distillbert_ktrain.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "distillbert_ktrain.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPUkDTpy80KBzuemuD2EQzW",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU",
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "00b7fb1f7f6e4746a8cdfa272b4fc0b3": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "state": {
            "_view_name": "HBoxView",
            "_dom_classes": [],
            "_model_name": "HBoxModel",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "box_style": "",
            "layout": "IPY_MODEL_8352869889034943af0ec4e63a775f39",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_6dd386c38f2b4f14b30560010e688835",
              "IPY_MODEL_1b1cb9a4aa0a4a9bb346a95cc448f6de"
            ]
          }
        },
        "8352869889034943af0ec4e63a775f39": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "6dd386c38f2b4f14b30560010e688835": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_dfb839ef4fe447239402fd470518ed09",
            "_dom_classes": [],
            "description": "Downloading: 100%",
            "_model_name": "FloatProgressModel",
            "bar_style": "success",
            "max": 231508,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 231508,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_faed80e2cab743d19dd1cbb390c928b8"
          }
        },
        "1b1cb9a4aa0a4a9bb346a95cc448f6de": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_fe38ad6100e64f939c25997ef4756b2c",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 232k/232k [00:00&lt;00:00, 373kB/s]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_53322720e0d64583983bd73d0f320906"
          }
        },
        "dfb839ef4fe447239402fd470518ed09": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "ProgressStyleModel",
            "description_width": "initial",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "bar_color": null,
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "faed80e2cab743d19dd1cbb390c928b8": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "fe38ad6100e64f939c25997ef4756b2c": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "DescriptionStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "53322720e0d64583983bd73d0f320906": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "baee758203f04793baed0e9ae63617ac": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "state": {
            "_view_name": "HBoxView",
            "_dom_classes": [],
            "_model_name": "HBoxModel",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "box_style": "",
            "layout": "IPY_MODEL_1341319acb704a92b7cf88d89c18beb5",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_026dd26c52db4c3c99c01663ebc308d7",
              "IPY_MODEL_d2f9e7e54f354492aa24bde4526dc662"
            ]
          }
        },
        "1341319acb704a92b7cf88d89c18beb5": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "026dd26c52db4c3c99c01663ebc308d7": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_633d4a0c11d546398878b77ad2abcb3e",
            "_dom_classes": [],
            "description": "Downloading: 100%",
            "_model_name": "FloatProgressModel",
            "bar_style": "success",
            "max": 442,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 442,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_d11bc486dc7047fca4a499d9939f2019"
          }
        },
        "d2f9e7e54f354492aa24bde4526dc662": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_818022badb5f4ebaaf656fee58324615",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 442/442 [00:00&lt;00:00, 957B/s]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_71d4a7fb6249426c930f7b18a8727cda"
          }
        },
        "633d4a0c11d546398878b77ad2abcb3e": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "ProgressStyleModel",
            "description_width": "initial",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "bar_color": null,
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "d11bc486dc7047fca4a499d9939f2019": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "818022badb5f4ebaaf656fee58324615": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "DescriptionStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "71d4a7fb6249426c930f7b18a8727cda": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "cbb7f089bb61489298af0ab08044a2eb": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "state": {
            "_view_name": "HBoxView",
            "_dom_classes": [],
            "_model_name": "HBoxModel",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "box_style": "",
            "layout": "IPY_MODEL_4c483df384224a63861f5159f781a76f",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_008f536cff7d4d639d15da055e1a90f4",
              "IPY_MODEL_6b53e62cc24d4a83aa9d8d5635accc2e"
            ]
          }
        },
        "4c483df384224a63861f5159f781a76f": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "008f536cff7d4d639d15da055e1a90f4": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_f3bec629aa7b43f889d408cde3c64b0b",
            "_dom_classes": [],
            "description": "Downloading: 100%",
            "_model_name": "FloatProgressModel",
            "bar_style": "success",
            "max": 363423424,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 363423424,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_7ca1d517616e4fe098b1385bb301b337"
          }
        },
        "6b53e62cc24d4a83aa9d8d5635accc2e": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_9fa1cb21a78e41fc9a47b8a1aa1c01f6",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 363M/363M [00:08&lt;00:00, 45.1MB/s]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_97b69555087a44108c6214f9e217171a"
          }
        },
        "f3bec629aa7b43f889d408cde3c64b0b": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "ProgressStyleModel",
            "description_width": "initial",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "bar_color": null,
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "7ca1d517616e4fe098b1385bb301b337": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "9fa1cb21a78e41fc9a47b8a1aa1c01f6": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "DescriptionStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "97b69555087a44108c6214f9e217171a": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Jishan-works/Sentiment-analysis-using-DistillBERT-and-ktrain/blob/master/distillbert_ktrain.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MsF7oM5LLpow",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "bb27cad7-a764-49dd-f2bf-3964d03db7e2"
      },
      "source": [
        "!pip install ktrain"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting ktrain\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/83/93/66cfb8610a66e926a40b700f113b63759824225bce9ae0d6a39c21059978/ktrain-0.19.6.tar.gz (25.2MB)\n",
            "\u001b[K     |████████████████████████████████| 25.3MB 51.5MB/s \n",
            "\u001b[?25hCollecting tensorflow==2.1.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/85/d4/c0cd1057b331bc38b65478302114194bd8e1b9c2bbc06e300935c0e93d90/tensorflow-2.1.0-cp36-cp36m-manylinux2010_x86_64.whl (421.8MB)\n",
            "\u001b[K     |████████████████████████████████| 421.8MB 23kB/s \n",
            "\u001b[?25hRequirement already satisfied: scipy==1.4.1 in /usr/local/lib/python3.6/dist-packages (from ktrain) (1.4.1)\n",
            "Collecting scikit-learn==0.21.3\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/a0/c5/d2238762d780dde84a20b8c761f563fe882b88c5a5fb03c056547c442a19/scikit_learn-0.21.3-cp36-cp36m-manylinux1_x86_64.whl (6.7MB)\n",
            "\u001b[K     |████████████████████████████████| 6.7MB 30.6MB/s \n",
            "\u001b[?25hRequirement already satisfied: matplotlib>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from ktrain) (3.2.2)\n",
            "Requirement already satisfied: pandas>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from ktrain) (1.0.5)\n",
            "Requirement already satisfied: fastprogress>=0.1.21 in /usr/local/lib/python3.6/dist-packages (from ktrain) (0.2.5)\n",
            "Collecting keras_bert>=0.81.0\n",
            "  Downloading https://files.pythonhosted.org/packages/e2/7f/95fabd29f4502924fa3f09ff6538c5a7d290dfef2c2fe076d3d1a16e08f0/keras-bert-0.86.0.tar.gz\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from ktrain) (2.23.0)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.6/dist-packages (from ktrain) (0.16.0)\n",
            "Collecting langdetect\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/56/a3/8407c1e62d5980188b4acc45ef3d94b933d14a2ebc9ef3505f22cf772570/langdetect-1.0.8.tar.gz (981kB)\n",
            "\u001b[K     |████████████████████████████████| 983kB 13.1MB/s \n",
            "\u001b[?25hRequirement already satisfied: jieba in /usr/local/lib/python3.6/dist-packages (from ktrain) (0.42.1)\n",
            "Collecting cchardet==2.1.5\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/fa/4e/847feebfc3e71c773b23ee06c74687b8c50a5a6d6aaff452a0a4f4eb9a32/cchardet-2.1.5-cp36-cp36m-manylinux1_x86_64.whl (241kB)\n",
            "\u001b[K     |████████████████████████████████| 245kB 43.0MB/s \n",
            "\u001b[?25hRequirement already satisfied: networkx>=2.3 in /usr/local/lib/python3.6/dist-packages (from ktrain) (2.4)\n",
            "Requirement already satisfied: bokeh in /usr/local/lib/python3.6/dist-packages (from ktrain) (2.1.1)\n",
            "Collecting seqeval\n",
            "  Downloading https://files.pythonhosted.org/packages/34/91/068aca8d60ce56dd9ba4506850e876aba5e66a6f2f29aa223224b50df0de/seqeval-0.0.12.tar.gz\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.6/dist-packages (from ktrain) (20.4)\n",
            "Requirement already satisfied: tensorflow_datasets in /usr/local/lib/python3.6/dist-packages (from ktrain) (2.1.0)\n",
            "Collecting transformers>=2.11.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/27/3c/91ed8f5c4e7ef3227b4119200fc0ed4b4fd965b1f0172021c25701087825/transformers-3.0.2-py3-none-any.whl (769kB)\n",
            "\u001b[K     |████████████████████████████████| 778kB 24.7MB/s \n",
            "\u001b[?25hRequirement already satisfied: ipython in /usr/local/lib/python3.6/dist-packages (from ktrain) (5.5.0)\n",
            "Collecting syntok\n",
            "  Downloading https://files.pythonhosted.org/packages/8c/76/a49e73a04b3e3a14ce232e8e28a1587f8108baa665644fe8c40e307e792e/syntok-1.3.1.tar.gz\n",
            "Collecting whoosh\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ba/19/24d0f1f454a2c1eb689ca28d2f178db81e5024f42d82729a4ff6771155cf/Whoosh-2.7.4-py2.py3-none-any.whl (468kB)\n",
            "\u001b[K     |████████████████████████████████| 471kB 36.8MB/s \n",
            "\u001b[?25hCollecting shap\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/a8/77/b504e43e21a2ba543a1ac4696718beb500cfa708af2fb57cb54ce299045c/shap-0.35.0.tar.gz (273kB)\n",
            "\u001b[K     |████████████████████████████████| 276kB 41.4MB/s \n",
            "\u001b[?25hRequirement already satisfied: wheel>=0.26; python_version >= \"3\" in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.1.0->ktrain) (0.34.2)\n",
            "Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.1.0->ktrain) (1.31.0)\n",
            "Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.1.0->ktrain) (0.8.1)\n",
            "Requirement already satisfied: keras-preprocessing>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.1.0->ktrain) (1.1.2)\n",
            "Requirement already satisfied: numpy<2.0,>=1.16.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.1.0->ktrain) (1.18.5)\n",
            "Collecting gast==0.2.2\n",
            "  Downloading https://files.pythonhosted.org/packages/4e/35/11749bf99b2d4e3cceb4d55ca22590b0d7c2c62b9de38ac4a4a7f4687421/gast-0.2.2.tar.gz\n",
            "Requirement already satisfied: protobuf>=3.8.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.1.0->ktrain) (3.12.4)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.1.0->ktrain) (1.1.0)\n",
            "Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.1.0->ktrain) (0.9.0)\n",
            "Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.1.0->ktrain) (1.15.0)\n",
            "Collecting keras-applications>=1.0.8\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/71/e3/19762fdfc62877ae9102edf6342d71b28fbfd9dea3d2f96a882ce099b03f/Keras_Applications-1.0.8-py3-none-any.whl (50kB)\n",
            "\u001b[K     |████████████████████████████████| 51kB 6.0MB/s \n",
            "\u001b[?25hCollecting tensorboard<2.2.0,>=2.1.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/d9/41/bbf49b61370e4f4d245d4c6051dfb6db80cec672605c91b1652ac8cc3d38/tensorboard-2.1.1-py3-none-any.whl (3.8MB)\n",
            "\u001b[K     |████████████████████████████████| 3.9MB 32.3MB/s \n",
            "\u001b[?25hRequirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.1.0->ktrain) (3.3.0)\n",
            "Collecting tensorflow-estimator<2.2.0,>=2.1.0rc0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/18/90/b77c328a1304437ab1310b463e533fa7689f4bfc41549593056d812fab8e/tensorflow_estimator-2.1.0-py2.py3-none-any.whl (448kB)\n",
            "\u001b[K     |████████████████████████████████| 450kB 40.2MB/s \n",
            "\u001b[?25hRequirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.1.0->ktrain) (1.12.1)\n",
            "Requirement already satisfied: google-pasta>=0.1.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.1.0->ktrain) (0.2.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=3.0.0->ktrain) (0.10.0)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=3.0.0->ktrain) (2.4.7)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=3.0.0->ktrain) (1.2.0)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=3.0.0->ktrain) (2.8.1)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=1.0.1->ktrain) (2018.9)\n",
            "Requirement already satisfied: Keras>=2.4.3 in /usr/local/lib/python3.6/dist-packages (from keras_bert>=0.81.0->ktrain) (2.4.3)\n",
            "Collecting keras-transformer>=0.38.0\n",
            "  Downloading https://files.pythonhosted.org/packages/89/6c/d6f0c164f4cc16fbc0d0fea85f5526e87a7d2df7b077809e422a7e626150/keras-transformer-0.38.0.tar.gz\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->ktrain) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->ktrain) (2020.6.20)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->ktrain) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->ktrain) (2.10)\n",
            "Requirement already satisfied: decorator>=4.3.0 in /usr/local/lib/python3.6/dist-packages (from networkx>=2.3->ktrain) (4.4.2)\n",
            "Requirement already satisfied: pillow>=4.0 in /usr/local/lib/python3.6/dist-packages (from bokeh->ktrain) (7.0.0)\n",
            "Requirement already satisfied: tornado>=5.1 in /usr/local/lib/python3.6/dist-packages (from bokeh->ktrain) (5.1.1)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4 in /usr/local/lib/python3.6/dist-packages (from bokeh->ktrain) (3.7.4.2)\n",
            "Requirement already satisfied: Jinja2>=2.7 in /usr/local/lib/python3.6/dist-packages (from bokeh->ktrain) (2.11.2)\n",
            "Requirement already satisfied: PyYAML>=3.10 in /usr/local/lib/python3.6/dist-packages (from bokeh->ktrain) (3.13)\n",
            "Requirement already satisfied: attrs>=18.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow_datasets->ktrain) (19.3.0)\n",
            "Requirement already satisfied: tensorflow-metadata in /usr/local/lib/python3.6/dist-packages (from tensorflow_datasets->ktrain) (0.22.2)\n",
            "Requirement already satisfied: future in /usr/local/lib/python3.6/dist-packages (from tensorflow_datasets->ktrain) (0.16.0)\n",
            "Requirement already satisfied: dill in /usr/local/lib/python3.6/dist-packages (from tensorflow_datasets->ktrain) (0.3.2)\n",
            "Requirement already satisfied: promise in /usr/local/lib/python3.6/dist-packages (from tensorflow_datasets->ktrain) (2.3)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (from tensorflow_datasets->ktrain) (4.41.1)\n",
            "Requirement already satisfied: dataclasses; python_version < \"3.7\" in /usr/local/lib/python3.6/dist-packages (from transformers>=2.11.0->ktrain) (0.7)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.6/dist-packages (from transformers>=2.11.0->ktrain) (3.0.12)\n",
            "Collecting sentencepiece!=0.1.92\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/d4/a4/d0a884c4300004a78cca907a6ff9a5e9fe4f090f5d95ab341c53d28cbc58/sentencepiece-0.1.91-cp36-cp36m-manylinux1_x86_64.whl (1.1MB)\n",
            "\u001b[K     |████████████████████████████████| 1.1MB 36.9MB/s \n",
            "\u001b[?25hCollecting sacremoses\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/7d/34/09d19aff26edcc8eb2a01bed8e98f13a1537005d31e95233fd48216eed10/sacremoses-0.0.43.tar.gz (883kB)\n",
            "\u001b[K     |████████████████████████████████| 890kB 34.1MB/s \n",
            "\u001b[?25hCollecting tokenizers==0.8.1.rc1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/40/d0/30d5f8d221a0ed981a186c8eb986ce1c94e3a6e87f994eae9f4aa5250217/tokenizers-0.8.1rc1-cp36-cp36m-manylinux1_x86_64.whl (3.0MB)\n",
            "\u001b[K     |████████████████████████████████| 3.0MB 32.1MB/s \n",
            "\u001b[?25hRequirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.6/dist-packages (from transformers>=2.11.0->ktrain) (2019.12.20)\n",
            "Requirement already satisfied: pexpect; sys_platform != \"win32\" in /usr/local/lib/python3.6/dist-packages (from ipython->ktrain) (4.8.0)\n",
            "Requirement already satisfied: pygments in /usr/local/lib/python3.6/dist-packages (from ipython->ktrain) (2.1.3)\n",
            "Requirement already satisfied: pickleshare in /usr/local/lib/python3.6/dist-packages (from ipython->ktrain) (0.7.5)\n",
            "Requirement already satisfied: prompt-toolkit<2.0.0,>=1.0.4 in /usr/local/lib/python3.6/dist-packages (from ipython->ktrain) (1.0.18)\n",
            "Requirement already satisfied: traitlets>=4.2 in /usr/local/lib/python3.6/dist-packages (from ipython->ktrain) (4.3.3)\n",
            "Requirement already satisfied: simplegeneric>0.8 in /usr/local/lib/python3.6/dist-packages (from ipython->ktrain) (0.8.1)\n",
            "Requirement already satisfied: setuptools>=18.5 in /usr/local/lib/python3.6/dist-packages (from ipython->ktrain) (49.2.0)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.8->tensorflow==2.1.0->ktrain) (2.10.0)\n",
            "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.2.0,>=2.1.0->tensorflow==2.1.0->ktrain) (0.4.1)\n",
            "Requirement already satisfied: google-auth<2,>=1.6.3 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.2.0,>=2.1.0->tensorflow==2.1.0->ktrain) (1.17.2)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.2.0,>=2.1.0->tensorflow==2.1.0->ktrain) (3.2.2)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.2.0,>=2.1.0->tensorflow==2.1.0->ktrain) (1.0.1)\n",
            "Collecting keras-pos-embd>=0.11.0\n",
            "  Downloading https://files.pythonhosted.org/packages/09/70/b63ed8fc660da2bb6ae29b9895401c628da5740c048c190b5d7107cadd02/keras-pos-embd-0.11.0.tar.gz\n",
            "Collecting keras-multi-head>=0.27.0\n",
            "  Downloading https://files.pythonhosted.org/packages/e6/32/45adf2549450aca7867deccfa04af80a0ab1ca139af44b16bc669e0e09cd/keras-multi-head-0.27.0.tar.gz\n",
            "Collecting keras-layer-normalization>=0.14.0\n",
            "  Downloading https://files.pythonhosted.org/packages/a4/0e/d1078df0494bac9ce1a67954e5380b6e7569668f0f3b50a9531c62c1fc4a/keras-layer-normalization-0.14.0.tar.gz\n",
            "Collecting keras-position-wise-feed-forward>=0.6.0\n",
            "  Downloading https://files.pythonhosted.org/packages/e3/59/f0faa1037c033059e7e9e7758e6c23b4d1c0772cd48de14c4b6fd4033ad5/keras-position-wise-feed-forward-0.6.0.tar.gz\n",
            "Collecting keras-embed-sim>=0.8.0\n",
            "  Downloading https://files.pythonhosted.org/packages/57/ef/61a1e39082c9e1834a2d09261d4a0b69f7c818b359216d4e1912b20b1c86/keras-embed-sim-0.8.0.tar.gz\n",
            "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.6/dist-packages (from Jinja2>=2.7->bokeh->ktrain) (1.1.1)\n",
            "Requirement already satisfied: googleapis-common-protos in /usr/local/lib/python3.6/dist-packages (from tensorflow-metadata->tensorflow_datasets->ktrain) (1.52.0)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.6/dist-packages (from sacremoses->transformers>=2.11.0->ktrain) (7.1.2)\n",
            "Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.6/dist-packages (from pexpect; sys_platform != \"win32\"->ipython->ktrain) (0.6.0)\n",
            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.6/dist-packages (from prompt-toolkit<2.0.0,>=1.0.4->ipython->ktrain) (0.2.5)\n",
            "Requirement already satisfied: ipython-genutils in /usr/local/lib/python3.6/dist-packages (from traitlets>=4.2->ipython->ktrain) (0.2.0)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.2.0,>=2.1.0->tensorflow==2.1.0->ktrain) (1.3.0)\n",
            "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard<2.2.0,>=2.1.0->tensorflow==2.1.0->ktrain) (4.1.1)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4; python_version >= \"3\" in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard<2.2.0,>=2.1.0->tensorflow==2.1.0->ktrain) (4.6)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard<2.2.0,>=2.1.0->tensorflow==2.1.0->ktrain) (0.2.8)\n",
            "Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from markdown>=2.6.8->tensorboard<2.2.0,>=2.1.0->tensorflow==2.1.0->ktrain) (1.7.0)\n",
            "Collecting keras-self-attention==0.46.0\n",
            "  Downloading https://files.pythonhosted.org/packages/15/6b/c804924a056955fa1f3ff767945187103cfc851ba9bd0fc5a6c6bc18e2eb/keras-self-attention-0.46.0.tar.gz\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.2.0,>=2.1.0->tensorflow==2.1.0->ktrain) (3.1.0)\n",
            "Requirement already satisfied: pyasn1>=0.1.3 in /usr/local/lib/python3.6/dist-packages (from rsa<5,>=3.1.4; python_version >= \"3\"->google-auth<2,>=1.6.3->tensorboard<2.2.0,>=2.1.0->tensorflow==2.1.0->ktrain) (0.4.8)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard<2.2.0,>=2.1.0->tensorflow==2.1.0->ktrain) (3.1.0)\n",
            "Building wheels for collected packages: ktrain, keras-bert, langdetect, seqeval, syntok, shap, gast, keras-transformer, sacremoses, keras-pos-embd, keras-multi-head, keras-layer-normalization, keras-position-wise-feed-forward, keras-embed-sim, keras-self-attention\n",
            "  Building wheel for ktrain (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for ktrain: filename=ktrain-0.19.6-cp36-none-any.whl size=25268110 sha256=beb8adebe7fd7305ca5366ede9586f59b6a401ce611db109eb2be0075b4da98b\n",
            "  Stored in directory: /root/.cache/pip/wheels/e4/a0/10/90232fd95a8d337b7de4611df9a3af4d9c2c18ee9e891f7944\n",
            "  Building wheel for keras-bert (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for keras-bert: filename=keras_bert-0.86.0-cp36-none-any.whl size=34143 sha256=9f60e22f55a16b95b640b44b6971168c527bc2637ab449c60936ee257104f7b1\n",
            "  Stored in directory: /root/.cache/pip/wheels/66/f0/b1/748128b58562fc9e31b907bb5e2ab6a35eb37695e83911236b\n",
            "  Building wheel for langdetect (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for langdetect: filename=langdetect-1.0.8-cp36-none-any.whl size=993193 sha256=a8f2c5c23655197c8a0ef4a955507a896591a03c926d8824efd8bc5cb04af6ae\n",
            "  Stored in directory: /root/.cache/pip/wheels/8d/b3/aa/6d99de9f3841d7d3d40a60ea06e6d669e8e5012e6c8b947a57\n",
            "  Building wheel for seqeval (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for seqeval: filename=seqeval-0.0.12-cp36-none-any.whl size=7424 sha256=f8bbe83ea2a713dacb3c3b2271c3778b70973ef76561e07d8a9d7926c32e7b97\n",
            "  Stored in directory: /root/.cache/pip/wheels/4f/32/0a/df3b340a82583566975377d65e724895b3fad101a3fb729f68\n",
            "  Building wheel for syntok (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for syntok: filename=syntok-1.3.1-cp36-none-any.whl size=20918 sha256=2a0289064814bdf0e82743981aaa91052722d382ff0c245e6e10b157d2277c9c\n",
            "  Stored in directory: /root/.cache/pip/wheels/51/c6/a4/be1920586c49469846bcd2888200bdecfe109ec421dab9be2d\n",
            "  Building wheel for shap (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for shap: filename=shap-0.35.0-cp36-cp36m-linux_x86_64.whl size=394119 sha256=5a9cbb524ffd893f8cf1c2b4e0cb89f31f9340d15a2910ad4bd8f8fa670fbaaa\n",
            "  Stored in directory: /root/.cache/pip/wheels/e7/f7/0f/b57055080cf8894906b3bd3616d2fc2bfd0b12d5161bcb24ac\n",
            "  Building wheel for gast (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for gast: filename=gast-0.2.2-cp36-none-any.whl size=7540 sha256=51bbeca633b9386b207c02228722445b1c0ed165b704b72e3c8ea26cf3d1dea2\n",
            "  Stored in directory: /root/.cache/pip/wheels/5c/2e/7e/a1d4d4fcebe6c381f378ce7743a3ced3699feb89bcfbdadadd\n",
            "  Building wheel for keras-transformer (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for keras-transformer: filename=keras_transformer-0.38.0-cp36-none-any.whl size=12942 sha256=521e487b4581af17fbac8df3e4b497b7223277c9e78f7639f3de15c9194bc8d2\n",
            "  Stored in directory: /root/.cache/pip/wheels/e5/fb/3a/37b2b9326c799aa010ae46a04ddb04f320d8c77c0b7e837f4e\n",
            "  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sacremoses: filename=sacremoses-0.0.43-cp36-none-any.whl size=893260 sha256=abd3789e8f5645847f3d13d6d52b0537471e6f074ce799edd5a4456f0e953ad0\n",
            "  Stored in directory: /root/.cache/pip/wheels/29/3c/fd/7ce5c3f0666dab31a50123635e6fb5e19ceb42ce38d4e58f45\n",
            "  Building wheel for keras-pos-embd (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for keras-pos-embd: filename=keras_pos_embd-0.11.0-cp36-none-any.whl size=7554 sha256=ca3c9897f131bdcc41a2008033b0097236b4a52c1a268a4e00d30402d3f0a1e8\n",
            "  Stored in directory: /root/.cache/pip/wheels/5b/a1/a0/ce6b1d49ba1a9a76f592e70cf297b05c96bc9f418146761032\n",
            "  Building wheel for keras-multi-head (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for keras-multi-head: filename=keras_multi_head-0.27.0-cp36-none-any.whl size=15611 sha256=37ba836f02bccdcfc855acc0cd29ac64bc61e6e45e21d482ef77d26cda5be879\n",
            "  Stored in directory: /root/.cache/pip/wheels/b5/b4/49/0a0c27dcb93c13af02fea254ff51d1a43a924dd4e5b7a7164d\n",
            "  Building wheel for keras-layer-normalization (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for keras-layer-normalization: filename=keras_layer_normalization-0.14.0-cp36-none-any.whl size=5268 sha256=db58f9190cde7803bb461a5b8ad2d2c4835158be133a5d13a7e86cfcc7c731da\n",
            "  Stored in directory: /root/.cache/pip/wheels/54/80/22/a638a7d406fd155e507aa33d703e3fa2612b9eb7bb4f4fe667\n",
            "  Building wheel for keras-position-wise-feed-forward (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for keras-position-wise-feed-forward: filename=keras_position_wise_feed_forward-0.6.0-cp36-none-any.whl size=5623 sha256=8d6ff1c0868da9c4c516e600522040027047a55ab778d4585cc5653017a792d5\n",
            "  Stored in directory: /root/.cache/pip/wheels/39/e2/e2/3514fef126a00574b13bc0b9e23891800158df3a3c19c96e3b\n",
            "  Building wheel for keras-embed-sim (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for keras-embed-sim: filename=keras_embed_sim-0.8.0-cp36-none-any.whl size=4558 sha256=9537d577014c62594f371b2de2d11eb05866ea64ecfd9e2f4d30831000825907\n",
            "  Stored in directory: /root/.cache/pip/wheels/49/45/8b/c111f6cc8bec253e984677de73a6f4f5d2f1649f42aac191c8\n",
            "  Building wheel for keras-self-attention (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for keras-self-attention: filename=keras_self_attention-0.46.0-cp36-none-any.whl size=17278 sha256=d8b99b41227be4742a520c1548a3e5938e0f5ac972eda4649611ad7518e7527f\n",
            "  Stored in directory: /root/.cache/pip/wheels/d2/2e/80/fec4c05eb23c8e13b790e26d207d6e0ffe8013fad8c6bdd4d2\n",
            "Successfully built ktrain keras-bert langdetect seqeval syntok shap gast keras-transformer sacremoses keras-pos-embd keras-multi-head keras-layer-normalization keras-position-wise-feed-forward keras-embed-sim keras-self-attention\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",
            "Installing collected packages: gast, keras-applications, tensorboard, tensorflow-estimator, tensorflow, scikit-learn, keras-pos-embd, keras-self-attention, keras-multi-head, keras-layer-normalization, keras-position-wise-feed-forward, keras-embed-sim, keras-transformer, keras-bert, langdetect, cchardet, seqeval, sentencepiece, sacremoses, tokenizers, transformers, syntok, whoosh, shap, ktrain\n",
            "  Found existing installation: gast 0.3.3\n",
            "    Uninstalling gast-0.3.3:\n",
            "      Successfully uninstalled gast-0.3.3\n",
            "  Found existing installation: tensorboard 2.3.0\n",
            "    Uninstalling tensorboard-2.3.0:\n",
            "      Successfully uninstalled tensorboard-2.3.0\n",
            "  Found existing installation: tensorflow-estimator 2.3.0\n",
            "    Uninstalling tensorflow-estimator-2.3.0:\n",
            "      Successfully uninstalled tensorflow-estimator-2.3.0\n",
            "  Found existing installation: tensorflow 2.3.0\n",
            "    Uninstalling tensorflow-2.3.0:\n",
            "      Successfully uninstalled tensorflow-2.3.0\n",
            "  Found existing installation: scikit-learn 0.22.2.post1\n",
            "    Uninstalling scikit-learn-0.22.2.post1:\n",
            "      Successfully uninstalled scikit-learn-0.22.2.post1\n",
            "Successfully installed cchardet-2.1.5 gast-0.2.2 keras-applications-1.0.8 keras-bert-0.86.0 keras-embed-sim-0.8.0 keras-layer-normalization-0.14.0 keras-multi-head-0.27.0 keras-pos-embd-0.11.0 keras-position-wise-feed-forward-0.6.0 keras-self-attention-0.46.0 keras-transformer-0.38.0 ktrain-0.19.6 langdetect-1.0.8 sacremoses-0.0.43 scikit-learn-0.21.3 sentencepiece-0.1.91 seqeval-0.0.12 shap-0.35.0 syntok-1.3.1 tensorboard-2.1.1 tensorflow-2.1.0 tensorflow-estimator-2.1.0 tokenizers-0.8.1rc1 transformers-3.0.2 whoosh-2.7.4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GJ9CtNs9NJB9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        },
        "outputId": "4e52034d-c89e-4550-9915-76b78739a8d4"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 3,
      "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": "EU8DYvLVMknz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "outputId": "3f78ad14-77a7-4e81-b023-895e36d32dd4"
      },
      "source": [
        "!git clone https://github.com/laxmimerit/IMDB-Movie-Reviews-Large-Dataset-50k.git"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'IMDB-Movie-Reviews-Large-Dataset-50k'...\n",
            "remote: Enumerating objects: 10, done.\u001b[K\n",
            "remote: Counting objects: 100% (10/10), done.\u001b[K\n",
            "remote: Compressing objects: 100% (8/8), done.\u001b[K\n",
            "remote: Total 10 (delta 1), reused 0 (delta 0), pack-reused 0\u001b[K\n",
            "Unpacking objects: 100% (10/10), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2aYgP4W7MkqQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "deea880a-47e4-4690-c75f-6647e7f4e456"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import ktrain\n",
        "from ktrain import text\n",
        "import tensorflow as tf\n",
        "tf.__version__"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'2.1.0'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6DX4XZloMktL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data_test = pd.read_excel('/content/IMDB-Movie-Reviews-Large-Dataset-50k/test.xlsx', dtype= str)\n",
        "data_train = pd.read_excel('/content/IMDB-Movie-Reviews-Large-Dataset-50k/train.xlsx', dtype = str)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o9Ds23jSMkvm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "be80043b-f0b8-48c5-9e32-c3e18defab73"
      },
      "source": [
        "data_train.sample(5)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Reviews</th>\n",
              "      <th>Sentiment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>20890</th>\n",
              "      <td>Wow, what can I say about this film? It's a lo...</td>\n",
              "      <td>neg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23789</th>\n",
              "      <td>The 1st season was amazing, the whole idea of ...</td>\n",
              "      <td>pos</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4495</th>\n",
              "      <td>This is a great movie from the lost age of rea...</td>\n",
              "      <td>pos</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12996</th>\n",
              "      <td>I couldn't believe it. I had to rub my eyes a ...</td>\n",
              "      <td>neg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>200</th>\n",
              "      <td>Oh, man! This thing scared the heck out of me ...</td>\n",
              "      <td>neg</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                 Reviews Sentiment\n",
              "20890  Wow, what can I say about this film? It's a lo...       neg\n",
              "23789  The 1st season was amazing, the whole idea of ...       pos\n",
              "4495   This is a great movie from the lost age of rea...       pos\n",
              "12996  I couldn't believe it. I had to rub my eyes a ...       neg\n",
              "200    Oh, man! This thing scared the heck out of me ...       neg"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bTIrgslxMkyD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 140
        },
        "outputId": "5cafa234-a9c4-4c6a-b9da-38c9a5eb01d3"
      },
      "source": [
        "text.print_text_classifiers()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "fasttext: a fastText-like model [http://arxiv.org/pdf/1607.01759.pdf]\n",
            "logreg: logistic regression using a trainable Embedding layer\n",
            "nbsvm: NBSVM model [http://www.aclweb.org/anthology/P12-2018]\n",
            "bigru: Bidirectional GRU with pretrained fasttext word vectors [https://fasttext.cc/docs/en/crawl-vectors.html]\n",
            "standard_gru: simple 2-layer GRU with randomly initialized embeddings\n",
            "bert: Bidirectional Encoder Representations from Transformers (BERT) [https://arxiv.org/abs/1810.04805]\n",
            "distilbert: distilled, smaller, and faster BERT from Hugging Face [https://arxiv.org/abs/1910.01108]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qLx9SPdkMk2P",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295,
          "referenced_widgets": [
            "00b7fb1f7f6e4746a8cdfa272b4fc0b3",
            "8352869889034943af0ec4e63a775f39",
            "6dd386c38f2b4f14b30560010e688835",
            "1b1cb9a4aa0a4a9bb346a95cc448f6de",
            "dfb839ef4fe447239402fd470518ed09",
            "faed80e2cab743d19dd1cbb390c928b8",
            "fe38ad6100e64f939c25997ef4756b2c",
            "53322720e0d64583983bd73d0f320906"
          ]
        },
        "outputId": "0c38d658-0ab7-4477-e36e-c9f152d08832"
      },
      "source": [
        "(train, val, preproc) = text.texts_from_df(train_df=data_train, text_column='Reviews', label_columns='Sentiment',\n",
        "                   val_df = data_test,\n",
        "                   maxlen = 400,\n",
        "                   preprocess_mode = 'distilbert')"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "preprocessing train...\n",
            "language: en\n",
            "train sequence lengths:\n",
            "\tmean : 234\n",
            "\t95percentile : 598\n",
            "\t99percentile : 913\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "00b7fb1f7f6e4746a8cdfa272b4fc0b3",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=231508.0, 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 : 234\n",
            "\t95percentile : 598\n",
            "\t99percentile : 913\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dj8aAoDKMk6x",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 169,
          "referenced_widgets": [
            "baee758203f04793baed0e9ae63617ac",
            "1341319acb704a92b7cf88d89c18beb5",
            "026dd26c52db4c3c99c01663ebc308d7",
            "d2f9e7e54f354492aa24bde4526dc662",
            "633d4a0c11d546398878b77ad2abcb3e",
            "d11bc486dc7047fca4a499d9939f2019",
            "818022badb5f4ebaaf656fee58324615",
            "71d4a7fb6249426c930f7b18a8727cda",
            "cbb7f089bb61489298af0ab08044a2eb",
            "4c483df384224a63861f5159f781a76f",
            "008f536cff7d4d639d15da055e1a90f4",
            "6b53e62cc24d4a83aa9d8d5635accc2e",
            "f3bec629aa7b43f889d408cde3c64b0b",
            "7ca1d517616e4fe098b1385bb301b337",
            "9fa1cb21a78e41fc9a47b8a1aa1c01f6",
            "97b69555087a44108c6214f9e217171a"
          ]
        },
        "outputId": "483b0bc5-0cc6-48b4-b379-68d39d15ec52"
      },
      "source": [
        "model = text.text_classifier(name = 'distilbert', train_data = train, preproc=preproc)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Is Multi-Label? False\n",
            "maxlen is 400\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "baee758203f04793baed0e9ae63617ac",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=442.0, 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": "cbb7f089bb61489298af0ab08044a2eb",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=363423424.0, style=ProgressStyle(descri…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5wb0x0GJMlBn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "learner = ktrain.get_learner(model = model,\n",
        "                             train_data = train,\n",
        "                             val_data = val,\n",
        "                             batch_size = 6)"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ERaI956UMlEN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "4c1660a6-c0ae-4763-86af-1fa633a827a5"
      },
      "source": [
        "learner.fit_onecycle(lr = 2e-5, epochs=2)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "begin training using onecycle policy with max lr of 2e-05...\n",
            "Train for 4167 steps, validate for 782 steps\n",
            "Epoch 1/2\n",
            "4167/4167 [==============================] - 3029s 727ms/step - loss: 0.2854 - accuracy: 0.8816 - val_loss: 0.1655 - val_accuracy: 0.9412\n",
            "Epoch 2/2\n",
            "4167/4167 [==============================] - 3013s 723ms/step - loss: 0.1587 - accuracy: 0.9414 - val_loss: 0.0633 - val_accuracy: 0.9842\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f3c3e8fb470>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5WHGKwDSMlLb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "predictor = ktrain.get_predictor(learner.model, preproc)"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kcjc19K2MlOg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "predictor.save('/content/drive/My Drive/projects/DistillBert_ktrain')"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6M5qLwtKMk-O",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = ['I like her']"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qM98B2pEMk5M",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "93fb2bef-95fc-4643-87a9-39389c242872"
      },
      "source": [
        "predictor.predict(data, return_proba=True)"
      ],
      "execution_count": 22,
      "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([[0.01607376, 0.98392624]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xFUZgoQuMk09",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = ['I will die for my country']"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xeVjxbxoDV55",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5969edec-c897-4d85-8254-cd7a8c0674ea"
      },
      "source": [
        "predictor.predict(data, return_proba=True)"
      ],
      "execution_count": 20,
      "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([[0.08347354, 0.91652644]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G0_6gOOPElE2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = ['He is a mean person with mean attitude']"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z9KKh6LzEvnb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f6b65f18-95ea-43fc-8c8b-352251e30e98"
      },
      "source": [
        "predictor.predict(data, return_proba=True)"
      ],
      "execution_count": 24,
      "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([[0.73907137, 0.26092863]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    }
  ]
}