violence-sixteen.ipynb

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  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "%%capture\n",
        "!pip install evaluate\n",
        "!pip install transformers"
      ],
      "metadata": {
        "id": "gYWuFHWVN4lr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import subprocess\n",
        "\n",
        "def get_gpu_name():\n",
        "    try:\n",
        "        output = subprocess.check_output(['nvidia-smi', '--query-gpu=name', '--format=csv,noheader'], encoding='utf-8')\n",
        "        gpu_name = output.strip()\n",
        "        return gpu_name\n",
        "    except (subprocess.CalledProcessError, FileNotFoundError):\n",
        "        return None\n",
        "print(get_gpu_name())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SPNJf1KvSER4",
        "outputId": "746b3a77-31ef-4920-8bfc-5b0b853ebe8c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tesla T4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p1KKqqoTRp0C",
        "outputId": "bcad8f44-970b-41e5-ade2-07beb91b4461"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xAnoGNU9MtQC"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import evaluate\n",
        "import re\n",
        "\n",
        "from enum import Enum\n",
        "from datasets import Dataset\n",
        "from transformers import AutoTokenizer, AutoModelForPreTraining\n",
        "from transformers import DataCollatorWithPadding\n",
        "from transformers import AutoModelForSequenceClassification, TrainingArguments, Trainer\n",
        "from transformers import EarlyStoppingCallback, IntervalStrategy\n",
        "\n",
        "from sklearn.metrics import f1_score, roc_auc_score, accuracy_score\n",
        "from transformers import EvalPrediction\n",
        "import torch"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"/content/drive/MyDrive/violence/violence-v1.csv\")"
      ],
      "metadata": {
        "id": "Hum-3AgWOT62"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(df))\n",
        "df = df[df['text'].notna()]\n",
        "print(len(df))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fp5wWlLWPKr5",
        "outputId": "b7350e81-d64e-402d-bb38-7d3f20f7e73f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "8148\n",
            "8147\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class ViolenceType(Enum):\n",
        "    DEROGATION = 1\n",
        "    ANTIPATHY = 2\n",
        "    PREJUDICATION = 3\n",
        "    REPRESSION = 4\n",
        "    \n",
        "class Annotation:\n",
        "    def __init__(self, text, religio, ethno, nondenominational, noncommunal):\n",
        "        self.text = text\n",
        "        self.religio = religio\n",
        "        self.ethno = ethno\n",
        "        self.nondenominational = nondenominational\n",
        "        self.noncommunal = noncommunal"
      ],
      "metadata": {
        "id": "IbWc-gpyPPwt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def categorize_violence(violences, text):\n",
        "    ore = [violences]\n",
        "    violences = str(violences)\n",
        "    violences = re.findall(r'\\d',violences)\n",
        "    return_list = list()\n",
        "    for v in violences:\n",
        "        if int(v)!=0:\n",
        "            return_list.append(int(v))\n",
        "    return return_list"
      ],
      "metadata": {
        "id": "X8urJlaVPR7J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "annotation_list = []\n",
        "for idx, row in df.iterrows():\n",
        "    text = row.text\n",
        "    annotation = Annotation(\n",
        "        row.text,\n",
        "        categorize_violence(row['religio-communal'],text),\n",
        "        categorize_violence(row['ethno-communal'], text),\n",
        "        categorize_violence(row['other-communal'], text),\n",
        "        categorize_violence(row['generic-violence'], text)\n",
        "    )\n",
        "    annotation_list.append(annotation)"
      ],
      "metadata": {
        "id": "7B1GTI7RPTlF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for idx, row in df.iterrows():\n",
        "    if not isinstance(row.text, str) :\n",
        "        print(row)"
      ],
      "metadata": {
        "id": "JG2TlxkQPU2K"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class AnnotationSixteen:\n",
        "    def __init__(self,text):\n",
        "        self.text = text\n",
        "        self.religio_derogation = False\n",
        "        self.religio_antipathy = False\n",
        "        self.religio_prejudication = False\n",
        "        self.religio_repression = False\n",
        "        self.ethno_derogation = False\n",
        "        self.ethno_antipathy = False\n",
        "        self.ethno_prejudication = False\n",
        "        self.ethno_repression = False\n",
        "        self.nondenominational_derogation = False\n",
        "        self.nondenominational_antipathy = False\n",
        "        self.nondenominational_prejudication = False\n",
        "        self.nondenominational_repression = False\n",
        "        self.noncommunal_derogation = False\n",
        "        self.noncommunal_antipathy = False\n",
        "        self.noncommunal_prejudication = False\n",
        "        self.noncommunal_repression = False\n",
        "\n",
        "annotation_sixteen_list = list()\n",
        "list_a = ['religio','ethno','nondenominational','noncommunal']\n",
        "list_b = ['derogation','antipathy','prejudication','repression']\n",
        "\n",
        "for i in annotation_list:\n",
        "    annotation = AnnotationSixteen(i.text)\n",
        "    if len(i.religio)>0:\n",
        "        for j in i.religio:\n",
        "            setattr(annotation, f\"religio_{list_b[j-1]}\",True)\n",
        "    if len(i.ethno)>0:\n",
        "        for j in i.ethno:\n",
        "            setattr(annotation, f\"ethno_{list_b[j-1]}\",True)\n",
        "    if len(i.nondenominational)>0:\n",
        "        for j in i.nondenominational:\n",
        "            setattr(annotation, f\"nondenominational_{list_b[j-1]}\",True)\n",
        "    if len(i.noncommunal)>0:\n",
        "        for j in i.noncommunal:\n",
        "            setattr(annotation, f\"noncommunal_{list_b[j-1]}\",True)\n",
        "    annotation_sixteen_list.append(annotation)\n",
        "            \n",
        "tempdf_v2 = pd.DataFrame([x.__dict__ for x in annotation_sixteen_list])"
      ],
      "metadata": {
        "id": "9xShpXCbPXsx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tempdf_v2.head()"
      ],
      "metadata": {
        "colab": {
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                text  religio_derogation  \\\n",
              "0  দাদা নাম শুনে বিন্দাবনের টিকিট কাটছি টাকাটা দি...               False   \n",
              "1  সবই সিষ্টেম রে ভাই, সবই সিষ্টেম। এই বেডারে হাই...               False   \n",
              "2  দিদিমনি কতবার কেন্দ্র কে বর্ডার সুরক্ষা নিয়ে ক...               False   \n",
              "3  সালাম সেই যুদ্ধাদের প্রতি,  যাদের বীরত্বের জন্...               False   \n",
              "4  বাল পাকনা চেতনাবাজ গুলো আছে চঞ্চল চৌধুরী নিয়ে ...               False   \n",
              "\n",
              "   religio_antipathy  religio_prejudication  religio_repression  \\\n",
              "0              False                  False               False   \n",
              "1              False                  False               False   \n",
              "2              False                  False               False   \n",
              "3              False                  False               False   \n",
              "4              False                  False               False   \n",
              "\n",
              "   ethno_derogation  ethno_antipathy  ethno_prejudication  ethno_repression  \\\n",
              "0             False            False                False             False   \n",
              "1             False            False                False             False   \n",
              "2             False            False                False             False   \n",
              "3             False            False                False             False   \n",
              "4             False            False                False             False   \n",
              "\n",
              "   nondenominational_derogation  nondenominational_antipathy  \\\n",
              "0                         False                        False   \n",
              "1                         False                        False   \n",
              "2                         False                        False   \n",
              "3                         False                        False   \n",
              "4                         False                        False   \n",
              "\n",
              "   nondenominational_prejudication  nondenominational_repression  \\\n",
              "0                            False                         False   \n",
              "1                            False                         False   \n",
              "2                            False                         False   \n",
              "3                            False                         False   \n",
              "4                            False                         False   \n",
              "\n",
              "   noncommunal_derogation  noncommunal_antipathy  noncommunal_prejudication  \\\n",
              "0                   False                  False                      False   \n",
              "1                    True                  False                      False   \n",
              "2                   False                  False                      False   \n",
              "3                   False                  False                      False   \n",
              "4                    True                  False                      False   \n",
              "\n",
              "   noncommunal_repression  \n",
              "0                   False  \n",
              "1                   False  \n",
              "2                   False  \n",
              "3                   False  \n",
              "4                   False  "
            ],
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            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import DatasetDict\n",
        "\n",
        "dataset = Dataset.from_pandas(tempdf_v2)\n",
        "train_test_valid = dataset.train_test_split(test_size=0.4)\n",
        "test_valid = train_test_valid['test'].train_test_split(test_size=0.5)\n",
        "\n",
        "dataset = DatasetDict({\n",
        "    'train': train_test_valid['train'],\n",
        "    'test': test_valid['test'],\n",
        "    'valid': test_valid['train']})\n",
        "print(dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "66ZxE5DPPgIl",
        "outputId": "2c1e214e-f1f5-44bd-9ea0-0615d75ec84d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DatasetDict({\n",
            "    train: Dataset({\n",
            "        features: ['text', 'religio_derogation', 'religio_antipathy', 'religio_prejudication', 'religio_repression', 'ethno_derogation', 'ethno_antipathy', 'ethno_prejudication', 'ethno_repression', 'nondenominational_derogation', 'nondenominational_antipathy', 'nondenominational_prejudication', 'nondenominational_repression', 'noncommunal_derogation', 'noncommunal_antipathy', 'noncommunal_prejudication', 'noncommunal_repression'],\n",
            "        num_rows: 4888\n",
            "    })\n",
            "    test: Dataset({\n",
            "        features: ['text', 'religio_derogation', 'religio_antipathy', 'religio_prejudication', 'religio_repression', 'ethno_derogation', 'ethno_antipathy', 'ethno_prejudication', 'ethno_repression', 'nondenominational_derogation', 'nondenominational_antipathy', 'nondenominational_prejudication', 'nondenominational_repression', 'noncommunal_derogation', 'noncommunal_antipathy', 'noncommunal_prejudication', 'noncommunal_repression'],\n",
            "        num_rows: 1630\n",
            "    })\n",
            "    valid: Dataset({\n",
            "        features: ['text', 'religio_derogation', 'religio_antipathy', 'religio_prejudication', 'religio_repression', 'ethno_derogation', 'ethno_antipathy', 'ethno_prejudication', 'ethno_repression', 'nondenominational_derogation', 'nondenominational_antipathy', 'nondenominational_prejudication', 'nondenominational_repression', 'noncommunal_derogation', 'noncommunal_antipathy', 'noncommunal_prejudication', 'noncommunal_repression'],\n",
            "        num_rows: 1629\n",
            "    })\n",
            "})\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "labels = [label for label in dataset['train'].features.keys() if label not in ['text']]\n",
        "id2label = {idx:label for idx, label in enumerate(labels)}\n",
        "label2id = {label:idx for idx, label in enumerate(labels)}\n",
        "labels"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ICTpb8zjPk2i",
        "outputId": "18bb44d6-88a7-4843-9107-4b532d87e7e3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['religio_derogation',\n",
              " 'religio_antipathy',\n",
              " 'religio_prejudication',\n",
              " 'religio_repression',\n",
              " 'ethno_derogation',\n",
              " 'ethno_antipathy',\n",
              " 'ethno_prejudication',\n",
              " 'ethno_repression',\n",
              " 'nondenominational_derogation',\n",
              " 'nondenominational_antipathy',\n",
              " 'nondenominational_prejudication',\n",
              " 'nondenominational_repression',\n",
              " 'noncommunal_derogation',\n",
              " 'noncommunal_antipathy',\n",
              " 'noncommunal_prejudication',\n",
              " 'noncommunal_repression']"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(\"csebuetnlp/banglabert\")\n",
        "model = AutoModelForPreTraining.from_pretrained(\"csebuetnlp/banglabert\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317,
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        "outputId": "bffe27b5-ca98-4a70-89c6-a4e312434a46"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
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          "metadata": {}
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        {
          "output_type": "display_data",
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            ],
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def preprocess_data(examples):\n",
        "  text = examples[\"text\"]\n",
        "  encoding = tokenizer(text, padding=\"max_length\", truncation=True, max_length=512)\n",
        "  labels_batch = {k: examples[k] for k in examples.keys() if k in labels}\n",
        "  labels_matrix = np.zeros((len(text), len(labels)))\n",
        "  for idx, label in enumerate(labels):\n",
        "    labels_matrix[:, idx] = labels_batch[label]\n",
        "  encoding[\"labels\"] = labels_matrix.tolist()\n",
        "  return encoding"
      ],
      "metadata": {
        "id": "bqEV4tN0QisO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "encoded_dataset = dataset.map(preprocess_data, batched=True, remove_columns=dataset['train'].column_names)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17,
          "referenced_widgets": [
            "35c06b0ade3c4544a9ab9a4d60a57e91",
            "08093013b9264b39acbc23a7a06bb16a",
            "316b500f61fe42e09457be7d6646893b",
            "45d10aad32a7491ea532b488f0935b68",
            "7aef4914ac934d68b43e85312ae9a286",
            "e1b3295ce49e44ea9739df6774c9c151",
            "b133122ce41b429c8756fa0c7abcda82",
            "9bfb0f3560c6456d844a801f4bb81717",
            "865f3f8bd68b48e1ace7abc25765f1f9",
            "4db15866bbe844ab9accf899eaaf7b4b",
            "b4de86d99538487a83d62bb222f51cde",
            "e1ebc21202d941fa8bff12f13e159952",
            "9966febb89354c1e9a70ad05c9720f7c",
            "62d1a665d5934a8997222501f26cb1e8",
            "8c29b5eb029e42129c17932c63cdaf5d",
            "e4aad980ab354945bf71b24617061a7a",
            "7df660644f604db5b55ad6a77e1360fa",
            "28dffa6e45db4d28a90beab47ca7f63b",
            "ed3697189867452c99d38049ca7de636",
            "e6a29e162ee14757804c24593f6901c9",
            "2a44a4d549864befb93d0d2231378622",
            "45088054b77940a192db2f20ab5efad0",
            "5fbd57a0ab944edfbef53cd957288d58",
            "81a288ff17884afc9aac79cfe5c2cd8f",
            "c646350ea76246489a95b9d73d979913",
            "08761cf8b291433987eb2877e1b14c6b",
            "e8a54297bb0c413eb4df1872de986b1b",
            "4d075e44b8654027ab26b0fef3e51115",
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            "0e49466e23ae4047b9a041360b7c36ff",
            "26f4e6cc725f4f43aa0aa36cccf43971"
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        },
        "id": "80fJNwtQQpsi",
        "outputId": "575e20de-a6d9-43b5-c8bb-9f1c906c5f6b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/4888 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "35c06b0ade3c4544a9ab9a4d60a57e91"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/1630 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e1ebc21202d941fa8bff12f13e159952"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/1629 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5fbd57a0ab944edfbef53cd957288d58"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "example = encoded_dataset['train'][0]\n",
        "print(example.keys())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PngFvbXHQsAc",
        "outputId": "776c783d-95ec-4bb6-cb7e-c9e9f6654b48"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dict_keys(['input_ids', 'token_type_ids', 'attention_mask', 'labels'])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "encoded_dataset.set_format(\"torch\")\n",
        "print(encoded_dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UMA-uf7dQu3k",
        "outputId": "482de75b-0db4-4768-fce1-a1a4e62fa105"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DatasetDict({\n",
            "    train: Dataset({\n",
            "        features: ['input_ids', 'token_type_ids', 'attention_mask', 'labels'],\n",
            "        num_rows: 4888\n",
            "    })\n",
            "    test: Dataset({\n",
            "        features: ['input_ids', 'token_type_ids', 'attention_mask', 'labels'],\n",
            "        num_rows: 1630\n",
            "    })\n",
            "    valid: Dataset({\n",
            "        features: ['input_ids', 'token_type_ids', 'attention_mask', 'labels'],\n",
            "        num_rows: 1629\n",
            "    })\n",
            "})\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = AutoModelForSequenceClassification.from_pretrained(\n",
        "    \"csebuetnlp/banglabert\", \n",
        "    problem_type=\"multi_label_classification\", \n",
        "    num_labels=len(labels), \n",
        "    id2label=id2label, \n",
        "    label2id=label2id\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OjedIjxKQyZ1",
        "outputId": "bdc49fdf-bbb6-47fa-8bc0-5d3ec7e04a06"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at csebuetnlp/banglabert were not used when initializing ElectraForSequenceClassification: ['discriminator_predictions.dense.bias', 'discriminator_predictions.dense_prediction.weight', 'discriminator_predictions.dense.weight', 'discriminator_predictions.dense_prediction.bias']\n",
            "- This IS expected if you are initializing ElectraForSequenceClassification 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 ElectraForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of ElectraForSequenceClassification were not initialized from the model checkpoint at csebuetnlp/banglabert and are newly initialized: ['classifier.dense.bias', 'classifier.dense.weight', 'classifier.out_proj.weight', 'classifier.out_proj.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "metric_name = \"f1\"\n",
        "\n",
        "training_args = TrainingArguments(\n",
        "    output_dir=\"violence\",\n",
        "    learning_rate=2e-5,\n",
        "    per_device_train_batch_size=16,\n",
        "    per_device_eval_batch_size=16,\n",
        "    num_train_epochs=30,\n",
        "    weight_decay=0.01,\n",
        "    evaluation_strategy=\"epoch\",\n",
        "    save_strategy=\"epoch\",\n",
        "    load_best_model_at_end=True,\n",
        "    metric_for_best_model='f1',\n",
        ")"
      ],
      "metadata": {
        "id": "lY1xHqu3Q5PX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def multi_label_metrics(predictions, labels, threshold=0.5):\n",
        "    sigmoid = torch.nn.Sigmoid()\n",
        "    probs = sigmoid(torch.Tensor(predictions))\n",
        "    y_pred = np.zeros(probs.shape)\n",
        "    y_pred[np.where(probs >= threshold)] = 1\n",
        "    y_true = labels\n",
        "    f1_micro_average = f1_score(y_true=y_true, y_pred=y_pred, average='micro')\n",
        "    roc_auc = roc_auc_score(y_true, y_pred, average = 'micro')\n",
        "    accuracy = accuracy_score(y_true, y_pred)\n",
        "    metrics = {'f1': f1_micro_average,\n",
        "               'roc_auc': roc_auc,\n",
        "               'accuracy': accuracy}\n",
        "    return metrics\n",
        "\n",
        "def compute_metrics(p: EvalPrediction):\n",
        "    preds = p.predictions[0] if isinstance(p.predictions, \n",
        "            tuple) else p.predictions\n",
        "    result = multi_label_metrics(\n",
        "        predictions=preds, \n",
        "        labels=p.label_ids)\n",
        "    return result"
      ],
      "metadata": {
        "id": "8kNAEDSOQ7Vd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=encoded_dataset[\"train\"],\n",
        "    eval_dataset=encoded_dataset[\"valid\"],\n",
        "    tokenizer=tokenizer,\n",
        "    compute_metrics=compute_metrics,\n",
        "    callbacks = [EarlyStoppingCallback(early_stopping_patience=3)]\n",
        ")\n",
        "\n",
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "wRimzSMNRD72",
        "outputId": "86a6fac1-3136-43c7-92c1-060464bcc875"
      },
      "execution_count": null,
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/transformers/optimization.py:391: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  warnings.warn(\n",
            "You're using a ElectraTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
          ]
        },
        {
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              "      [4910/9180 2:15:44 < 1:58:05, 0.60 it/s, Epoch 16.04/30]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>F1</th>\n",
              "      <th>Roc Auc</th>\n",
              "      <th>Accuracy</th>\n",
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              "  <tbody>\n",
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              "    <tr>\n",
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              "      <td>0.235925</td>\n",
              "      <td>0.572017</td>\n",
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              "    <tr>\n",
              "      <td>3</td>\n",
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              "      <td>0.129989</td>\n",
              "      <td>0.232495</td>\n",
              "      <td>0.570213</td>\n",
              "      <td>0.511357</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.123100</td>\n",
              "      <td>0.128763</td>\n",
              "      <td>0.294306</td>\n",
              "      <td>0.594635</td>\n",
              "      <td>0.517495</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.104300</td>\n",
              "      <td>0.129734</td>\n",
              "      <td>0.272606</td>\n",
              "      <td>0.584689</td>\n",
              "      <td>0.516882</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.104300</td>\n",
              "      <td>0.130282</td>\n",
              "      <td>0.317654</td>\n",
              "      <td>0.606506</td>\n",
              "      <td>0.515040</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.084000</td>\n",
              "      <td>0.134456</td>\n",
              "      <td>0.377944</td>\n",
              "      <td>0.643275</td>\n",
              "      <td>0.497851</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.084000</td>\n",
              "      <td>0.141034</td>\n",
              "      <td>0.400983</td>\n",
              "      <td>0.664409</td>\n",
              "      <td>0.480663</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.068700</td>\n",
              "      <td>0.153729</td>\n",
              "      <td>0.391825</td>\n",
              "      <td>0.658313</td>\n",
              "      <td>0.487416</td>\n",
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              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>0.053800</td>\n",
              "      <td>0.158908</td>\n",
              "      <td>0.409341</td>\n",
              "      <td>0.678781</td>\n",
              "      <td>0.457336</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11</td>\n",
              "      <td>0.053800</td>\n",
              "      <td>0.171708</td>\n",
              "      <td>0.390434</td>\n",
              "      <td>0.660565</td>\n",
              "      <td>0.467158</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12</td>\n",
              "      <td>0.043300</td>\n",
              "      <td>0.173937</td>\n",
              "      <td>0.407407</td>\n",
              "      <td>0.672333</td>\n",
              "      <td>0.482505</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13</td>\n",
              "      <td>0.043300</td>\n",
              "      <td>0.181145</td>\n",
              "      <td>0.413826</td>\n",
              "      <td>0.675774</td>\n",
              "      <td>0.485574</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14</td>\n",
              "      <td>0.036100</td>\n",
              "      <td>0.191333</td>\n",
              "      <td>0.386673</td>\n",
              "      <td>0.654362</td>\n",
              "      <td>0.482505</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15</td>\n",
              "      <td>0.029300</td>\n",
              "      <td>0.196778</td>\n",
              "      <td>0.421882</td>\n",
              "      <td>0.692336</td>\n",
              "      <td>0.457950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16</td>\n",
              "      <td>0.029300</td>\n",
              "      <td>0.205381</td>\n",
              "      <td>0.414503</td>\n",
              "      <td>0.684900</td>\n",
              "      <td>0.461633</td>\n",
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              "  </tbody>\n",
              "</table><p>"
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              "      [5508/9180 2:32:25 < 1:41:39, 0.60 it/s, Epoch 18/30]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
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              "      <th>Validation Loss</th>\n",
              "      <th>F1</th>\n",
              "      <th>Roc Auc</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.143856</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.500000</td>\n",
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              "      <td>0.138817</td>\n",
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              "      <td>0.232495</td>\n",
              "      <td>0.570213</td>\n",
              "      <td>0.511357</td>\n",
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              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.123100</td>\n",
              "      <td>0.128763</td>\n",
              "      <td>0.294306</td>\n",
              "      <td>0.594635</td>\n",
              "      <td>0.517495</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.104300</td>\n",
              "      <td>0.129734</td>\n",
              "      <td>0.272606</td>\n",
              "      <td>0.584689</td>\n",
              "      <td>0.516882</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.104300</td>\n",
              "      <td>0.130282</td>\n",
              "      <td>0.317654</td>\n",
              "      <td>0.606506</td>\n",
              "      <td>0.515040</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.084000</td>\n",
              "      <td>0.134456</td>\n",
              "      <td>0.377944</td>\n",
              "      <td>0.643275</td>\n",
              "      <td>0.497851</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.084000</td>\n",
              "      <td>0.141034</td>\n",
              "      <td>0.400983</td>\n",
              "      <td>0.664409</td>\n",
              "      <td>0.480663</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.068700</td>\n",
              "      <td>0.153729</td>\n",
              "      <td>0.391825</td>\n",
              "      <td>0.658313</td>\n",
              "      <td>0.487416</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>0.053800</td>\n",
              "      <td>0.158908</td>\n",
              "      <td>0.409341</td>\n",
              "      <td>0.678781</td>\n",
              "      <td>0.457336</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11</td>\n",
              "      <td>0.053800</td>\n",
              "      <td>0.171708</td>\n",
              "      <td>0.390434</td>\n",
              "      <td>0.660565</td>\n",
              "      <td>0.467158</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12</td>\n",
              "      <td>0.043300</td>\n",
              "      <td>0.173937</td>\n",
              "      <td>0.407407</td>\n",
              "      <td>0.672333</td>\n",
              "      <td>0.482505</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13</td>\n",
              "      <td>0.043300</td>\n",
              "      <td>0.181145</td>\n",
              "      <td>0.413826</td>\n",
              "      <td>0.675774</td>\n",
              "      <td>0.485574</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14</td>\n",
              "      <td>0.036100</td>\n",
              "      <td>0.191333</td>\n",
              "      <td>0.386673</td>\n",
              "      <td>0.654362</td>\n",
              "      <td>0.482505</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15</td>\n",
              "      <td>0.029300</td>\n",
              "      <td>0.196778</td>\n",
              "      <td>0.421882</td>\n",
              "      <td>0.692336</td>\n",
              "      <td>0.457950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16</td>\n",
              "      <td>0.029300</td>\n",
              "      <td>0.205381</td>\n",
              "      <td>0.414503</td>\n",
              "      <td>0.684900</td>\n",
              "      <td>0.461633</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>17</td>\n",
              "      <td>0.025400</td>\n",
              "      <td>0.210634</td>\n",
              "      <td>0.419442</td>\n",
              "      <td>0.686477</td>\n",
              "      <td>0.461019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>18</td>\n",
              "      <td>0.020600</td>\n",
              "      <td>0.213153</td>\n",
              "      <td>0.421438</td>\n",
              "      <td>0.684589</td>\n",
              "      <td>0.478821</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=5508, training_loss=0.07153615914420451, metrics={'train_runtime': 9149.9879, 'train_samples_per_second': 16.026, 'train_steps_per_second': 1.003, 'total_flos': 2.315247300299981e+16, 'train_loss': 0.07153615914420451, 'epoch': 18.0})"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.evaluate(encoded_dataset[\"test\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 180
        },
        "id": "4BzZE03SRPKK",
        "outputId": "e405aedf-f374-4fa6-eb29-30eab60bf957"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
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              "      \n",
              "      <progress value='204' max='102' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [102/102 02:14]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'eval_loss': 0.18914318084716797,\n",
              " 'eval_f1': 0.41171251109139306,\n",
              " 'eval_roc_auc': 0.6869023213819666,\n",
              " 'eval_accuracy': 0.4699386503067485,\n",
              " 'eval_runtime': 58.5759,\n",
              " 'eval_samples_per_second': 27.827,\n",
              " 'eval_steps_per_second': 1.741,\n",
              " 'epoch': 18.0}"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "encoded_dataset.save_to_disk(\"/content/drive/MyDrive/violence/violence-v1-16.hf\")\n"
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            "fd2901949a2b45fab51ed1405a298c1c",
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        "outputId": "108293f4-19e6-4c92-e36f-db45315e0223"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Saving the dataset (0/1 shards):   0%|          | 0/4888 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "569a8700592e4be8a7a451c859439437"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Saving the dataset (0/1 shards):   0%|          | 0/1630 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6edc042c22c343bcbd47e278aa4fddaf"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Saving the dataset (0/1 shards):   0%|          | 0/1629 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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    {
      "cell_type": "code",
      "source": [
        "trainer.save_model(\"/content/drive/MyDrive/violence/violence-sixteen-model\")\n"
      ],
      "metadata": {
        "id": "n_XPs1EW2RLf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
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      "metadata": {
        "id": "a7UqnLL14GfW"
      },
      "execution_count": null,
      "outputs": []
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  ]
}