samples/public_transformers_in_ktrain.ipynb

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      "source": [
        "### A Simplied Interface to Text Classification With Hugging Face Transformers in TensorFlow Using [ktrain](https://github.com/amaiya/ktrain)\n",
        "\n",
        "*ktrain* requires TensorFlow 2.  \n",
        "  \n",
        "  \n",
        "Réference : https://towardsdatascience.com/text-classification-with-hugging-face-transformers-in-tensorflow-2-without-tears-ee50e4f3e7ed"
      ]
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      "source": [
        "!pip3 install -q tensorflow_gpu==2.1.0"
      ],
      "execution_count": null,
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          "output_type": "stream",
          "text": [
            "\u001b[K     |████████████████████████████████| 421.8MB 39kB/s \n",
            "\u001b[K     |████████████████████████████████| 51kB 7.7MB/s \n",
            "\u001b[K     |████████████████████████████████| 450kB 47.0MB/s \n",
            "\u001b[K     |████████████████████████████████| 3.9MB 46.8MB/s \n",
            "\u001b[?25h  Building wheel for gast (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[31mERROR: tensorflow 2.3.0 has requirement gast==0.3.3, but you'll have gast 0.2.2 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: tensorflow 2.3.0 has requirement tensorboard<3,>=2.3.0, but you'll have tensorboard 2.1.1 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: tensorflow 2.3.0 has requirement tensorflow-estimator<2.4.0,>=2.3.0, but you'll have tensorflow-estimator 2.1.0 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: tensorflow-probability 0.11.0 has requirement gast>=0.3.2, but you'll have gast 0.2.2 which is incompatible.\u001b[0m\n"
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          "height": 34
        },
        "outputId": "45dd7141-cc98-403a-f504-b7f98dcabacc"
      },
      "source": [
        "import tensorflow as tf\n",
        "print(tf.__version__)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.1.0\n"
          ],
          "name": "stdout"
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      "cell_type": "markdown",
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      "source": [
        "We then need to install *ktrain* library using pip."
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      "source": [
        "!pip3 install -q ktrain"
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          "output_type": "stream",
          "text": [
            "\u001b[K     |████████████████████████████████| 25.3MB 130kB/s \n",
            "\u001b[K     |████████████████████████████████| 421.8MB 27kB/s \n",
            "\u001b[K     |████████████████████████████████| 6.7MB 55.2MB/s \n",
            "\u001b[K     |████████████████████████████████| 983kB 14.5MB/s \n",
            "\u001b[K     |████████████████████████████████| 245kB 56.5MB/s \n",
            "\u001b[K     |████████████████████████████████| 778kB 52.1MB/s \n",
            "\u001b[K     |████████████████████████████████| 471kB 52.7MB/s \n",
            "\u001b[K     |████████████████████████████████| 276kB 14.6MB/s \n",
            "\u001b[K     |████████████████████████████████| 890kB 53.1MB/s \n",
            "\u001b[K     |████████████████████████████████| 1.1MB 51.9MB/s \n",
            "\u001b[K     |████████████████████████████████| 3.0MB 54.1MB/s \n",
            "\u001b[?25h  Building wheel for ktrain (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for keras-bert (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for langdetect (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for seqeval (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for syntok (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for shap (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for keras-transformer (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for keras-pos-embd (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for keras-multi-head (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for keras-layer-normalization (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for keras-position-wise-feed-forward (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for keras-embed-sim (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for keras-self-attention (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eiZcfFe9R-fg",
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      "source": [
        "### Load a Dataset Into Arrays"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2vV81nuXR9XP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "outputId": "6073306a-2681-4922-94e1-2642a8a9994a"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly&response_type=code\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qZyvsheOSfoy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "! cp -R /content/drive/\"My Drive\"/\"foo\"/Data /content"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VluJ0Jo4SlfV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gNtsgquVS0FW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.read_csv('/content/Data/EULA_Training_Data_Set_1_v1.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JVdVye7fS3pd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x, y = df['Clause Text'].values, df['Classification'].values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5ZYB0GMYS_Se",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "random_state = 1234\n",
        "test_ratio = 0.2\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = test_ratio, random_state = random_state)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "loiu_Qs2T1zk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "44696fbd-f68c-44bd-93e0-a0d794bae36f"
      },
      "source": [
        "classes = list(set(y))\n",
        "classes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0, 1]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Us9cYSd5P4j",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import ktrain\n",
        "from ktrain import text"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aJo1BnahmCOq",
        "colab_type": "text"
      },
      "source": [
        "# **DistilBERT**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "isnP0jacTgjM",
        "colab_type": "text"
      },
      "source": [
        "## STEP 1:  Preprocess Data and Create a Transformer Model\n",
        "\n",
        "We will use [DistilBERT](https://arxiv.org/abs/1910.01108)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q-rf-Ji_TWiw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439,
          "referenced_widgets": [
            "72d164feaa81475297c3346404afaaa9",
            "a7d98ddc466c43c98e37ab3d5f263745",
            "7e95c59a902e4da998724e7cf830fe26",
            "b359c148e43e47dd8d2b8b4420465cde",
            "b8ded37080e746d18cace1b18292f0b7",
            "74930399ce0c4ae1998057f6019b6c3e",
            "30572ec5e99d4f2697f93d176042539c",
            "a411610f57894581ada1edff7da23e31",
            "35b51e3c65d7458fb50c5c3a1c563c55",
            "e5a2f67c4ae441a7b11bb672e321f851",
            "cd5aae628d5c453fb010e878aff73b5a",
            "4bd68126e84341c4a6f1613bdc2705a4",
            "06536d67ec3241b29d91e4ebd718b3b1",
            "9257fcd2b36e42bd879c98330a8309c7",
            "a197cd4e929342e2b05d11c337442b15",
            "cc92c246e9ce45dfa46206ec6e6776cf",
            "822c9e51e6414c4e961a88ed31e28650",
            "d30dfdb79d894250b9382883bfcb4a12",
            "7b9e24498a414860ab08616dc90badd1",
            "3da62815dff4497890a38b33dcff3436",
            "bd019e378813455da904c4e022cea93e",
            "8f4e842dfe5d4e019dcd01fe2f5ea621",
            "b0a89fa65e9e4846a500c593e74e659d",
            "a818811084214671aeacaa248b3046bd"
          ]
        },
        "outputId": "34f140e4-4de4-477d-d2ef-74b1b9f77434"
      },
      "source": [
        "MODEL_NAME = 'distilbert-base-uncased'\n",
        "t = text.Transformer(MODEL_NAME, maxlen=500, classes= classes)\n",
        "trn = t.preprocess_train(x_train, y_train)\n",
        "val = t.preprocess_test(x_test, y_test)\n",
        "model = t.get_classifier()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ktrain/text/preprocessor.py:400: UserWarning: The class_names argument is replacing the classes argument. Please update your code.\n",
            "  warnings.warn('The class_names argument is replacing the classes argument. Please update your code.')\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "preprocessing train...\n",
            "language: en\n",
            "train sequence lengths:\n",
            "\tmean : 80\n",
            "\t95percentile : 262\n",
            "\t99percentile : 466\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "72d164feaa81475297c3346404afaaa9",
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=231508.0, style=ProgressStyle(descripti…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Is Multi-Label? False\n",
            "preprocessing test...\n",
            "language: en\n",
            "test sequence lengths:\n",
            "\tmean : 81\n",
            "\t95percentile : 265\n",
            "\t99percentile : 512\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
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        {
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=442.0, style=ProgressStyle(description_…"
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        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "822c9e51e6414c4e961a88ed31e28650",
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=363423424.0, style=ProgressStyle(descri…"
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        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jEWwWGygYxBu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "learner = ktrain.get_learner(model, train_data=trn, val_data=val, batch_size=16)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZhekygrbbYtw",
        "colab_type": "text"
      },
      "source": [
        "## STEP 2 : Estimate the Learning Rate *(opitonal)*  \n",
        "\n",
        "\n",
        "```\n",
        "learner.lr_find()\n",
        "learner.lr_plot()\n",
        "```\n",
        "Or\n",
        "```\n",
        "learner.lr_find(show_plot = True, max_epochs = ?)\n",
        "```\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dmZ-BFM2Bku4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        },
        "outputId": "846f4408-27c3-49b1-aef0-766f47f55dac"
      },
      "source": [
        "learner.lr_find()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "simulating training for different learning rates... this may take a few moments...\n",
            "Train for 393 steps\n",
            "Epoch 1/1024\n",
            "393/393 [==============================] - 356s 907ms/step - loss: 0.6340 - accuracy: 0.5663\n",
            "Epoch 2/1024\n",
            "393/393 [==============================] - 352s 895ms/step - loss: 0.4132 - accuracy: 0.8094\n",
            "Epoch 3/1024\n",
            "393/393 [==============================] - 351s 893ms/step - loss: 0.5162 - accuracy: 0.8048\n",
            "Epoch 4/1024\n",
            "212/393 [===============>..............] - ETA: 2:38 - loss: 3.4280 - accuracy: 0.8071\n",
            "\n",
            "done.\n",
            "Please invoke the Learner.lr_plot() method to visually inspect the loss plot to help identify the maximal learning rate associated with falling loss.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CK3bji-qG221",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "6372efd5-4480-4f9c-dca2-4b457d8a2442"
      },
      "source": [
        "learner.lr_plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xbPAq2V4bbBH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "lr = 1e-4"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZUIyjj3SUEKq",
        "colab_type": "text"
      },
      "source": [
        "## STEP 3:  Train the Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i-0W-WQAUE-p",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "outputId": "124d5546-8aa7-49ce-9aa6-dd5dd9f2c691"
      },
      "source": [
        "# learner.fit_onecycle(5e-6, 4)\n",
        "learner.fit_onecycle(lr, 6)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "begin training using onecycle policy with max lr of 0.0001...\n",
            "Train for 394 steps, validate for 50 steps\n",
            "Epoch 1/6\n",
            "394/394 [==============================] - 750s 2s/step - loss: 0.3979 - accuracy: 0.8095 - val_loss: 0.3517 - val_accuracy: 0.8179\n",
            "Epoch 2/6\n",
            "394/394 [==============================] - 748s 2s/step - loss: 0.3257 - accuracy: 0.8490 - val_loss: 0.3367 - val_accuracy: 0.8490\n",
            "Epoch 3/6\n",
            "394/394 [==============================] - 748s 2s/step - loss: 0.2759 - accuracy: 0.8799 - val_loss: 0.3788 - val_accuracy: 0.8547\n",
            "Epoch 4/6\n",
            "394/394 [==============================] - 748s 2s/step - loss: 0.2101 - accuracy: 0.9137 - val_loss: 0.3524 - val_accuracy: 0.8668\n",
            "Epoch 5/6\n",
            "394/394 [==============================] - 748s 2s/step - loss: 0.1179 - accuracy: 0.9535 - val_loss: 0.4085 - val_accuracy: 0.8642\n",
            "Epoch 6/6\n",
            "394/394 [==============================] - 748s 2s/step - loss: 0.0564 - accuracy: 0.9805 - val_loss: 0.5214 - val_accuracy: 0.8706\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fb98a22c7b8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zD31MyUQURqA",
        "colab_type": "text"
      },
      "source": [
        "## STEP 4: Evaluate and Inspect the Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3QyEWx_cUSih",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "6db3578a-d956-4515-e952-8f5e6e481f3b"
      },
      "source": [
        "learner.validate(class_names=t.get_classes())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.91      0.93      0.92      1287\n",
            "           1       0.66      0.60      0.63       289\n",
            "\n",
            "    accuracy                           0.87      1576\n",
            "   macro avg       0.79      0.77      0.78      1576\n",
            "weighted avg       0.87      0.87      0.87      1576\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1198,   89],\n",
              "       [ 115,  174]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n-YrCoCNU7N5",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "Let's examine the validation example about which we were the most wrong.a"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bTvZdCrgU_8o",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "d9ffccf7-72ad-4bc1-d1a6-12a0782ec4cb"
      },
      "source": [
        "learner.view_top_losses(n=1, preproc=t)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "----------\n",
            "id:1298 | loss:7.57 | true:1 | pred:0)\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HyQ6AVMmUxB4",
        "colab_type": "text"
      },
      "source": [
        "## STEP 5: Making Predictions on New Data in Deployment"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HzwHZltfUzy2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "predictor = ktrain.get_predictor(learner.model, preproc=t)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YuTH4hl7do-l",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "eula = \"Right to use the Software: Subject to the terms and conditions of this Agreement, McAfee grants Company a non-exclusive,\"\n",
        "eula = eula + \" non-transferable right to use the Software listed in the Grant Letter solely for Company’s own internal business operations.\" \n",
        "eula = eula + \" In this Agreement, to use the Software includes to download, install and access the Software. Company is not granted rights to\"\n",
        "eula = eula + \" Updates and Upgrades unless Company has purchased Support (or a service subscription granting rights to Updates and Upgrades).\" "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I0M3yGp2VV2L",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e35a7de3-1dfc-40cc-b6b0-8ea2b5397df2"
      },
      "source": [
        "predictor.predict(eula)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cODyw6P4VSsg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b360570e-5c50-4f40-8a3b-5a3f2182d961"
      },
      "source": [
        "# predicted probability scores for each category\n",
        "predictor.predict_proba(eula)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.93959993, 0.06040014], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2XgE0wwYVYCE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d86cd1a3-896d-4890-b886-01060306f4c1"
      },
      "source": [
        "predictor.get_classes()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0, 1]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HXX9gjPZVldx",
        "colab_type": "text"
      },
      "source": [
        "As expected, `soc.religion.christian` is assigned the highest probability.\n",
        "\n",
        "Let's invoke the `explain` method to see which words contribute most to the classification.\n",
        "\n",
        "We will need a forked version of the **eli5** library that supportes TensorFlow Keras, so let's install it first."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bKFiApbXVqm_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e41ac148-10a5-4e3a-9aae-9eef96a483f7"
      },
      "source": [
        "!pip3 install -q git+https://github.com/amaiya/eli5@tfkeras_0_10_1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  Building wheel for eli5 (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1uifEyz7VvQC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 220
        },
        "outputId": "fc97d8c8-8b0f-4bd4-a5db-f46f3c0f5d52"
      },
      "source": [
        "predictor.explain(eula)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "    <style>\n",
              "    table.eli5-weights tr:hover {\n",
              "        filter: brightness(85%);\n",
              "    }\n",
              "</style>\n",
              "\n",
              "\n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "        \n",
              "\n",
              "    \n",
              "\n",
              "        \n",
              "\n",
              "        \n",
              "    \n",
              "        \n",
              "        \n",
              "    \n",
              "        <p style=\"margin-bottom: 0.5em; margin-top: 0em\">\n",
              "            <b>\n",
              "    \n",
              "        y=0\n",
              "    \n",
              "</b>\n",
              "\n",
              "    \n",
              "    (probability <b>0.537</b>, score <b>-0.148</b>)\n",
              "\n",
              "top features\n",
              "        </p>\n",
              "    \n",
              "    <table class=\"eli5-weights\"\n",
              "           style=\"border-collapse: collapse; border: none; margin-top: 0em; table-layout: auto; margin-bottom: 2em;\">\n",
              "        <thead>\n",
              "        <tr style=\"border: none;\">\n",
              "            \n",
              "                <th style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\" title=\"Feature contribution already accounts for the feature value (for linear models, contribution = weight * feature value), and the sum of feature contributions is equal to the score or, for some classifiers, to the probability. Feature values are shown if &quot;show_feature_values&quot; is True.\">\n",
              "                    Contribution<sup>?</sup>\n",
              "                </th>\n",
              "            \n",
              "            <th style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">Feature</th>\n",
              "            \n",
              "        </tr>\n",
              "        </thead>\n",
              "        <tbody>\n",
              "        \n",
              "            <tr style=\"background-color: hsl(120, 100.00%, 80.00%); border: none;\">\n",
              "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
              "        +1.238\n",
              "    </td>\n",
              "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
              "        &lt;BIAS&gt;\n",
              "    </td>\n",
              "    \n",
              "</tr>\n",
              "        \n",
              "        \n",
              "\n",
              "        \n",
              "        \n",
              "            <tr style=\"background-color: hsl(0, 100.00%, 81.70%); border: none;\">\n",
              "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
              "        -1.091\n",
              "    </td>\n",
              "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
              "        Highlighted in text (sum)\n",
              "    </td>\n",
              "    \n",
              "</tr>\n",
              "        \n",
              "\n",
              "        </tbody>\n",
              "    </table>\n",
              "\n",
              "    \n",
              "\n",
              "\n",
              "\n",
              "    <p style=\"margin-bottom: 2.5em; margin-top:-0.5em;\">\n",
              "        <span style=\"background-color: hsl(120, 100.00%, 61.64%); opacity: 0.99\" title=\"1.356\">right</span><span style=\"opacity: 0.80\"> </span><span style=\"background-color: hsl(120, 100.00%, 69.83%); opacity: 0.93\" title=\"0.962\">to</span><span style=\"opacity: 0.80\"> </span><span style=\"background-color: hsl(0, 100.00%, 84.28%); opacity: 0.85\" title=\"-0.379\">use</span><span style=\"opacity: 0.80\"> </span><span style=\"background-color: hsl(120, 100.00%, 93.67%); opacity: 0.81\" title=\"0.103\">the</span><span style=\"opacity: 0.80\"> </span><span style=\"background-color: hsl(120, 100.00%, 84.57%); opacity: 0.85\" title=\"0.369\">software</span><span style=\"opacity: 0.80\">: </span><span style=\"background-color: hsl(120, 100.00%, 88.15%); opacity: 0.84\" title=\"0.253\">subject</span><span style=\"opacity: 0.80\"> </span><span style=\"background-color: hsl(120, 100.00%, 87.71%); opacity: 0.84\" title=\"0.267\">to</span><span style=\"opacity: 0.80\"> </span><span style=\"background-color: hsl(120, 100.00%, 95.94%); opacity: 0.81\" title=\"0.055\">the</span><span style=\"opacity: 0.80\"> </span><span style=\"background-color: hsl(0, 100.00%, 72.35%); opacity: 0.92\" title=\"-0.849\">terms</span><span style=\"opacity: 0.80\"> </span><span style=\"background-color: hsl(120, 100.00%, 95.17%); opacity: 0.81\" title=\"0.070\">and</span><span style=\"opacity: 0.80\"> </span><span style=\"background-color: hsl(0, 100.00%, 89.26%); opacity: 0.83\" title=\"-0.220\">conditions</span><span style=\"opacity: 0.80\"> </span><span style=\"background-color: hsl(120, 100.00%, 96.03%); opacity: 0.81\" title=\"0.053\">of</span><span style=\"opacity: 0.80\"> </span><span style=\"background-color: hsl(120, 100.00%, 87.43%); opacity: 0.84\" title=\"0.275\">this</span><span style=\"opacity: 0.80\"> </span><span style=\"background-color: hsl(120, 100.00%, 91.09%); opacity: 0.82\" title=\"0.169\">agreement</span><span style=\"opacity: 0.80\">, </span><span style=\"background-color: hsl(0, 100.00%, 76.04%); opacity: 0.90\" title=\"-0.692\">mcafee</span><span style=\"opacity: 0.80\"> </span><span style=\"background-color: hsl(0, 100.00%, 82.68%); opacity: 0.86\" title=\"-0.435\">grants</span><span style=\"opacity: 0.80\"> </span><span style=\"background-color: hsl(0, 100.00%, 82.56%); opacity: 0.86\" title=\"-0.440\">company</span><span style=\"opacity: 0.80\"> </span><span style=\"background-color: hsl(0, 100.00%, 96.17%); opacity: 0.81\" title=\"-0.050\">a</span><span style=\"opacity: 0.80\"> </span><span style=\"background-color: hsl(0, 100.00%, 81.52%); opacity: 0.87\" title=\"-0.478\">non</span><span style=\"opacity: 0.80\">-</span><span style=\"background-color: hsl(0, 100.00%, 85.63%); opacity: 0.85\" title=\"-0.333\">exclusive</span><span style=\"opacity: 0.80\">, </span><span style=\"background-color: hsl(0, 100.00%, 84.81%); opacity: 0.85\" title=\"-0.361\">non</span><span style=\"opacity: 0.80\">-</span><span style=\"background-color: hsl(0, 100.00%, 60.00%); opacity: 1.00\" title=\"-1.440\">transferable</span><span style=\"opacity: 0.80\"> </span><span style=\"background-color: hsl(120, 100.00%, 81.27%); opacity: 0.87\" title=\"0.487\">right</span><span style=\"opacity: 0.80\"> </span><span style=\"background-color: hsl(120, 100.00%, 69.83%); opacity: 0.93\" title=\"0.962\">to</span><span style=\"opacity: 0.80\"> </span><span style=\"background-color: hsl(0, 100.00%, 84.28%); opacity: 0.85\" title=\"-0.379\">use</span><span style=\"opacity: 0.80\"> </span><span style=\"background-color: hsl(120, 100.00%, 93.67%); opacity: 0.81\" title=\"0.103\">the</span><span style=\"opacity: 0.80\"> </span><span style=\"background-color: hsl(0, 100.00%, 97.09%); opacity: 0.80\" title=\"-0.034\">software</span><span style=\"opacity: 0.80\"> </span><span style=\"background-color: hsl(120, 100.00%, 98.01%); opacity: 0.80\" title=\"0.020\">listed</span><span style=\"opacity: 0.80\"> </span><span style=\"background-color: hsl(120, 100.00%, 85.55%); opacity: 0.85\" title=\"0.336\">in</span><span style=\"opacity: 0.80\"> </span><span style=\"background-color: hsl(120, 100.00%, 93.78%); opacity: 0.81\" title=\"0.101\">the</span><span style=\"opacity: 0.80\"> </span><span style=\"background-color: hsl(120, 100.00%, 76.01%); opacity: 0.90\" title=\"0.693\">grant</span><span style=\"opacity: 0.80\"> </span><span style=\"background-color: hsl(120, 100.00%, 96.57%); opacity: 0.81\" title=\"0.043\">letter</span><span style=\"opacity: 0.80\"> </span><span style=\"background-color: hsl(0, 100.00%, 79.95%); opacity: 0.87\" title=\"-0.537\">solely</span><span style=\"opacity: 0.80\"> </span><span style=\"background-color: hsl(120, 100.00%, 94.88%); opacity: 0.81\" title=\"0.076\">for</span><span style=\"opacity: 0.80\"> </span><span style=\"background-color: hsl(0, 100.00%, 91.54%); opacity: 0.82\" title=\"-0.156\">company</span><span style=\"opacity: 0.80\">’</span><span style=\"background-color: hsl(120, 100.00%, 90.08%); opacity: 0.83\" title=\"0.197\">s</span><span style=\"opacity: 0.80\"> </span><span style=\"background-color: hsl(120, 100.00%, 95.57%); opacity: 0.81\" title=\"0.062\">own</span><span style=\"opacity: 0.80\"> </span><span style=\"background-color: hsl(120, 100.00%, 89.41%); opacity: 0.83\" title=\"0.216\">internal</span><span style=\"opacity: 0.80\"> </span><span style=\"background-color: hsl(0, 100.00%, 88.34%); opacity: 0.83\" title=\"-0.247\">business</span><span style=\"opacity: 0.80\"> </span><span style=\"background-color: hsl(0, 100.00%, 98.12%); opacity: 0.80\" title=\"-0.018\">operations</span><span style=\"opacity: 0.80\">. </span><span style=\"background-color: hsl(120, 100.00%, 91.38%); opacity: 0.82\" title=\"0.161\">in</span><span style=\"opacity: 0.80\"> </span><span style=\"background-color: hsl(120, 100.00%, 91.28%); opacity: 0.82\" title=\"0.163\">this</span><span style=\"opacity: 0.80\"> </span><span style=\"background-color: hsl(0, 100.00%, 95.45%); opacity: 0.81\" title=\"-0.065\">agreement</span><span style=\"opacity: 0.80\">, </span><span style=\"background-color: hsl(120, 100.00%, 89.59%); opacity: 0.83\" title=\"0.211\">to</span><span style=\"opacity: 0.80\"> </span><span style=\"background-color: hsl(0, 100.00%, 84.28%); opacity: 0.85\" title=\"-0.379\">use</span><span style=\"opacity: 0.80\"> </span><span style=\"background-color: hsl(120, 100.00%, 93.67%); opacity: 0.81\" title=\"0.103\">the</span><span style=\"opacity: 0.80\"> </span><span style=\"background-color: hsl(120, 100.00%, 93.13%); opacity: 0.82\" title=\"0.116\">software</span><span style=\"opacity: 0.80\"> </span><span style=\"background-color: hsl(0, 100.00%, 90.21%); opacity: 0.83\" title=\"-0.193\">includes</span><span style=\"opacity: 0.80\"> </span><span style=\"background-color: hsl(120, 100.00%, 86.50%); opacity: 0.84\" title=\"0.305\">to</span><span style=\"opacity: 0.80\"> </span><span style=\"background-color: hsl(0, 100.00%, 86.53%); opacity: 0.84\" title=\"-0.304\">download</span><span style=\"opacity: 0.80\">, </span><span style=\"background-color: hsl(0, 100.00%, 93.58%); opacity: 0.81\" title=\"-0.105\">install</span><span style=\"opacity: 0.80\"> </span><span style=\"background-color: hsl(0, 100.00%, 85.60%); opacity: 0.85\" title=\"-0.334\">and</span><span style=\"opacity: 0.80\"> </span><span style=\"background-color: hsl(0, 100.00%, 90.81%); opacity: 0.82\" title=\"-0.176\">access</span><span style=\"opacity: 0.80\"> </span><span style=\"background-color: hsl(120, 100.00%, 93.04%); opacity: 0.82\" title=\"0.118\">the</span><span style=\"opacity: 0.80\"> </span><span style=\"background-color: hsl(120, 100.00%, 95.23%); opacity: 0.81\" title=\"0.069\">software</span><span style=\"opacity: 0.80\">. </span><span style=\"background-color: hsl(0, 100.00%, 78.15%); opacity: 0.88\" title=\"-0.607\">company</span><span style=\"opacity: 0.80\"> </span><span style=\"background-color: hsl(0, 100.00%, 78.14%); opacity: 0.88\" title=\"-0.607\">is</span><span style=\"opacity: 0.80\"> </span><span style=\"background-color: hsl(120, 100.00%, 98.12%); opacity: 0.80\" title=\"0.018\">not</span><span style=\"opacity: 0.80\"> </span><span style=\"background-color: hsl(0, 100.00%, 87.57%); opacity: 0.84\" title=\"-0.271\">granted</span><span style=\"opacity: 0.80\"> </span><span style=\"background-color: hsl(0, 100.00%, 88.61%); opacity: 0.83\" title=\"-0.239\">rights</span><span style=\"opacity: 0.80\"> </span><span style=\"background-color: hsl(0, 100.00%, 99.36%); opacity: 0.80\" title=\"-0.004\">to</span><span style=\"opacity: 0.80\"> </span><span style=\"background-color: hsl(0, 100.00%, 94.79%); opacity: 0.81\" title=\"-0.078\">updates</span><span style=\"opacity: 0.80\"> </span><span style=\"background-color: hsl(120, 100.00%, 96.11%); opacity: 0.81\" title=\"0.052\">and</span><span style=\"opacity: 0.80\"> </span><span style=\"background-color: hsl(0, 100.00%, 85.94%); opacity: 0.84\" title=\"-0.323\">upgrades</span><span style=\"opacity: 0.80\"> </span><span style=\"background-color: hsl(0, 100.00%, 74.38%); opacity: 0.91\" title=\"-0.762\">unless</span><span style=\"opacity: 0.80\"> </span><span style=\"background-color: hsl(0, 100.00%, 79.43%); opacity: 0.88\" title=\"-0.557\">company</span><span style=\"opacity: 0.80\"> </span><span style=\"background-color: hsl(0, 100.00%, 95.63%); opacity: 0.81\" title=\"-0.061\">has</span><span style=\"opacity: 0.80\"> </span><span style=\"background-color: hsl(0, 100.00%, 81.74%); opacity: 0.87\" title=\"-0.470\">purchased</span><span style=\"opacity: 0.80\"> </span><span style=\"background-color: hsl(0, 100.00%, 91.25%); opacity: 0.82\" title=\"-0.164\">support</span><span style=\"opacity: 0.80\"> (</span><span style=\"background-color: hsl(120, 100.00%, 89.33%); opacity: 0.83\" title=\"0.218\">or</span><span style=\"opacity: 0.80\"> </span><span style=\"background-color: hsl(0, 100.00%, 89.91%); opacity: 0.83\" title=\"-0.201\">a</span><span style=\"opacity: 0.80\"> </span><span style=\"background-color: hsl(0, 100.00%, 86.62%); opacity: 0.84\" title=\"-0.301\">service</span><span style=\"opacity: 0.80\"> </span><span style=\"background-color: hsl(0, 100.00%, 75.59%); opacity: 0.90\" title=\"-0.711\">subscription</span><span style=\"opacity: 0.80\"> </span><span style=\"background-color: hsl(120, 100.00%, 99.02%); opacity: 0.80\" title=\"0.007\">granting</span><span style=\"opacity: 0.80\"> </span><span style=\"background-color: hsl(0, 100.00%, 94.19%); opacity: 0.81\" title=\"-0.092\">rights</span><span style=\"opacity: 0.80\"> </span><span style=\"background-color: hsl(0, 100.00%, 99.36%); opacity: 0.80\" title=\"-0.004\">to</span><span style=\"opacity: 0.80\"> </span><span style=\"background-color: hsl(0, 100.00%, 94.79%); opacity: 0.81\" title=\"-0.078\">updates</span><span style=\"opacity: 0.80\"> </span><span style=\"background-color: hsl(120, 100.00%, 96.11%); opacity: 0.81\" title=\"0.052\">and</span><span style=\"opacity: 0.80\"> </span><span style=\"background-color: hsl(0, 100.00%, 86.17%); opacity: 0.84\" title=\"-0.316\">upgrades</span><span style=\"opacity: 0.80\">).</span>\n",
              "    </p>\n",
              "\n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "\n",
              "\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K_sQ9YN3V2cp",
        "colab_type": "text"
      },
      "source": [
        "The words in the darkest shade of green contribute most to the classification and agree with what you would expect for this example.\n",
        "\n",
        "We can save and reload our predictor for later deployment."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3nbgrK9DV3fy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "predictor.save('my_distilbert_predictor')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y6XmzOE0V7TN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "reloaded_predictor = ktrain.load_predictor('/tmp/my_distilbert_predictor')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DCXCdlFsV-ye",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a889b10f-f006-49b7-b555-7e187338eee9"
      },
      "source": [
        "reloaded_predictor.predict(eula)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yOxHhe_n93cA",
        "colab_type": "text"
      },
      "source": [
        "# **Albert**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jyjE8B7595_y",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439,
          "referenced_widgets": [
            "94373da67a05419388feb5ff0ca6430f",
            "847c1cfc5c48472e8779fa057c9ff245",
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            "a4dda24c79fe475284fe05095aa34480",
            "f0b2f1b81d134cdbb525f3b9d2c37bf4",
            "a28e560d9ccd4e9ca47b2042a541139c",
            "cb9bc3125f4d4daaa9f5a946dbdffd87",
            "832019cc4a374984a9db4c653b939a83",
            "1841b2ccb9944ae583f3d954619d5979",
            "bb7be258b216434daed3d86797051491",
            "9f806cfee64f40429f67d77aac99bc13",
            "1e6d8a7a30524242a45a30d216a9c7b8",
            "2ba6725d5bc144a7b95f61fe2c8e6429",
            "e08da35a85fe4972bddd583e53dce983",
            "bafb35557cab417a984bab9c1d7ed014"
          ]
        },
        "outputId": "fe4f8571-3e93-43fc-c580-bcededd310ac"
      },
      "source": [
        "MODEL_NAME = 'albert-base-v2'\n",
        "t2 = text.Transformer(MODEL_NAME, maxlen=500, classes= classes)\n",
        "trn2 = t2.preprocess_train(x_train, y_train)\n",
        "val2 = t2.preprocess_test(x_test, y_test)\n",
        "albert = t2.get_classifier()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ktrain/text/preprocessor.py:400: UserWarning: The class_names argument is replacing the classes argument. Please update your code.\n",
            "  warnings.warn('The class_names argument is replacing the classes argument. Please update your code.')\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "preprocessing train...\n",
            "language: en\n",
            "train sequence lengths:\n",
            "\tmean : 80\n",
            "\t95percentile : 262\n",
            "\t99percentile : 466\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "94373da67a05419388feb5ff0ca6430f",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=760289.0, style=ProgressStyle(descripti…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Is Multi-Label? False\n",
            "preprocessing test...\n",
            "language: en\n",
            "test sequence lengths:\n",
            "\tmean : 81\n",
            "\t95percentile : 265\n",
            "\t99percentile : 512\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "bf659642b40d480bb392d2c992e55836",
              "version_minor": 0,
              "version_major": 2
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=684.0, style=ProgressStyle(description_…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "832019cc4a374984a9db4c653b939a83",
              "version_minor": 0,
              "version_major": 2
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            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=63048440.0, style=ProgressStyle(descrip…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PIPiQnWx-fZk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "learner2 = ktrain.get_learner(albert, train_data=trn2, val_data=val2, batch_size=8)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RguJ_pAP-pwE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "81cf609d-9e29-45cb-bf65-fda3064d0a92"
      },
      "source": [
        "learner2.lr_find()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "simulating training for different learning rates... this may take a few moments...\n",
            "Train for 787 steps\n",
            "Epoch 1/1024\n",
            "787/787 [==============================] - 1368s 2s/step - loss: 0.5159 - accuracy: 0.7508\n",
            "Epoch 2/1024\n",
            "425/787 [===============>..............] - ETA: 10:27 - loss: 0.7379 - accuracy: 0.7638\n",
            "\n",
            "done.\n",
            "Please invoke the Learner.lr_plot() method to visually inspect the loss plot to help identify the maximal learning rate associated with falling loss.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pZCWaAdKT604",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "8be71521-a0f0-4bd3-92e5-297244608769"
      },
      "source": [
        "learner2.lr_plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rEA0wSrsUhs_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3b31f748-f21c-4693-dc5b-8b84acad3065"
      },
      "source": [
        "lr2 = (1e-5 + 1e-4)/2\n",
        "lr2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5.5e-05"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iJCac_md_J68",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "learner2.fit_onecycle(lr2, 6)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HEffFNts_IQ3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        },
        "outputId": "6bc7e95a-630f-402c-eb17-7690c8187ca3"
      },
      "source": [
        "learner2.validate(class_names = t2.get_classes())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.82      1.00      0.90      1287\n",
            "           1       0.00      0.00      0.00       289\n",
            "\n",
            "    accuracy                           0.82      1576\n",
            "   macro avg       0.41      0.50      0.45      1576\n",
            "weighted avg       0.67      0.82      0.73      1576\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
            "  'precision', 'predicted', average, warn_for)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1287,    0],\n",
              "       [ 289,    0]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8Yn2TLi_-2kt",
        "colab_type": "text"
      },
      "source": [
        "# **Roberta**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7T6d9WiRAith",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488,
          "referenced_widgets": [
            "018d9f436810444182e59412e7fb8c2c",
            "7a1f17e145e944489e2057d195710ec9",
            "b200b3caab144f5a8ff9886b1e4f4bc2",
            "6ca680c4688c494cb3b834138a678fc1",
            "68bbb05a0e1c429c864a0586a5416b86",
            "d4102dde8e2e430c8abbe05dbe477775",
            "e47a326c6d5c40c79e8f9ef4e5fc554f",
            "c6cce5ebd0574f1f9d7cc5c4891e9e95",
            "c5c455172f2141308dee5f05e4a9bca0",
            "a9ddc94866b84101bfe0cc2a5d08b345",
            "e39c675ab5d94d4a9245d07804084512",
            "dbd4a651118548d8ad161c35eafc8d75",
            "0fd8a3e37c654bc28d9bd723f940abbf",
            "e241bfe5dc3c484f909d4b7892191edd",
            "ee52133299d0483fa47eabe3dfea86de",
            "8142341924e64d6cac2ba44cbee5e5ae",
            "fc992e13544f445a9457e021a6233ada",
            "0fc986c4a0af49d7ba54b5597bd86396",
            "8aa19aafec2942f6bd62a791ac906ecf",
            "f363aba35a104c46aa62a1dbc855d57b",
            "b1b3789e9bc2489ba7217fd4122c6439",
            "938c82b0c30b441eba42e04208bb247d",
            "4fd38cb44be642d98a454209201ef7ee",
            "23314c8c832240d8a7c1edd5edb9b4ea",
            "d90a19b1e15849ca85b7169aa0dc34f4",
            "6d6bcd643ca3452b88d3b3743685db16",
            "9377d5e54332447e825b3aaf67ac06a3",
            "78f2d34975354cb59c21990d8fcd4d6f",
            "dd2ba4e0b4714746a7d0830274fb5c82",
            "b91cbed2b9e64bde94053415dc8adc31",
            "36059f46a7334798b17ce97bb4045997",
            "542da56997ca42f68c4bc6859732f75a"
          ]
        },
        "outputId": "06de2df4-12f3-4f8f-dd22-a19441a9ef9b"
      },
      "source": [
        "MODEL_NAME = 'roberta-base'\n",
        "t3 = text.Transformer(MODEL_NAME, maxlen=500, classes = classes)\n",
        "trn3 = t3.preprocess_train(x_train, y_train)\n",
        "val3 = t3.preprocess_test(x_test, y_test)\n",
        "roberta = t3.get_classifier()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ktrain/text/preprocessor.py:400: UserWarning: The class_names argument is replacing the classes argument. Please update your code.\n",
            "  warnings.warn('The class_names argument is replacing the classes argument. Please update your code.')\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "preprocessing train...\n",
            "language: en\n",
            "train sequence lengths:\n",
            "\tmean : 80\n",
            "\t95percentile : 262\n",
            "\t99percentile : 466\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "018d9f436810444182e59412e7fb8c2c",
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            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=898823.0, style=ProgressStyle(descripti…"
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        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=456318.0, style=ProgressStyle(descripti…"
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          "metadata": {
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          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Is Multi-Label? False\n",
            "preprocessing test...\n",
            "language: en\n",
            "test sequence lengths:\n",
            "\tmean : 81\n",
            "\t95percentile : 265\n",
            "\t99percentile : 512\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
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            "tags": []
          }
        },
        {
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            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qtfCV7hQAz1k",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "learner3 = ktrain.get_learner(roberta, train_data = trn3, val_data = val3, batch_size = 8)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6PlhxU7HUlrQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "7c0b7f18-a6e7-4b92-ef1f-93e564681051"
      },
      "source": [
        "learner3.lr_find()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "simulating training for different learning rates... this may take a few moments...\n",
            "Train for 787 steps\n",
            "Epoch 1/1024\n",
            "787/787 [==============================] - 760s 965ms/step - loss: 0.4760 - accuracy: 0.8208\n",
            "Epoch 2/1024\n",
            "320/787 [===========>..................] - ETA: 7:27 - loss: 0.6413 - accuracy: 0.7862\n",
            "\n",
            "done.\n",
            "Please invoke the Learner.lr_plot() method to visually inspect the loss plot to help identify the maximal learning rate associated with falling loss.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VL_i3r3wBy-q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "860a32b9-c89c-4ec6-992a-b23cfde7bb9c"
      },
      "source": [
        "learner3.lr_plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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dRhOBqjZ8FTIhJSY6itO6tQVg9W8uZu2uYq55YSHlVV7GPj67ptzvrziNwd3bkRwXQ/e0pGCFa4zxo6zSE7DpKWtzNe2IyHgRWS8ieSJyVwNlvisia0RktYi85mY8kSI5PoacrDTWP3IJ7/3krDrbfvXOSsb/+TPG/GE2hSXlrN5ZREkARzk0xjSsrNJDYmzgO2Q2tddQs4lINDAFuBDIBxaJyDRVXVOrTF/gbmC0qh4UERuXoYUN7dGeLY9NYM2uYjJS4nnkg7XOk87A8Edn1pR78qohTBrSNVhhGmOAoxWtr0YwAshT1c2qWgFMBSbVK/MjYIqqHgQI1rAVrZ2IMKhLWzqmJPDU5KFs/d1EbjuvT50yt09dxqHSiiBFaIwBKKvykNjKEkFXYEet5Xzfutr6Af1E5AsRWSAi412Mx9Ty84tOYevvJvLKDSNqvnjvr9gV5KiMiWxHKzwkxLWuRNAUMUBfYBwwGfibiLSrX0hEbhKRXBHJLSwsDHCIrdvYfhmseehi+ndK4bHpa1m90yaeMyZYyiq9JMS0rkRQAHSvtdzNt662fGCaqlaq6hZgA05iqENVn1fVHFXNycg4fugGc3JEhCe+O4TSCg8Tn/o84E81GmMcZZUeEuMC//vczSMuAvqKSLaIxAFXAdPqlfk3Tm0AEUnHaSra7GJMpgEDu6Ry78QBANz51nK8XqWy3gNqxhh3Ha30tK4agapWAbcCM4C1wJuqulpEHhKRy3zFZgD7RWQNMBu4U1X3uxWTadwPx/Ti7D7pfLRqN71+PZ2+93xElSUDYwJi/qb9lFZ4AjppfTVX6yCqOl1V+6lqb1V91LfuflWd5nutqvozVR2oqqep6lQ34zFf74Uf5NAu6dgYRYu2HgxiNMZEjpe/dAaWzN0W+P9zwb5ZbEJMQmw0s352Dm/cdCYAL37hfDnnbigk664PWVVgN5ONcUNasjMm2IistIAf2xKBOU6HNvGM7NWBy4d25b9r9nDu/83hBy9+BcAT/90Q5OiMaZ0SfE8UP3jZoIAf2xKBadA1Z/YEYMu+IzXr8vYeDlY4xrRqh0or6Z6W2OqeLDZh7oye7fno9jF8Y3AX0pLj6NI2ge0HSrn51VzCbfhyY0LdgSMVpCXFBeXYro01ZFqHAZ1TeXqyMyFOYUk5wx+dyYzVe/h03V7OH5AZ5OiMaT0OllbQPkiJwGoEpskyUuJ57tphgPUmMuZkbNt/hDveWEZ5ladm3YEjFaQlWyIwYWD8qZ0BeG7uJuZtsOE+TGSZuWYPWXd9yIhHZzJ95YmPzfXgtNW8t7SAj1ftprCknKMVHnYVldG1XWILRtt01jRkmq1TagK7i8t4dcE2xvZzhvx4cNpqemUkU+VRBnROZVTvDkGO0piW9/O3lgOwt6Scn/xryQlPKRnre8/tU5cRFx3F7Rf0xeNVzshq36LxNpUlAtNsb/14FNf+fSGz1+1lx4FSuqcl8fKXW+uUuWBAR0b3Sef60dnBCdIYFwzp3o65tWrCfe75iBe+n8MFA5t3v6xN/LFLb4XHy+Mz1hMdJYzrF5yx1KxpyDRb97Qk/v6DHLyqPP3pRr89iGau3ctv3l/DdS99xfrdJUGI0piWM3/Tfi59+jPmbijkklM78dU959ds++Erze9FFxdz/KXX41VE5KRjPRGWCMwJ6dMxheFZabyZm8/aXQ1f6OesL+SaFxYGMDJjTt6OA6WMe3w2WXd9yKvzt/K9vy9kVUExABcNyqyZ5KnanuLyZu2/9jASPxnXu0ViPhmWCMwJu/0CZ8TwFz4/NmDsOb6qbY+0JL5zRjcA9h0ut9nPTNgoLClnzB9ms3V/KQD3/Wc1VV6lvW8MrpHZzv2vywZ3qXnP3A1Nn1yxvMpD3t7D3HpuHxbfewG/HN+feycOqBn9NxjsHoE5YWf1TueMnu15d4kzzcSUq4cx7pQMkmu1f353eHe+89x8/vjJBh7+5qnBCtWYJqt+ej4zNZ5hPdrz0ardAMz6+bjjundueWwC5z8xl7cX53Pl8B5N2n/BwaMAZKcn06GNM77QD8f0aqnwT4jVCMxJuWbksS//xNM710kCAMOz0rj09M68umAbM1bvDnR4JkLtKS7jDx+vY2ut4VGaquioU3t96boRnNHT6cXzraFd/fbxFxEuG9yFRVsPUtLE4aNzfc/g9O+c0uzY3GKJwJyUy4d146FJg5jqG63Un1+N7w/Aza8u5qlZGwMVmolgT83ayDNzNnHRn+Y1+QJd7VCpU75tUiyThnSlU2oC3xvVs8Hyp2Q6F/Rt+0vZW1LG5sLDnPP4bN5enF9Tpvpm8lu5O3hlwVYSY6MZ2Dm1uaflGmsaMift+6OyGt3ePS2J07q2ZWVBEU/8dwM/Pf+42UiNaTFllR4+9jXnVHi8nPbgJ3xw29lkpiaQkRL/te8/4Luf1S4xluT4GBb8+vxGy/f3XdD/MGN9nYcsf/HWci49vTMHSysY9dinRAl4fZ2LstOTg9ZDyB+rEZiAeOK7gwHI6pAU5EhMa7R2VzH3vLeSxdsOcv1Li9h/pIKHJh0bzvnSpz9n+KMzmbV2T6P7Wb2ziD98vJ6OKfHHNXM2JDs9ma7tEmuSQNvEWPp3cmoJ/e/7mMdnrAeOJQEg5DpPuJoIRGS8iKwXkTwRucvP9utEpFBElvn+fuhmPCZ4+mamcOu5fdhx8CgVVTb9pWk+VWXB5v38c8G247a99MUW/rVwO1c8+yXzNzuz3Q7onMpfrh5ap9yN/8hl4x7/3Z3X7y7hBy8uAuCOC/s1K7aKWlO6Ln/gIj66fUzNcnVnijrn0qy9u8+1RCAi0cAU4BJgIDBZRAb6KfqGqg7x/b3gVjwm+HplJOPxKtsPlAY7FBNmSsoqGf7oTK56fgH3/nsVd7yxjCXbj/XF9/frvVv7RCae1vm49dOW7wTA61WOVhwb9O0vs/Mor/LwwW1nM3lE03oAVevbsQ0Af/t+DuDcRH7p+uE12687K6tO+e7tQ6tm7OY9ghFAnqpuBhCRqcAkYI2LxzQhrFeG859lc+Fh+vj+4xhT3+6iMm57fQkPf/NUurdP4rfT1/KvhdvrlHlvaQHvLS1g6+8mAs4N3m7tE/n8V+fx0cpdbCo8TOe2zgBuI7PTWLjlAKt/czEX/3keU2bnsWZnMbPWOX3/X/vRSM7qnc7qgiLO7pPOqV3bNjvmZ64Zxocrd3HBgI416849pSMrHryIzzbs4/wBHbnx7GwyUuL5z7ICzunXsZG9BZ6biaArsKPWcj4w0k+5K0RkLLABuENVd/gpY1qB7PRkADafQJc+0/p8uGIXg7u3pVutX8cer/KHj9exaOtBxv/5szrlLx6UyXPXnkH23dNr1u0uKuPhD9bw4cpdnOa7gF9Srxbwyo0j2L6/lOT4GCae3pm/zt1ckwQA/rVwOzk909h2oJQJfmoQTdEuKY5rRh7fsyg1IZaJpzv77J7mnGdTnzcIpGD3GnofeF1Vy0XkZuAfwHn1C4nITcBNAD16hN6HaJqmbWIs6W3i2Fx4mDU7i1m/p5hvDe0W7LBMgD320Vr+Otd5Gv3cUzJ46foRTJmdx8tfbqWwxP9QDc9dewbjT+0EOH3631vqtLuf+dismjJZvh8a9cXHRNPX18XzquE9ao6dlhzHyOw0lmw7yBu5O/B4ld4d/e+jtXPzZnEB0L3Wcjffuhqqul9Vq//lXwDO8LcjVX1eVXNUNScjIzij85mW0Su9DVv2HWHCU59xxxvLOVJeFeyQTADtLS6ruRADHCl32uifnLmxThI4v39H3vvJWXRPc5p3zut/rCnl91eczis3jKiz3xHZaVx3VsN9/atlpydz49nZZKbG88FtZ3NWn3R2FZVx379XATCoS/ObhVoDN2sEi4C+IpKNkwCuAq6uXUBEOqtq9ewOlwFrXYzHhIBeGclMXXSs9S/nkZmsfXg8e4vLuO31pfzswn6M7GVzGbRWL9UbrnxX8VEOlVZQ4fFy4cBMEmOj6ZfZhlvG9SE6Spjzi3M5UlFVZ7TOuJgoxvbLIC46igqPl4cmDfraZ1lqu+/Sgdx3qdNvZVSt71qH5Liam76RxrUagapWAbcCM3Au8G+q6moReUhELvMV+6mIrBaR5cBPgevciseEhrP6pNdZPlrpocrj5ZEP17JwywGufH4Bh62WEFbeXpzP7VOXUlbpabRccVklry3cziWndmLr7yby0/P7kn/wKJ/n7QPgxrOzeWryUG49ry/RUc7DVtFRQmpCrN/9VXfZHJGddsKx98441hT0tx/khNRDXoHk6nMEqjpdVfupam9VfdS37n5VneZ7fbeqDlLVwap6rqquczMeE3zjB3Xi1K51H60f/ftPa7r0AdzwktOX+8mZG/np60vrzOtqQs+zc/L4z7Kd9L/vYzYXHm6w3Nz1hRQdrazpSjkyOw1V+N1H64iNFoZ0b9es414xzLm/1DvjxH/FiwgP+x486xakaSJDgT1ZbAIqLiaKD24bw9bfTWTZ/RfSPim2Ziz357/n3CL6ausB1u0u5k8zNzBt+U4W1xq73YQWr1fZXVRWs/zZxn0Nlt1T7JQ7xffUbfWAbvkHjzKoS1sSYqObdezHLj+NxfdeUDPt44m69syerH1oPB1TE05qP+HMEoEJmnZJcSy9/yJ6+LrVjTulI5/cMRagTtfBxVstEQTSku0H8Xqb9uzr1v1HOFLhqRlLf//hhidoKTxcTlxMFG0Tnaae2hf+gkNHmx1nXExUzTDOJ0NESIxrXhJqbSwRmKD7+H/HMPNn5xAXE0W/zJQ6T3X2Sk+uM5uTcUdZpYer/7aAO99azuXPfMmrC7Z9bZs/wEtfbCU6Srh4UCe6pyWyed+RBqdt3Fx4hK7tEuu0w08e4XQsrPTYsCPBZInABF1SXEydJ40fu/w0cu+9gOUPXMTIXmnN+oVqTszLX27ly037ecs3dPID01bT/76POXik4cHRnpu7iVcXbOPK4d3pnpZEdnobPlixi2v/fvzUpF6vkrv1QE1zULUHvuG0z397mD1PEkyWCExISm8TT9vEWHJ6plFSVsWGvQ3Pi2xO3icNTBr01KcNzx8xf5MzuNs9E5xmoQsHZgLwRd7+4wYW/L9P1nOwtJIRWXV7+CTERrPiwYu4e0Lwpmk0lghMiMvJcn5B5tp9Atds31/Kku2HuPPiU3jkm6fSL9OpnQ3t0Y6XvtjKmp3FlJRV8ur8rXVqZjsOlDJ+UKeaAd8617rZ+ou3ltc0ER04UsEzczYBMNxPV8/UhNia7qImOII9xIQxjeqRlkRmajzzN+3n2jO//slR0zxllR7GPj4bgG+c3oUeHZK4fFhXvszbT3SUcP3Li5jw1GdcPrQr7y4toG9mCiOz0+j16+moUjOODsDIXmmkJcdx4EgF05bvpG/HNuRkpfGvhc6w0Z1SE2w+ihBlicCENBHhwoGZvLkon/2Hy1ukl4hx7DhQyhXPflmz3MN3kU6Ki+GCgZnsq9UDKM/3fMDGvYfpkZZE9f3g6vF/AFISYlly34V8+9kvyd12kD/+d0Od48375bkR+8BWqLOmIRPyJo/oQYXHy8Mf2AjmLenBaavZ6xvf56Xrhh+3Pb1NPLef3xcR8Pqu/M/MzmNVQREAU2860+/YPG/cPIrLh3U9bn3tYSJMaLF/GRPyBnVpy/Wjs/j3sp0862trNidn3oZCDh11Jmn/9YT+nNvf//j4/TuloAqrCooB2FVUxu8/XkdKfAxDe/h/Ejg6Snj824PdCdy4whKBCQu3ntsHgN9/vI51u4uDHE34uvvdFWTd9SHff/ErFm87yHn9O3LT2N4Nlj+3f0c6JMcB8I3BXQDYVHiE0X3SiY9p+CGs6CjhlRtG0CE5jge+MZAPbju7ZU/EtChLBCYsdGgTz50XnwJw3IQlpmlUlde/qjvvU2Zq4/dcEmKjeePmM7l8aFd+6fv8AX40Nvtrjze2XwaL77uQ60dnn9CsXyZw7GaxCRtdaw0K5vGqdTlspuoxnWprykBvfTqm8MSVQwB8cwQkkW437VsVSwQmbEw4rTOPz1hPwaGjbNt/pGYOZNM0S32TvU+5ehjjT+3EkYqqBod4bsjQHu2/vpAJO9Y0ZMJGXEwUf6xHpPAAABNASURBVPWNULpml90naI5/Ly3gln8tAZxRPxsb599EHksEJqz0y0whOS66ZngD8/V2HCjlf99YBjgTwHdqG7nDLRv/rGnIhJW4mCjG9M3g03V7gx1K2Fi90+n3f/+lA/mBb1IYY2pztUYgIuNFZL2I5InIXY2Uu0JEVERy3IzHtA45We3ZVVTW6MiYxnG4vIof/9NpErp6ZA+7wW78ci0RiEg0MAW4BBgITBaRgX7KpQC3A8ePXWuMH71888z+z2tLghxJ6Jv61XbAmde3uTOAmcjhZo1gBJCnqptVtQKYCkzyU+5h4PdAmZ9txhwnxzeU8Zeb9jc4CYqBjXtKeOTDtQzonMqbN48KdjgmhLmZCLoCtZ9eyfetqyEiw4Duqvqhi3GYViY1IZbbznOeND6RKQ4jxdOf5gEwodbAcMb4E7ReQyISBTwB/LwJZW8SkVwRyS0sLHQ/OBPyRvXuAMAlT9pTxg3ZuPcw7ZNi+fG4hoeQMAbcTQQFQPday91866qlAKcCc0RkK3AmMM3fDWNVfV5Vc1Q1JyMjw8WQTbg4vZvzRGxJWRXlVV8/t26kWbe7mLW7iunTsQ2x0dZL3DTOzW/IIqCviGSLSBxwFTCteqOqFqlquqpmqWoWsAC4TFVzXYzJtBJt4mN43vdw2Wcb9gU5mtBT/ZnccWG/IEdiwoFriUBVq4BbgRnAWuBNVV0tIg+JyGVuHddEjrH9MoiPiWL+Znu4rL59h8uJj4liVK8OwQ7FhAFXHyhT1enA9Hrr7m+g7Dg3YzGtT0JsNGf0bM+X9pTxcXYVlZHeJt5mBDNNYo2HJqyd1bsDa3cVc8AeLquxqfAw05bvJCvd5gc2TWOJwIS1Ub3TARj28H+DHEnomLPe6Vl378Tjnt80xi9LBCasnd7t2IQne0si95nEh95fw+u+p4iXbj9I13aJDOicGuSoTLiwRGDCWmx0FPdd6vzyXbTlYJCjCZ4Xv9jC3e+u5OCRCpZuP8SQBuYTNsYfSwQm7H1/VE9SEmJ4ctaGiBxy4mjFsecorv37QgoOHWVoE2YeM6aaJQIT9mKjo+jZIYkNew7z7pKCr39DK1NYcmwKytU7nQl7hlqNwDSDJQLTKjz+7cEA/HtZ5CWC+Zudh8duPqdXzbpBXWyyeNN0NjGNaRUGdE7l5rG9ePGLLRwpryIuJirkh1Z4atZGVuQX8cSVg5m/aT/JcTGc3Te9Wfuo9Hi57z+rAbhrfH/iY6LJP1BqQ06bZgnt/ynGNMPZfdOp9Ci/fm8lfe/5iJ0hMjKpqvLmoh2UVlTVrPN4lSf+u4GZa/fw9KyN3PzqYq79+8I67f1Ncf4f51JR5QVARPjZhf144sohLRq/af0sEZhWY3hWGnExUfxn2U4Abvnn4qDfPF5VUET23dP55TsrePiDNTXrl24/1sPpb59tqXn95KyNTd53YUk52w+UAnDNyB4tEK2JVJYITKuREBvN8Kz2NcvL84t4Kzef33+8jsPlVY280z33/WdVzesl2w7VvP4izxkW44bR2XXKPzd3Exv3lHztfr1e5Xt/X0hcTBSf3DGWR791WgtFbCKRJQLTqpzdxxmmvGcHZ3iFX76zgmfnbOLSp4Izb0FUrbF+1u8p4Vdvr0BV2bLvMF3bJTLx9GOTxvzcN1LohX+ax6qCIvYUOw/I7So6yhP/3cDekjI+XbeHOev3snZ3Met2l/A/4/rQLzMlsCdlWh1LBKZVGeO72XpW7w4kxR27Ybp1fyn7D5c39Da/Cg4dxeN1mpZmr99LcVlls96//3A5i7cd5Mqc7jzyzVMBeCN3BysLitiy7wjZ6cl1evf8cMyxXj+XPv05I387i4oqL6Me+5SnZm1kxKOzuOHlXK57aRHTfM1fVw7vjjEnyxKBaVUGdk5lTN90zumXwZSrhwFw8aBMAFYWFDX4vhc+28yEWrOd3fLPxYz+3af0/vV0fviPXK5/aRG/fnclFVVe/rlg29cOcnfwSAUTatVCRman1by+7C9fsDy/iOz0ZBJio7llXG9e+H4OiXHRbHjkEqKjjtUiTv/NDL/7/+u8zbRNjKVT24RG4zCmKaz7qGlVoqKEV28cWbM89aYzGdAplRmrP2HZjkOMO6Wj3/c98uFaAJbtOMTAzql8tGp3zbaZa/cA8MGKXXhVmb5yN+8syee9n4xuMI5fvbOCPcVODeTW8/rQrX3icWWuPbOnU3Z8/5p1cb45BD7Pc54NKKv0ktUhia37nZvCz14zjFv+tQSAoqPNq6EY0xCrEZhW7cxeHWibFMvwrPb8eeZGvtpywG+5vh3bAPCvBdtq2ubvnTiAOy7ox/dH9aR3RjIA01c6CWLp9kN4vQ33SMrbexiAh795Kt3TkhAR7pkwgB+NySYpLpqnJg/llE7+2/bvu3QgZ/U+NqHMQ5NOpb+vbK+MNvzvBX0B6JfZpsmfgzGNkWB3r2uunJwczc212SxN8/xnWQG3T10GwEvXD+eON5bxm8sGMWlIVwpLyhn+6Mzj3vPPG0fWPOC1ufAwU2Zv4p0l+TXb37/1bE7r5v8J3pG/ncnYvhk8/p3BJxzz4zPWMWX2JtY+NJ6CQ0d5beF27pk4gOgo4eNVuxme1Z4ObeJPeP8msojIYlU9bk54cLlGICLjRWS9iOSJyF1+tv9YRFaKyDIR+VxEbAB144oJp3XmklOdHjrXv7SIQ6WVPDtnEwC/ne40C3VIjqvzngGdj/1i75XRhj9+dzAdU45deC9/9ouaX/61lVd52FNcTrf2JzcxzM8vPIU1D11MYlw0fTq24f5vDKy5fzD+1E6WBEyLcS0RiEg0MAW4BBgITPZzoX9NVU9T1SHAH4An3IrHRLbY6CievfaMOnP4rttdQtZdH/LeUmd8oum3j2HSkC4AXD86y++F9tNfjGPmz87hnH4ZVHqUC56YWzPom9Mt9Ag7DzlNS1393BdojqgoISnObuMZ97lZIxgB5KnqZlWtAKYCk2oXUNXiWovJQHi1U5mwc+t5fQB4evLQOut7ZySTmZrAk1cNZevvJvLANwb5fX+b+Bj6dGzD7684vWaEzymz8yguqyTnkZmc+39z+Otcp6bh7waxMaHIzUTQFdhRaznft64OEfkfEdmEUyP4qYvxGMPoPunk3nsBl57emdd+5PQuSm8Tx8O+fv5N1altAu/9ZDQdU+J5f/lOVuwoYr+vS+nURc7XPjs9uWWDN8YlQe81pKpTVLU38CvgXn9lROQmEckVkdzCwsLABmhanfQ28YgIZ/VOZ96d57Lg7vM5q3fzRv2s9tPz+7L/SAXzNjrfy5euHw5AYmw0manWx9+EBzcbIAuA2o89dvOta8hU4Fl/G1T1eeB5cHoNtVSAxvTocHI3dIf4ZgJ7Zf5WAMb0Seej28fQLin2JCMzJnDcrBEsAvqKSLaIxAFXAdNqFxCRvrUWJwJNH3rRmBBQ/SxAWaUzFHRMdBQDOqfSua3dHzDhw7UagapWicitwAwgGnhRVVeLyENArqpOA24VkQuASuAg8AO34jHGDaE++Y0xTeFq3zRVnQ5Mr7fu/lqvb3fz+MYEwju3jOLbz83n5rG9gx2KMSfEOikbc5LO6JnGlscmBjsMY06Y1WuNMSbCWSIwxpgIZ4nAGGMinCUCY4yJcJYIjDEmwlkiMMaYCGeJwBhjIpwlAmOMiXBhN1WliBQC23yLbYEiP6/rL6cD+1oohPrHOZmyjW33t62xc6y/HArn35TyDW1vyvnXX2ffgcB8BxqK7UTLtvbvQFPKn8xn0NTvQE9VzfB7dFUN2z/geX+v/WzLdeOYJ1u2se3+tjV2jo19HsE6/5P5DJpy/vYdCM53IFCfQWv5Drj9GTT1O9DYX7g3Db3fwGt/y24c82TLNrbd37avO8fGPo+W0tz9nuhn0JTzr7/OvgOB+Q40d9+R/h1oSvmT+QxO+jsQdk1DJ0JEclU1J9hxBEuknz/YZxDp5w/2GTQm3GsETfV8sAMIskg/f7DPINLPH+wzaFBE1AiMMcY0LFJqBMYYYxpgicAYYyKcJQJjjIlwEZ0IRGSMiDwnIi+IyJfBjicYRCRKRB4VkadFJOLmjBaRcSLyme97MC7Y8QSLiCSLSK6IXBrsWAJNRAb4/v3fFpFbgh1PMIRtIhCRF0Vkr4isqrd+vIisF5E8EbmrsX2o6meq+mPgA+Afbsbrhpb4DIBJQDegEsh3K1Y3tND5K3AYSCDMzh9a7DMA+BXwpjtRuqeFrgNrfdeB7wKj3Yw3VIVtryERGYvzH/gVVT3Vty4a2ABciPOfehEwGYgGHqu3ixtUda/vfW8CN6pqSYDCbxEt8Rn4/g6q6l9F5G1V/Xag4j9ZLXT++1TVKyKZwBOqek2g4m8JLfQZDAY64CTDfar6QWCiP3ktdR0QkcuAW4BXVfW1QMUfKsJ28npVnSciWfVWjwDyVHUzgIhMBSap6mOA3yqviPQAisItCUDLfAYikg9U+BY97kXb8lrqO+BzEIh3I043tdB3YByQDAwEjorIdFX1uhl3S2mp74CqTgOmiciHgCWCMNcV2FFrOR8Y+TXvuRF4ybWIAq+5n8G7wNMiMgaY52ZgAdKs8xeRy4GLgXbAX9wNLWCa9Rmo6j0AInIdvhqSq9G5r7nfgXHA5Tg/BKa7GlmIam2JoNlU9YFgxxBMqlqKkwwjkqq+i5MMI56qvhzsGIJBVecAc4IcRlCF7c3iBhQA3Wstd/OtiySR/hlE+vmDfQaRfv7N1toSwSKgr4hki0gccBUwLcgxBVqkfwaRfv5gn0Gkn3+zhW0iEJHXgfnAKSKSLyI3qmoVcCswA1gLvKmqq4MZp5si/TOI9PMH+wwi/fxbSth2HzXGGNMywrZGYIwxpmVYIjDGmAhnicAYYyKcJQJjjIlwlgiMMSbCWSIwxpgIZ4nAuE5EDgfgGD8Wke+7fZx6x/ymiAw8wffd73v9oIj8ouWjaz7f3AyNjjwqIqeJyMsBCskESMSPNWTCh4hEq6rfEVJV9blAHxP4Js5cFmuaudtfApedVGBBoqorRaSbiPRQ1e3Bjse0DKsRmIASkTtFZJGIrBCR39Ra/28RWSwiq0XkplrrD4vIH0VkOTDKt/yoiCwXkQW+eQTq/LIWkTki8nsR+UpENvhGVkVEkkTkTRFZIyLvichCEcnxE+NW3/uXAN8RkR/5Yl4uIu/49nMWzsX8cRFZJiK9fX8f+87jMxHp72ff/YByVd3nZ9sQ3zmt8MXX3rd+uG/dMhF5XOpNwuIr01lE5vnKrKp1zuNFZIkv9lm+dSNEZL6ILBWRL0XkFD/7SxZn0pevfOUm1dr8Ps6wDaaVsERgAkZELgL64owXPwQ4Q5yJRcCZIOQMIAf4qYh08K1PBhaq6mBV/dy3vEBVB+MMm/2jBg4Xo6ojgP8FqkeY/QnOJDwDgfuAMxoJd7+qDlPVqcC7qjrcd8y1OJMYfYkzfs2dqjpEVTcBzwO3+c7jF8AzfvY7GljSwDFfAX6lqqcDK2vF/RJws6oOoeE5I64GZvjKDAaWiUgG8DfgCl/s3/GVXQeMUdWhwP3Ab/3s7x7gU99neC5Owkv2bcsFxjQQhwlD1jRkAuki399S33IbnMQwD+fi/y3f+u6+9ftxLnzv1NpHBU5zDMBinFmo/Hm3Vpks3+uzgScBVHWViKxoJNY3ar0+VUQewZmzoA3OGDZ1iEgb4CzgLRGpXu1vopvOQKGf97cF2qnqXN+qf/j21Q5IUdX5vvWv4X9ylUXAiyISC/xbVZeJM87+PFXd4jvnA76ybYF/iEhfnKk6Y/3s7yLgslr3LxKAHjiJcC/Qxc97TJiyRGACSYDHVPWvdVY6F6wLgFGqWioic3AuPABl9droK/XYAFkeGv4OlzehTGOO1Hr9MvBNVV0uzuQt4/yUjwIO+X6RN+YozoW4Rflm6hoLTAReFpEncGZd8+dhYLaqfkuc2b3m+CkjODWJ9X62JeCch2klrGnIBNIM4Abfr2dEpKuIdMS5MB70JYH+wJkuHf8LnAnK8fX2Oa2J70sBdvl+bdee07jEtw1VLQa2iMh3fPsXERnsZ19rgT71V6pqEXCwum0f+B4wV1UPASUiUj3Dlt+2eRHpCexR1b8BLwDDgAXAWBHJ9pVJ8xVvy7Hx+a9r4JxnALeJr3ojIkNrbesHHHefwoQvSwQmYFT1E5ymjfkishJ4G+dC+jEQIyJrgd/hXMDc8AyQISJrgEeA1UBRE953H7AQJ5Gsq7V+KnCn72Zqb5wkcaPvxvZqYNJxe3KawYZWX2Dr+QFOW/wKnHsoD/nW3wj8TUSW4dwj8RfzOGC5iCwFrgSeVNVC4CbgXV9M1c1dfwAe85VtqLb0ME6T0QoRWe1brnYu8GED7zNhyIahNhFDRKKBWFUt8124ZwKnqGpFgON4EnhfVWc2sXwbVT3se30X0FlVb3czxkZiiQfmAmf7xv03rYDdIzCRJAmY7WviEeAngU4CPr+lkcnU/ZgoInfj/H/dRsPNOYHQA7jLkkDrYjUCY4yJcHaPwBhjIpwlAmOMiXCWCIwxJsJZIjDGmAhnicAYYyKcJQJjjIlw/w9tloGTyo2KbwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "99dVBpe8Un1F",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "lr3 = 4e-5"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VuyFtci6BxXE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "7ff053b3-d080-41c6-bd67-a8c746c3e5e0"
      },
      "source": [
        "learner3.fit_onecycle(lr3, 2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "begin training using onecycle policy with max lr of 4e-05...\n",
            "Train for 788 steps, validate for 50 steps\n",
            "788/788 [==============================] - 811s 1s/step - loss: 0.2879 - accuracy: 0.8774 - val_loss: 0.2626 - val_accuracy: 0.8788\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7feb2504bba8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bU2nMmR5Bv2K",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "18eafc9b-c284-4b67-d9dc-e927e12c2408"
      },
      "source": [
        "learner3.validate(class_names = t3.get_classes())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.95      0.90      0.92      1287\n",
            "           1       0.64      0.77      0.70       289\n",
            "\n",
            "    accuracy                           0.88      1576\n",
            "   macro avg       0.79      0.84      0.81      1576\n",
            "weighted avg       0.89      0.88      0.88      1576\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1162,  125],\n",
              "       [  66,  223]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z7YLTmhmArbk",
        "colab_type": "text"
      },
      "source": [
        "# **XLNet**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0H4sdFwy-3hG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439,
          "referenced_widgets": [
            "d8c283e5666a40a7afc6fa916d5c0059",
            "ff81bbbd95c04d488a0a13e464670f47",
            "87c17a1e02974af3a37b64bc5ab3e3b1",
            "4003ddb41dd84cecbb92dd1543f22478",
            "2a6bf295d6ed4ae4ab7a0817ef3c57ff",
            "12819438417e4746bf1f8bcb2083fae6",
            "b1a8091344604b22a375ff2d52a12df1",
            "f411041b447547da92dbe207f40ef0fc",
            "7f67df93b98b42179d135224819aa484",
            "a3a212546cf142b4960ef45f0ad1b9ca",
            "445fb1b5b9c6426e811c43446ec397fe",
            "920429cf183244bc8b68617202f18449",
            "340dc52a12754e9da13115fdc25cd12c",
            "ca4ba386f3164118b72fc0de12c89fde",
            "080c0e2630ff41c39df22bf8d323a706",
            "d83d204ec3ea4bd9ab1136eeb878c0f0",
            "4a254b048a5f4c5a820bd4dad67a62a1",
            "a13ed0f0083a49339ff9b26596027635",
            "8ccbd2d0b74a4d959a6688d1ab9b2637",
            "9ac0bf9fdd64474480bb23788dfd8215",
            "edee3daf90a64a559c7f0ae5591139bc",
            "0e37351c7fa84848b6aa59c2817070b7",
            "a21906e907994bb58116316ead228777",
            "0b6816399a244ef5a83b03c18722fa84"
          ]
        },
        "outputId": "447146fd-d88d-405f-a438-c49c500136a8"
      },
      "source": [
        "MODEL_NAME = 'xlnet-base-cased'\n",
        "t4 = text.Transformer(MODEL_NAME, maxlen = 500, classes = classes)\n",
        "trn4 = t4.preprocess_train(x_train, y_train)\n",
        "val4 = t4.preprocess_test(x_test, y_test)\n",
        "xlnet = t4.get_classifier()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ktrain/text/preprocessor.py:400: UserWarning: The class_names argument is replacing the classes argument. Please update your code.\n",
            "  warnings.warn('The class_names argument is replacing the classes argument. Please update your code.')\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "preprocessing train...\n",
            "language: en\n",
            "train sequence lengths:\n",
            "\tmean : 80\n",
            "\t95percentile : 262\n",
            "\t99percentile : 466\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d8c283e5666a40a7afc6fa916d5c0059",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=798011.0, style=ProgressStyle(descripti…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Is Multi-Label? False\n",
            "preprocessing test...\n",
            "language: en\n",
            "test sequence lengths:\n",
            "\tmean : 81\n",
            "\t95percentile : 265\n",
            "\t99percentile : 512\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7f67df93b98b42179d135224819aa484",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=760.0, style=ProgressStyle(description_…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4a254b048a5f4c5a820bd4dad67a62a1",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=565485600.0, style=ProgressStyle(descri…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rLG5LoC3CGps",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "learner4 = ktrain.get_learner(xlnet, train_data = trn4, val_data = val4, batch_size = 4)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AjnR_vzjVLnP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "e8b792bd-7413-4a13-a457-935cfc0f26fa"
      },
      "source": [
        "learner4.lr_find()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "simulating training for different learning rates... this may take a few moments...\n",
            "Train for 1575 steps\n",
            "Epoch 1/1024\n",
            "1230/1575 [======================>.......] - ETA: 11:14 - loss: 0.6348 - accuracy: 0.7492\n",
            "\n",
            "done.\n",
            "Please invoke the Learner.lr_plot() method to visually inspect the loss plot to help identify the maximal learning rate associated with falling loss.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sW3L0WZHVONZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "3a5271ca-28c5-4768-a96b-ea4022dce929"
      },
      "source": [
        "learner4.lr_plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PI5FIjFpCunP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "lr4 = 1e-5"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "itVmZitWCmoi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "edffcd7f-6cf3-4b65-c2b8-7fcb17dff092"
      },
      "source": [
        "learner4.fit_onecycle(lr4, 2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "begin training using onecycle policy with max lr of 1e-05...\n",
            "Train for 1576 steps, validate for 50 steps\n",
            "1576/1576 [==============================] - 3344s 2s/step - loss: 0.3054 - accuracy: 0.8610 - val_loss: 0.2737 - val_accuracy: 0.8794\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f981fd48b38>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cYfxz-zpCo2V",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "a967bfe2-ad94-466f-e8c9-e922a7447ed3"
      },
      "source": [
        "learner4.validate(class_names = t4.get_classes())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.90      0.96      0.93      1287\n",
            "           1       0.75      0.52      0.61       289\n",
            "\n",
            "    accuracy                           0.88      1576\n",
            "   macro avg       0.82      0.74      0.77      1576\n",
            "weighted avg       0.87      0.88      0.87      1576\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1236,   51],\n",
              "       [ 139,  150]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    }
  ]
}