violence-four.ipynb

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  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MpWGtWK62pVK"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "!pip install evaluate\n",
        "!pip install transformers"
      ]
    },
    {
      "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": "7R_ZuT-s2wKT",
        "outputId": "25cb1386-dc09-4d4a-ce95-4ce58db7d575"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tesla T4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "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"
      ],
      "metadata": {
        "id": "q77GiOyT2xf9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"/content/drive/MyDrive/violence/violence-v1.csv\")"
      ],
      "metadata": {
        "id": "IxKGyXm620CD"
      },
      "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": "xz0GP-3y23Lt",
        "outputId": "e74a6a1a-7cf3-4f50-cba2-16332c9f0037"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "8148\n",
            "8147\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "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": "T9dl_lxj24vE"
      },
      "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",
        "    if(len(return_list)>0):\n",
        "        return True\n",
        "    return False"
      ],
      "metadata": {
        "id": "y5bvM0FF2-tG"
      },
      "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": "PFzdzr5z2_jz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame([x.__dict__ for x in annotation_list])"
      ],
      "metadata": {
        "id": "zy6iYap43Bnu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import DatasetDict\n",
        "\n",
        "dataset = Dataset.from_pandas(df)\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": "TMcS27nk3DcG",
        "outputId": "ad2f05ff-65b9-4ed7-ee0e-fed8d78b8a98"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DatasetDict({\n",
            "    train: Dataset({\n",
            "        features: ['text', 'religio', 'ethno', 'nondenominational', 'noncommunal'],\n",
            "        num_rows: 4888\n",
            "    })\n",
            "    test: Dataset({\n",
            "        features: ['text', 'religio', 'ethno', 'nondenominational', 'noncommunal'],\n",
            "        num_rows: 1630\n",
            "    })\n",
            "    valid: Dataset({\n",
            "        features: ['text', 'religio', 'ethno', 'nondenominational', 'noncommunal'],\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": "swhGZdd33VIJ",
        "outputId": "b9b1c26c-342e-4376-bede-bafc2ee2347d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['religio', 'ethno', 'nondenominational', 'noncommunal']"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(\"csebuetnlp/banglabert\")\n",
        "model = AutoModelForPreTraining.from_pretrained(\"csebuetnlp/banglabert\")"
      ],
      "metadata": {
        "colab": {
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          "height": 317,
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/119 [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/586 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "27508764a2114e74ae01cd349b1aa264"
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/528k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/112 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/443M [00:00<?, ?B/s]"
            ],
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def preprocess_data(examples):\n",
        "  # take a batch of texts\n",
        "  text = examples[\"text\"]\n",
        "  # encode them\n",
        "  encoding = tokenizer(text, padding=\"max_length\", truncation=True, max_length=512)\n",
        "  # add labels\n",
        "  labels_batch = {k: examples[k] for k in examples.keys() if k in labels}\n",
        "  # create numpy array of shape (batch_size, num_labels)\n",
        "  labels_matrix = np.zeros((len(text), len(labels)))\n",
        "  # fill numpy array\n",
        "  for idx, label in enumerate(labels):\n",
        "    labels_matrix[:, idx] = labels_batch[label]\n",
        "\n",
        "  encoding[\"labels\"] = labels_matrix.tolist()\n",
        "  \n",
        "  return encoding"
      ],
      "metadata": {
        "id": "YVGHXnQb3ZL6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "encoded_dataset = dataset.map(preprocess_data, batched=True, remove_columns=dataset['train'].column_names)\n",
        "\n",
        "example = encoded_dataset['train'][0]\n",
        "print(example.keys())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35,
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        "outputId": "c0fde7ce-f7d5-4259-fd61-6369cc36ecc1"
      },
      "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": "df992e4be03441e187acdbdf948c67c9"
            }
          },
          "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": "2d2090540f2c4673ba24f01f3e267179"
            }
          },
          "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": "a512a32266b340c7908dbbac9313e5d8"
            }
          },
          "metadata": {}
        },
        {
          "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",
        "encoded_dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qrsFbnGb3beN",
        "outputId": "05376f2b-4cd7-411a-a265-2a6f74367646"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "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",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "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": "C96VvTJ33fCS",
        "outputId": "8e11549a-b49c-4f24-f623-08f722d8a02b"
      },
      "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_prediction.weight', 'discriminator_predictions.dense.weight', 'discriminator_predictions.dense.bias', '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.out_proj.weight', 'classifier.dense.bias', 'classifier.out_proj.bias', 'classifier.dense.weight']\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": "C5A4o7P23gxB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import f1_score, roc_auc_score, accuracy_score\n",
        "from transformers import EvalPrediction\n",
        "import torch\n",
        "    \n",
        "# source: https://jesusleal.io/2021/04/21/Longformer-multilabel-classification/\n",
        "def multi_label_metrics(predictions, labels, threshold=0.5):\n",
        "    # first, apply sigmoid on predictions which are of shape (batch_size, num_labels)\n",
        "    sigmoid = torch.nn.Sigmoid()\n",
        "    probs = sigmoid(torch.Tensor(predictions))\n",
        "    # next, use threshold to turn them into integer predictions\n",
        "    y_pred = np.zeros(probs.shape)\n",
        "    y_pred[np.where(probs >= threshold)] = 1\n",
        "    # finally, compute metrics\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",
        "    # return as dictionary\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": "FlbFBg8d3ips"
      },
      "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[\"test\"],\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": 610
        },
        "id": "8VhnxEU33kXk",
        "outputId": "934e3a67-3481-41d5-9904-59135166cdef"
      },
      "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"
          ]
        },
        {
          "data": {
            "text/html": [
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              "    <div>\n",
              "      \n",
              "      <progress value='1081' max='9180' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [1081/9180 29:24 < 3:40:47, 0.61 it/s, Epoch 3.53/30]\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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.256129</td>\n",
              "      <td>0.470405</td>\n",
              "      <td>0.665641</td>\n",
              "      <td>0.619632</td>\n",
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              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.286100</td>\n",
              "      <td>0.234332</td>\n",
              "      <td>0.526937</td>\n",
              "      <td>0.696468</td>\n",
              "      <td>0.637423</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.286100</td>\n",
              "      <td>0.250677</td>\n",
              "      <td>0.606324</td>\n",
              "      <td>0.762725</td>\n",
              "      <td>0.664417</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
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          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
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            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='2142' max='9180' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [2142/9180 59:36 < 3:16:01, 0.60 it/s, Epoch 7/30]\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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.256129</td>\n",
              "      <td>0.470405</td>\n",
              "      <td>0.665641</td>\n",
              "      <td>0.619632</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.286100</td>\n",
              "      <td>0.234332</td>\n",
              "      <td>0.526937</td>\n",
              "      <td>0.696468</td>\n",
              "      <td>0.637423</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.286100</td>\n",
              "      <td>0.250677</td>\n",
              "      <td>0.606324</td>\n",
              "      <td>0.762725</td>\n",
              "      <td>0.664417</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.186400</td>\n",
              "      <td>0.284951</td>\n",
              "      <td>0.617377</td>\n",
              "      <td>0.797525</td>\n",
              "      <td>0.627607</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.127300</td>\n",
              "      <td>0.326504</td>\n",
              "      <td>0.522124</td>\n",
              "      <td>0.694357</td>\n",
              "      <td>0.641104</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.127300</td>\n",
              "      <td>0.359797</td>\n",
              "      <td>0.608123</td>\n",
              "      <td>0.788121</td>\n",
              "      <td>0.621472</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.077700</td>\n",
              "      <td>0.411344</td>\n",
              "      <td>0.563399</td>\n",
              "      <td>0.730933</td>\n",
              "      <td>0.643558</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=2142, training_loss=0.16233879199771542, metrics={'train_runtime': 3582.0682, 'train_samples_per_second': 40.937, 'train_steps_per_second': 2.563, 'total_flos': 9002769531764736.0, 'train_loss': 0.16233879199771542, 'epoch': 7.0})"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.evaluate(encoded_dataset[\"test\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 180
        },
        "id": "BMZIi7YM3sHS",
        "outputId": "ebe3828a-5269-4764-ee47-f6a29503e516"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='102' max='102' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [102/102 00:57]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'eval_loss': 0.28495144844055176,\n",
              " 'eval_f1': 0.6173772261198057,\n",
              " 'eval_roc_auc': 0.7975247370111944,\n",
              " 'eval_accuracy': 0.6276073619631902,\n",
              " 'eval_runtime': 58.414,\n",
              " 'eval_samples_per_second': 27.904,\n",
              " 'eval_steps_per_second': 1.746,\n",
              " 'epoch': 7.0}"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.save_model(\"/content/drive/MyDrive/violence/violence-bert-four\")"
      ],
      "metadata": {
        "id": "FzIK1mt45GIs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "HujTtfFFHu-0"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}