text_generation.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "text-generation.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "mount_file_id": "1SsYKniB48_XJTuSX26nRFWYa9Y51BMay",
      "authorship_tag": "ABX9TyOL4rf+yBkuBNzWQG3oZD5Q",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/parvathysarat/gpt2-text-generation/blob/master/text_generation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VhWmzd1llZsS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 877
        },
        "outputId": "8fa743ad-e19c-4854-d88b-87fc76fc191d"
      },
      "source": [
        "!pip install transformers\n",
        "!git clone https://github.com/huggingface/transformers\n",
        "import os\n",
        "os.chdir('transformers')\n",
        "print(os.getcwd())\n",
        "!pip install ."
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: transformers in /usr/local/lib/python3.6/dist-packages (3.0.2)\n",
            "Requirement already satisfied: sacremoses in /usr/local/lib/python3.6/dist-packages (from transformers) (0.0.43)\n",
            "Requirement already satisfied: sentencepiece!=0.1.92 in /usr/local/lib/python3.6/dist-packages (from transformers) (0.1.91)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.6/dist-packages (from transformers) (20.4)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from transformers) (1.18.5)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.6/dist-packages (from transformers) (2019.12.20)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.6/dist-packages (from transformers) (4.41.1)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.6/dist-packages (from transformers) (3.0.12)\n",
            "Requirement already satisfied: dataclasses; python_version < \"3.7\" in /usr/local/lib/python3.6/dist-packages (from transformers) (0.7)\n",
            "Requirement already satisfied: tokenizers==0.8.1.rc2 in /usr/local/lib/python3.6/dist-packages (from transformers) (0.8.1rc2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from transformers) (2.23.0)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.6/dist-packages (from sacremoses->transformers) (0.16.0)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.6/dist-packages (from sacremoses->transformers) (7.1.2)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from sacremoses->transformers) (1.15.0)\n",
            "Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.6/dist-packages (from packaging->transformers) (2.4.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->transformers) (2020.6.20)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->transformers) (2.10)\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->transformers) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->transformers) (3.0.4)\n",
            "fatal: destination path 'transformers' already exists and is not an empty directory.\n",
            "/content/transformers\n",
            "Processing /content/transformers\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from transformers==3.0.2) (1.18.5)\n",
            "Requirement already satisfied: tokenizers==0.8.1.rc2 in /usr/local/lib/python3.6/dist-packages (from transformers==3.0.2) (0.8.1rc2)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.6/dist-packages (from transformers==3.0.2) (20.4)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.6/dist-packages (from transformers==3.0.2) (3.0.12)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from transformers==3.0.2) (2.23.0)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.6/dist-packages (from transformers==3.0.2) (4.41.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.6/dist-packages (from transformers==3.0.2) (2019.12.20)\n",
            "Requirement already satisfied: sentencepiece!=0.1.92 in /usr/local/lib/python3.6/dist-packages (from transformers==3.0.2) (0.1.91)\n",
            "Requirement already satisfied: sacremoses in /usr/local/lib/python3.6/dist-packages (from transformers==3.0.2) (0.0.43)\n",
            "Requirement already satisfied: dataclasses in /usr/local/lib/python3.6/dist-packages (from transformers==3.0.2) (0.7)\n",
            "Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.6/dist-packages (from packaging->transformers==3.0.2) (2.4.7)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from packaging->transformers==3.0.2) (1.15.0)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->transformers==3.0.2) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->transformers==3.0.2) (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->transformers==3.0.2) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->transformers==3.0.2) (2.10)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.6/dist-packages (from sacremoses->transformers==3.0.2) (0.16.0)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.6/dist-packages (from sacremoses->transformers==3.0.2) (7.1.2)\n",
            "Building wheels for collected packages: transformers\n",
            "  Building wheel for transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for transformers: filename=transformers-3.0.2-cp36-none-any.whl size=858594 sha256=a21ee48732261c5ad167a51f833bc3e56a0c13e946133fd9a2f0fa0730c13f03\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-jn7ptc0s/wheels/23/19/dd/2561a4e47240cf6b307729d58e56f8077dd0c698f5992216cf\n",
            "Successfully built transformers\n",
            "Installing collected packages: transformers\n",
            "  Found existing installation: transformers 3.0.2\n",
            "    Uninstalling transformers-3.0.2:\n",
            "      Successfully uninstalled transformers-3.0.2\n",
            "Successfully installed transformers-3.0.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KjlxwycXspwm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "os.chdir('examples/language-modeling/')"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9PtLOtOa1FO2",
        "colab_type": "text"
      },
      "source": [
        "## Fine tuning GPT-2 language model on our dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VB47o4Jn0N9T",
        "colab_type": "text"
      },
      "source": [
        "Add to transformers/example/language-modeling/run-language-modeling.py file the following snippet : <br>\n",
        "\n",
        "```\n",
        "special_tokens_dict = {'bos_token': '<BOS>', 'eos_token': '<EOS>', 'pad_token': '<PAD>'}\n",
        "num_added_toks = tokenizer.add_special_tokens(special_tokens_dict)\n",
        "model.resize_token_embeddings(len(tokenizer))\n",
        "```\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9CnhcYntssjc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "8c8b4c5e-c0be-4723-dfbe-2a504fdf45fd"
      },
      "source": [
        "N=\"1\"\n",
        "try:os.mkdir('../../../model')\n",
        "except:pass\n",
        "OUTPUT_DIR='../../../model/'\n",
        "print(os.getcwd())\n",
        "TRAIN_FILE='../../../dataset/train.txt'\n",
        "VALID_FILE='../../../dataset/valid.txt'\n"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/transformers/examples/language-modeling\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JaTfjcvNtfEQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "9162bca9-2b0e-4fb0-ad47-c79e1f90b7fd"
      },
      "source": [
        "os.environ[\"CUDA_VISIBLE_DEVICES\"]= N\n",
        "!python run_language_modeling.py \\\n",
        "--output_dir=$OUTPUT_DIR \\\n",
        "--model_type=gpt2 \\\n",
        "--model_name_or_path=gpt2 \\\n",
        "--do_train \\\n",
        "--train_data_file=$TRAIN_FILE \\\n",
        "--do_eval \\\n",
        "--eval_data_file=$VALID_FILE \\\n",
        "--per_device_train_batch_size=2 \\\n",
        "--per_device_eval_batch_size=2 \\\n",
        "--line_by_line \\\n",
        "--evaluate_during_training \\\n",
        "--learning_rate=5e-5 \\\n",
        "--num_train_epochs=2 \\\n",
        "--overwrite_output_dir"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "{'eval_loss': 3.838087332456611, 'epoch': 0.7267441860465116, 'step': 1000}\n",
            "08/16/2020 16:39:42 - INFO - transformers.trainer -   Saving model checkpoint to ../../../model/checkpoint-1000\n",
            "08/16/2020 16:39:42 - INFO - transformers.configuration_utils -   Configuration saved in ../../../model/checkpoint-1000/config.json\n",
            "08/16/2020 16:39:43 - INFO - transformers.modeling_utils -   Model weights saved in ../../../model/checkpoint-1000/pytorch_model.bin\n",
            "\n",
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            "08/16/2020 17:19:36 - INFO - transformers.trainer -   ***** Running Evaluation *****\n",
            "08/16/2020 17:19:36 - INFO - transformers.trainer -     Num examples = 2752\n",
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            "\n",
            "Evaluation:  13% 181/1376 [01:10<07:38,  2.60it/s]\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "Evaluation:  25% 343/1376 [02:12<04:37,  3.72it/s]\u001b[A\u001b[A\n",
            "\n",
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            "Evaluation:  34% 465/1376 [02:55<06:24,  2.37it/s]\u001b[A\u001b[A\n",
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            "\n",
            "Evaluation:  34% 467/1376 [02:56<06:03,  2.50it/s]\u001b[A\u001b[A\n",
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            "Evaluation:  34% 468/1376 [02:56<06:13,  2.43it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  34% 469/1376 [02:56<05:30,  2.75it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  34% 470/1376 [02:57<05:46,  2.62it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  34% 471/1376 [02:57<05:36,  2.69it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  34% 472/1376 [02:57<04:54,  3.07it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  34% 473/1376 [02:58<04:59,  3.02it/s]\u001b[A\u001b[A\n",
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            "Evaluation:  34% 474/1376 [02:58<05:42,  2.64it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  35% 475/1376 [02:59<05:51,  2.57it/s]\u001b[A\u001b[A\n",
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            "Evaluation:  35% 478/1376 [03:00<04:54,  3.05it/s]\u001b[A\u001b[A\n",
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            "Evaluation:  35% 484/1376 [03:02<05:05,  2.92it/s]\u001b[A\u001b[A\n",
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            "Evaluation:  35% 488/1376 [03:03<06:15,  2.37it/s]\u001b[A\u001b[A\n",
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            "Evaluation:  36% 489/1376 [03:04<05:00,  2.95it/s]\u001b[A\u001b[A\n",
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            "Evaluation:  36% 490/1376 [03:04<05:04,  2.91it/s]\u001b[A\u001b[A\n",
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            "Evaluation:  36% 491/1376 [03:04<04:33,  3.23it/s]\u001b[A\u001b[A\n",
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            "Evaluation:  36% 492/1376 [03:04<04:40,  3.15it/s]\u001b[A\u001b[A\n",
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            "Evaluation:  36% 493/1376 [03:05<04:10,  3.53it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  36% 494/1376 [03:05<04:04,  3.61it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  36% 495/1376 [03:05<03:37,  4.04it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  36% 496/1376 [03:05<03:34,  4.09it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  36% 497/1376 [03:06<03:14,  4.53it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  36% 498/1376 [03:06<03:53,  3.76it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  36% 499/1376 [03:06<03:25,  4.26it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  36% 500/1376 [03:06<03:06,  4.71it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  36% 501/1376 [03:06<02:48,  5.18it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  36% 502/1376 [03:07<02:47,  5.22it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  37% 503/1376 [03:07<02:40,  5.43it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  37% 504/1376 [03:07<02:42,  5.36it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  37% 505/1376 [03:07<03:45,  3.87it/s]\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "Evaluation:  45% 620/1376 [03:51<04:30,  2.79it/s]\u001b[A\u001b[A\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
            "Evaluation:  48% 655/1376 [04:07<06:49,  1.76it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  48% 656/1376 [04:08<06:32,  1.84it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  48% 657/1376 [04:08<05:50,  2.05it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  48% 658/1376 [04:08<04:46,  2.51it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  48% 659/1376 [04:09<04:37,  2.59it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  48% 660/1376 [04:09<04:03,  2.95it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  48% 661/1376 [04:09<04:32,  2.62it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  48% 662/1376 [04:10<04:27,  2.67it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  48% 663/1376 [04:10<04:42,  2.52it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  48% 664/1376 [04:10<04:11,  2.84it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  48% 665/1376 [04:11<04:13,  2.81it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  48% 666/1376 [04:11<04:10,  2.83it/s]\u001b[A\u001b[A\n",
            "\n",
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            "Evaluation:  58% 792/1376 [04:56<04:28,  2.18it/s]\u001b[A\u001b[A\n",
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            "Evaluation:  58% 794/1376 [04:57<03:59,  2.43it/s]\u001b[A\u001b[A\n",
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            "\n",
            "Evaluation:  58% 804/1376 [05:01<04:05,  2.33it/s]\u001b[A\u001b[A\n",
            "\n",
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            "Evaluation:  59% 810/1376 [05:04<04:58,  1.90it/s]\u001b[A\u001b[A\n",
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            "Evaluation:  59% 815/1376 [05:06<04:16,  2.19it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  59% 816/1376 [05:07<03:39,  2.56it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  59% 817/1376 [05:07<03:15,  2.85it/s]\u001b[A\u001b[A\n",
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            "Evaluation:  59% 818/1376 [05:07<03:09,  2.94it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  60% 819/1376 [05:08<02:53,  3.21it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  60% 820/1376 [05:08<02:39,  3.48it/s]\u001b[A\u001b[A\n",
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            "\n",
            "Evaluation:  60% 822/1376 [05:08<02:29,  3.70it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  60% 823/1376 [05:09<02:59,  3.07it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  60% 824/1376 [05:09<02:29,  3.70it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  60% 825/1376 [05:09<03:15,  2.81it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  60% 826/1376 [05:10<02:48,  3.27it/s]\u001b[A\u001b[A\n",
            "\n",
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            "Evaluation:  69% 953/1376 [05:59<03:00,  2.35it/s]\u001b[A\u001b[A\n",
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            "Evaluation:  69% 954/1376 [05:59<02:35,  2.72it/s]\u001b[A\u001b[A\n",
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            "Evaluation:  69% 955/1376 [06:00<02:18,  3.04it/s]\u001b[A\u001b[A\n",
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            "Evaluation:  69% 956/1376 [06:00<02:00,  3.49it/s]\u001b[A\u001b[A\n",
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            "Evaluation:  70% 957/1376 [06:00<01:53,  3.70it/s]\u001b[A\u001b[A\n",
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            "Evaluation:  70% 958/1376 [06:00<01:39,  4.19it/s]\u001b[A\u001b[A\n",
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            "Evaluation:  70% 966/1376 [06:03<02:06,  3.24it/s]\u001b[A\u001b[A\n",
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            "Evaluation:  71% 971/1376 [06:04<02:26,  2.77it/s]\u001b[A\u001b[A\n",
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            "Evaluation:  71% 972/1376 [06:05<02:17,  2.94it/s]\u001b[A\u001b[A\n",
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            "Evaluation:  71% 974/1376 [06:05<01:58,  3.39it/s]\u001b[A\u001b[A\n",
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            "Evaluation:  71% 975/1376 [06:06<01:53,  3.54it/s]\u001b[A\u001b[A\n",
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            "Evaluation:  71% 976/1376 [06:06<02:34,  2.59it/s]\u001b[A\u001b[A\n",
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            "Evaluation:  71% 977/1376 [06:07<02:42,  2.45it/s]\u001b[A\u001b[A\n",
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            "Evaluation:  71% 978/1376 [06:07<02:20,  2.83it/s]\u001b[A\u001b[A\n",
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            "Evaluation:  71% 979/1376 [06:08<03:22,  1.96it/s]\u001b[A\u001b[A\n",
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            "Evaluation:  71% 980/1376 [06:08<03:52,  1.70it/s]\u001b[A\u001b[A\n",
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            "Evaluation:  71% 981/1376 [06:09<04:03,  1.62it/s]\u001b[A\u001b[A\n",
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            "Evaluation:  71% 982/1376 [06:10<04:21,  1.51it/s]\u001b[A\u001b[A\n",
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            "Evaluation:  71% 983/1376 [06:10<04:03,  1.62it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  72% 984/1376 [06:11<03:41,  1.77it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  72% 985/1376 [06:11<02:57,  2.20it/s]\u001b[A\u001b[A\n",
            "\n",
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            "Evaluation:  79% 1081/1376 [06:51<02:16,  2.16it/s]\u001b[A\u001b[A\n",
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            "\n",
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            "\n",
            "Evaluation:  79% 1085/1376 [06:52<01:49,  2.65it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  79% 1086/1376 [06:53<01:55,  2.52it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  79% 1087/1376 [06:53<01:57,  2.46it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  79% 1088/1376 [06:54<02:03,  2.33it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  79% 1089/1376 [06:54<01:58,  2.43it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  79% 1090/1376 [06:54<01:58,  2.41it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  79% 1091/1376 [06:55<02:00,  2.36it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  79% 1092/1376 [06:55<01:59,  2.38it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  79% 1093/1376 [06:55<01:42,  2.75it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  80% 1094/1376 [06:56<01:49,  2.56it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  80% 1095/1376 [06:56<01:53,  2.48it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  80% 1096/1376 [06:57<02:36,  1.79it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  80% 1097/1376 [06:58<02:13,  2.09it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  80% 1098/1376 [06:58<02:25,  1.92it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  80% 1099/1376 [06:59<03:28,  1.33it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  80% 1100/1376 [07:00<03:09,  1.46it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  80% 1101/1376 [07:00<02:53,  1.59it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  80% 1102/1376 [07:01<02:37,  1.74it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  80% 1103/1376 [07:02<02:40,  1.70it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  80% 1104/1376 [07:03<03:16,  1.39it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  80% 1105/1376 [07:03<02:40,  1.69it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  80% 1106/1376 [07:03<02:15,  2.00it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  80% 1107/1376 [07:03<01:52,  2.40it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  81% 1108/1376 [07:04<01:43,  2.60it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  81% 1109/1376 [07:04<02:14,  1.99it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  81% 1110/1376 [07:05<02:11,  2.03it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  81% 1111/1376 [07:05<01:51,  2.39it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  81% 1112/1376 [07:05<01:31,  2.87it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  81% 1113/1376 [07:06<01:22,  3.21it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  81% 1114/1376 [07:06<01:30,  2.88it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  81% 1115/1376 [07:06<01:34,  2.75it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  81% 1116/1376 [07:07<01:24,  3.09it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  81% 1117/1376 [07:07<01:26,  3.00it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  81% 1118/1376 [07:07<01:29,  2.87it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  81% 1119/1376 [07:08<01:26,  2.98it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  81% 1120/1376 [07:08<01:27,  2.94it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  81% 1121/1376 [07:08<01:15,  3.38it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  82% 1122/1376 [07:08<01:07,  3.77it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  82% 1123/1376 [07:09<01:30,  2.79it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  82% 1124/1376 [07:09<01:15,  3.32it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  82% 1125/1376 [07:09<01:12,  3.47it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  82% 1126/1376 [07:10<01:22,  3.03it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  82% 1127/1376 [07:10<01:19,  3.15it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  82% 1128/1376 [07:10<01:09,  3.56it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  82% 1129/1376 [07:11<01:29,  2.77it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  82% 1130/1376 [07:12<02:18,  1.78it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  82% 1131/1376 [07:13<02:34,  1.58it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  82% 1132/1376 [07:13<02:13,  1.83it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  82% 1133/1376 [07:13<02:01,  2.01it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  82% 1134/1376 [07:14<01:50,  2.18it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  82% 1135/1376 [07:14<01:55,  2.10it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  83% 1136/1376 [07:15<01:40,  2.38it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  83% 1137/1376 [07:15<01:27,  2.72it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  83% 1138/1376 [07:15<01:29,  2.65it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  83% 1139/1376 [07:16<01:33,  2.53it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  83% 1140/1376 [07:16<01:45,  2.24it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  83% 1141/1376 [07:17<01:45,  2.23it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  83% 1142/1376 [07:17<02:05,  1.87it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  83% 1143/1376 [07:18<01:53,  2.05it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  83% 1144/1376 [07:18<02:03,  1.88it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  83% 1145/1376 [07:19<01:58,  1.95it/s]\u001b[A\u001b[A\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
            "Evaluation:  91% 1247/1376 [08:02<00:56,  2.28it/s]\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "Evaluation:  91% 1249/1376 [08:03<00:43,  2.95it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  91% 1250/1376 [08:03<00:51,  2.45it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  91% 1251/1376 [08:04<00:45,  2.75it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  91% 1252/1376 [08:04<00:52,  2.37it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  91% 1253/1376 [08:04<00:50,  2.45it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  91% 1254/1376 [08:05<00:54,  2.25it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  91% 1255/1376 [08:05<00:51,  2.35it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  91% 1256/1376 [08:06<00:51,  2.34it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  91% 1257/1376 [08:06<00:41,  2.85it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  91% 1258/1376 [08:06<00:40,  2.94it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  91% 1259/1376 [08:07<00:37,  3.16it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  92% 1260/1376 [08:07<00:32,  3.54it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  92% 1261/1376 [08:07<00:33,  3.39it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  92% 1262/1376 [08:07<00:28,  4.06it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  92% 1263/1376 [08:07<00:26,  4.20it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  92% 1264/1376 [08:08<00:25,  4.41it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  92% 1265/1376 [08:08<00:26,  4.11it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  92% 1266/1376 [08:08<00:27,  4.05it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  92% 1267/1376 [08:08<00:24,  4.45it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  92% 1268/1376 [08:09<00:27,  3.91it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  92% 1269/1376 [08:09<00:28,  3.80it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  92% 1270/1376 [08:09<00:28,  3.68it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  92% 1271/1376 [08:10<00:32,  3.23it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  92% 1272/1376 [08:10<00:30,  3.41it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  93% 1273/1376 [08:10<00:31,  3.27it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  93% 1274/1376 [08:11<00:31,  3.23it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  93% 1275/1376 [08:11<00:30,  3.32it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  93% 1276/1376 [08:11<00:26,  3.76it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  93% 1277/1376 [08:11<00:28,  3.53it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  93% 1278/1376 [08:12<00:29,  3.35it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  93% 1279/1376 [08:12<00:26,  3.73it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  93% 1280/1376 [08:12<00:24,  3.90it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  93% 1281/1376 [08:13<00:32,  2.95it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  93% 1282/1376 [08:13<00:28,  3.27it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  93% 1283/1376 [08:13<00:26,  3.56it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  93% 1284/1376 [08:13<00:27,  3.41it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  93% 1285/1376 [08:14<00:24,  3.74it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  93% 1286/1376 [08:14<00:22,  3.97it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  94% 1287/1376 [08:14<00:19,  4.46it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  94% 1288/1376 [08:14<00:20,  4.29it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  94% 1289/1376 [08:14<00:20,  4.22it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  94% 1290/1376 [08:15<00:23,  3.68it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  94% 1291/1376 [08:15<00:24,  3.47it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  94% 1292/1376 [08:16<00:25,  3.26it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  94% 1293/1376 [08:16<00:22,  3.66it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  94% 1294/1376 [08:16<00:20,  3.95it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  94% 1295/1376 [08:16<00:21,  3.85it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  94% 1296/1376 [08:16<00:19,  4.01it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  94% 1297/1376 [08:17<00:19,  4.15it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  94% 1298/1376 [08:17<00:23,  3.38it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  94% 1299/1376 [08:18<00:26,  2.93it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  94% 1300/1376 [08:18<00:28,  2.70it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  95% 1301/1376 [08:18<00:29,  2.53it/s]\u001b[A\u001b[A\n",
            "\n",
            "Evaluation:  95% 1302/1376 [08:19<00:28,  2.56it/s]\u001b[A\u001b[A\n",
            "\n",
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            "{'eval_loss': 3.4340166661801725, 'epoch': 1.4534883720930232, 'step': 2000}\n",
            "08/16/2020 17:28:21 - INFO - transformers.trainer -   Saving model checkpoint to ../../../model/checkpoint-2000\n",
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            "Iteration: 100% 1376/1376 [1:03:14<00:00,  2.76s/it]\n",
            "Epoch: 100% 2/2 [2:07:11<00:00, 3815.66s/it]\n",
            "08/16/2020 17:57:50 - INFO - transformers.trainer -   \n",
            "\n",
            "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
            "\n",
            "\n",
            "08/16/2020 17:57:50 - INFO - transformers.trainer -   Saving model checkpoint to ../../../model/\n",
            "08/16/2020 17:57:50 - INFO - transformers.configuration_utils -   Configuration saved in ../../../model/config.json\n",
            "08/16/2020 17:57:51 - INFO - transformers.modeling_utils -   Model weights saved in ../../../model/pytorch_model.bin\n",
            "/usr/local/lib/python3.6/dist-packages/transformers/trainer.py:810: FutureWarning: This method is deprecated, use `Trainer.is_world_process_zero()` instead.\n",
            "  warnings.warn(\"This method is deprecated, use `Trainer.is_world_process_zero()` instead.\", FutureWarning)\n",
            "08/16/2020 17:57:51 - INFO - __main__ -   *** Evaluate ***\n",
            "08/16/2020 17:57:51 - INFO - transformers.trainer -   ***** Running Evaluation *****\n",
            "08/16/2020 17:57:51 - INFO - transformers.trainer -     Num examples = 2752\n",
            "08/16/2020 17:57:51 - INFO - transformers.trainer -     Batch size = 2\n",
            "Evaluation: 100% 1376/1376 [08:50<00:00,  2.60it/s]\n",
            "{'eval_loss': 3.346235140811565, 'epoch': 2.0, 'step': 2752}\n",
            "08/16/2020 18:06:42 - INFO - __main__ -   ***** Eval results *****\n",
            "08/16/2020 18:06:42 - INFO - __main__ -     perplexity = 28.395626613070057\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-2GnJODBdMaM",
        "colab_type": "text"
      },
      "source": [
        "Vocabulary size : 50260\n",
        "\n",
        "Total number of tokens : 244000+\n",
        "\n",
        "Final loss : 3.346"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e5ODoTep7a3h",
        "colab_type": "text"
      },
      "source": [
        "## Evaluate perplexity on test set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sBkJ1KXh7eTi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "960b4ff9-856b-4b88-8227-7705e3d7868e"
      },
      "source": [
        "TEST_FILE='../../../dataset/valid.txt'\n",
        "CUDA_VISIBLE_DEVICES=\"1\" \n",
        "!python run_language_modeling.py \\\n",
        "--output_dir=$OUTPUT_DIR \\\n",
        "--model_type=gpt2 \\\n",
        "--model_name_or_path=$OUTPUT_DIR \\\n",
        "--do_eval \\\n",
        "--eval_data_file=$TEST_FILE \\\n",
        "--per_device_eval_batch_size=2 \\\n",
        "--line_by_line"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2020-08-16 18:07:24.266399: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\n",
            "08/16/2020 18:07:26 - INFO - transformers.training_args -   PyTorch: setting up devices\n",
            "08/16/2020 18:07:26 - WARNING - __main__ -   Process rank: -1, device: cpu, n_gpu: 0, distributed training: False, 16-bits training: False\n",
            "08/16/2020 18:07:26 - INFO - __main__ -   Training/evaluation parameters TrainingArguments(output_dir='../../../model/', overwrite_output_dir=False, do_train=False, do_eval=True, do_predict=False, evaluate_during_training=False, prediction_loss_only=False, per_device_train_batch_size=8, per_device_eval_batch_size=2, per_gpu_train_batch_size=None, per_gpu_eval_batch_size=None, gradient_accumulation_steps=1, learning_rate=5e-05, weight_decay=0.0, adam_beta1=0.9, adam_beta2=0.999, adam_epsilon=1e-08, max_grad_norm=1.0, num_train_epochs=3.0, max_steps=-1, warmup_steps=0, logging_dir='runs/Aug16_18-07-26_cd2c91bd504b', logging_first_step=False, logging_steps=500, save_steps=500, save_total_limit=None, no_cuda=False, seed=42, fp16=False, fp16_opt_level='O1', local_rank=-1, tpu_num_cores=None, tpu_metrics_debug=False, debug=False, dataloader_drop_last=False, eval_steps=1000, past_index=-1, run_name=None)\n",
            "08/16/2020 18:07:26 - INFO - transformers.configuration_utils -   loading configuration file ../../../model/config.json\n",
            "08/16/2020 18:07:26 - INFO - transformers.configuration_utils -   Model config GPT2Config {\n",
            "  \"activation_function\": \"gelu_new\",\n",
            "  \"architectures\": [\n",
            "    \"GPT2LMHeadModel\"\n",
            "  ],\n",
            "  \"attn_pdrop\": 0.1,\n",
            "  \"bos_token_id\": 50256,\n",
            "  \"embd_pdrop\": 0.1,\n",
            "  \"eos_token_id\": 50256,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"layer_norm_epsilon\": 1e-05,\n",
            "  \"model_type\": \"gpt2\",\n",
            "  \"n_ctx\": 1024,\n",
            "  \"n_embd\": 768,\n",
            "  \"n_head\": 12,\n",
            "  \"n_inner\": null,\n",
            "  \"n_layer\": 12,\n",
            "  \"n_positions\": 1024,\n",
            "  \"resid_pdrop\": 0.1,\n",
            "  \"summary_activation\": null,\n",
            "  \"summary_first_dropout\": 0.1,\n",
            "  \"summary_proj_to_labels\": true,\n",
            "  \"summary_type\": \"cls_index\",\n",
            "  \"summary_use_proj\": true,\n",
            "  \"task_specific_params\": {\n",
            "    \"text-generation\": {\n",
            "      \"do_sample\": true,\n",
            "      \"max_length\": 50\n",
            "    }\n",
            "  },\n",
            "  \"vocab_size\": 50260\n",
            "}\n",
            "\n",
            "08/16/2020 18:07:26 - INFO - transformers.configuration_utils -   loading configuration file ../../../model/config.json\n",
            "08/16/2020 18:07:26 - INFO - transformers.configuration_utils -   Model config GPT2Config {\n",
            "  \"activation_function\": \"gelu_new\",\n",
            "  \"architectures\": [\n",
            "    \"GPT2LMHeadModel\"\n",
            "  ],\n",
            "  \"attn_pdrop\": 0.1,\n",
            "  \"bos_token_id\": 50256,\n",
            "  \"embd_pdrop\": 0.1,\n",
            "  \"eos_token_id\": 50256,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"layer_norm_epsilon\": 1e-05,\n",
            "  \"model_type\": \"gpt2\",\n",
            "  \"n_ctx\": 1024,\n",
            "  \"n_embd\": 768,\n",
            "  \"n_head\": 12,\n",
            "  \"n_inner\": null,\n",
            "  \"n_layer\": 12,\n",
            "  \"n_positions\": 1024,\n",
            "  \"resid_pdrop\": 0.1,\n",
            "  \"summary_activation\": null,\n",
            "  \"summary_first_dropout\": 0.1,\n",
            "  \"summary_proj_to_labels\": true,\n",
            "  \"summary_type\": \"cls_index\",\n",
            "  \"summary_use_proj\": true,\n",
            "  \"task_specific_params\": {\n",
            "    \"text-generation\": {\n",
            "      \"do_sample\": true,\n",
            "      \"max_length\": 50\n",
            "    }\n",
            "  },\n",
            "  \"vocab_size\": 50260\n",
            "}\n",
            "\n",
            "08/16/2020 18:07:26 - INFO - transformers.tokenization_utils_base -   Model name '../../../model/' not found in model shortcut name list (gpt2, gpt2-medium, gpt2-large, gpt2-xl, distilgpt2). Assuming '../../../model/' is a path, a model identifier, or url to a directory containing tokenizer files.\n",
            "08/16/2020 18:07:26 - INFO - transformers.tokenization_utils_base -   Didn't find file ../../../model/tokenizer.json. We won't load it.\n",
            "08/16/2020 18:07:26 - INFO - transformers.tokenization_utils_base -   loading file ../../../model/vocab.json\n",
            "08/16/2020 18:07:26 - INFO - transformers.tokenization_utils_base -   loading file ../../../model/merges.txt\n",
            "08/16/2020 18:07:26 - INFO - transformers.tokenization_utils_base -   loading file ../../../model/added_tokens.json\n",
            "08/16/2020 18:07:26 - INFO - transformers.tokenization_utils_base -   loading file ../../../model/special_tokens_map.json\n",
            "08/16/2020 18:07:26 - INFO - transformers.tokenization_utils_base -   loading file ../../../model/tokenizer_config.json\n",
            "08/16/2020 18:07:26 - INFO - transformers.tokenization_utils_base -   loading file None\n",
            "08/16/2020 18:07:27 - INFO - transformers.tokenization_utils -   Adding <BOS> to the vocabulary\n",
            "08/16/2020 18:07:27 - INFO - transformers.tokenization_utils -   Adding <EOS> to the vocabulary\n",
            "08/16/2020 18:07:27 - INFO - transformers.tokenization_utils -   Adding <PAD> to the vocabulary\n",
            "/usr/local/lib/python3.6/dist-packages/transformers/modeling_auto.py:819: FutureWarning: The class `AutoModelWithLMHead` is deprecated and will be removed in a future version. Please use `AutoModelForCausalLM` for causal language models, `AutoModelForMaskedLM` for masked language models and `AutoModelForSeq2SeqLM` for encoder-decoder models.\n",
            "  FutureWarning,\n",
            "08/16/2020 18:07:27 - INFO - transformers.modeling_utils -   loading weights file ../../../model/pytorch_model.bin\n",
            "08/16/2020 18:07:33 - INFO - transformers.modeling_utils -   All model checkpoint weights were used when initializing GPT2LMHeadModel.\n",
            "\n",
            "08/16/2020 18:07:33 - INFO - transformers.modeling_utils -   All the weights of GPT2LMHeadModel were initialized from the model checkpoint at ../../../model/.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use GPT2LMHeadModel for predictions without further training.\n",
            "08/16/2020 18:07:33 - INFO - transformers.tokenization_utils_base -   Assigning <BOS> to the bos_token key of the tokenizer\n",
            "08/16/2020 18:07:33 - INFO - transformers.tokenization_utils_base -   Assigning <EOS> to the eos_token key of the tokenizer\n",
            "08/16/2020 18:07:33 - INFO - transformers.tokenization_utils_base -   Assigning <PAD> to the pad_token key of the tokenizer\n",
            "/usr/local/lib/python3.6/dist-packages/transformers/tokenization_utils_base.py:1319: FutureWarning: The `max_len` attribute has been deprecated and will be removed in a future version, use `model_max_length` instead.\n",
            "  FutureWarning,\n",
            "08/16/2020 18:07:33 - INFO - transformers.data.datasets.language_modeling -   Creating features from dataset file at ../../../dataset/valid.txt\n",
            "/usr/local/lib/python3.6/dist-packages/transformers/trainer.py:189: FutureWarning: Passing `prediction_loss_only` as a keyword argument is deprecated and won't be possible in a future version. Use `args.prediction_loss_only` instead.\n",
            "  FutureWarning,\n",
            "08/16/2020 18:07:34 - INFO - transformers.trainer -   You are instantiating a Trainer but W&B is not installed. To use wandb logging, run `pip install wandb; wandb login` see https://docs.wandb.com/huggingface.\n",
            "08/16/2020 18:07:34 - INFO - transformers.trainer -   To use comet_ml logging, run `pip/conda install comet_ml` see https://www.comet.ml/docs/python-sdk/huggingface/\n",
            "08/16/2020 18:07:34 - INFO - __main__ -   *** Evaluate ***\n",
            "08/16/2020 18:07:34 - INFO - transformers.trainer -   ***** Running Evaluation *****\n",
            "08/16/2020 18:07:34 - INFO - transformers.trainer -     Num examples = 2752\n",
            "08/16/2020 18:07:34 - INFO - transformers.trainer -     Batch size = 2\n",
            "Evaluation: 100% 1376/1376 [09:11<00:00,  2.49it/s]\n",
            "{'eval_loss': 3.346235140811565, 'step': 0}\n",
            "/usr/local/lib/python3.6/dist-packages/transformers/trainer.py:810: FutureWarning: This method is deprecated, use `Trainer.is_world_process_zero()` instead.\n",
            "  warnings.warn(\"This method is deprecated, use `Trainer.is_world_process_zero()` instead.\", FutureWarning)\n",
            "08/16/2020 18:16:45 - INFO - __main__ -   ***** Eval results *****\n",
            "08/16/2020 18:16:45 - INFO - __main__ -     perplexity = 28.395626613070057\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X9Y1HEJ97oRR",
        "colab_type": "text"
      },
      "source": [
        "## Text generation\n",
        "\n",
        "Setting prompt text: \"I always wonder\"\n",
        "\n",
        "Using top-k sampling decoder from GPT-2 (k=50 has been proven to be more effective at generating non-repetitive texts.) Other decoders are also available.\n",
        "\n",
        "Set <code>add_special_tokens=True</code> in the transformers/examples/text-generation/run_generation.py:\n",
        "<code>\n",
        "encoded_prompt = tokenizer.encode(prompt_text, add_special_tokens=True, return_tensors='pt')</code>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n_WakQt07-sL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "dcaace76-d490-4517-867b-3f42dd6b8004"
      },
      "source": [
        "import os\n",
        "# os.chdir('./transformers/examples/text-generation')\n",
        "\n",
        "K=50\n",
        "CUDA_VISIBLE_DEVICES=\"1\" \n",
        "!python run_generation.py \\\n",
        "--model_type gpt2 \\\n",
        "--model_name_or_path $OUTPUT_DIR \\\n",
        "--length 300 \\\n",
        "--prompt \"<BOS>\" \\\n",
        "--stop_token \"<EOS>\" \\\n",
        "--k $K \\\n",
        "--num_return_sequences 5"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2020-08-16 18:49:26.289587: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\n",
            "08/16/2020 18:49:28 - INFO - transformers.tokenization_utils_base -   Model name '../../../model' not found in model shortcut name list (gpt2, gpt2-medium, gpt2-large, gpt2-xl, distilgpt2). Assuming '../../../model' is a path, a model identifier, or url to a directory containing tokenizer files.\n",
            "08/16/2020 18:49:28 - INFO - transformers.tokenization_utils_base -   Didn't find file ../../../model/tokenizer.json. We won't load it.\n",
            "08/16/2020 18:49:28 - INFO - transformers.tokenization_utils_base -   loading file ../../../model/vocab.json\n",
            "08/16/2020 18:49:28 - INFO - transformers.tokenization_utils_base -   loading file ../../../model/merges.txt\n",
            "08/16/2020 18:49:28 - INFO - transformers.tokenization_utils_base -   loading file ../../../model/added_tokens.json\n",
            "08/16/2020 18:49:28 - INFO - transformers.tokenization_utils_base -   loading file ../../../model/special_tokens_map.json\n",
            "08/16/2020 18:49:28 - INFO - transformers.tokenization_utils_base -   loading file ../../../model/tokenizer_config.json\n",
            "08/16/2020 18:49:28 - INFO - transformers.tokenization_utils_base -   loading file None\n",
            "08/16/2020 18:49:28 - INFO - transformers.tokenization_utils -   Adding <BOS> to the vocabulary\n",
            "08/16/2020 18:49:28 - INFO - transformers.tokenization_utils -   Adding <EOS> to the vocabulary\n",
            "08/16/2020 18:49:28 - INFO - transformers.tokenization_utils -   Adding <PAD> to the vocabulary\n",
            "08/16/2020 18:49:28 - INFO - transformers.configuration_utils -   loading configuration file ../../../model/config.json\n",
            "08/16/2020 18:49:28 - INFO - transformers.configuration_utils -   Model config GPT2Config {\n",
            "  \"activation_function\": \"gelu_new\",\n",
            "  \"architectures\": [\n",
            "    \"GPT2LMHeadModel\"\n",
            "  ],\n",
            "  \"attn_pdrop\": 0.1,\n",
            "  \"bos_token_id\": 50256,\n",
            "  \"embd_pdrop\": 0.1,\n",
            "  \"eos_token_id\": 50256,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"layer_norm_epsilon\": 1e-05,\n",
            "  \"model_type\": \"gpt2\",\n",
            "  \"n_ctx\": 1024,\n",
            "  \"n_embd\": 768,\n",
            "  \"n_head\": 12,\n",
            "  \"n_inner\": null,\n",
            "  \"n_layer\": 12,\n",
            "  \"n_positions\": 1024,\n",
            "  \"resid_pdrop\": 0.1,\n",
            "  \"summary_activation\": null,\n",
            "  \"summary_first_dropout\": 0.1,\n",
            "  \"summary_proj_to_labels\": true,\n",
            "  \"summary_type\": \"cls_index\",\n",
            "  \"summary_use_proj\": true,\n",
            "  \"task_specific_params\": {\n",
            "    \"text-generation\": {\n",
            "      \"do_sample\": true,\n",
            "      \"max_length\": 50\n",
            "    }\n",
            "  },\n",
            "  \"vocab_size\": 50260\n",
            "}\n",
            "\n",
            "08/16/2020 18:49:28 - INFO - transformers.modeling_utils -   loading weights file ../../../model/pytorch_model.bin\n",
            "08/16/2020 18:49:34 - INFO - transformers.modeling_utils -   All model checkpoint weights were used when initializing GPT2LMHeadModel.\n",
            "\n",
            "08/16/2020 18:49:34 - INFO - transformers.modeling_utils -   All the weights of GPT2LMHeadModel were initialized from the model checkpoint at ../../../model.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use GPT2LMHeadModel for predictions without further training.\n",
            "08/16/2020 18:49:37 - INFO - __main__ -   Namespace(device=device(type='cuda'), k=50, length=300, model_name_or_path='../../../model', model_type='gpt2', n_gpu=1, no_cuda=False, num_return_sequences=5, p=0.9, padding_text='', prompt='<BOS>', repetition_penalty=1.0, seed=42, stop_token='<EOS>', temperature=1.0, xlm_language='')\n",
            "08/16/2020 18:49:37 - WARNING - transformers.generation_utils -   Setting `pad_token_id` to 50256 (first `eos_token_id`) to generate sequence\n",
            "=== GENERATED SEQUENCE 1 ===\n",
            "<BOS> Its the moment that leaves you with the most anxiety. \n",
            "=== GENERATED SEQUENCE 2 ===\n",
            "<BOS> There are still days left until the end of our semester - it is Saturday. \n",
            "=== GENERATED SEQUENCE 3 ===\n",
            "<BOS> But my brother and I know each other, we also know he always wears a green t-shirt and grey jeans with green-ish socks that cover a half-inch under his left thigh. \n",
            "=== GENERATED SEQUENCE 4 ===\n",
            "<BOS> I've always wanted to be a part of society, or a part of people. \n",
            "=== GENERATED SEQUENCE 5 ===\n",
            "<BOS> It takes less than 5 minutes for an hour (and sometimes even longer if you leave home). \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2aoxCk96i0b4",
        "colab_type": "text"
      },
      "source": [
        "Wow. This is my first time using a pre-trained model on my data set for text generation (I tried to build my own LM using unidirectional LSTM and the results were underwhelming at best). \n",
        "These sentences really sound like me - I'm still processing it. Let me recover from this, and I would love to see what GPT-3 is capable of! \n",
        "\n",
        "(Although maybe we aren't ready for it? Maybe it's good it isn't open-source yet? Very curious)!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ojuTM1NcNoEK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        },
        "outputId": "424aca4e-7a93-40ce-dd01-eb99d4b79d6d"
      },
      "source": [
        "import glob\n",
        "from google.colab import files\n",
        "print(os.getcwd())\n",
        "fles=glob.glob('../../../model/checkpoint-1000/*')\n",
        "print(fles)\n",
        "for file in fles:\n",
        "  files.download(file)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/transformers/examples/text-generation\n",
            "['../../../model/checkpoint-1000/config.json', '../../../model/checkpoint-1000/optimizer.pt', '../../../model/checkpoint-1000/training_args.bin', '../../../model/checkpoint-1000/scheduler.pt', '../../../model/checkpoint-1000/pytorch_model.bin']\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_45997722-5a7c-49dd-ae7f-84a9a2b5ab8c\", \"config.json\", 685)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_2b5e3869-25fa-4433-a5e4-bf43ba3e2584\", \"optimizer.pt\", 995622846)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_6f215368-e302-47e0-9bf7-5d5b34559252\", \"training_args.bin\", 1519)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_021e572c-0988-4f04-84d5-8e927c62bd41\", \"scheduler.pt\", 559)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_1416d1ed-8d8c-4393-adba-a9c2db11074c\", \"pytorch_model.bin\", 510413407)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
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          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cy6n3l_XP8Vi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}