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":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GBfltjGHT6KG","outputId":"d1b8d162-3554-4478-934f-2c6d3cd4f2bc","executionInfo":{"status":"ok","timestamp":1646021656982,"user_tz":-330,"elapsed":139088,"user":{"displayName":"Aryan Bhosale","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgMTOcxuJ_HEUMAQkjggmXzsZ3mAYICIEVShOR4=s64","userId":"16291163758591043198"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive/\n"]}],"source":["from google.colab import drive\n","drive.mount('/content/drive/')"]},{"cell_type":"code","execution_count":2,"metadata":{"id":"T8fgmjaqUErq","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1646021701436,"user_tz":-330,"elapsed":9723,"user":{"displayName":"Aryan Bhosale","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgMTOcxuJ_HEUMAQkjggmXzsZ3mAYICIEVShOR4=s64","userId":"16291163758591043198"}},"outputId":"051883f5-017a-430b-f4cf-697d16c2c9ff"},"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[K     |████████████████████████████████| 3.5 MB 5.4 MB/s \n","\u001b[K     |████████████████████████████████| 895 kB 49.9 MB/s \n","\u001b[K     |████████████████████████████████| 67 kB 4.9 MB/s \n","\u001b[K     |████████████████████████████████| 596 kB 49.9 MB/s \n","\u001b[K     |████████████████████████████████| 6.8 MB 36.8 MB/s \n","\u001b[?25h"]}],"source":["!pip -q install transformers"]},{"cell_type":"code","execution_count":3,"metadata":{"id":"EtCreyG8UG1s","executionInfo":{"status":"ok","timestamp":1646021785006,"user_tz":-330,"elapsed":587,"user":{"displayName":"Aryan Bhosale","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgMTOcxuJ_HEUMAQkjggmXzsZ3mAYICIEVShOR4=s64","userId":"16291163758591043198"}}},"outputs":[],"source":["import os\n","os.chdir(\"/content/drive/My Drive/\")"]},{"cell_type":"code","execution_count":4,"metadata":{"id":"dnv5kT-mLsB-","executionInfo":{"status":"ok","timestamp":1646021814662,"user_tz":-330,"elapsed":8721,"user":{"displayName":"Aryan Bhosale","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgMTOcxuJ_HEUMAQkjggmXzsZ3mAYICIEVShOR4=s64","userId":"16291163758591043198"}}},"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":5,"metadata":{"id":"ftBYBoOoV_Er","executionInfo":{"status":"ok","timestamp":1646022229734,"user_tz":-330,"elapsed":957,"user":{"displayName":"Aryan Bhosale","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgMTOcxuJ_HEUMAQkjggmXzsZ3mAYICIEVShOR4=s64","userId":"16291163758591043198"}}},"outputs":[],"source":["!mkdir ~/.kaggle\n","!cp kaggle.json ~/.kaggle/kaggle.json"]},{"cell_type":"code","execution_count":6,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"fbITTMcLVbI_","outputId":"eca84120-6da5-4653-94b5-34ffc0d7b8b8","executionInfo":{"status":"ok","timestamp":1646022234443,"user_tz":-330,"elapsed":1297,"user":{"displayName":"Aryan Bhosale","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgMTOcxuJ_HEUMAQkjggmXzsZ3mAYICIEVShOR4=s64","userId":"16291163758591043198"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading Harry%20Potter%201.csv to /content/drive/MyDrive\n","\r  0% 0.00/65.9k [00:00<?, ?B/s]\n","\r100% 65.9k/65.9k [00:00<00:00, 4.48MB/s]\n"]}],"source":["!kaggle datasets download gulsahdemiryurek/harry-potter-dataset -f \"Harry Potter 1.csv\""]},{"cell_type":"code","source":["!head Harry%20Potter%201.csv"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"PxSbN78Wkt83","executionInfo":{"status":"ok","timestamp":1646022324330,"user_tz":-330,"elapsed":397,"user":{"displayName":"Aryan Bhosale","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgMTOcxuJ_HEUMAQkjggmXzsZ3mAYICIEVShOR4=s64","userId":"16291163758591043198"}},"outputId":"40704f07-77dd-42c5-a0e3-8359da85b9bc"},"execution_count":7,"outputs":[{"output_type":"stream","name":"stdout","text":["﻿Character;Sentence\r\n","Dumbledore;I should've known that you would be here, Professor McGonagall.\r\n","McGonagall;Good evening, Professor Dumbledore.\r\n","McGonagall;Are the rumors true, Albus?\r\n","Dumbledore;I'm afraid so, professor.\r\n","Dumbledore;The good and the bad.\r\n","McGonagall;And the boy?\r\n","Dumbledore;Hagrid is bringing him.\r\n","McGonagall;Do you think it wise to trust Hagrid with something as important as this?\r\n","Dumbledore;Ah, Professor, I would trust Hagrid with my life.\r\n"]}]},{"cell_type":"code","execution_count":8,"metadata":{"id":"RXdJTSVwWGHj","executionInfo":{"status":"ok","timestamp":1646022541869,"user_tz":-330,"elapsed":323,"user":{"displayName":"Aryan Bhosale","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgMTOcxuJ_HEUMAQkjggmXzsZ3mAYICIEVShOR4=s64","userId":"16291163758591043198"}}},"outputs":[],"source":["data = pd.read_csv('Harry%20Potter%201.csv', sep=';')"]},{"cell_type":"code","source":["data.rename(columns={'Character': 'name', 'Sentence': 'line'}, inplace=True)"],"metadata":{"id":"GDkGpkc9mAYZ","executionInfo":{"status":"ok","timestamp":1646022762408,"user_tz":-330,"elapsed":417,"user":{"displayName":"Aryan Bhosale","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgMTOcxuJ_HEUMAQkjggmXzsZ3mAYICIEVShOR4=s64","userId":"16291163758591043198"}}},"execution_count":10,"outputs":[]},{"cell_type":"code","execution_count":11,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":238},"id":"h6kGx-9eG7qA","outputId":"3517460a-c01d-4d41-c1a5-9b9b390c429f","executionInfo":{"status":"ok","timestamp":1646022765782,"user_tz":-330,"elapsed":344,"user":{"displayName":"Aryan Bhosale","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgMTOcxuJ_HEUMAQkjggmXzsZ3mAYICIEVShOR4=s64","userId":"16291163758591043198"}}},"outputs":[{"output_type":"execute_result","data":{"text/html":["\n","  <div id=\"df-ae83498e-7ae2-48c0-8a81-be67e4944a3f\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    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</tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ae83498e-7ae2-48c0-8a81-be67e4944a3f')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  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</style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-ae83498e-7ae2-48c0-8a81-be67e4944a3f button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "],"text/plain":["         name                        line\n","1024  Filch              It's still hot. \n","353    Hagrid        Here's your ticket. \n","392       Ron                   The scar?\n","1333    Ron     Wait a minute...he's.... \n","814     Ron    Trolls are really stupid. \n","113    Vernon           Fine day, Sunday."]},"metadata":{},"execution_count":11}],"source":["data.sample(6)"]},{"cell_type":"code","source":["len(data)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ZjE589KAms7L","executionInfo":{"status":"ok","timestamp":1646022824145,"user_tz":-330,"elapsed":326,"user":{"displayName":"Aryan Bhosale","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgMTOcxuJ_HEUMAQkjggmXzsZ3mAYICIEVShOR4=s64","userId":"16291163758591043198"}},"outputId":"5fd3b986-fe48-4aee-9466-0fa0f39b7615"},"execution_count":12,"outputs":[{"output_type":"execute_result","data":{"text/plain":["1587"]},"metadata":{},"execution_count":12}]},{"cell_type":"code","source":["sum(data.name == 'Harry')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Gs1X895em3k5","executionInfo":{"status":"ok","timestamp":1646022866754,"user_tz":-330,"elapsed":3,"user":{"displayName":"Aryan Bhosale","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgMTOcxuJ_HEUMAQkjggmXzsZ3mAYICIEVShOR4=s64","userId":"16291163758591043198"}},"outputId":"82619ee7-d50c-4ffb-f6af-0e2f52a1a51b"},"execution_count":13,"outputs":[{"output_type":"execute_result","data":{"text/plain":["155"]},"metadata":{},"execution_count":13}]},{"cell_type":"code","execution_count":15,"metadata":{"id":"PG8v6--qWUwj","executionInfo":{"status":"ok","timestamp":1646023030692,"user_tz":-330,"elapsed":410,"user":{"displayName":"Aryan Bhosale","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgMTOcxuJ_HEUMAQkjggmXzsZ3mAYICIEVShOR4=s64","userId":"16291163758591043198"}}},"outputs":[],"source":["CHARACTER_NAME = 'Harry'"]},{"cell_type":"code","execution_count":16,"metadata":{"id":"GZUcEMd2WLDT","executionInfo":{"status":"ok","timestamp":1646023032742,"user_tz":-330,"elapsed":381,"user":{"displayName":"Aryan Bhosale","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgMTOcxuJ_HEUMAQkjggmXzsZ3mAYICIEVShOR4=s64","userId":"16291163758591043198"}}},"outputs":[],"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)"]},{"cell_type":"code","execution_count":17,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":386},"id":"4T5OlNZHUxij","outputId":"dfe8b876-d01b-48b5-bd0e-26058139a6d7","executionInfo":{"status":"ok","timestamp":1646023047215,"user_tz":-330,"elapsed":335,"user":{"displayName":"Aryan Bhosale","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgMTOcxuJ_HEUMAQkjggmXzsZ3mAYICIEVShOR4=s64","userId":"16291163758591043198"}}},"outputs":[{"output_type":"execute_result","data":{"text/html":["\n","  <div id=\"df-197ce7f6-7942-4910-a6d3-55f4bbf232bd\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>response</th>\n","      <th>context</th>\n","      <th>context/0</th>\n","      <th>context/1</th>\n","      <th>context/2</th>\n","      <th>context/3</th>\n","      <th>context/4</th>\n","      <th>context/5</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>86</th>\n","      <td>We'll take the lot.</td>\n","      <td>No, thanks. I'm all set.</td>\n","      <td>Anything off the trolley, dears?</td>\n","      <td>Wicked!</td>\n","      <td>The scar?</td>\n","      <td>The what?</td>\n","      <td>I mean, do you really have the...?</td>\n","      <td>So it's true!</td>\n","    </tr>\n","    <tr>\n","      <th>35</th>\n","      <td>I mean...l can't be a wizard.</td>\n","      <td>No, you've made a mistake.</td>\n","      <td>And a thumping good one, I'd wager, once you'r...</td>\n","      <td>A wizard.</td>\n","      <td>I'm a what?</td>\n","      <td>You're a wizard, Harry.</td>\n","      <td>Learnt what?</td>\n","      <td>No? Blimey, Harry, didn't you ever wonder wher...</td>\n","    </tr>\n","    <tr>\n","      <th>95</th>\n","      <td>Nothing. Nothing, I'm fine.</td>\n","      <td>Harry, what is it?</td>\n","      <td>Susan Bones.</td>\n","      <td>There isn't a witch or wizard who went bad who...</td>\n","      <td>Slytherin!</td>\n","      <td>Draco Malfoy.</td>\n","      <td>Okay. Gryffindor!</td>\n","      <td>Ah, right then... hmm... right.</td>\n","    </tr>\n","    <tr>\n","      <th>24</th>\n","      <td>Get off!</td>\n","      <td>Give me that letter!</td>\n","      <td>Give me that!</td>\n","      <td>Mummy, what's happening?</td>\n","      <td>Stop it!</td>\n","      <td>No, sir, not one blasted, miserable...</td>\n","      <td>Not one single bloody letter. Not one!</td>\n","      <td>No blasted letters today! No, sir.</td>\n","    </tr>\n","    <tr>\n","      <th>81</th>\n","      <td>Excuse me.</td>\n","      <td>I'm only joking. I am Fred.</td>\n","      <td>Oh, I'm sorry, George.</td>\n","      <td>Honestly, woman, you call yourself our mother?</td>\n","      <td>He's not Fred, I am.</td>\n","      <td>Fred, you next.</td>\n","      <td>All right, Percy, you first.</td>\n","      <td>Platform 9 3/4, this way.</td>\n","    </tr>\n","    <tr>\n","      <th>122</th>\n","      <td>Nicholas Flamel...Who's Nicholas Flamel?</td>\n","      <td>I should not have said that.</td>\n","      <td>I should not have said that.</td>\n","      <td>I shouldn't have said that.</td>\n","      <td>Nicholas Flamel?</td>\n","      <td>What that dog is guarding is strictly between ...</td>\n","      <td>It's dangerous.</td>\n","      <td>You're meddlin' in things that ought not to be...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-197ce7f6-7942-4910-a6d3-55f4bbf232bd')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-197ce7f6-7942-4910-a6d3-55f4bbf232bd button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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Ah, right then... hmm... right.\n","24                                   Get off!  ...                 No blasted letters today! No, sir.\n","81                                 Excuse me.  ...                          Platform 9 3/4, this way.\n","122  Nicholas Flamel...Who's Nicholas Flamel?  ...  You're meddlin' in things that ought not to be...\n","\n","[6 rows x 8 columns]"]},"metadata":{},"execution_count":17}],"source":["df.sample(6)"]},{"cell_type":"code","execution_count":18,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":337},"id":"NGy0MxMQVIAP","outputId":"65d727d6-91d1-4b0f-b60f-1af1006ab7a1","executionInfo":{"status":"ok","timestamp":1646023089923,"user_tz":-330,"elapsed":342,"user":{"displayName":"Aryan Bhosale","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgMTOcxuJ_HEUMAQkjggmXzsZ3mAYICIEVShOR4=s64","userId":"16291163758591043198"}}},"outputs":[{"output_type":"execute_result","data":{"text/html":["\n","  <div id=\"df-c051b86e-f550-4c21-8fb7-d45d71ee4b9d\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>response</th>\n","      <th>context</th>\n","      <th>context/0</th>\n","      <th>context/1</th>\n","      <th>context/2</th>\n","      <th>context/3</th>\n","      <th>context/4</th>\n","      <th>context/5</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>141</th>\n","      <td>Hagrid always wanted a dragon.</td>\n","      <td>Oh, dear.</td>\n","      <td>Malfoy.</td>\n","      <td>Who's that?</td>\n","      <td>He'll have to be trained up a bit, of course.</td>\n","      <td>Ohh! Oooh, ooh, ooh, well...</td>\n","      <td>Don't you, Norbert? Dededede.</td>\n","      <td>Yeah, well, he's got to have a name, doesn't he?</td>\n","    </tr>\n","    <tr>\n","      <th>21</th>\n","      <td>It'll fit me like bits of old elephant skin.</td>\n","      <td>That's Dudley's old uniform.</td>\n","      <td>This is what you'll going to be wearing, when ...</td>\n","      <td>Where you belong.</td>\n","      <td>You're going to go to the state school.</td>\n","      <td>Don't be so stupid!</td>\n","      <td>What? You? Go to Smeltings?</td>\n","      <td>Will I have to wear that too?</td>\n","    </tr>\n","    <tr>\n","      <th>138</th>\n","      <td>I knew the name sound familiar.</td>\n","      <td>For the discovery of the twelve uses of dragon...</td>\n","      <td>Go on!</td>\n","      <td>Dumbledore is particularly famous for his defe...</td>\n","      <td>I found him!</td>\n","      <td>Besides, if anyone cared to notice my eyebrows...</td>\n","      <td>I don't appreciate the insinuation, Longbottom.</td>\n","      <td>You to set my bloody kneecups on fire!</td>\n","    </tr>\n","    <tr>\n","      <th>108</th>\n","      <td>Listen.</td>\n","      <td>Hey Ron! Somebody broke into Gringotts.</td>\n","      <td>The only problem is, I can't remember what I'v...</td>\n","      <td>When the smoke turns red, it means you've forg...</td>\n","      <td>I've read about those.</td>\n","      <td>Hey, look! Neville's got a Remembrall</td>\n","      <td>Can I borrow this? Thanks.</td>\n","      <td>Mail's here.</td>\n","    </tr>\n","    <tr>\n","      <th>83</th>\n","      <td>No. Not at all.</td>\n","      <td>Everywhere else is full.</td>\n","      <td>Excuse me. Do you mind?</td>\n","      <td>Good luck.</td>\n","      <td>Best do it at a bit of a run if you're nervous.</td>\n","      <td>All you've got do is walk straight at the wall...</td>\n","      <td>It's Ron's first time to Hogwarts as well.</td>\n","      <td>Not to worry, dear.</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c051b86e-f550-4c21-8fb7-d45d71ee4b9d')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-c051b86e-f550-4c21-8fb7-d45d71ee4b9d button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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Will I have to wear that too?\n","138               I knew the name sound familiar.  ...            You to set my bloody kneecups on fire!\n","108                                       Listen.  ...                                      Mail's here.\n","83                                No. Not at all.  ...                               Not to worry, dear.\n","\n","[5 rows x 8 columns]"]},"metadata":{},"execution_count":18}],"source":["trn_df, val_df = train_test_split(df, test_size=0.1)\n","trn_df.head()"]},{"cell_type":"code","execution_count":19,"metadata":{"id":"aEeJQlAKWtiJ","executionInfo":{"status":"ok","timestamp":1646023152870,"user_tz":-330,"elapsed":321,"user":{"displayName":"Aryan Bhosale","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgMTOcxuJ_HEUMAQkjggmXzsZ3mAYICIEVShOR4=s64","userId":"16291163758591043198"}}},"outputs":[],"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":20,"metadata":{"id":"-3iHwoKlWyrs","executionInfo":{"status":"ok","timestamp":1646023159001,"user_tz":-330,"elapsed":415,"user":{"displayName":"Aryan Bhosale","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgMTOcxuJ_HEUMAQkjggmXzsZ3mAYICIEVShOR4=s64","userId":"16291163758591043198"}}},"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":21,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":232,"referenced_widgets":["e350f709f307484696536e20e57ccd83","d771ed6d6c4441f6be209e9af622b039","13a6c2276bc0453c8b62af0222c6c0ea","fec2c4c73a924d0caf73fbfe13affb9d","0d09f6e357fa48baa7c261486b058249","437b562c0dd644b28adf6c916e18df66","cebecba2fbbe49efa6378a8db5491e9e","284b64ad90a64561a3d27e6944a0887e","0a92c70650b049babc4d4bd8a9b270b6","8685258659924675849ba32f798f61ee","d56f75f67ce749c98e55f9761f30e1b4","a55ff52bfa9d41c3bbe5a5e36f8dfa94","2af2af525c2642709037e4236827c8df","09d03eb501ec486d8313db0d2f123ded","31dc210f54f54586bad78b9e988ebd51","e09bf661f00445bf8350207999bfac35","4684be83aad14566bcf86af3eb3bb684","0826b75259434e3a92a59d5217c357ca","55f9acb9200d407095dcb34ab940af23","7726675bacb947b5bec92eb43008e3d6","377211abbc6a493c81cd21104bca6e33","486561c31f0e4891a9b19de2677c27c9","e32ed4cbcd35442eb6696ff6e0f3afa6","9e5e67678421494d84618b9e17f62dd6","cf51635db71d4facab698e1cd7e542f2","b3430dcb7af045be892a029dd327f4fa","e485004ee70a4e77b9afbcb99e0c6bed","51ee4d80eb86454987d0798b02091d7f","de6fdea2c33f410085f4c9c1a741e918","aa0d3476b0344ae1a96b04096afbed1b","3608e492846040ab8768fd1c5dc9cdd1","046a23ca07d846508b4d29093d3bc693","697b4468d504495fb8b8d2a013cb7236","9f7e4b77d6504b4f96b5d3dee6b10ac4","46b7046954164c0ba412f1901ecdbe6b","f9032aa124a24f6998c268897b7f72a5","c03566c6287b4e028073245a4b8d5f14","a6d9114b9e144c3891c9bd5c9d649a5b","0c3e483af5924ee5a0782bcce905d9e9","e77f6ad3d48a4861a3f3de70dbdad75f","4b0a7221c8e2416eb2489811decda95b","b213ce1955a64bbfbee9e3576d1488d9","5fe0b123a1ed4551bebf89f4d538955f","e14b4cabcab14d59b05baba9a8eb2df5","7403983cded44749a99161bfceb4ae61","14d6933d8082433cb6bd3b508a875eef","c1d7ed24671e46aa884b00e3d77b9f58","8704fadd6d124fd2b368a89b4be1e6d9","11cae5e52f5d4d388f4e393ce4644883","cb25a66b326847c9904fc61a558f6dea","71eb68755b874081908e8067a5a407ce","6c258ab8f23e4d08ac87ae9e237df5a4","72d9b3486b6745289065f6af88a32371","2dcaacbe7b114d97904d8fad0d7f6ca8","690cc0bebef545db8533f2bcd620b750"]},"id":"r2cE0fY5UHpz","outputId":"204d34ae-bb5b-4142-a4c0-c173f304a751","executionInfo":{"status":"ok","timestamp":1646023290479,"user_tz":-330,"elapsed":31433,"user":{"displayName":"Aryan Bhosale","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgMTOcxuJ_HEUMAQkjggmXzsZ3mAYICIEVShOR4=s64","userId":"16291163758591043198"}}},"outputs":[{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"e350f709f307484696536e20e57ccd83","version_minor":0,"version_major":2},"text/plain":["Downloading:   0%|          | 0.00/26.0 [00:00<?, ?B/s]"]},"metadata":{}},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"a55ff52bfa9d41c3bbe5a5e36f8dfa94","version_minor":0,"version_major":2},"text/plain":["Downloading:   0%|          | 0.00/642 [00:00<?, ?B/s]"]},"metadata":{}},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"e32ed4cbcd35442eb6696ff6e0f3afa6","version_minor":0,"version_major":2},"text/plain":["Downloading:   0%|          | 0.00/0.99M [00:00<?, ?B/s]"]},"metadata":{}},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"9f7e4b77d6504b4f96b5d3dee6b10ac4","version_minor":0,"version_major":2},"text/plain":["Downloading:   0%|          | 0.00/446k [00:00<?, ?B/s]"]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/transformers/models/auto/modeling_auto.py:807: 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"]},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"7403983cded44749a99161bfceb4ae61","version_minor":0,"version_major":2},"text/plain":["Downloading:   0%|          | 0.00/823M [00:00<?, ?B/s]"]},"metadata":{}}],"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":22,"metadata":{"id":"ra2vsRp-UMXo","executionInfo":{"status":"ok","timestamp":1646023315762,"user_tz":-330,"elapsed":319,"user":{"displayName":"Aryan Bhosale","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgMTOcxuJ_HEUMAQkjggmXzsZ3mAYICIEVShOR4=s64","userId":"16291163758591043198"}}},"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":23,"metadata":{"id":"2OnASqJjUNJa","executionInfo":{"status":"ok","timestamp":1646023420125,"user_tz":-330,"elapsed":410,"user":{"displayName":"Aryan Bhosale","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgMTOcxuJ_HEUMAQkjggmXzsZ3mAYICIEVShOR4=s64","userId":"16291163758591043198"}}},"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-small'\n","        self.model_type = 'gpt2'\n","        self.model_name_or_path = 'microsoft/DialoGPT-small'\n","        self.config_name = 'microsoft/DialoGPT-small'\n","        self.tokenizer_name = 'microsoft/DialoGPT-small'\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 = 4\n","        self.per_gpu_eval_batch_size = 4\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 = 4\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":24,"metadata":{"id":"PaarIDZrW81h","executionInfo":{"status":"ok","timestamp":1646023495492,"user_tz":-330,"elapsed":1011,"user":{"displayName":"Aryan Bhosale","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgMTOcxuJ_HEUMAQkjggmXzsZ3mAYICIEVShOR4=s64","userId":"16291163758591043198"}}},"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":25,"metadata":{"id":"SCnGAJWbXD9C","executionInfo":{"status":"ok","timestamp":1646023504299,"user_tz":-330,"elapsed":410,"user":{"displayName":"Aryan Bhosale","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgMTOcxuJ_HEUMAQkjggmXzsZ3mAYICIEVShOR4=s64","userId":"16291163758591043198"}}},"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":26,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":872,"referenced_widgets":["ca2c4682ff33494ca31cad10b3935c6e","8e19028ff1d640c990efde507614a2e1","363d543a3ede482b940cacd5b34cbe9f","4996bd5d3e054c5fb065f7a7f867c8f2","42fe94bbf0c0401a96b37eaa644a52d5","7428024967264da2a3aef3580fb69c07","ede5256164ab448a91ca5ae4d031f3c5","281b0846053241b18b8ed5553aad4e3e","067ef6b887e742c28a908b2c16de191f","9a4b20f7dbd340988a81b3541d3d2833","0b914ac5270d4989ac5b00c67494965a","a84e826e49694fea97345c1edc20651a","db802a54ee2e46f686c5a421c0602256","a367ab1db5dd44609c97c533d50e01c5","7b78fabbbf7a4ca2bef22cee1ce28894","de7aa910450440d887054f6330f04c39","a6a2db3f928d4aebaa8ab8b634847f46","dd3e136bf6134a4eac8c14a7d9e41212","f35ceb2fec0943478057c5b279c1338b","f49b3c325caf4d41b8a77b170d31e941","ab887cc144824bfda3ee0eab8fa18f98","3e51558d1b884fdda6f01789e47ca022","2c80139f7dcf4ca486a43e5f4563e086","1e57f288d78c41ee84860d62feea282b","20c4548d799044b49dd9a9e574ee050b","f91f6e182c234161add5a4c32421c838","dd98de6a498746e0ae03fae956b76a94","27bd3dc61f7244fdbe5c6a8996eca6f6","9a5f125fcf3a44b9a3b138f03d6f6311","ec8ebc8cc3174953aaf5d7f9a504cfb2","f0f2728042c64c2588e88d2763fdf317","eaf4b71d1c2b4a2da1dfa7b49abeccbf","b36a91fdd7da431e9adfab15e4b3f5cd","8dc862dda963461a878fcedef23a5fe4","2f1b21e07a264f8ab2339d5c8d0887a2","dc1a2bf522cf4c858e7b3e822fa8f959","36730bc33e7149a6bc65d3bcd5e06655","71b5c0423eb54b87849f1088b4bb7210","deaf7e734c8d46c0bc1e20e0ea13bb63","dff05a354ca845e8a5880f0b98d189cb","3d9903d33db84f85bdb725a2c1dd4e10","3c4c72c2f052494397f9f8d8e264aac6","6c8b2db0d06f4ac79ea37983bed49c2c","dba9e91ec0c645bfbee3fd9c25e8857b","dad66a7b4a1f473d915064669f40373f","db067e6c6b374ddc8656aa7fcf53d581","a4538cb94b204882bb7ecce5370dde6b","f31820246f614b198437b707faa1d8aa","d4a6f8c1f84743bf8e28682fbf6ceaa7","cf54c57e15a5401c9915e42590a9889b","b2b56e30e10443f49bb17d2b86d21042","dada18c075a44240838535097e308241","d925b73580114389a10add0a5c90ee13","1b49e1ccba5744029ca2dfd263cfdb7e","aa7bee5826944fc6b91e657e1440bec4","f6e740530cc846bc9735ec09d22f2a2e","78263ac220994e2f9a9ca3df7331a257","c9043be912ab4181bff2f8f62c197590","3535b55cb3c14cd881c66deb6a03e839","c94f3a28ebf142a1ab2f33159875610a","7c624225daf043e0b52747fe6e877e1f","8aea1aae54494b4f85cb36f426ab5bb7","899da2c25c7b4d019ffadfc165a88d5e","fec9c0afcd8f472d92f7d40a9d2333c4","c3a8ea527a5a42f5b6fc9b8feeef7d5c","1d2da2c53ddb40f4bd766c49a769e09a","d49ed10eec1a47e2bf324c5ce8a22373","3bd632ef10034fc7a307995f00060d1c","2afedbca184c421193ee22071f60dffd","499aba5c6fbe4774a4fae0e29adc1d52","801de12705ce480ea70765ef5449d811","4ccb46bb23424a969682a52597632b70","d553f72c40bc4512a1e7763f521b8689","65f16c80d44d4103861955d6705c6316","8e43d0acfcf542a5aac095759f4f9a8a","9b059e24f91641d4b80e9a131588d3d5","005c53b8d3c9431191371fa2c778424c","4aed0508d3934c9080430fcb54abacdc","a1171686238c4930a555552d485f818a","13595a20060f4ee0b3edfd2e69b2b14e","e1b7adfd4ac146f8bcc08ef42fea11c4","8eb37dd98112497489ec5df27faf390a","b6ef80fab5c947988397d60ba935707e","c353318efd0f406bbf6a378630e45f34","8b70c1273b7b43daaab650330e7efa2d","fdfa23728056415eaeb1561b7ad77e60","a0b5c59ad94d4d0ab847032577b285db","9f6a4c763adc4c63ace512cb6c1dcaa2","b23e71794f3744b8b1198f7ab29592cc","39313f7fd5b94745ae8f9ed6ec6ecb7e","fa864a3673f24956b7eb58547602ed9f","52244fa9acef40cb8923664874ff2b0e","505cbf53cba946efb405deb18c760735","da4272d8f1f84f82baa95cbc2365787f","4f7b60c623844cd68dbbec1556f75a45","07fda03fb55f46bcb49c72412911e856","eafe7b850e8a494a884661becbb3fb8f","f137cffbd19845b2a0430c59fcbf4349","f69ee39725dd4b5296bcf6936f4cefa8","468361ef980a4168adc237e7d03170fb","36bb2db2a47d46f98796d6251fbb22d4","31a34998bf5349fb80c7a2ee7af9bd91","b2abce7ea5694f01823cf5978bf42109","1bbbec4bbc7847c2af8b06e358089074","5891db4f390349778d911f0f335a2683","601833fa93f4479f95ad3f5f69950c31","f0f0255db85841d683142796295f02e4","39ae0b5c56194850b93d2d10f41f2ce3","3dd9715ac17f4b91bfd51c624fae97a6","6d14d36cef21418d996aa41811ae9807","5a13c4a096e94cb5aa8596b481ab8113","e2bbe53138d44d0dbc8ea170aaffd0c3","0f2569b629ab4f73b0786b6783105bc5","b67619cd27d3424fb64513c4d6dd579f","d1af0a7c8f3e4e1daeaf7526ab6fd680","1552d56747e6442f95347b4b1e489a8f","04f6743e0ca646878e19d2ad28d02f0b","64981e196c1644f3a8c442b83f652dd1","ebb7c39a8bfc4992bcc532c8dc2fe2a3","c3caf1a53eb94199898356794aea55b5","ad43a5465789454c90f35747ecdc6afa","6286d4e136284a6aa740b908d9c85449","a6cf4c0f5749477eba3e2403c3bad3dc","69d6ef9144e244dbb84c7510e671f080","fe316407d7a049d68947fd5a6f207b4d","ca7b590d65284be5a6f18af0f8127e93","53a24d440b9640b4bca0d664a5e58ee5","b508fc548dd24c1cb0836a7cdf2053e4","6981ec74e50a4dc6905c9cc5b2f77368","246e6d57d47e4170914284165537054a","e9e3a9edbb7a4b848629c863c557634c","964d76bf7cb54d078f0d0f8c11e459b7"]},"id":"e61zo2JtXGNX","outputId":"156bc26a-fd25-40e5-9ff6-aa163c0aa496","executionInfo":{"status":"ok","timestamp":1646023608076,"user_tz":-330,"elapsed":95483,"user":{"displayName":"Aryan Bhosale","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgMTOcxuJ_HEUMAQkjggmXzsZ3mAYICIEVShOR4=s64","userId":"16291163758591043198"}}},"outputs":[{"output_type":"stream","name":"stderr","text":["02/28/2022 04:45:13 - WARNING - __main__ -   Process rank: -1, device: cuda, n_gpu: 1, distributed training: False, 16-bits training: False\n"]},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"ca2c4682ff33494ca31cad10b3935c6e","version_minor":0,"version_major":2},"text/plain":["Downloading:   0%|          | 0.00/641 [00:00<?, ?B/s]"]},"metadata":{}},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"a84e826e49694fea97345c1edc20651a","version_minor":0,"version_major":2},"text/plain":["Downloading:   0%|          | 0.00/26.0 [00:00<?, ?B/s]"]},"metadata":{}},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"2c80139f7dcf4ca486a43e5f4563e086","version_minor":0,"version_major":2},"text/plain":["Downloading:   0%|          | 0.00/0.99M [00:00<?, ?B/s]"]},"metadata":{}},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"8dc862dda963461a878fcedef23a5fe4","version_minor":0,"version_major":2},"text/plain":["Downloading:   0%|          | 0.00/446k [00:00<?, ?B/s]"]},"metadata":{}},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"dad66a7b4a1f473d915064669f40373f","version_minor":0,"version_major":2},"text/plain":["Downloading:   0%|          | 0.00/641 [00:00<?, ?B/s]"]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/transformers/models/auto/modeling_auto.py:807: 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"]},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"f6e740530cc846bc9735ec09d22f2a2e","version_minor":0,"version_major":2},"text/plain":["Downloading:   0%|          | 0.00/335M [00:00<?, ?B/s]"]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["02/28/2022 04:45:43 - INFO - __main__ -   Training/evaluation parameters <__main__.Args object at 0x7ff8ff6a5650>\n","02/28/2022 04:45:43 - INFO - __main__ -   Creating features from dataset file at cached\n","02/28/2022 04:45:43 - 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 thePyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n","  FutureWarning,\n","02/28/2022 04:45:43 - INFO - __main__ -   ***** Running training *****\n","02/28/2022 04:45:43 - INFO - __main__ -     Num examples = 139\n","02/28/2022 04:45:43 - INFO - __main__ -     Num Epochs = 4\n","02/28/2022 04:45:43 - INFO - __main__ -     Instantaneous batch size per GPU = 4\n","02/28/2022 04:45:43 - INFO - __main__ -     Total train batch size (w. parallel, distributed & accumulation) = 4\n","02/28/2022 04:45:43 - INFO - __main__ -     Gradient Accumulation steps = 1\n","02/28/2022 04:45:43 - INFO - __main__ -     Total optimization steps = 136\n"]},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"d49ed10eec1a47e2bf324c5ce8a22373","version_minor":0,"version_major":2},"text/plain":["Epoch:   0%|          | 0/4 [00:00<?, ?it/s]"]},"metadata":{}},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"4aed0508d3934c9080430fcb54abacdc","version_minor":0,"version_major":2},"text/plain":["Iteration:   0%|          | 0/34 [00:00<?, ?it/s]"]},"metadata":{}},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"b23e71794f3744b8b1198f7ab29592cc","version_minor":0,"version_major":2},"text/plain":["Iteration:   0%|          | 0/34 [00:00<?, ?it/s]"]},"metadata":{}},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"468361ef980a4168adc237e7d03170fb","version_minor":0,"version_major":2},"text/plain":["Iteration:   0%|          | 0/34 [00:00<?, ?it/s]"]},"metadata":{}},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"5a13c4a096e94cb5aa8596b481ab8113","version_minor":0,"version_major":2},"text/plain":["Iteration:   0%|          | 0/34 [00:00<?, ?it/s]"]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["02/28/2022 04:46:40 - INFO - __main__ -    global_step = 136, average loss = 3.0892218281241024\n","02/28/2022 04:46:40 - INFO - __main__ -   Saving model checkpoint to output-small\n","02/28/2022 04:46:45 - INFO - __main__ -   Evaluate the following checkpoints: ['output-small']\n","02/28/2022 04:46:47 - INFO - __main__ -   Creating features from dataset file at cached\n","02/28/2022 04:46:47 - INFO - __main__ -   Saving features into cached file cached/gpt2_cached_lm_512\n","02/28/2022 04:46:47 - INFO - __main__ -   ***** Running evaluation  *****\n","02/28/2022 04:46:47 - INFO - __main__ -     Num examples = 16\n","02/28/2022 04:46:47 - INFO - __main__ -     Batch size = 4\n"]},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"6286d4e136284a6aa740b908d9c85449","version_minor":0,"version_major":2},"text/plain":["Evaluating:   0%|          | 0/4 [00:00<?, ?it/s]"]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["02/28/2022 04:46:48 - INFO - __main__ -   ***** Eval results  *****\n","02/28/2022 04:46:48 - INFO - __main__ -     perplexity = tensor(10.4054)\n"]},{"output_type":"execute_result","data":{"text/plain":["{'perplexity_': tensor(10.4054)}"]},"metadata":{},"execution_count":26}],"source":["main(trn_df, val_df)"]},{"cell_type":"markdown","metadata":{"id":"YRpQ_n2zXQj-"},"source":["## Load the Trained Model"]},{"cell_type":"code","execution_count":29,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"HGw3qgfaXQHX","outputId":"eb34619e-9769-443e-a6df-4f64366b8404","executionInfo":{"status":"ok","timestamp":1646023793999,"user_tz":-330,"elapsed":4411,"user":{"displayName":"Aryan Bhosale","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgMTOcxuJ_HEUMAQkjggmXzsZ3mAYICIEVShOR4=s64","userId":"16291163758591043198"}}},"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/transformers/models/auto/modeling_auto.py:807: 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-small')"]},{"cell_type":"code","execution_count":30,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"lAWsiAvNXbxd","outputId":"538a6789-205e-453c-9d07-e453c8570dc4","executionInfo":{"status":"ok","timestamp":1646023911965,"user_tz":-330,"elapsed":42811,"user":{"displayName":"Aryan Bhosale","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgMTOcxuJ_HEUMAQkjggmXzsZ3mAYICIEVShOR4=s64","userId":"16291163758591043198"}}},"outputs":[{"output_type":"stream","name":"stdout","text":[">> User:Hello!\n","HarryPotterBot: Hello, friend.\n",">> User:How's quidditch?\n","HarryPotterBot: Hey.\n",">> User:Go Gryffindor!\n","HarryPotterBot: Hey, Harry.\n",">> User:h\n","HarryPotterBot: !!!?!!.!!,!!:!!You!!\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(\"HarryPotterBot: {}\".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","source":["os.chdir('/content/')"],"metadata":{"id":"kc8aao87r284","executionInfo":{"status":"ok","timestamp":1646024384644,"user_tz":-330,"elapsed":397,"user":{"displayName":"Aryan Bhosale","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgMTOcxuJ_HEUMAQkjggmXzsZ3mAYICIEVShOR4=s64","userId":"16291163758591043198"}}},"execution_count":31,"outputs":[]},{"cell_type":"code","source":["!pip install huggingface_hub"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"i0ilpNFgsHog","executionInfo":{"status":"ok","timestamp":1646024391097,"user_tz":-330,"elapsed":4188,"user":{"displayName":"Aryan Bhosale","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgMTOcxuJ_HEUMAQkjggmXzsZ3mAYICIEVShOR4=s64","userId":"16291163758591043198"}},"outputId":"0a12cf12-cfc3-4b4d-9e46-de4b2960dd84"},"execution_count":32,"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: huggingface_hub in /usr/local/lib/python3.7/dist-packages (0.4.0)\n","Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.7/dist-packages (from huggingface_hub) (21.3)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from huggingface_hub) (3.6.0)\n","Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from huggingface_hub) (2.23.0)\n","Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from huggingface_hub) (4.62.3)\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: pyyaml in /usr/local/lib/python3.7/dist-packages (from huggingface_hub) (6.0)\n","Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from huggingface_hub) (4.11.1)\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: 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","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: 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"]}]},{"cell_type":"code","source":["!huggingface-cli login"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"LPBJXcsnsMWA","executionInfo":{"status":"ok","timestamp":1646024482259,"user_tz":-330,"elapsed":86249,"user":{"displayName":"Aryan Bhosale","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgMTOcxuJ_HEUMAQkjggmXzsZ3mAYICIEVShOR4=s64","userId":"16291163758591043198"}},"outputId":"a4bfdf68-e6a5-448b-c765-c0591eeecfca"},"execution_count":33,"outputs":[{"output_type":"stream","name":"stdout","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"]}]},{"cell_type":"code","source":["!huggingface-cli repo create DialoGPT-medium-harrypotter"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"KsUNcBIMsQz3","executionInfo":{"status":"ok","timestamp":1646024825749,"user_tz":-330,"elapsed":9895,"user":{"displayName":"Aryan Bhosale","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgMTOcxuJ_HEUMAQkjggmXzsZ3mAYICIEVShOR4=s64","userId":"16291163758591043198"}},"outputId":"7c0ee835-bc89-4621-d89a-c9ae8f771db8"},"execution_count":37,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[90mgit version 2.17.1\u001b[0m\n","\u001b[1m\u001b[31mLooks like you do not have git-lfs installed, please install. You can install from https://git-lfs.github.com/. Then run `git lfs install` (you only have to do this once).\u001b[0m\n","\n","You are about to create \u001b[1maryanbhosale/DialoGPT-medium-harrypotter\u001b[0m\n","Proceed? [Y/n] Y\n","403 Client Error: Forbidden for url: https://huggingface.co/api/repos/create - You don't have the rights to create a model under this namespace\n","\u001b[1m\u001b[31m{\"error\":\"You don't have the rights to create a model under this namespace\"}\u001b[0m\n"]}]},{"cell_type":"code","execution_count":38,"metadata":{"id":"VgnHRgHKXwDd","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1646024995197,"user_tz":-330,"elapsed":6098,"user":{"displayName":"Aryan Bhosale","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgMTOcxuJ_HEUMAQkjggmXzsZ3mAYICIEVShOR4=s64","userId":"16291163758591043198"}},"outputId":"3a473f8c-b20b-4265-9bab-08b2060e1e1e"},"outputs":[{"output_type":"stream","name":"stdout","text":["Reading package lists... Done\n","Building dependency tree       \n","Reading state information... Done\n","The following package was automatically installed and is no longer required:\n","  libnvidia-common-470\n","Use 'sudo apt autoremove' to remove it.\n","The following NEW packages will be installed:\n","  git-lfs\n","0 upgraded, 1 newly installed, 0 to remove and 39 not upgraded.\n","Need to get 2,129 kB of archives.\n","After this operation, 7,662 kB of additional disk space will be used.\n","Get:1 http://archive.ubuntu.com/ubuntu bionic/universe amd64 git-lfs amd64 2.3.4-1 [2,129 kB]\n","Fetched 2,129 kB in 1s (1,837 kB/s)\n","debconf: unable to initialize frontend: Dialog\n","debconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 76, <> line 1.)\n","debconf: falling back to frontend: Readline\n","debconf: unable to initialize frontend: Readline\n","debconf: (This frontend requires a controlling tty.)\n","debconf: falling back to frontend: Teletype\n","dpkg-preconfigure: unable to re-open stdin: \n","Selecting previously unselected package git-lfs.\n","(Reading database ... 155320 files and directories currently installed.)\n","Preparing to unpack .../git-lfs_2.3.4-1_amd64.deb ...\n","Unpacking git-lfs (2.3.4-1) ...\n","Setting up git-lfs (2.3.4-1) ...\n","Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n"]}],"source":["!sudo apt-get install git-lfs"]},{"cell_type":"code","source":["!git clone https://aryanbhosale: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@huggingface.co/aryanbhosale/DialoGPT-medium-harrypotter"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"bsAJuKV2vkxF","executionInfo":{"status":"ok","timestamp":1646025297795,"user_tz":-330,"elapsed":1179,"user":{"displayName":"Aryan Bhosale","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgMTOcxuJ_HEUMAQkjggmXzsZ3mAYICIEVShOR4=s64","userId":"16291163758591043198"}},"outputId":"10d09dca-95ab-409a-cb24-cffeda17d912"},"execution_count":39,"outputs":[{"output_type":"stream","name":"stdout","text":["Cloning into 'DialoGPT-medium-harrypotter'...\n","remote: Enumerating objects: 6, done.\u001b[K\n","remote: Counting objects: 100% (6/6), done.\u001b[K\n","remote: Compressing objects: 100% (4/4), done.\u001b[K\n","remote: Total 6 (delta 0), reused 0 (delta 0)\u001b[K\n","Unpacking objects: 100% (6/6), done.\n"]}]},{"cell_type":"code","source":["!ls \"/content/drive/My Drive/output-small/\""],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5ii0E8RxwWbt","executionInfo":{"status":"ok","timestamp":1646025458259,"user_tz":-330,"elapsed":423,"user":{"displayName":"Aryan Bhosale","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgMTOcxuJ_HEUMAQkjggmXzsZ3mAYICIEVShOR4=s64","userId":"16291163758591043198"}},"outputId":"aee33bdb-7401-4a7f-8ab1-efc34434e25d"},"execution_count":40,"outputs":[{"output_type":"stream","name":"stdout","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"]}]},{"cell_type":"code","source":["!mv \"/content/drive/My\\ Drive/output-small/* DialoGPT-medium-harrypotter/\""],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"q86RcCpUw1Sm","executionInfo":{"status":"ok","timestamp":1646025958964,"user_tz":-330,"elapsed":345,"user":{"displayName":"Aryan Bhosale","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgMTOcxuJ_HEUMAQkjggmXzsZ3mAYICIEVShOR4=s64","userId":"16291163758591043198"}},"outputId":"0af11f82-6ead-401c-d9c5-2e930c0570fc"},"execution_count":48,"outputs":[{"output_type":"stream","name":"stdout","text":["mv: missing destination file operand after '/content/drive/My\\ Drive/output-small/* DialoGPT-medium-harrypotter/'\n","Try 'mv --help' for more information.\n"]}]},{"cell_type":"code","source":["os.chdir('DialoGPT-medium-harrypotter')"],"metadata":{"id":"eGO-I6XMyu2R","executionInfo":{"status":"ok","timestamp":1646026008627,"user_tz":-330,"elapsed":407,"user":{"displayName":"Aryan Bhosale","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgMTOcxuJ_HEUMAQkjggmXzsZ3mAYICIEVShOR4=s64","userId":"16291163758591043198"}}},"execution_count":49,"outputs":[]},{"cell_type":"code","source":["!git lfs install"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1sB_sYPCzAkk","executionInfo":{"status":"ok","timestamp":1646026040900,"user_tz":-330,"elapsed":325,"user":{"displayName":"Aryan Bhosale","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgMTOcxuJ_HEUMAQkjggmXzsZ3mAYICIEVShOR4=s64","userId":"16291163758591043198"}},"outputId":"3d8df0fb-15e6-4e54-d65e-6d81ffc5d54f"},"execution_count":50,"outputs":[{"output_type":"stream","name":"stdout","text":["Updated git hooks.\n","Git LFS initialized.\n"]}]},{"cell_type":"code","source":["!ls"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"uT869AF1zDMj","executionInfo":{"status":"ok","timestamp":1646026062083,"user_tz":-330,"elapsed":414,"user":{"displayName":"Aryan Bhosale","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgMTOcxuJ_HEUMAQkjggmXzsZ3mAYICIEVShOR4=s64","userId":"16291163758591043198"}},"outputId":"43704dcd-b437-4256-9504-3694756f06e2"},"execution_count":51,"outputs":[{"output_type":"stream","name":"stdout","text":["README.md\n"]}]},{"cell_type":"code","source":["!pwd"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"fc-R3VJuzNYf","executionInfo":{"status":"ok","timestamp":1646026095463,"user_tz":-330,"elapsed":1044,"user":{"displayName":"Aryan Bhosale","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgMTOcxuJ_HEUMAQkjggmXzsZ3mAYICIEVShOR4=s64","userId":"16291163758591043198"}},"outputId":"1200fb4e-6914-4cba-91ae-7361c66e2092"},"execution_count":52,"outputs":[{"output_type":"stream","name":"stdout","text":["/content/DialoGPT-medium-harrypotter\n"]}]},{"cell_type":"code","source":["!git status"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"LzsP7Q6jzWnF","executionInfo":{"status":"ok","timestamp":1646026129563,"user_tz":-330,"elapsed":1059,"user":{"displayName":"Aryan Bhosale","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgMTOcxuJ_HEUMAQkjggmXzsZ3mAYICIEVShOR4=s64","userId":"16291163758591043198"}},"outputId":"8ef63083-ec0b-4419-e504-f05941424312"},"execution_count":53,"outputs":[{"output_type":"stream","name":"stdout","text":["On branch main\n","Your branch is up to date with 'origin/main'.\n","\n","nothing to commit, working tree clean\n"]}]},{"cell_type":"code","source":["!git add"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"lszB2RLvz1EP","executionInfo":{"status":"ok","timestamp":1646026253810,"user_tz":-330,"elapsed":341,"user":{"displayName":"Aryan Bhosale","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgMTOcxuJ_HEUMAQkjggmXzsZ3mAYICIEVShOR4=s64","userId":"16291163758591043198"}},"outputId":"739d0d2f-6591-431b-814d-8f7ff42d31ab"},"execution_count":54,"outputs":[{"output_type":"stream","name":"stdout","text":["Nothing specified, nothing added.\n","Maybe you wanted to say 'git add .'?\n"]}]},{"cell_type":"code","execution_count":55,"metadata":{"id":"uhqMtvfmXei8","executionInfo":{"status":"ok","timestamp":1646026279251,"user_tz":-330,"elapsed":993,"user":{"displayName":"Aryan Bhosale","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgMTOcxuJ_HEUMAQkjggmXzsZ3mAYICIEVShOR4=s64","userId":"16291163758591043198"}}},"outputs":[],"source":["!git config --global user.email \"aryan.bhosale1719@gmail.com\"\n","# Tip: using the same email as your huggingface.co account will link your commits to your profile\n","!git config --global user.name \"Aryan Bhosale\""]},{"cell_type":"code","source":["!git commit -m \"Initial commit\""],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"jNihUOKV0EjV","executionInfo":{"status":"ok","timestamp":1646026335729,"user_tz":-330,"elapsed":338,"user":{"displayName":"Aryan Bhosale","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgMTOcxuJ_HEUMAQkjggmXzsZ3mAYICIEVShOR4=s64","userId":"16291163758591043198"}},"outputId":"7c77892b-19b5-4c87-cb37-5f514fde773a"},"execution_count":56,"outputs":[{"output_type":"stream","name":"stdout","text":["On branch main\n","Your branch is up to date with 'origin/main'.\n","\n","nothing to commit, working tree clean\n"]}]},{"cell_type":"code","source":["!git push"],"metadata":{"id":"RdKmf8oJ0NaQ","executionInfo":{"status":"ok","timestamp":1646026353719,"user_tz":-330,"elapsed":1025,"user":{"displayName":"Aryan 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