Spam_Detection_using_BERT_Model.ipynb

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            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "Installing collected packages: evaluate\n",
            "Successfully installed evaluate-0.4.0\n"
          ]
        }
      ],
      "source": [
        "!pip install datasets\n",
        "!pip install transformers\n",
        "!pip install evaluate"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "# data from https://www.kaggle.com/datasets/balaka18/email-spam-classification-dataset-csv\n",
        "file_path=\"spam.csv\"\n",
        "df = pd.read_csv(file_path,encoding = \"ISO-8859-1\")"
      ],
      "metadata": {
        "id": "nANMnXg3kWVS"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = df[[\"v1\",\"v2\"]]"
      ],
      "metadata": {
        "id": "1bUwPO1Ko927"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "lI8jo7wro-97",
        "outputId": "c3214519-87bb-4532-8392-bddd51b752e1"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        v1                                                 v2\n",
              "0      ham  Go until jurong point, crazy.. Available only ...\n",
              "1      ham                      Ok lar... Joking wif u oni...\n",
              "2     spam  Free entry in 2 a wkly comp to win FA Cup fina...\n",
              "3      ham  U dun say so early hor... U c already then say...\n",
              "4      ham  Nah I don't think he goes to usf, he lives aro...\n",
              "...    ...                                                ...\n",
              "5567  spam  This is the 2nd time we have tried 2 contact u...\n",
              "5568   ham              Will Ì_ b going to esplanade fr home?\n",
              "5569   ham  Pity, * was in mood for that. So...any other s...\n",
              "5570   ham  The guy did some bitching but I acted like i'd...\n",
              "5571   ham                         Rofl. Its true to its name\n",
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              "[5572 rows x 2 columns]"
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              "      <td>Rofl. Its true to its name</td>\n",
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    {
      "cell_type": "code",
      "source": [
        "df['label'] = df.v1.map({'ham':0, 'spam':1})"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UNyV3doxpUwW",
        "outputId": "4ae381cf-63bb-45eb-cd12-15f656f2e559"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-5-1756a03d9724>:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['label'] = df.v1.map({'ham':0, 'spam':1})\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-uncased\")"
      ],
      "metadata": {
        "colab": {
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        "id": "EnYJSMWipj2e",
        "outputId": "dc8f8718-4f26-488e-9df6-bbdec87dd7cb"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
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          },
          "metadata": {}
        },
        {
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            }
          },
          "metadata": {}
        },
        {
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
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          },
          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "kK_hJD-dquq7"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModelForSequenceClassification\n",
        "model = AutoModelForSequenceClassification.from_pretrained('bert-base-uncased',num_labels=2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156,
          "referenced_widgets": [
            "b45394399e4541b7a0974dbaeeb50a98",
            "4492aa71766f4219af965bcf61ac3128",
            "c77198376f2446ada47857c042ab2063",
            "7932d4de7cad41829d7474fdb0e78547",
            "3e7ac013b03e4f43861049306aeb0299",
            "dc41143c020d46ce8283ad28de7d981f",
            "3d817140205b48478b362990321751ac",
            "3d2bc038dc844735bdc55c1fdb9a3423",
            "34fa060b761045c8bf36d63cc79c8484",
            "ed7f2ba681bf4452ba2df8a3067a2132",
            "e8456d87a40148d6b6880c44eddaecf3"
          ]
        },
        "id": "9J-jDJISrdD-",
        "outputId": "56d903e2-f55c-429d-c60c-34eb7dfe6ba7"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)\"pytorch_model.bin\";:   0%|          | 0.00/440M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b45394399e4541b7a0974dbaeeb50a98"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.dense.bias', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.decoder.weight', 'cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.bias']\n",
            "- This IS expected if you are initializing BertForSequenceClassification 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 BertForSequenceClassification 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 BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.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": [
        "## test the model accuracy on sample data\n",
        "#sample_data = df['v2'].to_list()[4000:4100]\n",
        "#sample_labels = df['label'].to_list()[4000:4100]"
      ],
      "metadata": {
        "id": "WaNuLcohr4Se"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#token_sample_output = tokenizer(sample_data,padding=True,return_tensors=\"pt\")\n",
        "#output_prediction = model(**token_sample_output)\n",
        "#numpy_output = output_prediction.logits.detach().numpy()\n",
        "#import numpy as np\n",
        "#preds = np.argmax(numpy_output, axis=-1)\n",
        "#from sklearn.metrics import accuracy_score\n",
        "#accuracy_score(sample_labels, preds)"
      ],
      "metadata": {
        "id": "Boq6JpfAtP_C"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import Dataset\n",
        "train_data_df = df.sample(200, random_state=42)\n",
        "eval_data_df = df.sample(200, random_state=45)\n",
        "train_dataset = Dataset.from_pandas(train_data_df)\n",
        "eval_dataset = Dataset.from_pandas(eval_data_df)\n",
        "test_data_df = df.iloc[4000:4100]\n",
        "test_dataset = Dataset.from_pandas(test_data_df)"
      ],
      "metadata": {
        "id": "mYo6TEQ5xbeK"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def tokenize_function(examples):\n",
        "    return tokenizer(examples[\"v2\"], padding=\"max_length\", truncation=True)\n",
        "tokenized_datasets_train = train_dataset.map(tokenize_function, batched=True)\n",
        "tokenized_datasets_eval = eval_dataset.map(tokenize_function, batched=True)\n",
        "tokenizer_datasets_test = test_dataset.map(tokenize_function, batched=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "a2d71148aa7246ca9b40ab343773742b",
            "a74b374cdd4247939717734bd205b798",
            "0e78a7ccf97f4878885dbd95451c3016",
            "59c55bfcee704976b636a5bb67181d74",
            "d61c4cf18b944da3936c61331ce33991",
            "578289ce58fe482fa7aafaa88f66edab",
            "afae1b38126546d2a4ae017a3c81c64e",
            "46f3570584c144a182faccc761b7c8cb",
            "60cd47e91d94428d98c17c98cb3f2b73",
            "c710eed008c7485e92e3ee8c296655c5",
            "0a5e4ba5bea04df39f9f8341339f9c4b",
            "8b08586ba9a049e38aa43647573e55b7",
            "302ee07af9204601afc45ff44392b3c4",
            "62a59b3486a6478894e247471818f20e",
            "9471081823c946ef8d601b9937c1a036",
            "49085954f678421e913f394a4d0aa16d",
            "bdf3f588e38a4c5191107c88afa1e4d3",
            "0db4dc16e88d4abfbddc25116af8b946",
            "ace1d7f4997840f2a2bb04b05b5855eb",
            "3229f1f703724b72ad4e46e9f9948d02",
            "3d712b2dd535469fafe205834040e4c6",
            "a06258a1a22f41a78bfeec37264085da",
            "8a0e337d6dff4d91a2f768cbc34444a2",
            "a645786a00ad4851997831d7036f7d33",
            "c3c0ba2be405426eb2fa2c11fecbec17",
            "ffee2e01b3d34484800b767918dfe375",
            "25a47c15b24c4c1ea3d9ef2ba4f92305",
            "c8fab88f7b3c4f3a8e2176a2f78722c1",
            "d3c3e830449f492b950a49293530fecb",
            "ef8d5ada8c094498ab1dbd0f528c04db",
            "118ad1b38f064980b40c75b16388cd6f",
            "a0457f577d1a43169b32997a995ad6f4",
            "cce366d9395140a49c7c31c2b75bafa1"
          ]
        },
        "id": "0nBMXwh-1IWv",
        "outputId": "62552cf1-3779-4a6c-c631-2811a3b607c6"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a2d71148aa7246ca9b40ab343773742b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8b08586ba9a049e38aa43647573e55b7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8a0e337d6dff4d91a2f768cbc34444a2"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "djt3rqIe1afK"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import TrainingArguments,Trainer\n",
        "training_args = TrainingArguments(output_dir=\"test_trainer\")\n"
      ],
      "metadata": {
        "id": "Q0B1QAydz-HF"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import evaluate\n",
        "metric = evaluate.load(\"accuracy\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "135e546259ff4af092d7ec9a3db5f9a1",
            "fd613bbd32f145a88cad11d1a76ba512",
            "ddd704356a4c447e9608f6b87edb9446",
            "ce33e46f99ee4904b034cd1c8c305efd",
            "b9772977906a4bc3872d6ecd9624ebab",
            "7f0169deae5e4334a3f089a710dbd3e3",
            "0a9d4b45328d44b7ac511e00484a0f18",
            "adf72e8f18fc4f5498b7a7ca09101f0f",
            "da8819cf02244cb59b843f5d90a3c67b",
            "c2e6d8b842584230a53daec439f2939d",
            "170f296fbc4b499e91d655317c87b96d"
          ]
        },
        "id": "IpJ-qkVB0C3n",
        "outputId": "49198ded-bf74-4f39-f963-df52a3eb550a"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/4.20k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "135e546259ff4af092d7ec9a3db5f9a1"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def compute_metrics(eval_pred):\n",
        "    logits, labels = eval_pred\n",
        "    predictions = np.argmax(logits, axis=-1)\n",
        "    return metric.compute(predictions=predictions, references=labels)"
      ],
      "metadata": {
        "id": "Gz5uwKoX0mPs"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=tokenized_datasets_train,\n",
        "    eval_dataset=tokenized_datasets_eval,\n",
        "    compute_metrics=compute_metrics,\n",
        ")"
      ],
      "metadata": {
        "id": "XKkTh4NY06dW"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "id": "_Vpkppqb0-Md",
        "outputId": "b2987994-a1d8-404a-a0d3-1fa76458e067"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The following columns in the training set don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: __index_level_0__, v2, v1. If __index_level_0__, v2, v1 are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.\n",
            "***** Running training *****\n",
            "  Num examples = 200\n",
            "  Num Epochs = 3\n",
            "  Instantaneous batch size per device = 8\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 8\n",
            "  Gradient Accumulation steps = 1\n",
            "  Total optimization steps = 75\n",
            "  Number of trainable parameters = 109483778\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='75' max='75' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [75/75 00:56, Epoch 3/3]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "\n",
            "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=75, training_loss=0.0024451325337092084, metrics={'train_runtime': 57.4994, 'train_samples_per_second': 10.435, 'train_steps_per_second': 1.304, 'total_flos': 157866633216000.0, 'train_loss': 0.0024451325337092084, 'epoch': 3.0})"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions_output = trainer.predict(tokenizer_datasets_test)\n",
        "accuracy_score = compute_metrics((predictions_output.predictions,tokenizer_datasets_test['label']))\n",
        "print(accuracy_score)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 124
        },
        "id": "LJhe7giC4yCC",
        "outputId": "3ab2e567-ffab-4363-8c2f-283e5e2ed578"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The following columns in the test set don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: v2, v1. If v2, v1 are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.\n",
            "***** Running Prediction *****\n",
            "  Num examples = 100\n",
            "  Batch size = 8\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'accuracy': 0.97}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Tg3oF7PU42dl"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}