autogluon_text.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "autogluon-text.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyOhrAD9OGcwVou/p/rEmnA7",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/tmtong/ai-library-exploration/blob/main/autogluon_text.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "kiZHRFFhGMtE",
        "outputId": "9fddc346-27ab-48d8-8c61-8f23b152a2f9"
      },
      "source": [
        "!pip install -U pip\n",
        "!pip install -U setuptools wheel\n",
        "!pip install -U \"mxnet<2.0.0\"\n",
        "!pip install autogluon\n",
        "!pip install -q kaggle\n",
        "from google.colab import files"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/47/ca/f0d790b6e18b3a6f3bd5e80c2ee4edbb5807286c21cdd0862ca933f751dd/pip-21.1.3-py3-none-any.whl (1.5MB)\n",
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            "\u001b[?25hInstalling collected packages: pip\n",
            "  Found existing installation: pip 19.3.1\n",
            "    Uninstalling pip-19.3.1:\n",
            "      Successfully uninstalled pip-19.3.1\n",
            "Successfully installed pip-21.1.3\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (57.0.0)\n",
            "Collecting setuptools\n",
            "  Downloading setuptools-57.1.0-py3-none-any.whl (818 kB)\n",
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            "\u001b[?25hRequirement already satisfied: wheel in /usr/local/lib/python3.7/dist-packages (0.36.2)\n",
            "Installing collected packages: setuptools\n",
            "  Attempting uninstall: setuptools\n",
            "    Found existing installation: setuptools 57.0.0\n",
            "    Uninstalling setuptools-57.0.0:\n",
            "      Successfully uninstalled setuptools-57.0.0\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\u001b[0m\n",
            "Successfully installed setuptools-57.1.0\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n"
          ],
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        },
        {
          "output_type": "display_data",
          "data": {
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              "pip_warning": {
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          "metadata": {
            "tags": []
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          "text": [
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            "Installing collected packages: graphviz, mxnet\n",
            "  Attempting uninstall: graphviz\n",
            "    Found existing installation: graphviz 0.10.1\n",
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            "      Successfully uninstalled graphviz-0.10.1\n",
            "Successfully installed graphviz-0.8.4 mxnet-1.8.0.post0\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n",
            "Collecting autogluon\n",
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            "Requirement already satisfied: heapdict in /usr/local/lib/python3.7/dist-packages (from zict>=0.1.3->distributed>=2.6.0->autogluon.core==0.2.0->autogluon) (1.0.1)\n",
            "Collecting jmespath<1.0.0,>=0.7.1\n",
            "  Downloading jmespath-0.10.0-py2.py3-none-any.whl (24 kB)\n",
            "Collecting s3transfer<0.5.0,>=0.4.0\n",
            "  Downloading s3transfer-0.4.2-py2.py3-none-any.whl (79 kB)\n",
            "\u001b[K     |████████████████████████████████| 79 kB 8.7 MB/s \n",
            "\u001b[?25hCollecting botocore<1.21.0,>=1.20.109\n",
            "  Downloading botocore-1.20.109-py2.py3-none-any.whl (7.7 MB)\n",
            "\u001b[K     |████████████████████████████████| 7.7 MB 54.3 MB/s \n",
            "\u001b[?25hCollecting urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1\n",
            "  Downloading urllib3-1.25.11-py2.py3-none-any.whl (127 kB)\n",
            "\u001b[K     |████████████████████████████████| 127 kB 68.1 MB/s \n",
            "\u001b[?25hCollecting immutables>=0.9\n",
            "  Downloading immutables-0.15-cp37-cp37m-manylinux1_x86_64.whl (101 kB)\n",
            "\u001b[K     |████████████████████████████████| 101 kB 13.6 MB/s \n",
            "\u001b[?25hCollecting pycodestyle<2.8.0,>=2.7.0\n",
            "  Downloading pycodestyle-2.7.0-py2.py3-none-any.whl (41 kB)\n",
            "\u001b[K     |████████████████████████████████| 41 kB 713 kB/s \n",
            "\u001b[?25hCollecting mccabe<0.7.0,>=0.6.0\n",
            "  Downloading mccabe-0.6.1-py2.py3-none-any.whl (8.6 kB)\n",
            "Collecting pyflakes<2.4.0,>=2.3.0\n",
            "  Downloading pyflakes-2.3.1-py2.py3-none-any.whl (68 kB)\n",
            "\u001b[K     |████████████████████████████████| 68 kB 8.9 MB/s \n",
            "\u001b[?25hRequirement already satisfied: python-slugify in /usr/local/lib/python3.7/dist-packages (from kaggle->d8<1.0,>=0.0.2->autogluon.vision==0.2.0->autogluon) (5.0.2)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->autogluon.core==0.2.0->autogluon) (1.3.1)\n",
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            "Collecting liac-arff>=2.4.0\n",
            "  Downloading liac-arff-2.5.0.tar.gz (13 kB)\n",
            "Collecting xmltodict\n",
            "  Downloading xmltodict-0.12.0-py2.py3-none-any.whl (9.2 kB)\n",
            "Collecting minio\n",
            "  Downloading minio-7.1.0-py3-none-any.whl (75 kB)\n",
            "\u001b[K     |████████████████████████████████| 75 kB 5.3 MB/s \n",
            "\u001b[?25hRequirement already satisfied: retrying>=1.3.3 in /usr/local/lib/python3.7/dist-packages (from plotly->catboost<0.26,>=0.24.0->autogluon.tabular[all]==0.2.0->autogluon) (1.3.3)\n",
            "Requirement already satisfied: more-itertools>=4.0.0 in /usr/local/lib/python3.7/dist-packages (from pytest->autogluon.extra==0.2.0->autogluon) (8.8.0)\n",
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            "Requirement already satisfied: py>=1.5.0 in /usr/local/lib/python3.7/dist-packages (from pytest->autogluon.extra==0.2.0->autogluon) (1.10.0)\n",
            "Requirement already satisfied: attrs>=17.4.0 in /usr/local/lib/python3.7/dist-packages (from pytest->autogluon.extra==0.2.0->autogluon) (21.2.0)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.7/dist-packages (from python-slugify->kaggle->d8<1.0,>=0.0.2->autogluon.vision==0.2.0->autogluon) (1.3)\n",
            "Collecting portalocker\n",
            "  Downloading portalocker-2.0.0-py2.py3-none-any.whl (11 kB)\n",
            "Building wheels for collected packages: ConfigSpace, contextvars, openml, liac-arff\n",
            "  Building wheel for ConfigSpace (PEP 517) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for ConfigSpace: filename=ConfigSpace-0.4.18-cp37-cp37m-linux_x86_64.whl size=2880047 sha256=c1e5cb008ae4109c05d6c7c499e6d4517e305910fcf70152ae24ac4614d46790\n",
            "  Stored in directory: /root/.cache/pip/wheels/36/f7/0f/36f368c419ea1a8024fc3d6c078c3111dfef43fa1d14cfebe0\n",
            "  Building wheel for contextvars (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for contextvars: filename=contextvars-2.4-py3-none-any.whl size=7680 sha256=0a239e5bba7ee9a12fddbfaabdb96ae40804878f9f9a1b66392d53224d98e47b\n",
            "  Stored in directory: /root/.cache/pip/wheels/0a/11/79/e70e668095c0bb1f94718af672ef2d35ee7a023fee56ef54d9\n",
            "  Building wheel for openml (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for openml: filename=openml-0.12.2-py3-none-any.whl size=137326 sha256=ac16075cafc6cdce1ebab89a81b6ef270a187aa3578414545f4c4e6196b806fa\n",
            "  Stored in directory: /root/.cache/pip/wheels/6a/20/88/cf4ac86aa18e2cd647ed16ebe274a5dacee9d0075fa02af250\n",
            "  Building wheel for liac-arff (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for liac-arff: filename=liac_arff-2.5.0-py3-none-any.whl size=11730 sha256=8869f9663a28c2f32d9e15e9c7bdf731f399640d364f72eb6e13898bdd4f9073\n",
            "  Stored in directory: /root/.cache/pip/wheels/1f/0f/15/332ca86cbebf25ddf98518caaf887945fbe1712b97a0f2493b\n",
            "Successfully built ConfigSpace contextvars openml liac-arff\n",
            "Installing collected packages: urllib3, locket, jmespath, partd, fsspec, cloudpickle, botocore, threadpoolctl, scipy, s3transfer, pynacl, psutil, dask, cryptography, bcrypt, scikit-learn, paramiko, distributed, dill, ConfigSpace, boto3, xmltodict, pyflakes, pycodestyle, portalocker, minio, mccabe, liac-arff, immutables, autogluon.core, yacs, xxhash, tokenizers, sentencepiece, sacremoses, sacrebleu, openml, flake8, fastcore, contextvars, autogluon.features, autocfg, xgboost, lightgbm, gluoncv, fastai, d8, catboost, autogluon.tabular, autogluon.mxnet, autogluon-contrib-nlp, autogluon.vision, autogluon.text, autogluon.extra, autogluon\n",
            "  Attempting uninstall: urllib3\n",
            "    Found existing installation: urllib3 1.24.3\n",
            "    Uninstalling urllib3-1.24.3:\n",
            "      Successfully uninstalled urllib3-1.24.3\n",
            "  Attempting uninstall: cloudpickle\n",
            "    Found existing installation: cloudpickle 1.3.0\n",
            "    Uninstalling cloudpickle-1.3.0:\n",
            "      Successfully uninstalled cloudpickle-1.3.0\n",
            "  Attempting uninstall: scipy\n",
            "    Found existing installation: scipy 1.4.1\n",
            "    Uninstalling scipy-1.4.1:\n",
            "      Successfully uninstalled scipy-1.4.1\n",
            "  Attempting uninstall: psutil\n",
            "    Found existing installation: psutil 5.4.8\n",
            "    Uninstalling psutil-5.4.8:\n",
            "      Successfully uninstalled psutil-5.4.8\n",
            "  Attempting uninstall: dask\n",
            "    Found existing installation: dask 2.12.0\n",
            "    Uninstalling dask-2.12.0:\n",
            "      Successfully uninstalled dask-2.12.0\n",
            "  Attempting uninstall: scikit-learn\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",
            "  Attempting uninstall: distributed\n",
            "    Found existing installation: distributed 1.25.3\n",
            "    Uninstalling distributed-1.25.3:\n",
            "      Successfully uninstalled distributed-1.25.3\n",
            "  Attempting uninstall: dill\n",
            "    Found existing installation: dill 0.3.4\n",
            "    Uninstalling dill-0.3.4:\n",
            "      Successfully uninstalled dill-0.3.4\n",
            "  Attempting uninstall: xgboost\n",
            "    Found existing installation: xgboost 0.90\n",
            "    Uninstalling xgboost-0.90:\n",
            "      Successfully uninstalled xgboost-0.90\n",
            "  Attempting uninstall: lightgbm\n",
            "    Found existing installation: lightgbm 2.2.3\n",
            "    Uninstalling lightgbm-2.2.3:\n",
            "      Successfully uninstalled lightgbm-2.2.3\n",
            "  Attempting uninstall: fastai\n",
            "    Found existing installation: fastai 1.0.61\n",
            "    Uninstalling fastai-1.0.61:\n",
            "      Successfully uninstalled fastai-1.0.61\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "multiprocess 0.70.12.2 requires dill>=0.3.4, but you have dill 0.3.3 which is incompatible.\n",
            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\n",
            "albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.\u001b[0m\n",
            "Successfully installed ConfigSpace-0.4.18 autocfg-0.0.8 autogluon-0.2.0 autogluon-contrib-nlp-0.0.1b20210201 autogluon.core-0.2.0 autogluon.extra-0.2.0 autogluon.features-0.2.0 autogluon.mxnet-0.2.0 autogluon.tabular-0.2.0 autogluon.text-0.2.0 autogluon.vision-0.2.0 bcrypt-3.2.0 boto3-1.17.109 botocore-1.20.109 catboost-0.25.1 cloudpickle-1.6.0 contextvars-2.4 cryptography-3.4.7 d8-0.0.2.post0 dask-2021.7.0 dill-0.3.3 distributed-2021.7.0 fastai-2.4 fastcore-1.3.20 flake8-3.9.2 fsspec-2021.6.1 gluoncv-0.10.3 immutables-0.15 jmespath-0.10.0 liac-arff-2.5.0 lightgbm-3.2.1 locket-0.2.1 mccabe-0.6.1 minio-7.1.0 openml-0.12.2 paramiko-2.7.2 partd-1.2.0 portalocker-2.0.0 psutil-5.8.0 pycodestyle-2.7.0 pyflakes-2.3.1 pynacl-1.4.0 s3transfer-0.4.2 sacrebleu-1.5.1 sacremoses-0.0.45 scikit-learn-0.24.2 scipy-1.6.3 sentencepiece-0.1.95 threadpoolctl-2.2.0 tokenizers-0.9.4 urllib3-1.25.11 xgboost-1.3.3 xmltodict-0.12.0 xxhash-2.0.2 yacs-0.1.8\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "contextvars",
                  "psutil"
                ]
              }
            }
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 97
        },
        "id": "p2PMceFkGkwT",
        "outputId": "64b2a100-ee8c-4079-fcad-8f8561ed9e78"
      },
      "source": [
        "files.upload()\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-190482ef-0537-4165-8a2d-a7d4214230f4\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-190482ef-0537-4165-8a2d-a7d4214230f4\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving kaggle.json to kaggle.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'kaggle.json': b'{\"username\":\"tmtong\",\"key\":\"9848504c1b29e606ee38ce5565f547ee\"}'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G8tpMGddG3Iy",
        "outputId": "03a2d4ea-92a1-43de-9b66-4586a8eff4c0"
      },
      "source": [
        "!mkdir ~/.kaggle\n",
        "!cp kaggle.json ~/.kaggle/\n",
        "!chmod 600 ~/.kaggle/kaggle.json\n",
        "!kaggle datasets list"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Warning: Looks like you're using an outdated API Version, please consider updating (server 1.5.12 / client 1.5.4)\n",
            "ref                                                           title                                              size  lastUpdated          downloadCount  \n",
            "------------------------------------------------------------  ------------------------------------------------  -----  -------------------  -------------  \n",
            "gpreda/reddit-vaccine-myths                                   Reddit Vaccine Myths                              233KB  2021-07-07 19:18:09           8995  \n",
            "crowww/a-large-scale-fish-dataset                             A Large Scale Fish Dataset                          3GB  2021-04-28 17:03:01           5508  \n",
            "imsparsh/musicnet-dataset                                     MusicNet Dataset                                   22GB  2021-02-18 14:12:19           1987  \n",
            "dhruvildave/wikibooks-dataset                                 Wikibooks Dataset                                   2GB  2021-07-03 18:37:20           2379  \n",
            "promptcloud/careerbuilder-job-listing-2020                    Careerbuilder Job Listing 2020                     42MB  2021-03-05 06:59:52           1305  \n",
            "fatiimaezzahra/famous-iconic-women                            Famous Iconic Women                               838MB  2021-02-28 14:56:00            917  \n",
            "nickuzmenkov/nih-chest-xrays-tfrecords                        NIH Chest X-rays TFRecords                         11GB  2021-03-09 04:49:23            711  \n",
            "coloradokb/dandelionimages                                    DandelionImages                                     4GB  2021-02-19 20:03:47            672  \n",
            "mathurinache/twitter-edge-nodes                               Twitter Edge Nodes                                342MB  2021-03-08 06:43:04            623  \n",
            "simiotic/github-code-snippets                                 GitHub Code Snippets                                7GB  2021-03-03 11:34:39            214  \n",
            "alsgroup/end-als                                              End ALS Kaggle Challenge                           12GB  2021-04-08 12:16:37            767  \n",
            "landrykezebou/lvzhdr-tone-mapping-benchmark-dataset-tmonet    LVZ-HDR Tone Mapping Benchmark Dataset (TMO-Net)   24GB  2021-03-01 05:03:40            154  \n",
            "mathurinache/the-lj-speech-dataset                            The LJ Speech Dataset                               3GB  2021-02-15 09:19:54            228  \n",
            "nickuzmenkov/ranzcr-clip-kfold-tfrecords                      RANZCR CLiP KFold TFRecords                         2GB  2021-02-21 13:29:51            100  \n",
            "imsparsh/accentdb-core-extended                               AccentDB - Core & Extended                          6GB  2021-02-17 14:22:54             89  \n",
            "stuartjames/lights                                            LightS: Light Specularity Dataset                  18GB  2021-02-18 14:32:26             88  \n",
            "jessicali9530/animal-crossing-new-horizons-nookplaza-dataset  Animal Crossing New Horizons Catalog              577KB  2021-06-08 15:05:09           4765  \n",
            "datasnaek/youtube-new                                         Trending YouTube Video Statistics                 201MB  2019-06-03 00:56:47         144179  \n",
            "zynicide/wine-reviews                                         Wine Reviews                                       51MB  2017-11-27 17:08:04         139852  \n",
            "residentmario/ramen-ratings                                   Ramen Ratings                                      40KB  2018-01-11 16:04:39          24769  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mAoB2AOfJrYO",
        "outputId": "ffa745b4-3efd-4ab1-e535-9a66ae4a239d"
      },
      "source": [
        "!kaggle datasets download -d lakshmi25npathi/imdb-dataset-of-50k-movie-reviews\n",
        "!unzip imdb-dataset-of-50k-movie-reviews.zip"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading imdb-dataset-of-50k-movie-reviews.zip to /content\n",
            "\r  0% 0.00/25.7M [00:00<?, ?B/s]\r 86% 22.0M/25.7M [00:00<00:00, 227MB/s]\n",
            "\r100% 25.7M/25.7M [00:00<00:00, 236MB/s]\n",
            "Archive:  imdb-dataset-of-50k-movie-reviews.zip\n",
            "  inflating: IMDB Dataset.csv        \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i1CNEcw8Louu"
      },
      "source": [
        "import numpy as np\n",
        "import warnings\n",
        "import matplotlib.pyplot as plt\n",
        "warnings.filterwarnings('ignore')\n",
        "np.random.seed(123)\n",
        "\n",
        "from autogluon.core.utils.loaders.load_pd import load\n",
        "import pandas as pd\n",
        "df = pd.read_csv('./IMDB Dataset.csv')\n",
        "\n",
        "msk = np.random.rand(len(df)) < 0.8\n",
        "train_df = df[msk]\n",
        "test_df = df[~msk]\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d3787Qt0TQKR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "00b41552-21c9-4501-d643-e2c7f2136000"
      },
      "source": [
        "from autogluon.text import TextPredictor\n",
        "predictor = TextPredictor(label='sentiment', eval_metric='acc')\n",
        "predictor.fit(train_df, time_limit=60)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:root:NumPy-shape semantics has been activated in your code. This is required for creating and manipulating scalar and zero-size tensors, which were not supported in MXNet before, as in the official NumPy library. Please DO NOT manually deactivate this semantics while using `mxnet.numpy` and `mxnet.numpy_extension` modules.\n",
            "INFO:autogluon.text.text_prediction.mx.models:The GluonNLP V0 backend is used. We will use 2 cpus and 1 gpus to train each trial.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "All Logs will be saved to /content/AutogluonModels/ag-20210710_032404/task0/training.log\n",
            "Downloading /root/.mxnet/models/nlp/google_electra_base/model-5b35ca0b.yml from https://gluonnlp-numpy-data.s3-accelerate.amazonaws.com/models/google_electra_base/model-5b35ca0b.yml...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 477/477 [00:00<00:00, 133kiB/s]\n",
            "  0%|          | 0.00/323k [00:00<?, ?iB/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Downloading /root/.mxnet/models/nlp/google_electra_base/vocab-e6d2b21d.json from https://gluonnlp-numpy-data.s3-accelerate.amazonaws.com/models/google_electra_base/vocab-e6d2b21d.json...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 323k/323k [00:00<00:00, 1.32MiB/s]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Downloading /root/.mxnet/models/nlp/google_electra_base/model-31c235cc.params from https://gluonnlp-numpy-data.s3-accelerate.amazonaws.com/models/google_electra_base/model-31c235cc.params...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 436M/436M [00:08<00:00, 50.3MiB/s]\n",
            "INFO:root:Fitting and transforming the train data...\n",
            "INFO:root:Done! Preprocessor saved to /content/AutogluonModels/ag-20210710_032404/task0/preprocessor.pkl\n",
            "INFO:root:Process dev set...\n",
            "INFO:root:Done!\n",
            "INFO:root:Max length for chunking text: 512, Stochastic chunk: Train-False/Test-False, Test #repeat: 1.\n",
            "INFO:root:#Total Params/Fixed Params=108990466/0\n",
            "Level 15:root:Using gradient accumulation. Global batch size = 128\n",
            "INFO:root:Local training results will be saved to /content/AutogluonModels/ag-20210710_032404/task0/results_local.jsonl.\n",
            "Level 15:root:[Iter 15/2940, Epoch 0] train loss=7.79e-01, gnorm=7.17e+00, lr=5.10e-06, #samples processed=1920, #sample per second=0.11. ETA=58562.23min\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ygz1zi5heJ_G"
      },
      "source": [
        "test_score = predictor.evaluate(test_df)\n",
        "print('Accuracy = {:.2f}%'.format(test_score * 100))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jG8_60IPl1Ic"
      },
      "source": [
        "This took so long that collab run out of gpu time. \n"
      ]
    }
  ]
}