model_train_upload_workflow.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VTze-VbeU1c0"
      },
      "source": [
        "# Fine-tune a DialoGPT model\n",
        "\n",
        "Adapted from the notebook in [this Medium post](https://towardsdatascience.com/make-your-own-rick-sanchez-bot-with-transformers-and-dialogpt-fine-tuning-f85e6d1f4e30?gi=e4a72d1510f0)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y17kuzFNUSrZ"
      },
      "source": [
        "## Setup"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GBfltjGHT6KG",
        "outputId": "a5103f1a-648e-4211-ba7d-d2c50fe2defb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive/; to attempt to forcibly remount, call drive.mount(\"/content/drive/\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive/')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "T8fgmjaqUErq"
      },
      "outputs": [],
      "source": [
        "!pip -q install transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EtCreyG8UG1s"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "os.chdir(\"/content/drive/My Drive/HuTao Bot\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dnv5kT-mLsB-"
      },
      "outputs": [],
      "source": [
        "# all the imports\n",
        "\n",
        "import glob\n",
        "import logging\n",
        "import os\n",
        "import pickle\n",
        "import random\n",
        "import re\n",
        "import shutil\n",
        "from typing import Dict, List, Tuple\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "from torch.nn.utils.rnn import pad_sequence\n",
        "from torch.utils.data import DataLoader, Dataset, RandomSampler, SequentialSampler\n",
        "from torch.utils.data.distributed import DistributedSampler\n",
        "from tqdm.notebook import tqdm, trange\n",
        "\n",
        "from pathlib import Path\n",
        "\n",
        "from transformers import (\n",
        "    MODEL_WITH_LM_HEAD_MAPPING,\n",
        "    WEIGHTS_NAME,\n",
        "    AdamW,\n",
        "    AutoConfig,\n",
        "    PreTrainedModel,\n",
        "    PreTrainedTokenizer,\n",
        "    get_linear_schedule_with_warmup,\n",
        ")\n",
        "\n",
        "\n",
        "try:\n",
        "    from torch.utils.tensorboard import SummaryWriter\n",
        "except ImportError:\n",
        "    from tensorboardX import SummaryWriter"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BmrbGB8aUmBm"
      },
      "source": [
        "## Get Data from Kaggle"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ftBYBoOoV_Er",
        "outputId": "bdff2186-380d-4d45-c099-a209bea02fcf"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "mkdir: cannot create directory ‘/root/.kaggle’: File exists\n"
          ]
        }
      ],
      "source": [
        "!mkdir ~/.kaggle\n",
        "!cp kaggle.json ~/.kaggle/kaggle.json"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fbITTMcLVbI_",
        "outputId": "4dedccc2-3461-4bba-ee12-e7854ec584d0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "HuTaoScript.csv: Skipping, found more recently modified local copy (use --force to force download)\n"
          ]
        }
      ],
      "source": [
        "!kaggle datasets download cymohen/hutaoscript -f \"HuTaoScript.csv\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N4rJ3KqoVu9B",
        "outputId": "d31ffa62-d451-4cf7-9e1e-f92d9c7656c4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "name,line\r\n",
            "Hu Tao,\"Heya, Traveler!\"\r\n",
            "Hu Tao,Why'd you come find me out of the blue? Miss me?\r\n",
            "Traveler,How do you like it here?\r\n",
            "Hu Tao,\"You have mountains, and bodies of water, food and fun, and everything to like!\"\r\n",
            "Hu Tao,\"Besides, you're so kind to me. I really do love it here.\"\r\n",
            "Traveler,I'm happy when I'm with you.\r\n",
            "Hu Tao,\"Oh, really? Come, come, lend me your ear so I can tell you a secret.\"\r\n",
            "Hu Tao,\"Truthfully, I feel the same way.\"\r\n",
            "Traveler,Isn't it about time you invited me over?\r\n"
          ]
        }
      ],
      "source": [
        "!head HuTaoScript.csv"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RXdJTSVwWGHj"
      },
      "outputs": [],
      "source": [
        "data = pd.read_csv('HuTaoScript.csv', encoding=\"latin-1\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xoh_L1EgW44d"
      },
      "outputs": [],
      "source": [
        "data.rename(columns={'name ': 'name'}, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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        "id": "h6kGx-9eG7qA",
        "outputId": "a4a3662c-77e8-4496-e770-fb23b3fa81cd"
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              "      <th>105</th>\n",
              "      <td>Hu Tao</td>\n",
              "      <td>It took a long, long time, longer than you'd t...</td>\n",
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              "      <td>Can you reveal a bit more?</td>\n",
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              "      <th>141</th>\n",
              "      <td>Meng</td>\n",
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              "      <th>55</th>\n",
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              "      <td>\"Guard the border between life and death...\" T...</td>\n",
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              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>Paimon</td>\n",
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              "    <tr>\n",
              "      <th>29</th>\n",
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              "         name                                               line\n",
              "105    Hu Tao  It took a long, long time, longer than you'd t...\n",
              "108  Traveler                         Can you reveal a bit more?\n",
              "141      Meng  Either way, I don't think I'll ever return to ...\n",
              "55     Hu Tao  \"Guard the border between life and death...\" T...\n",
              "94     Paimon  \"Take the hint\"? Really? Of all of us, you're ...\n",
              "29     Hu Tao  Your place has mountains and bodies of water, ..."
            ]
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      "source": [
        "data.sample(6)"
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      "source": [
        "len(data)"
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      "source": [
        "sum(data.name == 'Hu Tao')"
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      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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      "source": [
        "CHARACTER_NAME = 'Hu Tao'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GZUcEMd2WLDT"
      },
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      "source": [
        "contexted = []\n",
        "\n",
        "# context window of size 7\n",
        "n = 7\n",
        "\n",
        "for i in data[data.name == CHARACTER_NAME].index:\n",
        "  if i < n:\n",
        "    continue\n",
        "  row = []\n",
        "  prev = i - 1 - n # we additionally substract 1, so row will contain current responce and 7 previous responces  \n",
        "  for j in range(i, prev, -1):\n",
        "    row.append(data.line[j])\n",
        "  contexted.append(row)\n",
        "\n",
        "columns = ['response', 'context'] \n",
        "columns = columns + ['context/' + str(i) for i in range(n - 1)]\n",
        "\n",
        "df = pd.DataFrame.from_records(contexted, columns=columns)"
      ]
    },
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              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>And where we're going, don't be surprised at w...</td>\n",
              "      <td>Of course not. After all, you were caught up i...</td>\n",
              "      <td>So what about us? Does it matter if we cross t...</td>\n",
              "      <td>\"Guard the border between life and death...\" T...</td>\n",
              "      <td>I can't tell if you're serious anymore.</td>\n",
              "      <td>Sounds terrifying.</td>\n",
              "      <td>Which explains why they were reluctant to pry ...</td>\n",
              "      <td>Nevertheless, when it comes to things like gho...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>How can words suffice? Hmm... In this world, s...</td>\n",
              "      <td>I want to hear you explain some mysterious phe...</td>\n",
              "      <td>So... what do you want to talk about, (Traveler)?</td>\n",
              "      <td>*sigh* Aiya, aiya, no one's as considerate and...</td>\n",
              "      <td>Oh, perfect timing! I was just looking for som...</td>\n",
              "      <td>Would you like to have a chat with me?</td>\n",
              "      <td>As long as I'm not busy, you're welcome any time!</td>\n",
              "      <td>Well, sure I can! If going to the Wangsheng Fu...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>Relax! I'll help you look for them, you have m...</td>\n",
              "      <td>Grr, stop saying creepy stuff!</td>\n",
              "      <td>Hehe, I'm not an expert on tracking down the l...</td>\n",
              "      <td>You don't sound like you mean it</td>\n",
              "      <td>I'll keep my eyes open in case anything crops ...</td>\n",
              "      <td>Hahaha, I was fishing around on your behalf th...</td>\n",
              "      <td>...The biggest pity of all is your lack of help!</td>\n",
              "      <td>I took you to the \"border\" and let you have a ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>What you make of my words is up to you. I do h...</td>\n",
              "      <td>Not a chance! Curiosity can be perilous, you k...</td>\n",
              "      <td>Can you reveal a bit more?</td>\n",
              "      <td>Hahaha, the previous directors all loved to te...</td>\n",
              "      <td>I didn't expect you to be such a storyteller.</td>\n",
              "      <td>It took a long, long time, longer than you'd t...</td>\n",
              "      <td>By that, I mean the remnants of the defeated g...</td>\n",
              "      <td>After generations, Liyue Harbor became a bustl...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Ah, I'm so happy I could die! You're such a fa...</td>\n",
              "      <td>Right? I knew you'd have a discerning eye to a...</td>\n",
              "      <td>You really have such amazing literary talent.</td>\n",
              "      <td>How's that? Give me your thoughts.</td>\n",
              "      <td>\"There perched two birds on a tree, one swoope...</td>\n",
              "      <td>Oh, you mean my poems, right? Ahem. It's been ...</td>\n",
              "      <td>I want to hear about your recent works.</td>\n",
              "      <td>Is your heart pounding? Wanna go? Hee-hee, the...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>If you have the courage, you can come with me ...</td>\n",
              "      <td>How can words suffice? Hmm... In this world, s...</td>\n",
              "      <td>I want to hear you explain some mysterious phe...</td>\n",
              "      <td>So... what do you want to talk about, (Traveler)?</td>\n",
              "      <td>*sigh* Aiya, aiya, no one's as considerate and...</td>\n",
              "      <td>Oh, perfect timing! I was just looking for som...</td>\n",
              "      <td>Would you like to have a chat with me?</td>\n",
              "      <td>As long as I'm not busy, you're welcome any time!</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "                                             response  \\\n",
              "35  And where we're going, don't be surprised at w...   \n",
              "6   How can words suffice? Hmm... In this world, s...   \n",
              "79  Relax! I'll help you look for them, you have m...   \n",
              "63  What you make of my words is up to you. I do h...   \n",
              "13  Ah, I'm so happy I could die! You're such a fa...   \n",
              "7   If you have the courage, you can come with me ...   \n",
              "\n",
              "                                              context  \\\n",
              "35  Of course not. After all, you were caught up i...   \n",
              "6   I want to hear you explain some mysterious phe...   \n",
              "79                     Grr, stop saying creepy stuff!   \n",
              "63  Not a chance! Curiosity can be perilous, you k...   \n",
              "13  Right? I knew you'd have a discerning eye to a...   \n",
              "7   How can words suffice? Hmm... In this world, s...   \n",
              "\n",
              "                                            context/0  \\\n",
              "35  So what about us? Does it matter if we cross t...   \n",
              "6   So... what do you want to talk about, (Traveler)?   \n",
              "79  Hehe, I'm not an expert on tracking down the l...   \n",
              "63                         Can you reveal a bit more?   \n",
              "13      You really have such amazing literary talent.   \n",
              "7   I want to hear you explain some mysterious phe...   \n",
              "\n",
              "                                            context/1  \\\n",
              "35  \"Guard the border between life and death...\" T...   \n",
              "6   *sigh* Aiya, aiya, no one's as considerate and...   \n",
              "79                  You don't sound like you mean it\n",
              "   \n",
              "63  Hahaha, the previous directors all loved to te...   \n",
              "13                 How's that? Give me your thoughts.   \n",
              "7   So... what do you want to talk about, (Traveler)?   \n",
              "\n",
              "                                            context/2  \\\n",
              "35            I can't tell if you're serious anymore.   \n",
              "6   Oh, perfect timing! I was just looking for som...   \n",
              "79  I'll keep my eyes open in case anything crops ...   \n",
              "63      I didn't expect you to be such a storyteller.   \n",
              "13  \"There perched two birds on a tree, one swoope...   \n",
              "7   *sigh* Aiya, aiya, no one's as considerate and...   \n",
              "\n",
              "                                            context/3  \\\n",
              "35                                 Sounds terrifying.   \n",
              "6              Would you like to have a chat with me?   \n",
              "79  Hahaha, I was fishing around on your behalf th...   \n",
              "63  It took a long, long time, longer than you'd t...   \n",
              "13  Oh, you mean my poems, right? Ahem. It's been ...   \n",
              "7   Oh, perfect timing! I was just looking for som...   \n",
              "\n",
              "                                            context/4  \\\n",
              "35  Which explains why they were reluctant to pry ...   \n",
              "6   As long as I'm not busy, you're welcome any time!   \n",
              "79   ...The biggest pity of all is your lack of help!   \n",
              "63  By that, I mean the remnants of the defeated g...   \n",
              "13            I want to hear about your recent works.   \n",
              "7              Would you like to have a chat with me?   \n",
              "\n",
              "                                            context/5  \n",
              "35  Nevertheless, when it comes to things like gho...  \n",
              "6   Well, sure I can! If going to the Wangsheng Fu...  \n",
              "79  I took you to the \"border\" and let you have a ...  \n",
              "63  After generations, Liyue Harbor became a bustl...  \n",
              "13  Is your heart pounding? Wanna go? Hee-hee, the...  \n",
              "7   As long as I'm not busy, you're welcome any time!  "
            ]
          },
          "execution_count": 199,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.sample(6)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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        "id": "NGy0MxMQVIAP",
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              "      <th>60</th>\n",
              "      <td>It took a long, long time, longer than you'd t...</td>\n",
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              "      <td>After generations, Liyue Harbor became a bustl...</td>\n",
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              "      <td>Uhhhh... ThThank you?</td>\n",
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              "      <td>Let's give them some space to go over the good...</td>\n",
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              "      <td>Yep, just dive off the edge here.</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Rest early and don't worry about nightmares, m...</td>\n",
              "      <td>Hehehe... good night, (Traveler).</td>\n",
              "      <td>Good night, Hu Tao.</td>\n",
              "      <td>Look at how full of life you are! Must've slep...</td>\n",
              "      <td>Morning~</td>\n",
              "      <td>Good morning, Hu Tao.</td>\n",
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              "      <td>If such a thing happens, I'll settle it myself!</td>\n",
              "      <td>Rest early and don't worry about nightmares, m...</td>\n",
              "      <td>Hehehe... good night, (Traveler).</td>\n",
              "      <td>Good night, Hu Tao.</td>\n",
              "      <td>Look at how full of life you are! Must've slep...</td>\n",
              "      <td>Morning~</td>\n",
              "      <td>Good morning, Hu Tao.</td>\n",
              "      <td>We have such a good relationship, so... surely...</td>\n",
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              "                                             response  \\\n",
              "60  It took a long, long time, longer than you'd t...   \n",
              "75  I took you to the \"border\" and let you have a ...   \n",
              "52  Let's give them some space to go over the good...   \n",
              "26  Rest early and don't worry about nightmares, m...   \n",
              "27    If such a thing happens, I'll settle it myself!   \n",
              "\n",
              "                                              context  \\\n",
              "60  By that, I mean the remnants of the defeated g...   \n",
              "75    You promised that you'll help us in our search.   \n",
              "52                        Hey, Traveler, take a hint.   \n",
              "26                  Hehehe... good night, (Traveler).   \n",
              "27  Rest early and don't worry about nightmares, m...   \n",
              "\n",
              "                                            context/0  \\\n",
              "60  After generations, Liyue Harbor became a bustl...   \n",
              "75       Uh... Aren't you forgetting about something?   \n",
              "52  I know you're excited, so feel free to have a ...   \n",
              "26                                Good night, Hu Tao.   \n",
              "27                  Hehehe... good night, (Traveler).   \n",
              "\n",
              "                                            context/1  \\\n",
              "60           They must've paid a huge price for that.   \n",
              "75  No worries, it's a good opportunity for you to...   \n",
              "52  Welcome to the farewell ceremony site! Our des...   \n",
              "26  Look at how full of life you are! Must've slep...   \n",
              "27                                Good night, Hu Tao.   \n",
              "\n",
              "                                            context/2  \\\n",
              "60  Such were the times when the Wangsheng Funeral...   \n",
              "75  Either way, I don't think I'll ever return to ...   \n",
              "52                                           Wahhhh!?   \n",
              "26                                           Morning~   \n",
              "27  Look at how full of life you are! Must've slep...   \n",
              "\n",
              "                                            context/3  \\\n",
              "60  During the Archon War, piles of corpses covere...   \n",
              "75                             Uhhhh... ThThank you?   \n",
              "52                  Yep, just dive off the edge here.   \n",
              "26                              Good morning, Hu Tao.   \n",
              "27                                           Morning~   \n",
              "\n",
              "                                            context/4  \\\n",
              "60  As I explained, Wangsheng Funeral Parlor was, ...   \n",
              "75  So enjoy your life while you can, and when you...   \n",
              "52   So that's the weird stuff out of the way, right?   \n",
              "26  We have such a good relationship, so... surely...   \n",
              "27                              Good morning, Hu Tao.   \n",
              "\n",
              "                                            context/5  \n",
              "60  Really? The end result is the same, though. Ex...  \n",
              "75  That's right. Also, think about this: Everyone...  \n",
              "52  That's exactly the point. Trespassers end up r...  \n",
              "26  Hee-hee, no matter what you say, I'm staying h...  \n",
              "27  We have such a good relationship, so... surely...  "
            ]
          },
          "execution_count": 200,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "trn_df, val_df = train_test_split(df, test_size=0.1)\n",
        "trn_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aEeJQlAKWtiJ"
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      "source": [
        "# create dataset suitable for our model\n",
        "def construct_conv(row, tokenizer, eos = True):\n",
        "    flatten = lambda l: [item for sublist in l for item in sublist]\n",
        "    conv = list(reversed([tokenizer.encode(x) + [tokenizer.eos_token_id] for x in row]))\n",
        "    conv = flatten(conv)\n",
        "    return conv\n",
        "\n",
        "class ConversationDataset(Dataset):\n",
        "    def __init__(self, tokenizer: PreTrainedTokenizer, args, df, block_size=512):\n",
        "\n",
        "        block_size = block_size - (tokenizer.model_max_length - tokenizer.max_len_single_sentence)\n",
        "\n",
        "        directory = args.cache_dir\n",
        "        cached_features_file = os.path.join(\n",
        "            directory, args.model_type + \"_cached_lm_\" + str(block_size)\n",
        "        )\n",
        "\n",
        "        if os.path.exists(cached_features_file) and not args.overwrite_cache:\n",
        "            logger.info(\"Loading features from cached file %s\", cached_features_file)\n",
        "            with open(cached_features_file, \"rb\") as handle:\n",
        "                self.examples = pickle.load(handle)\n",
        "        else:\n",
        "            logger.info(\"Creating features from dataset file at %s\", directory)\n",
        "\n",
        "            self.examples = []\n",
        "            for _, row in df.iterrows():\n",
        "                conv = construct_conv(row, tokenizer)\n",
        "                self.examples.append(conv)\n",
        "\n",
        "            logger.info(\"Saving features into cached file %s\", cached_features_file)\n",
        "            with open(cached_features_file, \"wb\") as handle:\n",
        "                pickle.dump(self.examples, handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.examples)\n",
        "\n",
        "    def __getitem__(self, item):\n",
        "        return torch.tensor(self.examples[item], dtype=torch.long)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-3iHwoKlWyrs"
      },
      "outputs": [],
      "source": [
        "# Cacheing and storing of data/checkpoints\n",
        "\n",
        "def load_and_cache_examples(args, tokenizer, df_trn, df_val, evaluate=False):\n",
        "    return ConversationDataset(tokenizer, args, df_val if evaluate else df_trn)\n",
        "\n",
        "\n",
        "def set_seed(args):\n",
        "    random.seed(args.seed)\n",
        "    np.random.seed(args.seed)\n",
        "    torch.manual_seed(args.seed)\n",
        "    if args.n_gpu > 0:\n",
        "        torch.cuda.manual_seed_all(args.seed)\n",
        "\n",
        "\n",
        "def _sorted_checkpoints(args, checkpoint_prefix=\"checkpoint\", use_mtime=False) -> List[str]:\n",
        "    ordering_and_checkpoint_path = []\n",
        "\n",
        "    glob_checkpoints = glob.glob(os.path.join(args.output_dir, \"{}-*\".format(checkpoint_prefix)))\n",
        "\n",
        "    for path in glob_checkpoints:\n",
        "        if use_mtime:\n",
        "            ordering_and_checkpoint_path.append((os.path.getmtime(path), path))\n",
        "        else:\n",
        "            regex_match = re.match(\".*{}-([0-9]+)\".format(checkpoint_prefix), path)\n",
        "            if regex_match and regex_match.groups():\n",
        "                ordering_and_checkpoint_path.append((int(regex_match.groups()[0]), path))\n",
        "\n",
        "    checkpoints_sorted = sorted(ordering_and_checkpoint_path)\n",
        "    checkpoints_sorted = [checkpoint[1] for checkpoint in checkpoints_sorted]\n",
        "    return checkpoints_sorted\n",
        "\n",
        "\n",
        "def _rotate_checkpoints(args, checkpoint_prefix=\"checkpoint\", use_mtime=False) -> None:\n",
        "    if not args.save_total_limit:\n",
        "        return\n",
        "    if args.save_total_limit <= 0:\n",
        "        return\n",
        "\n",
        "    # Check if we should delete older checkpoint(s)\n",
        "    checkpoints_sorted = _sorted_checkpoints(args, checkpoint_prefix, use_mtime)\n",
        "    if len(checkpoints_sorted) <= args.save_total_limit:\n",
        "        return\n",
        "\n",
        "    number_of_checkpoints_to_delete = max(0, len(checkpoints_sorted) - args.save_total_limit)\n",
        "    checkpoints_to_be_deleted = checkpoints_sorted[:number_of_checkpoints_to_delete]\n",
        "    for checkpoint in checkpoints_to_be_deleted:\n",
        "        logger.info(\"Deleting older checkpoint [{}] due to args.save_total_limit\".format(checkpoint))\n",
        "        shutil.rmtree(checkpoint)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EEDdTJTqUwZJ"
      },
      "source": [
        "## Build Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r2cE0fY5UHpz",
        "outputId": "a2633183-0e7e-4ba4-c7bf-24bcde89e4c1"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/models/auto/modeling_auto.py:882: 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"
          ]
        }
      ],
      "source": [
        "from transformers import AutoModelWithLMHead, AutoModelForCausalLM, AutoTokenizer\n",
        "import torch\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"microsoft/DialoGPT-medium\")\n",
        "model = AutoModelWithLMHead.from_pretrained(\"microsoft/DialoGPT-medium\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ra2vsRp-UMXo"
      },
      "outputs": [],
      "source": [
        "\"\"\"\n",
        "Fine-tuning the library models for language modeling on a text file (GPT, GPT-2, BERT, RoBERTa).\n",
        "GPT and GPT-2 are fine-tuned using a causal language modeling (CLM) loss while BERT and RoBERTa are fine-tuned\n",
        "using a masked language modeling (MLM) loss.\n",
        "\"\"\"\n",
        "\n",
        "# Configs\n",
        "logger = logging.getLogger(__name__)\n",
        "\n",
        "MODEL_CONFIG_CLASSES = list(MODEL_WITH_LM_HEAD_MAPPING.keys())\n",
        "MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2OnASqJjUNJa"
      },
      "outputs": [],
      "source": [
        "# Args to allow for easy convertion of python script to notebook\n",
        "class Args():\n",
        "    def __init__(self):\n",
        "        self.output_dir = 'output-medium'\n",
        "        self.model_type = 'gpt2'\n",
        "        self.model_name_or_path = 'microsoft/DialoGPT-medium'\n",
        "        self.config_name = 'microsoft/DialoGPT-medium'\n",
        "        self.tokenizer_name = 'microsoft/DialoGPT-medium'\n",
        "        self.cache_dir = 'cached'\n",
        "        self.block_size = 512\n",
        "        self.do_train = True\n",
        "        self.do_eval = True\n",
        "        self.evaluate_during_training = False\n",
        "        self.per_gpu_train_batch_size = 2\n",
        "        self.per_gpu_eval_batch_size = 2\n",
        "        self.gradient_accumulation_steps = 1\n",
        "        self.learning_rate = 5e-5\n",
        "        self.weight_decay = 0.0\n",
        "        self.adam_epsilon = 1e-8\n",
        "        self.max_grad_norm = 1.0\n",
        "        self.num_train_epochs = 16\n",
        "        self.max_steps = -1\n",
        "        self.warmup_steps = 0\n",
        "        self.logging_steps = 1000\n",
        "        self.save_steps = 3500\n",
        "        self.save_total_limit = None\n",
        "        self.eval_all_checkpoints = False\n",
        "        self.no_cuda = False\n",
        "        self.overwrite_output_dir = True\n",
        "        self.overwrite_cache = True\n",
        "        self.should_continue = False\n",
        "        self.seed = 42\n",
        "        self.local_rank = -1\n",
        "        self.fp16 = False\n",
        "        self.fp16_opt_level = 'O1'\n",
        "\n",
        "args = Args()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9Q1dTFXxW9NE"
      },
      "source": [
        "## Train and Evaluate"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PaarIDZrW81h"
      },
      "outputs": [],
      "source": [
        "def train(args, train_dataset, model: PreTrainedModel, tokenizer: PreTrainedTokenizer) -> Tuple[int, float]:\n",
        "    \"\"\" Train the model \"\"\"\n",
        "    if args.local_rank in [-1, 0]:\n",
        "        tb_writer = SummaryWriter()\n",
        "\n",
        "    args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)\n",
        "\n",
        "    def collate(examples: List[torch.Tensor]):\n",
        "        if tokenizer._pad_token is None:\n",
        "            return pad_sequence(examples, batch_first=True)\n",
        "        return pad_sequence(examples, batch_first=True, padding_value=tokenizer.pad_token_id)\n",
        "\n",
        "    train_sampler = RandomSampler(train_dataset) if args.local_rank == -1 else DistributedSampler(train_dataset)\n",
        "    train_dataloader = DataLoader(\n",
        "        train_dataset, sampler=train_sampler, batch_size=args.train_batch_size, collate_fn=collate, drop_last = True\n",
        "    )\n",
        "\n",
        "    if args.max_steps > 0:\n",
        "        t_total = args.max_steps\n",
        "        args.num_train_epochs = args.max_steps // (len(train_dataloader) // args.gradient_accumulation_steps) + 1\n",
        "    else:\n",
        "        t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs\n",
        "\n",
        "    model = model.module if hasattr(model, \"module\") else model  # Take care of distributed/parallel training\n",
        "    model.resize_token_embeddings(len(tokenizer))\n",
        "    # add_special_tokens_(model, tokenizer)\n",
        "\n",
        "\n",
        "    # Prepare optimizer and schedule (linear warmup and decay)\n",
        "    no_decay = [\"bias\", \"LayerNorm.weight\"]\n",
        "    optimizer_grouped_parameters = [\n",
        "        {\n",
        "            \"params\": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],\n",
        "            \"weight_decay\": args.weight_decay,\n",
        "        },\n",
        "        {\"params\": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], \"weight_decay\": 0.0},\n",
        "    ]\n",
        "    optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)\n",
        "    scheduler = get_linear_schedule_with_warmup(\n",
        "        optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total\n",
        "    )\n",
        "\n",
        "    # Check if saved optimizer or scheduler states exist\n",
        "    if (\n",
        "        args.model_name_or_path\n",
        "        and os.path.isfile(os.path.join(args.model_name_or_path, \"optimizer.pt\"))\n",
        "        and os.path.isfile(os.path.join(args.model_name_or_path, \"scheduler.pt\"))\n",
        "    ):\n",
        "        # Load in optimizer and scheduler states\n",
        "        optimizer.load_state_dict(torch.load(os.path.join(args.model_name_or_path, \"optimizer.pt\")))\n",
        "        scheduler.load_state_dict(torch.load(os.path.join(args.model_name_or_path, \"scheduler.pt\")))\n",
        "\n",
        "    if args.fp16:\n",
        "        try:\n",
        "            from apex import amp\n",
        "        except ImportError:\n",
        "            raise ImportError(\"Please install apex from https://www.github.com/nvidia/apex to use fp16 training.\")\n",
        "        model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level)\n",
        "\n",
        "    # multi-gpu training (should be after apex fp16 initialization)\n",
        "    if args.n_gpu > 1:\n",
        "        model = torch.nn.DataParallel(model)\n",
        "\n",
        "    # Distributed training (should be after apex fp16 initialization)\n",
        "    if args.local_rank != -1:\n",
        "        model = torch.nn.parallel.DistributedDataParallel(\n",
        "            model, device_ids=[args.local_rank], output_device=args.local_rank, find_unused_parameters=True\n",
        "        )\n",
        "\n",
        "    # Train!\n",
        "    logger.info(\"***** Running training *****\")\n",
        "    logger.info(\"  Num examples = %d\", len(train_dataset))\n",
        "    logger.info(\"  Num Epochs = %d\", args.num_train_epochs)\n",
        "    logger.info(\"  Instantaneous batch size per GPU = %d\", args.per_gpu_train_batch_size)\n",
        "    logger.info(\n",
        "        \"  Total train batch size (w. parallel, distributed & accumulation) = %d\",\n",
        "        args.train_batch_size\n",
        "        * args.gradient_accumulation_steps\n",
        "        * (torch.distributed.get_world_size() if args.local_rank != -1 else 1),\n",
        "    )\n",
        "    logger.info(\"  Gradient Accumulation steps = %d\", args.gradient_accumulation_steps)\n",
        "    logger.info(\"  Total optimization steps = %d\", t_total)\n",
        "\n",
        "    global_step = 0\n",
        "    epochs_trained = 0\n",
        "    steps_trained_in_current_epoch = 0\n",
        "    # Check if continuing training from a checkpoint\n",
        "    if args.model_name_or_path and os.path.exists(args.model_name_or_path):\n",
        "        try:\n",
        "            # set global_step to gobal_step of last saved checkpoint from model path\n",
        "            checkpoint_suffix = args.model_name_or_path.split(\"-\")[-1].split(\"/\")[0]\n",
        "            global_step = int(checkpoint_suffix)\n",
        "            epochs_trained = global_step // (len(train_dataloader) // args.gradient_accumulation_steps)\n",
        "            steps_trained_in_current_epoch = global_step % (len(train_dataloader) // args.gradient_accumulation_steps)\n",
        "\n",
        "            logger.info(\"  Continuing training from checkpoint, will skip to saved global_step\")\n",
        "            logger.info(\"  Continuing training from epoch %d\", epochs_trained)\n",
        "            logger.info(\"  Continuing training from global step %d\", global_step)\n",
        "            logger.info(\"  Will skip the first %d steps in the first epoch\", steps_trained_in_current_epoch)\n",
        "        except ValueError:\n",
        "            logger.info(\"  Starting fine-tuning.\")\n",
        "\n",
        "    tr_loss, logging_loss = 0.0, 0.0\n",
        "\n",
        "    model.zero_grad()\n",
        "    train_iterator = trange(\n",
        "        epochs_trained, int(args.num_train_epochs), desc=\"Epoch\", disable=args.local_rank not in [-1, 0]\n",
        "    )\n",
        "    set_seed(args)  # Added here for reproducibility\n",
        "    for _ in train_iterator:\n",
        "        epoch_iterator = tqdm(train_dataloader, desc=\"Iteration\", disable=args.local_rank not in [-1, 0])\n",
        "        for step, batch in enumerate(epoch_iterator):\n",
        "\n",
        "            # Skip past any already trained steps if resuming training\n",
        "            if steps_trained_in_current_epoch > 0:\n",
        "                steps_trained_in_current_epoch -= 1\n",
        "                continue\n",
        "\n",
        "            inputs, labels = (batch, batch)\n",
        "            if inputs.shape[1] > 1024: continue\n",
        "            inputs = inputs.to(args.device)\n",
        "            labels = labels.to(args.device)\n",
        "            model.train()\n",
        "            outputs = model(inputs, labels=labels)\n",
        "            loss = outputs[0]  # model outputs are always tuple in transformers (see doc)\n",
        "\n",
        "            if args.n_gpu > 1:\n",
        "                loss = loss.mean()  # mean() to average on multi-gpu parallel training\n",
        "            if args.gradient_accumulation_steps > 1:\n",
        "                loss = loss / args.gradient_accumulation_steps\n",
        "\n",
        "            if args.fp16:\n",
        "                with amp.scale_loss(loss, optimizer) as scaled_loss:\n",
        "                    scaled_loss.backward()\n",
        "            else:\n",
        "                loss.backward()\n",
        "\n",
        "            tr_loss += loss.item()\n",
        "            if (step + 1) % args.gradient_accumulation_steps == 0:\n",
        "                if args.fp16:\n",
        "                    torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm)\n",
        "                else:\n",
        "                    torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)\n",
        "                optimizer.step()\n",
        "                scheduler.step()  # Update learning rate schedule\n",
        "                model.zero_grad()\n",
        "                global_step += 1\n",
        "\n",
        "                if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0:\n",
        "                    # Log metrics\n",
        "                    if (\n",
        "                        args.local_rank == -1 and args.evaluate_during_training\n",
        "                    ):  # Only evaluate when single GPU otherwise metrics may not average well\n",
        "                        results = evaluate(args, model, tokenizer)\n",
        "                        for key, value in results.items():\n",
        "                            tb_writer.add_scalar(\"eval_{}\".format(key), value, global_step)\n",
        "                    tb_writer.add_scalar(\"lr\", scheduler.get_lr()[0], global_step)\n",
        "                    tb_writer.add_scalar(\"loss\", (tr_loss - logging_loss) / args.logging_steps, global_step)\n",
        "                    logging_loss = tr_loss\n",
        "\n",
        "                if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0:\n",
        "                    checkpoint_prefix = \"checkpoint\"\n",
        "                    # Save model checkpoint\n",
        "                    output_dir = os.path.join(args.output_dir, \"{}-{}\".format(checkpoint_prefix, global_step))\n",
        "                    os.makedirs(output_dir, exist_ok=True)\n",
        "                    model_to_save = (\n",
        "                        model.module if hasattr(model, \"module\") else model\n",
        "                    )  # Take care of distributed/parallel training\n",
        "                    model_to_save.save_pretrained(output_dir)\n",
        "                    tokenizer.save_pretrained(output_dir)\n",
        "\n",
        "                    torch.save(args, os.path.join(output_dir, \"training_args.bin\"))\n",
        "                    logger.info(\"Saving model checkpoint to %s\", output_dir)\n",
        "\n",
        "                    _rotate_checkpoints(args, checkpoint_prefix)\n",
        "\n",
        "                    torch.save(optimizer.state_dict(), os.path.join(output_dir, \"optimizer.pt\"))\n",
        "                    torch.save(scheduler.state_dict(), os.path.join(output_dir, \"scheduler.pt\"))\n",
        "                    logger.info(\"Saving optimizer and scheduler states to %s\", output_dir)\n",
        "\n",
        "            if args.max_steps > 0 and global_step > args.max_steps:\n",
        "                epoch_iterator.close()\n",
        "                break\n",
        "        if args.max_steps > 0 and global_step > args.max_steps:\n",
        "            train_iterator.close()\n",
        "            break\n",
        "\n",
        "    if args.local_rank in [-1, 0]:\n",
        "        tb_writer.close()\n",
        "\n",
        "    return global_step, tr_loss / global_step\n",
        "\n",
        "# Evaluation of some model\n",
        "\n",
        "def evaluate(args, model: PreTrainedModel, tokenizer: PreTrainedTokenizer, df_trn, df_val, prefix=\"\") -> Dict:\n",
        "    # Loop to handle MNLI double evaluation (matched, mis-matched)\n",
        "    eval_output_dir = args.output_dir\n",
        "\n",
        "    eval_dataset = load_and_cache_examples(args, tokenizer, df_trn, df_val, evaluate=True)\n",
        "    os.makedirs(eval_output_dir, exist_ok=True)\n",
        "    args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)\n",
        "    # Note that DistributedSampler samples randomly\n",
        "\n",
        "    def collate(examples: List[torch.Tensor]):\n",
        "        if tokenizer._pad_token is None:\n",
        "            return pad_sequence(examples, batch_first=True)\n",
        "        return pad_sequence(examples, batch_first=True, padding_value=tokenizer.pad_token_id)\n",
        "\n",
        "    eval_sampler = SequentialSampler(eval_dataset)\n",
        "    eval_dataloader = DataLoader(\n",
        "        eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size, collate_fn=collate, drop_last = True\n",
        "    )\n",
        "\n",
        "    # multi-gpu evaluate\n",
        "    if args.n_gpu > 1:\n",
        "        model = torch.nn.DataParallel(model)\n",
        "\n",
        "    # Eval!\n",
        "    logger.info(\"***** Running evaluation {} *****\".format(prefix))\n",
        "    logger.info(\"  Num examples = %d\", len(eval_dataset))\n",
        "    logger.info(\"  Batch size = %d\", args.eval_batch_size)\n",
        "    eval_loss = 0.0\n",
        "    nb_eval_steps = 0\n",
        "    model.eval()\n",
        "\n",
        "    for batch in tqdm(eval_dataloader, desc=\"Evaluating\"):\n",
        "        inputs, labels = (batch, batch)\n",
        "        inputs = inputs.to(args.device)\n",
        "        labels = labels.to(args.device)\n",
        "\n",
        "        with torch.no_grad():\n",
        "            outputs = model(inputs, labels=labels)\n",
        "            lm_loss = outputs[0]\n",
        "            eval_loss += lm_loss.mean().item()\n",
        "        nb_eval_steps += 1\n",
        "\n",
        "    eval_loss = eval_loss / nb_eval_steps\n",
        "    perplexity = torch.exp(torch.tensor(eval_loss))\n",
        "\n",
        "    result = {\"perplexity\": perplexity}\n",
        "\n",
        "    output_eval_file = os.path.join(eval_output_dir, prefix, \"eval_results.txt\")\n",
        "    with open(output_eval_file, \"w\") as writer:\n",
        "        logger.info(\"***** Eval results {} *****\".format(prefix))\n",
        "        for key in sorted(result.keys()):\n",
        "            logger.info(\"  %s = %s\", key, str(result[key]))\n",
        "            writer.write(\"%s = %s\\n\" % (key, str(result[key])))\n",
        "\n",
        "    return result"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SCnGAJWbXD9C"
      },
      "outputs": [],
      "source": [
        "# Main runner\n",
        "\n",
        "def main(df_trn, df_val):\n",
        "    args = Args()\n",
        "    \n",
        "    if args.should_continue:\n",
        "        sorted_checkpoints = _sorted_checkpoints(args)\n",
        "        if len(sorted_checkpoints) == 0:\n",
        "            raise ValueError(\"Used --should_continue but no checkpoint was found in --output_dir.\")\n",
        "        else:\n",
        "            args.model_name_or_path = sorted_checkpoints[-1]\n",
        "\n",
        "    if (\n",
        "        os.path.exists(args.output_dir)\n",
        "        and os.listdir(args.output_dir)\n",
        "        and args.do_train\n",
        "        and not args.overwrite_output_dir\n",
        "        and not args.should_continue\n",
        "    ):\n",
        "        raise ValueError(\n",
        "            \"Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.\".format(\n",
        "                args.output_dir\n",
        "            )\n",
        "        )\n",
        "\n",
        "    # Setup CUDA, GPU & distributed training\n",
        "    device = torch.device(\"cuda\")\n",
        "    args.n_gpu = torch.cuda.device_count()\n",
        "    args.device = device\n",
        "\n",
        "    # Setup logging\n",
        "    logging.basicConfig(\n",
        "        format=\"%(asctime)s - %(levelname)s - %(name)s -   %(message)s\",\n",
        "        datefmt=\"%m/%d/%Y %H:%M:%S\",\n",
        "        level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN,\n",
        "    )\n",
        "    logger.warning(\n",
        "        \"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s\",\n",
        "        args.local_rank,\n",
        "        device,\n",
        "        args.n_gpu,\n",
        "        bool(args.local_rank != -1),\n",
        "        args.fp16,\n",
        "    )\n",
        "\n",
        "    # Set seed\n",
        "    set_seed(args)\n",
        "\n",
        "    config = AutoConfig.from_pretrained(args.config_name, cache_dir=args.cache_dir)\n",
        "    tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, cache_dir=args.cache_dir)\n",
        "    model = AutoModelWithLMHead.from_pretrained(\n",
        "        args.model_name_or_path,\n",
        "        from_tf=False,\n",
        "        config=config,\n",
        "        cache_dir=args.cache_dir,\n",
        "    )\n",
        "    model.to(args.device)\n",
        "    \n",
        "    logger.info(\"Training/evaluation parameters %s\", args)\n",
        "\n",
        "    # Training\n",
        "    if args.do_train:\n",
        "        train_dataset = load_and_cache_examples(args, tokenizer, df_trn, df_val, evaluate=False)\n",
        "\n",
        "        global_step, tr_loss = train(args, train_dataset, model, tokenizer)\n",
        "        logger.info(\" global_step = %s, average loss = %s\", global_step, tr_loss)\n",
        "\n",
        "    # Saving best-practices: if you use save_pretrained for the model and tokenizer, you can reload them using from_pretrained()\n",
        "    if args.do_train:\n",
        "        # Create output directory if needed\n",
        "        os.makedirs(args.output_dir, exist_ok=True)\n",
        "\n",
        "        logger.info(\"Saving model checkpoint to %s\", args.output_dir)\n",
        "        # Save a trained model, configuration and tokenizer using `save_pretrained()`.\n",
        "        # They can then be reloaded using `from_pretrained()`\n",
        "        model_to_save = (\n",
        "            model.module if hasattr(model, \"module\") else model\n",
        "        )  # Take care of distributed/parallel training\n",
        "        model_to_save.save_pretrained(args.output_dir)\n",
        "        tokenizer.save_pretrained(args.output_dir)\n",
        "\n",
        "        # Good practice: save your training arguments together with the trained model\n",
        "        torch.save(args, os.path.join(args.output_dir, \"training_args.bin\"))\n",
        "\n",
        "        # Load a trained model and vocabulary that you have fine-tuned\n",
        "        model = AutoModelWithLMHead.from_pretrained(args.output_dir)\n",
        "        tokenizer = AutoTokenizer.from_pretrained(args.output_dir)\n",
        "        model.to(args.device)\n",
        "\n",
        "    # Evaluation\n",
        "    results = {}\n",
        "    if args.do_eval and args.local_rank in [-1, 0]:\n",
        "        checkpoints = [args.output_dir]\n",
        "        if args.eval_all_checkpoints:\n",
        "            checkpoints = list(\n",
        "                os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + \"/**/\" + WEIGHTS_NAME, recursive=True))\n",
        "            )\n",
        "            logging.getLogger(\"transformers.modeling_utils\").setLevel(logging.WARN)  # Reduce logging\n",
        "        logger.info(\"Evaluate the following checkpoints: %s\", checkpoints)\n",
        "        for checkpoint in checkpoints:\n",
        "            global_step = checkpoint.split(\"-\")[-1] if len(checkpoints) > 1 else \"\"\n",
        "            prefix = checkpoint.split(\"/\")[-1] if checkpoint.find(\"checkpoint\") != -1 else \"\"\n",
        "\n",
        "            model = AutoModelWithLMHead.from_pretrained(checkpoint)\n",
        "            model.to(args.device)\n",
        "            result = evaluate(args, model, tokenizer, df_trn, df_val, prefix=prefix)\n",
        "            result = dict((k + \"_{}\".format(global_step), v) for k, v in result.items())\n",
        "            results.update(result)\n",
        "\n",
        "    return results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7NWvkdR-XHeB"
      },
      "source": [
        "## Run the Main Function"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
        "id": "e61zo2JtXGNX",
        "outputId": "ad6f68fd-02d3-4a39-c5a8-d06211ae5bef"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "03/23/2022 23:43:14 - WARNING - __main__ -   Process rank: -1, device: cuda, n_gpu: 1, distributed training: False, 16-bits training: False\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/models/auto/modeling_auto.py:882: 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",
            "03/23/2022 23:43:25 - INFO - __main__ -   Training/evaluation parameters <__main__.Args object at 0x7fbe08f2e6d0>\n",
            "03/23/2022 23:43:25 - INFO - __main__ -   Creating features from dataset file at cached\n",
            "03/23/2022 23:43:25 - INFO - __main__ -   Saving features into cached file cached/gpt2_cached_lm_512\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/optimization.py:309: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  FutureWarning,\n",
            "03/23/2022 23:43:26 - INFO - __main__ -   ***** Running training *****\n",
            "03/23/2022 23:43:26 - INFO - __main__ -     Num examples = 75\n",
            "03/23/2022 23:43:26 - INFO - __main__ -     Num Epochs = 16\n",
            "03/23/2022 23:43:26 - INFO - __main__ -     Instantaneous batch size per GPU = 2\n",
            "03/23/2022 23:43:26 - INFO - __main__ -     Total train batch size (w. parallel, distributed & accumulation) = 2\n",
            "03/23/2022 23:43:26 - INFO - __main__ -     Gradient Accumulation steps = 1\n",
            "03/23/2022 23:43:26 - INFO - __main__ -     Total optimization steps = 592\n"
          ]
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            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1e2110b2b0f74ce2bc07e419caf7d3c3",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8418c721d73e427e814768526408a1b9",
              "version_major": 2,
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            },
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "a45eb609669b418ea1d9a31019bfdecc",
              "version_major": 2,
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "dd0ff19ebf87418aafc35fb84c1ec99d",
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "f1e79b96983f4d0fb48c3a78d8e202f2",
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "f7104616be9e442ab77c12362f3794b5",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "bf3d1c4c6b014f299e8d890189764d54",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "03/23/2022 23:52:01 - INFO - __main__ -    global_step = 592, average loss = 0.5389482952792801\n",
            "03/23/2022 23:52:01 - INFO - __main__ -   Saving model checkpoint to output-medium\n",
            "03/23/2022 23:52:15 - INFO - __main__ -   Evaluate the following checkpoints: ['output-medium']\n",
            "03/23/2022 23:52:26 - INFO - __main__ -   Creating features from dataset file at cached\n",
            "03/23/2022 23:52:26 - INFO - __main__ -   Saving features into cached file cached/gpt2_cached_lm_512\n",
            "03/23/2022 23:52:26 - INFO - __main__ -   ***** Running evaluation  *****\n",
            "03/23/2022 23:52:26 - INFO - __main__ -     Num examples = 9\n",
            "03/23/2022 23:52:26 - INFO - __main__ -     Batch size = 2\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "014687330bda4c3dab41ed91d7e2cc55",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Evaluating:   0%|          | 0/4 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "03/23/2022 23:52:27 - INFO - __main__ -   ***** Eval results  *****\n",
            "03/23/2022 23:52:27 - INFO - __main__ -     perplexity = tensor(1.2347)\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "{'perplexity_': tensor(1.2347)}"
            ]
          },
          "execution_count": 208,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "main(trn_df, val_df)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YRpQ_n2zXQj-"
      },
      "source": [
        "## Load the Trained Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HGw3qgfaXQHX",
        "outputId": "61e9ee5b-fb06-4a56-9b80-4559fb3ea3db"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/models/auto/modeling_auto.py:882: 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"
          ]
        }
      ],
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained('microsoft/DialoGPT-medium')\n",
        "model = AutoModelWithLMHead.from_pretrained('output-medium')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lAWsiAvNXbxd",
        "outputId": "4227b12e-c9db-4c99-ec0c-bc33160b7928"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            ">> User:Hi, how are ya?\n",
            "Hu Tao: I'm good. You?\n",
            ">> User:I'm doing great!\n",
            "Hu Tao: That's good. I'm happy when I'm with you.\n",
            ">> User:You think so? I'm happy when I'm with you too!\n",
            "Hu Tao: Oh, really? Come, come, lend me your ear so I can tell you a secret.\n",
            ">> User:What is it?\n",
            "Hu Tao: Truthfully, I do not know.\n"
          ]
        }
      ],
      "source": [
        "# Let's chat for 4 lines\n",
        "for step in range(4):\n",
        "    # encode the new user input, add the eos_token and return a tensor in Pytorch\n",
        "    new_user_input_ids = tokenizer.encode(input(\">> User:\") + tokenizer.eos_token, return_tensors='pt')\n",
        "    # print(new_user_input_ids)\n",
        "\n",
        "    # append the new user input tokens to the chat history\n",
        "    bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids\n",
        "\n",
        "    # generated a response while limiting the total chat history to 1000 tokens, \n",
        "    chat_history_ids = model.generate(\n",
        "        bot_input_ids, max_length=200,\n",
        "        pad_token_id=tokenizer.eos_token_id,  \n",
        "        no_repeat_ngram_size=3,       \n",
        "        do_sample=True, \n",
        "        top_k=100, \n",
        "        top_p=0.7,\n",
        "        temperature=0.8\n",
        "    )\n",
        "    \n",
        "    # pretty print last ouput tokens from bot\n",
        "    print(\"Hu Tao: {}\".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ANSQlQezXqwn"
      },
      "source": [
        "## Push Model to Hugging Face"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aKZZEXfq1Avv"
      },
      "outputs": [],
      "source": [
        "os.chdir('/content/')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YyoPMXcR1EOk",
        "outputId": "b9e3c439-122c-4319-8226-6d0e3977f28e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: huggingface_hub in /usr/local/lib/python3.7/dist-packages (0.4.0)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from huggingface_hub) (6.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from huggingface_hub) (3.6.0)\n",
            "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.7/dist-packages (from huggingface_hub) (21.3)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from huggingface_hub) (4.11.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from huggingface_hub) (2.23.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from huggingface_hub) (3.10.0.2)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from huggingface_hub) (4.63.0)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.9->huggingface_hub) (3.0.7)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->huggingface_hub) (3.7.0)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->huggingface_hub) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->huggingface_hub) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->huggingface_hub) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->huggingface_hub) (2021.10.8)\n"
          ]
        }
      ],
      "source": [
        "!pip install huggingface_hub"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MwsAy_TN1HWD",
        "outputId": "6d710a5e-0c45-463d-f95a-751535f6ce3b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "        _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n",
            "        _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n",
            "        _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n",
            "        _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n",
            "        _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n",
            "\n",
            "        To login, `huggingface_hub` now requires a token generated from https://huggingface.co/settings/token.\n",
            "        (Deprecated, will be removed in v0.3.0) To login with username and password instead, interrupt with Ctrl+C.\n",
            "        \n",
            "Token: \n",
            "Login successful\n",
            "Your token has been saved to /root/.huggingface/token\n",
            "\u001b[1m\u001b[31mAuthenticated through git-credential store but this isn't the helper defined on your machine.\n",
            "You might have to re-authenticate when pushing to the Hugging Face Hub. Run the following command in your terminal in case you want to set this credential helper as the default\n",
            "\n",
            "git config --global credential.helper store\u001b[0m\n"
          ]
        }
      ],
      "source": [
        "!huggingface-cli login"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WP6BDgytC0z6"
      },
      "outputs": [],
      "source": [
        "!git config --global credential.helper"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VgnHRgHKXwDd",
        "outputId": "45a35dd5-5f80-4600-df9f-b526cf5cab62"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "git-lfs is already the newest version (2.3.4-1).\n",
            "0 upgraded, 0 newly installed, 0 to remove and 39 not upgraded.\n"
          ]
        }
      ],
      "source": [
        "!sudo apt-get install git-lfs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a4yT4tYX2Ang",
        "outputId": "e27ffd14-5369-4038-b3d0-dc6e3dfdc540"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[90mgit version 2.17.1\u001b[0m\n",
            "Error: unknown flag: --version\n",
            "\n",
            "\u001b[90mSorry, no usage text found for \"git-lfs\"\u001b[0m\n",
            "\n",
            "You are about to create \u001b[1mCymoh/Dialogue-HuTaoBot\u001b[0m\n",
            "Proceed? [Y/n] y\n",
            "\n",
            "Your repo now lives at:\n",
            "  \u001b[1mhttps://huggingface.co/Cymoh/Dialogue-HuTaoBot\u001b[0m\n",
            "\n",
            "You can clone it locally with the command below, and commit/push as usual.\n",
            "\n",
            "  git clone https://huggingface.co/Cymoh/Dialogue-HuTaoBot\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!huggingface-cli repo create Dialogue-HuTaoBot"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N8z043pBJ-yt",
        "outputId": "5e58debd-a1be-4845-8de4-a114eb1293ac"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "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"
          ]
        }
      ],
      "source": [
        "!cat /root/.huggingface/token"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Idz0SK9Y27xf",
        "outputId": "669c9a7f-3cec-4b37-aad1-9b0bd5069fff"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Cloning into 'Dialogue-HuTaoBot'...\n",
            "remote: Enumerating objects: 3, done.\u001b[K\n",
            "remote: Counting objects: 100% (3/3), done.\u001b[K\n",
            "remote: Compressing objects: 100% (2/2), done.\u001b[K\n",
            "remote: Total 3 (delta 0), reused 0 (delta 0)\u001b[K\n",
            "Unpacking objects: 100% (3/3), done.\n"
          ]
        }
      ],
      "source": [
        "!git clone https://Cymoh: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@huggingface.co/Cymoh/Dialogue-HuTaoBot"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AbQ5CsIM3Kuv",
        "outputId": "ff277bc6-fedf-4d3e-d10c-35443429fe48"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "config.json\t  pytorch_model.bin\t   tokenizer.json\n",
            "eval_results.txt  special_tokens_map.json  training_args.bin\n",
            "merges.txt\t  tokenizer_config.json    vocab.json\n"
          ]
        }
      ],
      "source": [
        "!ls \"/content/drive/My Drive/HuTao Bot/output-medium/\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wQSbzu2F3YbS"
      },
      "outputs": [],
      "source": [
        "!mv /content/drive/My\\ Drive/HuTao\\ Bot/output-medium/* Dialogue-HuTaoBot/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lh9AZknO3wZc"
      },
      "outputs": [],
      "source": [
        "os.chdir('Dialogue-HuTaoBot')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rjXug0cE32jx",
        "outputId": "fa0af4e5-9b27-4afc-c60a-696ac00ce7bd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Updated git hooks.\n",
            "Git LFS initialized.\n"
          ]
        }
      ],
      "source": [
        "!git lfs install"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "239iMu4l36no",
        "outputId": "f8b4facf-0339-48fd-cbb2-9ae09ddb3ba6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "config.json\t  pytorch_model.bin\t   tokenizer.json\n",
            "eval_results.txt  special_tokens_map.json  training_args.bin\n",
            "merges.txt\t  tokenizer_config.json    vocab.json\n"
          ]
        }
      ],
      "source": [
        "!ls"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6qysEkxm387R",
        "outputId": "65e79cc1-7d42-4cb1-d656-54f7dfa6e403"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content/Dialogue-HuTaoBot\n"
          ]
        }
      ],
      "source": [
        "!pwd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FCC5ySwg3_fO",
        "outputId": "e008c30c-89fc-43be-f24b-e3b96f1ace2d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "On branch main\n",
            "Your branch is up to date with 'origin/main'.\n",
            "\n",
            "Untracked files:\n",
            "  (use \"git add <file>...\" to include in what will be committed)\n",
            "\n",
            "\t\u001b[31mconfig.json\u001b[m\n",
            "\t\u001b[31meval_results.txt\u001b[m\n",
            "\t\u001b[31mmerges.txt\u001b[m\n",
            "\t\u001b[31mpytorch_model.bin\u001b[m\n",
            "\t\u001b[31mspecial_tokens_map.json\u001b[m\n",
            "\t\u001b[31mtokenizer.json\u001b[m\n",
            "\t\u001b[31mtokenizer_config.json\u001b[m\n",
            "\t\u001b[31mtraining_args.bin\u001b[m\n",
            "\t\u001b[31mvocab.json\u001b[m\n",
            "\n",
            "nothing added to commit but untracked files present (use \"git add\" to track)\n"
          ]
        }
      ],
      "source": [
        "!git status"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3ans00og4DBC"
      },
      "outputs": [],
      "source": [
        "!git add ."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uhqMtvfmXei8"
      },
      "outputs": [],
      "source": [
        "!git config --global user.email \"\"\n",
        "# Tip: using the same email as your huggingface.co account will link your commits to your profile\n",
        "!git config --global user.name \"\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JQQBXuNh4QtY",
        "outputId": "37bf3a28-dfa2-4115-d7a4-dfbf64cf19f0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[main 77b59f6] Initial commit\n",
            " 9 files changed, 150350 insertions(+)\n",
            " create mode 100644 config.json\n",
            " create mode 100644 eval_results.txt\n",
            " create mode 100644 merges.txt\n",
            " create mode 100644 pytorch_model.bin\n",
            " create mode 100644 special_tokens_map.json\n",
            " create mode 100644 tokenizer.json\n",
            " create mode 100644 tokenizer_config.json\n",
            " create mode 100644 training_args.bin\n",
            " create mode 100644 vocab.json\n"
          ]
        }
      ],
      "source": [
        "!git commit -m \"Initial commit\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8Sr7Kglx4U-s",
        "outputId": "6e744d16-78be-4d56-fcd7-3690499ac517"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Git LFS: (2 of 2 files) 1.35 GB / 1.35 GB\n",
            "Counting objects: 11, done.\n",
            "Delta compression using up to 2 threads.\n",
            "Compressing objects: 100% (10/10), done.\n",
            "Writing objects: 100% (11/11), 1.09 MiB | 1.25 MiB/s, done.\n",
            "Total 11 (delta 1), reused 0 (delta 0)\n",
            "remote: tput: No value for $TERM and no -T specified\u001b[K\n",
            "remote: tput: No value for $TERM and no -T specified\u001b[K\n",
            "remote: tput: No value for $TERM and no -T specified\u001b[K\n",
            "remote: tput: No value for $TERM and no -T specified\u001b[K\n",
            "To https://huggingface.co/Cymoh/Dialogue-HuTaoBot\n",
            "   4139e45..77b59f6  main -> main\n"
          ]
        }
      ],
      "source": [
        "!git push"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tfUsrKR7YLT1"
      },
      "outputs": [],
      "source": [
        "MY_MODEL_NAME = 'DialoGPT-small-joshua'\n",
        "with open('HuggingFace-API-key.txt', 'rt') as f:\n",
        "  HUGGINGFACE_API_KEY = f.read().strip()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_65nsiLcYNXI"
      },
      "outputs": [],
      "source": [
        "model.push_to_hub(MY_MODEL_NAME, use_auth_token=HUGGINGFACE_API_KEY)\n",
        "tokenizer.push_to_hub(MY_MODEL_NAME, use_auth_token=HUGGINGFACE_API_KEY)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D_XfXTCrZKmO"
      },
      "source": [
        "## All Done!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_tIwK7G8ZLrd"
      },
      "outputs": [],
      "source": []
    }
  ],
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