",
            "Collecting huggingface-hub<1.0.0,>=0.1.0\n",
            "  Downloading huggingface_hub-0.8.1-py3-none-any.whl (101 kB)\n",
            "\u001b[K     |████████████████████████████████| 101 kB 12.5 MB/s \n",
            "\u001b[?25hRequirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.7/dist-packages (from datasets) (2.23.0)\n",
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            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from datasets) (1.21.6)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from huggingface-hub<1.0.0,>=0.1.0->datasets) (3.7.1)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub<1.0.0,>=0.1.0->datasets) (4.1.1)\n",
            "Collecting pyyaml>=5.1\n",
            "  Downloading PyYAML-6.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (596 kB)\n",
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            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->datasets) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->datasets) (2022.6.15)\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>=2.19.0->datasets) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->datasets) (2.10)\n",
            "Collecting urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1\n",
            "  Downloading urllib3-1.25.11-py2.py3-none-any.whl (127 kB)\n",
            "\u001b[K     |████████████████████████████████| 127 kB 73.6 MB/s \n",
            "\u001b[?25hRequirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.7/dist-packages (from aiohttp->datasets) (6.0.2)\n",
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            "Requirement already satisfied: charset-normalizer<3.0,>=2.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp->datasets) (2.1.0)\n",
            "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.7/dist-packages (from aiohttp->datasets) (4.0.2)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.7/dist-packages (from aiohttp->datasets) (1.2.0)\n",
            "Requirement already satisfied: asynctest==0.13.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp->datasets) (0.13.0)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.7/dist-packages (from aiohttp->datasets) (1.3.0)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->datasets) (3.8.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas->datasets) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas->datasets) (2022.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas->datasets) (1.15.0)\n",
            "Installing collected packages: urllib3, pyyaml, fsspec, xxhash, responses, huggingface-hub, datasets\n",
            "  Attempting uninstall: urllib3\n",
            "    Found existing installation: urllib3 1.24.3\n",
            "    Uninstalling urllib3-1.24.3:\n",
            "      Successfully uninstalled urllib3-1.24.3\n",
            "  Attempting uninstall: pyyaml\n",
            "    Found existing installation: PyYAML 3.13\n",
            "    Uninstalling PyYAML-3.13:\n",
            "      Successfully uninstalled PyYAML-3.13\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\u001b[0m\n",
            "Successfully installed datasets-2.4.0 fsspec-2022.7.1 huggingface-hub-0.8.1 pyyaml-6.0 responses-0.18.0 urllib3-1.25.11 xxhash-3.0.0\n"
          ]
        }
      ],
      "source": [
        "pip install datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lzgNImmuP58a",
        "outputId": "27f6d2f6-3f1b-46ee-bd7b-7cd891c1c669"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting transformers\n",
            "  Downloading transformers-4.21.0-py3-none-any.whl (4.7 MB)\n",
            "\u001b[K     |████████████████████████████████| 4.7 MB 30.3 MB/s \n",
            "\u001b[?25hRequirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.7/dist-packages (from transformers) (6.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers) (2.23.0)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (21.3)\n",
            "Collecting tokenizers!=0.11.3,<0.13,>=0.11.1\n",
            "  Downloading tokenizers-0.12.1-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (6.6 MB)\n",
            "\u001b[K     |████████████████████████████████| 6.6 MB 46.6 MB/s \n",
            "\u001b[?25hRequirement already satisfied: huggingface-hub<1.0,>=0.1.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (0.8.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (2022.6.2)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers) (4.64.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers) (3.7.1)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.21.6)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers) (4.12.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub<1.0,>=0.1.0->transformers) (4.1.1)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->transformers) (3.0.9)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers) (3.8.1)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2022.6.15)\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: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (1.25.11)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2.10)\n",
            "Installing collected packages: tokenizers, transformers\n",
            "Successfully installed tokenizers-0.12.1 transformers-4.21.0\n"
          ]
        }
      ],
      "source": [
        "pip install transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 232,
          "referenced_widgets": [
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          ]
        },
        "id": "QmBbstypQCDu",
        "outputId": "75c062ea-1614-4b4f-f011-da0f3feff400"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/1.85k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9ea5d2fe3c1c46b5b5bf04f2cdd97e9d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading metadata:   0%|          | 0.00/940 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "423366879f144bf5b1e47aaf16431c52"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset ethos/binary (download: 121.01 KiB, generated: 121.90 KiB, post-processed: Unknown size, total: 242.91 KiB) to /root/.cache/huggingface/datasets/ethos/binary/1.0.0/898d3d005459ee3ff80dbeec2f169c6b7ea13de31a08458193e27dec3dd9ae38...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6201612cb2ff43ce9460366484a6f549"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/50.3k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e13e6b0a6fec4fb0a8fff5d25fd4920c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extracting data files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8123bf662f8b4cff8a4ed95212abfd04"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/998 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2b9013c89dc844a9a6b0464ea3337f10"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset ethos downloaded and prepared to /root/.cache/huggingface/datasets/ethos/binary/1.0.0/898d3d005459ee3ff80dbeec2f169c6b7ea13de31a08458193e27dec3dd9ae38. Subsequent calls will reuse this data.\n"
          ]
        }
      ],
      "source": [
        "from datasets import load_dataset\n",
        "ds=load_dataset('ethos','binary', split='train')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "td2jDZxPQCEu",
        "outputId": "9d0f2b30-c9c6-4540-b031-2b3dd85044b3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset({\n",
            "    features: ['text', 'label'],\n",
            "    num_rows: 998\n",
            "})\n"
          ]
        }
      ],
      "source": [
        "print(ds)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "o_li4vbEQCIc"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "df=ds.to_pandas()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3jXIFG0iQCMr",
        "outputId": "965cc2c4-16ec-4ef5-e01e-9938e852757e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                                  text  label\n",
            "0            You should know women's sports are a joke      1\n",
            "1      You look like Sloth with deeper Down’s syndrome      1\n",
            "2    You look like Russian and speak like Indian. B...      1\n",
            "3                 Women deserve to be abused, I guess.      1\n",
            "4    Women are made for making babies and cooking d...      1\n",
            "..                                                 ...    ...\n",
            "993   From the midnight sun where the hot springs blow      0\n",
            "994                        Don't say I'm not your type      0\n",
            "995   And therefore never send to know for whom the...      0\n",
            "996                      And I can't stand another day      0\n",
            "997   All values, unless otherwise stated, are in U...      0\n",
            "\n",
            "[998 rows x 2 columns]\n"
          ]
        }
      ],
      "source": [
        "print(df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "awy_8HJXQCNq",
        "outputId": "4c549543-d21c-465f-bcd7-f0075fecdf62"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb9a0bf8b10>"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#df.groupby(['label']).size().plot.bar()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SRQCWCRijdYL",
        "outputId": "cbda31be-86d8-4cc7-f8a6-d41609fbcdb6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      1\n",
              "1      1\n",
              "2      1\n",
              "3      1\n",
              "4      1\n",
              "      ..\n",
              "993    0\n",
              "994    0\n",
              "995    0\n",
              "996    0\n",
              "997    0\n",
              "Name: label, Length: 998, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "df.label"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "7bc72dfac6c9490eb4016eecc3f3ef7b",
            "6a9517f55da349e69028d0bd84e29ea3",
            "7888952d72634eff949454217f9417b2",
            "81a6452568c649cd8295f17276fcfe0f",
            "1b875ed04e804196b62867484dff74d0",
            "159173cc503f49e5bd6e788b523b22cb",
            "28abd28101174efeb3ad8ebe853ee4c8",
            "70be4683b41a4079970c649f9870a3c8",
            "8c7f2441d57c40fea8fd0256d38f383e",
            "d5cc1159673244d2aef6fb4a1e721d2c",
            "2b7158b3e94e4db98004a491c06d2fee",
            "a955d88215224c11b85d6d04fc90974a",
            "6a67fc3dc0e14401add3dfb9cefeb58a",
            "cbdfc5a193014c32beff7aa82f33bf12",
            "2e58dff3e5f1410a9e4cd7fec64e521a",
            "fa2e2a1012a8463fb20e8a96f1ea9cc3",
            "ded5022689064b8a87c0ef9b85b6d454",
            "9b816afcbfcd4999baf56ba23df1b341",
            "fbe98a69f2424b6e886fae3c32ca28ef",
            "f178309519c6414b803202d0be575884",
            "ff20270e7fbe4dcba6688c86ef8c67e6",
            "76984e0a2f4946279a2275a32f49f356",
            "0bf4fa1fbb124c809e74eb8bc27febad",
            "4c6daffd5f3849a3979d1bbf861f7fbb",
            "9cc14975a8c945b4b27ee447646895eb",
            "ae2592e62ff241f7ba65c77ecb2f9a64",
            "aed735a85eb6438693ff733471f5d729",
            "5dfa6f1bc1154db6980445afb1bac268",
            "b705a732565f4912933d6d414651715c",
            "a3a825e38ff14597bd350140703c6da8",
            "c954c351e9d6489890af45bdefca3205",
            "47e75617a27e414a9f204c11fe7275de",
            "cc1022a4d7a34c548aa885ab258e0030"
          ]
        },
        "id": "m3nmiKvfjdlI",
        "outputId": "b67a70b1-aeaf-4e2e-853b-aaafbdc2ceb2"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading vocab.txt:   0%|          | 0.00/208k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7bc72dfac6c9490eb4016eecc3f3ef7b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading tokenizer_config.json:   0%|          | 0.00/29.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a955d88215224c11b85d6d04fc90974a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0bf4fa1fbb124c809e74eb8bc27febad"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "import torch\n",
        "import numpy as np\n",
        "from transformers import BertTokenizer\n",
        "\n",
        "\n",
        "\n",
        "tokenizer = BertTokenizer.from_pretrained('bert-base-cased')\n",
        "\n",
        "class mydataset(torch.utils.data.Dataset):\n",
        "\n",
        "  def __init__(self, df):\n",
        "    #self.df=df\n",
        "    #self.labels=df.label\n",
        "    self.labels=[x for x in df['label']]\n",
        "    #self.texts=df.text\n",
        "    self.texts=[tokenizer(x,padding='max_length', max_length = 10, \n",
        "                       truncation=True, return_tensors=\"pt\") for x in df['text']]\n",
        "\n",
        "  def __len__(self):\n",
        "     return len(self.labels)\n",
        "\n",
        "  #def __getitemm__(self, i):\n",
        "   # return self.texts[i] , self.labels[i]  \n",
        "\n",
        "  #def gettextt(self):\n",
        "    #return   self.texts\n",
        "\n",
        "  def get_batch_labels(self, idx):\n",
        "        # Fetch a batch of labels\n",
        "        return np.array(self.labels[idx])\n",
        "\n",
        "  def get_batch_texts(self, idx):\n",
        "        # Fetch a batch of inputs\n",
        "        return self.texts[idx]  \n",
        "\n",
        "  def __getitem__(self, i):\n",
        "        batch_texts = self.get_batch_texts(i)\n",
        "        batch_y = self.get_batch_labels(i)\n",
        "\n",
        "        return batch_texts, batch_y\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xu4IqZVwnuab",
        "outputId": "fe799215-415c-4a6b-e3aa-cbbaef4478eb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'input_ids': tensor([[  101,   146,  1209,  2824,  2508, 26173,  3568,   102,     0,     0]]), 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 0, 0]])}\n"
          ]
        }
      ],
      "source": [
        "tokenizer2 = BertTokenizer.from_pretrained('bert-base-cased')\n",
        "\n",
        "example_text = 'I will watch Memento tonight'\n",
        "bert_input = tokenizer2(example_text,padding='max_length', max_length = 10, \n",
        "                       truncation=True, return_tensors=\"pt\")\n",
        "\n",
        "print(bert_input)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rbpvq7NAjdor"
      },
      "outputs": [],
      "source": [
        "z=mydataset(df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N0Bt7QzZqGjy",
        "outputId": "6ca69075-7cdb-4bbb-e6ee-3912cbb547db"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0              You should know women's sports are a joke\n",
            "1        You look like Sloth with deeper Down’s syndrome\n",
            "2      You look like Russian and speak like Indian. B...\n",
            "3                   Women deserve to be abused, I guess.\n",
            "4      Women are made for making babies and cooking d...\n",
            "                             ...                        \n",
            "993     From the midnight sun where the hot springs blow\n",
            "994                          Don't say I'm not your type\n",
            "995     And therefore never send to know for whom the...\n",
            "996                        And I can't stand another day\n",
            "997     All values, unless otherwise stated, are in U...\n",
            "Name: text, Length: 998, dtype: object\n"
          ]
        }
      ],
      "source": [
        "print(df.text)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_HixdVPWH_7N",
        "outputId": "c643c5f0-25c5-44b8-c2c9-8ea6528f1e6f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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1, 1, 1, 1, 1]])}, {'input_ids': tensor([[  101,  5844,  9949, 10719,  1111,  1128,  2086,   107,  1398,   102]]), 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])}, {'input_ids': tensor([[  101, 16151, 13329,  9637,  1708, 22133,  2036, 24819,  1942,   102]]), 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])}, {'input_ids': tensor([[  101, 24107,  6548,  1112,  1518,   117,  1105,  4208,   117,   102]]), 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])}, {'input_ids': tensor([[ 101,  138, 1299,  112,  188, 2640, 1104, 2033, 4182,  102]]), 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])}, {'input_ids': tensor([[ 101,  123, 7640,  125, 7407, 1105, 1185, 4696,  176,  102]]), 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])}, {'input_ids': tensor([[  101,   112, 24883,  2195,  1122,  1228,   146,  1306,  1612,   102]]), 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])}, {'input_ids': tensor([[ 101, 2048, 1105,  151, 2240, 2293, 8481, 1189, 3702,  102]]), 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])}, {'input_ids': tensor([[  101,   146,  1198, 16445,  1294,  1128,  1631,  3008,   102,     0]]), 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 0]])}, {'input_ids': tensor([[  101,  1622,  1103,  9191,  3336,  1187,  1103,  2633, 12517,   102]]), 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])}, {'input_ids': tensor([[ 101, 1790,  112,  189, 1474,  146,  112,  182, 1136,  102]]), 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])}, {'input_ids': tensor([[ 101, 1262, 3335, 1309, 3952, 1106, 1221, 1111, 2292,  102]]), 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])}, {'input_ids': tensor([[ 101, 1262,  146, 1169,  112,  189, 2484, 1330, 1285,  102]]), 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])}, {'input_ids': tensor([[ 101, 1398, 4718,  117, 4895, 4303, 2202,  117, 1132,  102]]), 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])}]\n"
          ]
        }
      ],
      "source": [
        "print(z.texts)\n",
        "#print(z.lenn)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S5TkWArujdr0",
        "outputId": "87bfbb2e-f689-4383-c913-367ff3250eb5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "({'input_ids': tensor([[ 101, 1398, 4718,  117, 4895, 4303, 2202,  117, 1132,  102]]), 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])}, 0)\n"
          ]
        }
      ],
      "source": [
        "#print(z.df)\n",
        "print(z.__getitemm__(997))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RaENl0X4uW0f",
        "outputId": "7fa887fc-2a95-4bfa-eb3f-c01dd8a28325"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "798 100 100\n"
          ]
        }
      ],
      "source": [
        "np.random.seed(112)\n",
        "df_train, df_val, df_test = np.split(df.sample(frac=1, random_state=42), \n",
        "                                     [int(.8*len(df)), int(.9*len(df))])\n",
        "\n",
        "print(len(df_train),len(df_val), len(df_test))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dvdiowNLyk8z"
      },
      "outputs": [],
      "source": [
        "#test my own network\n",
        "from torch import nn\n",
        "import torch.nn.functional as F\n",
        "\n",
        "class net1(nn.Module):\n",
        "\n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "    self.hidd=nn.Linear(700,200)\n",
        "    self.output=nn.Linear(200,10)\n",
        "    #self.sigmoid=nn.Sigmoid()\n",
        "    #self.softmax=nn.Softmax()\n",
        "\n",
        "  def forward(self, x):\n",
        "    x=F.sigmoid(self.hidd(x))\n",
        "    #x= self.sigmoid(x)\n",
        "    x=F.softmax(self.output(x))\n",
        "    #x=self.softmax(x)\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GpdmmqTI5OWg",
        "outputId": "8415ed8b-b9a5-49fb-ca6b-1dcfa166b529"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:1960: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\n",
            "  warnings.warn(\"nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\")\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:17: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n"
          ]
        }
      ],
      "source": [
        "m=net1()\n",
        "x=torch.rand(700)\n",
        "m.forward(x)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n6oWmVBJ7XgQ",
        "outputId": "2c1d61d5-49ce-44a8-85ac-3f9300aed778"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              "        1.1618e-01, 1.3213e-01, 8.5231e-02, 9.8980e-01, 8.3274e-01, 9.3310e-01,\n",
              "        3.6933e-01, 8.4329e-02, 3.9514e-01, 3.6709e-01, 9.9229e-01, 1.0525e-01,\n",
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              "        7.2596e-01, 3.1466e-02, 2.9817e-02, 4.1332e-02, 2.2563e-01, 8.8673e-01,\n",
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              "        7.9400e-01, 6.2941e-01, 2.3219e-01, 9.9680e-01, 5.9080e-01, 4.3850e-01,\n",
              "        5.0578e-01, 9.6051e-01, 3.9114e-01, 4.3664e-02, 1.4836e-01, 3.3194e-01,\n",
              "        7.8424e-02, 4.0351e-01, 2.0744e-01, 3.2583e-01, 3.6044e-01, 4.8703e-01,\n",
              "        3.4176e-01, 3.2140e-01, 3.3874e-01, 8.1948e-02, 4.4040e-01, 3.6072e-01,\n",
              "        8.3357e-01, 4.2804e-01, 2.6362e-01, 3.4732e-01, 5.3415e-01, 8.2704e-01,\n",
              "        6.6860e-01, 3.6754e-01, 7.5471e-01, 8.8218e-01, 9.9809e-02, 2.9383e-01,\n",
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              "        5.6636e-01, 9.8980e-01, 2.8645e-01, 2.0916e-01, 5.1382e-01, 6.8433e-01,\n",
              "        6.0387e-01, 6.3023e-01, 2.3836e-01, 7.3343e-01, 8.7330e-01, 5.6772e-01,\n",
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              "        3.6175e-01, 6.2811e-01, 5.6044e-01, 1.4201e-01, 5.6855e-01, 4.8962e-01,\n",
              "        4.1030e-01, 4.5231e-01, 6.6863e-01, 8.9328e-01, 4.6766e-01, 4.6127e-01,\n",
              "        1.8506e-01, 7.8512e-01, 6.0992e-01, 8.5006e-01, 9.9388e-01, 1.2761e-01,\n",
              "        1.2240e-01, 1.6834e-02, 1.3551e-01, 1.1955e-02, 3.0452e-01, 8.9872e-01,\n",
              "        6.2934e-01, 7.5354e-01, 9.9046e-01, 7.7894e-01, 5.8085e-01, 5.1363e-02,\n",
              "        8.6578e-01, 5.1773e-01, 8.1934e-01, 1.6885e-01, 7.1334e-01, 9.0918e-01,\n",
              "        7.8082e-01, 9.1181e-01, 6.8439e-01, 2.0829e-01, 3.3843e-01, 9.3947e-01,\n",
              "        5.8262e-01, 5.4564e-01, 8.3728e-01, 6.2314e-01, 6.9468e-01, 4.8098e-01,\n",
              "        5.2547e-01, 5.9994e-01, 6.4700e-01, 1.4956e-01, 1.6075e-01, 1.8942e-01,\n",
              "        7.5590e-01, 5.8406e-01, 6.3948e-01, 4.9816e-01, 4.7282e-01, 7.4207e-01,\n",
              "        5.5033e-01, 5.7737e-01, 2.1761e-01, 5.1324e-01, 9.8065e-01, 9.5417e-01,\n",
              "        7.3995e-01, 2.4059e-01, 2.9922e-02, 7.1119e-01, 3.7336e-01, 5.2984e-01,\n",
              "        3.0946e-01, 6.7097e-01, 7.5430e-01, 2.1942e-01, 5.9176e-01, 7.4488e-01,\n",
              "        4.2163e-02, 3.5300e-01, 6.4071e-01, 2.9344e-01, 4.7309e-01, 1.1130e-01,\n",
              "        4.1599e-01, 8.8807e-02, 7.6209e-02, 9.4343e-01, 1.1435e-01, 4.3879e-02,\n",
              "        6.5682e-01, 2.9263e-02, 9.2533e-01, 3.9260e-02, 1.7794e-02, 1.2965e-02,\n",
              "        4.9586e-01, 5.2776e-01, 6.2628e-01, 4.3658e-01, 2.5820e-01, 9.4387e-01,\n",
              "        3.6722e-01, 6.7329e-02, 3.9674e-01, 5.6976e-02, 7.8467e-01, 9.9634e-02,\n",
              "        5.7026e-01, 2.1662e-01, 9.3828e-01, 6.7405e-01, 8.0360e-01, 5.6915e-01,\n",
              "        1.9190e-01, 6.3015e-01, 3.4238e-01, 2.6545e-01, 8.3125e-01, 7.5671e-01,\n",
              "        5.1607e-01, 1.3469e-01, 8.3108e-01, 4.5290e-01, 7.8267e-01, 7.3556e-01,\n",
              "        1.7594e-01, 8.8675e-01, 9.5061e-01, 6.0723e-01, 5.4321e-01, 8.1184e-02,\n",
              "        1.1492e-01, 7.2680e-01, 9.1083e-01, 1.3438e-01, 3.1899e-01, 6.7535e-01,\n",
              "        3.5248e-02, 1.6196e-01, 3.2858e-02, 4.6965e-01, 9.7970e-01, 5.3474e-01,\n",
              "        9.6623e-01, 7.2257e-01, 7.9488e-01, 2.0266e-01, 6.4856e-01, 3.8224e-01,\n",
              "        9.2342e-01, 1.4822e-01, 4.0041e-01, 7.4039e-01, 2.7946e-01, 1.1820e-01,\n",
              "        8.2598e-01, 6.8096e-01, 3.6206e-01, 9.8616e-01, 7.5510e-01, 1.8550e-01,\n",
              "        7.0999e-01, 8.2197e-01, 3.2442e-01, 4.0620e-01, 3.9628e-01, 3.4238e-01,\n",
              "        9.1607e-01, 1.3487e-01, 9.3363e-01, 3.1066e-01, 9.7473e-01, 8.4685e-01,\n",
              "        8.3451e-01, 3.2329e-01, 3.7143e-02, 1.5675e-01, 5.9878e-01, 4.6988e-01,\n",
              "        7.8211e-01, 5.1060e-01, 5.8593e-01, 2.0299e-01, 1.0022e-01, 7.3381e-01,\n",
              "        9.9470e-01, 5.4981e-01, 1.2943e-01, 4.7515e-01, 2.6933e-01, 5.8615e-01,\n",
              "        3.3436e-01, 9.1617e-01, 2.6206e-01, 9.2287e-01, 7.8427e-01, 8.2803e-01,\n",
              "        2.9109e-01, 9.3430e-01, 5.0068e-01, 3.5426e-01, 6.8402e-01, 8.7097e-01,\n",
              "        8.5606e-01, 5.5256e-01, 9.3439e-01, 7.7411e-01, 1.5591e-01, 6.2846e-01,\n",
              "        7.8393e-01, 5.5904e-01, 2.3118e-01, 7.6193e-01, 8.4720e-01, 5.6568e-01,\n",
              "        5.0263e-01, 9.7323e-01, 8.5144e-01, 1.0971e-01, 5.7024e-02, 2.8255e-02,\n",
              "        3.7701e-01, 2.2415e-01, 5.1832e-01, 3.0193e-01, 5.5406e-01, 4.7237e-01,\n",
              "        8.6471e-01, 7.4914e-01, 7.1239e-01, 8.3243e-01, 8.0763e-01, 5.3503e-01,\n",
              "        4.8987e-01, 9.4742e-04, 3.7267e-01, 7.6405e-01, 1.2736e-01, 3.9261e-01,\n",
              "        9.8933e-01, 5.9084e-01, 3.1655e-01, 1.8156e-01, 5.5639e-01, 7.2532e-01,\n",
              "        6.5511e-01, 6.1939e-01, 7.3462e-01, 6.2901e-01, 8.0894e-01, 7.8112e-01,\n",
              "        4.6174e-01, 5.9242e-02, 9.6355e-01, 9.5998e-01, 1.6539e-01, 6.3373e-01,\n",
              "        1.9908e-01, 9.2767e-01, 1.2066e-01, 2.6958e-01, 2.2202e-01, 1.6487e-01,\n",
              "        7.9331e-01, 8.1985e-01, 5.5184e-01, 7.3930e-01, 8.5413e-01, 8.3081e-01,\n",
              "        7.4694e-01, 6.0985e-01, 9.7871e-01, 6.8176e-01, 9.7294e-01, 8.6375e-01,\n",
              "        1.9868e-01, 8.7500e-01, 9.8800e-01, 6.3804e-01, 7.0002e-01, 7.6975e-01,\n",
              "        5.7059e-01, 2.7539e-01, 6.3201e-01, 2.5176e-02, 5.0652e-02, 1.9125e-01,\n",
              "        5.7388e-01, 5.8905e-01, 8.3232e-02, 7.1124e-01])"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "x"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ApYQo5E3uW3L"
      },
      "outputs": [],
      "source": [
        "from torch import nn\n",
        "from transformers import BertModel\n",
        "\n",
        "class BertClassifier(nn.Module):\n",
        "\n",
        "    def __init__(self, dropout=0.5):\n",
        "\n",
        "        super(BertClassifier, self).__init__()\n",
        "\n",
        "        self.bert = BertModel.from_pretrained('bert-base-cased')\n",
        "        self.dropout = nn.Dropout(dropout)\n",
        "        self.linear = nn.Linear(768, 5)\n",
        "        self.relu = nn.ReLU()\n",
        "\n",
        "    def forward(self, input_id, mask):\n",
        "\n",
        "        _, pooled_output = self.bert(input_ids= input_id, attention_mask=mask,return_dict=False)\n",
        "        dropout_output = self.dropout(pooled_output)\n",
        "        linear_output = self.linear(dropout_output)\n",
        "        final_layer = self.relu(linear_output)\n",
        "\n",
        "        return final_layer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_ixjit4huW7V"
      },
      "outputs": [],
      "source": [
        "#training fun\n",
        "from torch.optim import Adam\n",
        "from tqdm import tqdm\n",
        "\n",
        "def train(model, train_data, val_data, learning_rate, epochs):\n",
        "\n",
        "    train, val = mydataset(train_data), mydataset(val_data)\n",
        "\n",
        "    train_dataloader = torch.utils.data.DataLoader(train, batch_size=2, shuffle=True)\n",
        "    val_dataloader = torch.utils.data.DataLoader(val, batch_size=2)\n",
        "\n",
        "    use_cuda = torch.cuda.is_available()\n",
        "    device = torch.device(\"cuda\" if use_cuda else \"cpu\")\n",
        "\n",
        "    criterion = nn.CrossEntropyLoss()\n",
        "    optimizer = Adam(model.parameters(), lr= learning_rate)\n",
        "\n",
        "    if use_cuda:\n",
        "\n",
        "            model = model.cuda()\n",
        "            criterion = criterion.cuda()\n",
        "\n",
        "    for epoch_num in range(epochs):\n",
        "\n",
        "            total_acc_train = 0\n",
        "            total_loss_train = 0\n",
        "\n",
        "            for train_input, train_label in tqdm(train_dataloader):\n",
        "\n",
        "                train_label = train_label.to(device)\n",
        "                mask = train_input['attention_mask'].to(device)\n",
        "                input_id = train_input['input_ids'].squeeze(1).to(device)\n",
        "\n",
        "                output = model(input_id, mask)\n",
        "                \n",
        "                batch_loss = criterion(output, train_label.long())\n",
        "                total_loss_train += batch_loss.item()\n",
        "                \n",
        "                acc = (output.argmax(dim=1) == train_label).sum().item()\n",
        "                total_acc_train += acc\n",
        "\n",
        "                model.zero_grad()\n",
        "                batch_loss.backward()\n",
        "                optimizer.step()\n",
        "            \n",
        "            total_acc_val = 0\n",
        "            total_loss_val = 0\n",
        "\n",
        "            with torch.no_grad():\n",
        "\n",
        "                for val_input, val_label in val_dataloader:\n",
        "\n",
        "                    val_label = val_label.to(device)\n",
        "                    mask = val_input['attention_mask'].to(device)\n",
        "                    input_id = val_input['input_ids'].squeeze(1).to(device)\n",
        "\n",
        "                    output = model(input_id, mask)\n",
        "\n",
        "                    batch_loss = criterion(output, val_label.long())\n",
        "                    total_loss_val += batch_loss.item()\n",
        "                    \n",
        "                    acc = (output.argmax(dim=1) == val_label).sum().item()\n",
        "                    total_acc_val += acc\n",
        "            \n",
        "            print(\n",
        "                f'Epochs: {epoch_num + 1} | Train Loss: {total_loss_train / len(train_data): .3f} \\\n",
        "                | Train Accuracy: {total_acc_train / len(train_data): .3f} \\\n",
        "                | Val Loss: {total_loss_val / len(val_data): .3f} \\\n",
        "                | Val Accuracy: {total_acc_val / len(val_data): .3f}')\n",
        "                  \n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m2k1dDS1uW8J",
        "outputId": "e6e24dfc-ab38-4aec-e55c-ec472c1eb279"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at bert-base-cased were not used when initializing BertModel: ['cls.seq_relationship.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.weight', 'cls.predictions.decoder.weight', 'cls.predictions.bias']\n",
            "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "100%|██████████| 399/399 [00:26<00:00, 15.01it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epochs: 1 | Train Loss:  0.740                 | Train Accuracy:  0.427                 | Val Loss:  0.672                 | Val Accuracy:  0.570\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 399/399 [00:26<00:00, 14.85it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epochs: 2 | Train Loss:  0.623                 | Train Accuracy:  0.546                 | Val Loss:  0.526                 | Val Accuracy:  0.600\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 399/399 [00:26<00:00, 15.07it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epochs: 3 | Train Loss:  0.487                 | Train Accuracy:  0.662                 | Val Loss:  0.472                 | Val Accuracy:  0.600\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 399/399 [00:26<00:00, 14.81it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epochs: 4 | Train Loss:  0.397                 | Train Accuracy:  0.746                 | Val Loss:  0.434                 | Val Accuracy:  0.650\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 399/399 [00:26<00:00, 15.05it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epochs: 5 | Train Loss:  0.316                 | Train Accuracy:  0.861                 | Val Loss:  0.423                 | Val Accuracy:  0.610\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 399/399 [00:26<00:00, 15.09it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epochs: 6 | Train Loss:  0.254                 | Train Accuracy:  0.927                 | Val Loss:  0.441                 | Val Accuracy:  0.600\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 399/399 [00:26<00:00, 14.79it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epochs: 7 | Train Loss:  0.196                 | Train Accuracy:  0.961                 | Val Loss:  0.437                 | Val Accuracy:  0.600\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 399/399 [00:26<00:00, 15.04it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epochs: 8 | Train Loss:  0.156                 | Train Accuracy:  0.980                 | Val Loss:  0.428                 | Val Accuracy:  0.610\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 399/399 [00:26<00:00, 14.86it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epochs: 9 | Train Loss:  0.128                 | Train Accuracy:  0.991                 | Val Loss:  0.405                 | Val Accuracy:  0.670\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 399/399 [00:26<00:00, 15.05it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epochs: 10 | Train Loss:  0.104                 | Train Accuracy:  0.995                 | Val Loss:  0.401                 | Val Accuracy:  0.660\n"
          ]
        }
      ],
      "source": [
        "#training\n",
        "EPOCHS = 10\n",
        "model = BertClassifier()\n",
        "LR = 1e-6\n",
        "              \n",
        "train(model, df_train, df_val, LR, EPOCHS)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "m2NVv2s3HORr"
      },
      "outputs": [],
      "source": [
        "def evaluate(model, test_data):\n",
        "\n",
        "    test = mydataset(test_data)\n",
        "\n",
        "    test_dataloader = torch.utils.data.DataLoader(test, batch_size=2)\n",
        "\n",
        "    use_cuda = torch.cuda.is_available()\n",
        "    device = torch.device(\"cuda\" if use_cuda else \"cpu\")\n",
        "\n",
        "    if use_cuda:\n",
        "\n",
        "        model = model.cuda()\n",
        "\n",
        "    total_acc_test = 0\n",
        "    with torch.no_grad():\n",
        "\n",
        "        for test_input, test_label in test_dataloader:\n",
        "\n",
        "              test_label = test_label.to(device)\n",
        "              mask = test_input['attention_mask'].to(device)\n",
        "              input_id = test_input['input_ids'].squeeze(1).to(device)\n",
        "\n",
        "              output = model(input_id, mask)\n",
        "\n",
        "              acc = (output.argmax(dim=1) == test_label).sum().item()\n",
        "              total_acc_test += acc\n",
        "    \n",
        "    print(f'Test Accuracy: {total_acc_test / len(test_data): .3f}')\n",
        "    \n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KVc7YgXAHOYh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4c1e3168-b498-47e8-c7e3-0cb8596ea85f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Accuracy:  0.630\n"
          ]
        }
      ],
      "source": [
        "evaluate(model, df_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZtSFkxs8HOaC"
      },
      "outputs": [],
      "source": [
        ""
      ]
    }
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