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      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "import tensorflow as tf"
      ]
    },
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      "source": [
        "df = pd.read_csv('SMSSpamCollection' , sep='\\t' , names=['label' , 'message'])\n",
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              "  label                                            message\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..."
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-a4a6331a-abfb-47db-bbfc-68a6f21c8fdb button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZrNzzTzTXCpJ",
        "outputId": "fa8c6682-2fce-4b21-b957-390a6af7a68d"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5572, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = list(df['message'])\n",
        "y = list(df['label'])"
      ],
      "metadata": {
        "id": "6TeUxaMpXR7Q"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GasIHc0MX19-",
        "outputId": "d63ca88e-71ef-4682-eeef-5ef25f39938b"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...',\n",
              " 'Ok lar... Joking wif u oni...',\n",
              " \"Free entry in 2 a wkly comp to win FA Cup final tkts 21st May 2005. Text FA to 87121 to receive entry question(std txt rate)T&C's apply 08452810075over18's\",\n",
              " 'U dun say so early hor... U c already then say...',\n",
              " \"Nah I don't think he goes to usf, he lives around here though\"]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tynaJaplX8rv",
        "outputId": "7f8870c2-71b9-4fa8-b733-d0524e2b5e51"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['ham', 'ham', 'spam', 'ham', 'ham']"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y = list(pd.get_dummies(y)['spam'])\n",
        "y[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GcgVpVO3X-N4",
        "outputId": "ab3a68c7-dd95-4b48-bc72-fd80ab0128d4"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0, 0, 1, 0, 0]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train , x_test , y_train , y_test = train_test_split(x , y  ,test_size = 0.2 , random_state=0)"
      ],
      "metadata": {
        "id": "Y595det5Yhak"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(x_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0snMxb5nZIqX",
        "outputId": "37a3ffaa-8a1a-4d86-f963-568167651b67"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4457"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(x_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jrq5fmh6ZM_J",
        "outputId": "89f0c95c-e78c-4986-f682-154ed6dff89b"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1115"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import DistilBertTokenizerFast\n",
        "tokenizer = DistilBertTokenizerFast.from_pretrained('distilbert-base-uncased')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "1d6a3b0c00234ec8a2ca42e0fcacae12",
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            "c2012f0e8909444fb9b0417645caa361",
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            "ef174000b8234fe68ab3b7ddd2692000",
            "d71783a5e2d94181a56b44d7fb566d52",
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            "711af41870d04057b62081f48d4afb1e",
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            "d3616414bcbe4080a324fa59d7be335e",
            "bf3755bb0f96434a9756699568784681",
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            "1bf28dcf6b234e16b9d247eb985b7f01",
            "428e73a1d1bf4cfa97e2c72768f68323",
            "a375c9e83d4b47d08a0dfa551d32f0f7"
          ]
        },
        "id": "j1rKS8vGZUlT",
        "outputId": "dff61afb-db58-40b2-aee2-15814bcd47cc"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1d6a3b0c00234ec8a2ca42e0fcacae12"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a03619774dc24edaaa87a2680ad21c59"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c2012f0e8909444fb9b0417645caa361"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/483 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "711af41870d04057b62081f48d4afb1e"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_encoding = tokenizer(x_train , truncation=True , padding=True)\n",
        "test_encoding = tokenizer(x_test , truncation=True , padding=True)"
      ],
      "metadata": {
        "id": "h-tOKbK9ai3P"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset = tf.data.Dataset.from_tensor_slices((\n",
        "      dict(train_encoding),\n",
        "                  y_train\n",
        "      ))\n",
        "\n",
        "test_dataset = tf.data.Dataset.from_tensor_slices((\n",
        "    dict(test_encoding),\n",
        "                    y_test\n",
        "    ))"
      ],
      "metadata": {
        "id": "hw53B49WbRXC"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "id2label = {0: \"ham\", 1: \"spam\"}\n",
        "label2id = {\"ham\": 0, \"spam\": 1}"
      ],
      "metadata": {
        "id": "oZMbX1dhfJUi"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H3_WPWPmg3tf",
        "outputId": "a6acae5c-6800-4d12-bc45-74e1f45b9199"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<_TensorSliceDataset element_spec=({'input_ids': TensorSpec(shape=(221,), dtype=tf.int32, name=None), 'attention_mask': TensorSpec(shape=(221,), dtype=tf.int32, name=None)}, TensorSpec(shape=(), dtype=tf.int32, name=None))>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from transformers import TFDistilBertForSequenceClassification , TFTrainer , TFTrainingArguments\n",
        "\n",
        "training_args = TFTrainingArguments(\n",
        "    output_dir = './results',\n",
        "    num_train_epochs = 2,\n",
        "    per_device_train_batch_size = 8,\n",
        "    per_device_eval_batch_size = 16,\n",
        "    eval_steps=100,\n",
        "    warmup_steps = 500,\n",
        "    weight_decay = 0.01,\n",
        "    logging_dir = './logs',\n",
        "    logging_steps = 10,\n",
        ")"
      ],
      "metadata": {
        "id": "pH-kNycicDPY"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "with training_args.strategy.scope():\n",
        "  model = TFDistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased',id2label=id2label, label2id=label2id)\n",
        "\n",
        "trainer = TFTrainer(\n",
        "    model = model,\n",
        "    args = training_args,\n",
        "    train_dataset = train_dataset,\n",
        "    eval_dataset = test_dataset\n",
        ")\n",
        "\n",
        "model.config.use_cache = False\n",
        "trainer.train()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 190,
          "referenced_widgets": [
            "d3607926ad3344bfa3733c5aada91527",
            "4836025b3bd44e07ad6562d4f5982980",
            "c267e99678c94fa489f06e1bb89b4a01",
            "ea6499ba78304663909476952f1405b3",
            "c8e8a5e063b343e6bef3372c778674e3",
            "d6be679cb8f94ab9ba865d949a247115",
            "520562eae87649d49634677d581f8c55",
            "c1bc256f2571428b882373c2533fa965",
            "1719ebad8bbb47ee89e4683f57eb8c6d",
            "fda4bf2faf114cf2aaf51d6f22dd9c2b",
            "5f30d7d3cd374eb4b277be0a805c2074"
          ]
        },
        "id": "kxRiLtfmdXjR",
        "outputId": "4ecd0769-a0c0-43bf-ea1d-3f0323a16634"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/268M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d3607926ad3344bfa3733c5aada91527"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the PyTorch model were not used when initializing the TF 2.0 model TFDistilBertForSequenceClassification: ['vocab_transform.bias', 'vocab_transform.weight', 'vocab_layer_norm.bias', 'vocab_projector.bias', 'vocab_layer_norm.weight']\n",
            "- This IS expected if you are initializing TFDistilBertForSequenceClassification from a PyTorch model trained on another task or with another architecture (e.g. initializing a TFBertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing TFDistilBertForSequenceClassification from a PyTorch model that you expect to be exactly identical (e.g. initializing a TFBertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights or buffers of the TF 2.0 model TFDistilBertForSequenceClassification were not initialized from the PyTorch model and are newly initialized: ['pre_classifier.weight', 'pre_classifier.bias', 'classifier.weight', 'classifier.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/trainer_tf.py:118: FutureWarning: The class `TFTrainer` is deprecated and will be removed in version 5 of Transformers. We recommend using native Keras instead, by calling methods like `fit()` and `predict()` directly on the model object. Detailed examples of the Keras style can be found in our examples at https://github.com/huggingface/transformers/tree/main/examples/tensorflow\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.evaluate(test_dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BTLxDjhugmdV",
        "outputId": "1f945a3d-bf8a-4dcc-cd59-441bca7385bb"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'eval_loss': 0.017569826330457415}"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.predict(test_dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NjnEhjnRuzDN",
        "outputId": "3f05627f-83eb-41f1-b7fd-0f7871abcedf"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PredictionOutput(predictions=array([[ 3.8966346, -3.0628417],\n",
              "       [-2.890867 ,  2.7945273],\n",
              "       [ 3.6326494, -2.8260531],\n",
              "       ...,\n",
              "       [ 3.1717052, -2.436365 ],\n",
              "       [-2.9046443,  2.8221161],\n",
              "       [ 3.3059733, -2.4722457]], dtype=float32), label_ids=array([0, 1, 0, ..., 0, 1, 0], dtype=int32), metrics={'eval_loss': 0.017577954701014926})"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_out = trainer.predict(test_dataset)[1]"
      ],
      "metadata": {
        "id": "Z6rB2meRvQIc"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "confusion_matrix(y_test , y_out)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kjEdfQLvvehA",
        "outputId": "ca34ae5c-3b1f-4d80-be13-d8300b004ce1"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[955,   0],\n",
              "       [  0, 160]])"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "\n",
        "print(classification_report(y_test , y_out))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0jNg8Gd0v2ml",
        "outputId": "ca50a035-8629-402b-bc11-5fecb8dfaddb"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00       955\n",
            "           1       1.00      1.00      1.00       160\n",
            "\n",
            "    accuracy                           1.00      1115\n",
            "   macro avg       1.00      1.00      1.00      1115\n",
            "weighted avg       1.00      1.00      1.00      1115\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "0d6f94a1ee8746ffb2bec92d0b9c3e2a",
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}