superknowa

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{
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 "cells": [
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  {
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   "cell_type": "markdown",
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   "metadata": {
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    "id": "Spxf9v5Ke4py"
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   },
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   "source": [
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    "# Reward Model Training"
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   ]
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  },
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  {
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   "cell_type": "markdown",
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   "metadata": {
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    "id": "4b6HWwEtfdaZ"
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   },
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   "source": [
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    "## Installing and importing necessry packages"
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   ]
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  },
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  {
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   "cell_type": "code",
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   "execution_count": 11,
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   "metadata": {
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    "colab": {
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     "base_uri": "https://localhost:8080/"
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    },
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    "id": "dSw4HgoJMBYT",
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    "outputId": "559287b8-1bfb-452f-bf34-c7c52d26f694"
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   },
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   "outputs": [],
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   "source": [
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    "!pip install pandas\n",
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    "!pip install trl\n",
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    "!pip install plotly"
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   ]
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  },
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  {
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   "cell_type": "code",
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   "execution_count": 20,
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   "metadata": {
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    "id": "IbSU2uDhMBYU"
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   },
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   "outputs": [],
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   "source": [
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    "import random\n",
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    "import pandas as pd\n",
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    "from operator import itemgetter\n",
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    "import torch\n",
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    "import warnings\n",
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    "warnings.filterwarnings('ignore')\n",
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    "from datasets import Dataset, load_dataset\n",
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    "from transformers import AutoModelForSequenceClassification,AutoTokenizer,TrainingArguments\n",
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    "from trl import RewardTrainer"
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   ]
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  },
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  {
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   "cell_type": "markdown",
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   "metadata": {
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    "id": "n27wOGaKU8_R"
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   },
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   "source": [
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    "## Comparison Dataset\n",
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    "\n",
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    "In this section, we convert the dataset in form of (question,answer,feedback) tuple to comparison dataset (question, chosen answer and rejected answer). \n",
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    "Reward model training requires the data to be in form of (question, chosen answer, rejected answer) tuple."
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   ]
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  },
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  {
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   "cell_type": "markdown",
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   "metadata": {},
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   "source": [
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    "The *feedback.csv* file contains mutliple answers for a given questions rated by human. These ratings could be for any human-value that we want to be included in the model output.\n",
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    "\n",
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    "For eg: If we want the model outputs to be of *helpful* nature answers, we can instruct annotators to give high rewards(feedback score) for helpful answer in comparison to other answers for a particular question."
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   ]
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  },
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  {
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   "cell_type": "code",
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   "execution_count": 13,
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   "metadata": {
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    "id": "SLzkj5xRU6ZK",
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    "scrolled": true
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   },
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   "outputs": [
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    {
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       "</style>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "  <thead>\n",
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th></th>\n",
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       "      <th>question</th>\n",
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       "      <th>answer</th>\n",
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       "      <th>feedback</th>\n",
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       "    </tr>\n",
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       "  </thead>\n",
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       "  <tbody>\n",
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       "    <tr>\n",
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       "      <th>0</th>\n",
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       "      <td>Can Maximo Visual Inspection run on prem?​​​</td>\n",
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       "      <td>Answer: Yes, Maximo Visual Inspection can be ...</td>\n",
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       "      <td>2.0</td>\n",
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       "    </tr>\n",
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       "    <tr>\n",
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       "      <th>1</th>\n",
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       "      <td>What is watson knowledge catalog?</td>\n",
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       "      <td>Answer: The IBM Watson Knowledge Catalog is a...</td>\n",
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       "      <td>4.0</td>\n",
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       "    </tr>\n",
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       "    <tr>\n",
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       "      <th>2</th>\n",
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       "      <td>What is watson knowledge catalog?</td>\n",
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       "      <td>Answer: The IBM Watson Knowledge Catalog is a...</td>\n",
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       "      <td>3.0</td>\n",
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       "    </tr>\n",
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       "    <tr>\n",
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       "      <th>3</th>\n",
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       "      <td>Can Instana use OpenTelemetry trace data?​​​​​</td>\n",
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       "      <td>Answer: Yes, Instana can ingest OpenTelemetry...</td>\n",
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       "      <td>4.0</td>\n",
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       "    </tr>\n",
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       "    <tr>\n",
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       "      <th>4</th>\n",
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       "      <td>format it on a table</td>\n",
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       "      <td>with the following columns. The first column i...</td>\n",
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       "      <td>1.0</td>\n",
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       "    </tr>\n",
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       "  </tbody>\n",
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       "</table>\n",
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       "</div>"
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      ],
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      "text/plain": [
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       "                                         question  \\\n",
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       "0    Can Maximo Visual Inspection run on prem?​​​   \n",
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       "1               What is watson knowledge catalog?   \n",
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       "2               What is watson knowledge catalog?   \n",
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       "3  Can Instana use OpenTelemetry trace data?​​​​​   \n",
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       "4                            format it on a table   \n",
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       "\n",
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       "                                              answer  feedback  \n",
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       "0   Answer: Yes, Maximo Visual Inspection can be ...       2.0  \n",
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       "1   Answer: The IBM Watson Knowledge Catalog is a...       4.0  \n",
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       "2   Answer: The IBM Watson Knowledge Catalog is a...       3.0  \n",
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       "3   Answer: Yes, Instana can ingest OpenTelemetry...       4.0  \n",
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       "4  with the following columns. The first column i...       1.0  "
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      ]
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     },
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     "execution_count": 13,
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     "metadata": {},
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     "output_type": "execute_result"
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    }
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   ],
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   "source": [
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    "df = pd.read_csv('feedback.csv')\n",
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    "df.head()"
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   ]
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  },
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  {
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   "cell_type": "markdown",
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   "metadata": {},
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   "source": [
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    "Once we have the feedback recieved from the users, we can then convert this dataset into comparsion dataset for reward model training"
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   ]
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  },
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  {
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   "cell_type": "code",
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   "execution_count": 14,
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   "metadata": {
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    "id": "pr3SHxTTU5be"
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   },
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   "outputs": [],
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   "source": [
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    "df['tup'] = list(zip(df['answer'], df['feedback']))\n",
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    "df_g = df.groupby('question')['tup'].apply(list).reset_index()\n",
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    "df_g[\"sorted_tup\"] = df_g[\"tup\"].apply(lambda x :sorted(x,key=itemgetter(1)) )\n",
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    "df_g[\"chosen\"] = df_g[\"sorted_tup\"].apply(lambda x: x[-1][0])\n",
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    "df_g[\"chosen_score\"] = df_g[\"sorted_tup\"].apply(lambda x: x[-1][1])\n",
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    "df_g[\"rejected\"] = df_g[\"sorted_tup\"].apply(lambda x: x[0][0])\n",
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    "df_g[\"rejected_score\"] = df_g[\"sorted_tup\"].apply(lambda x: x[0][1])\n",
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    "df_g = df_g.dropna()\n",
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    "df_g = df_g[(df_g['chosen_score']>=4.0) & (df_g['rejected_score']<4.0)]\n",
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    "df_g.to_csv(\"feedback_comparison_dataset.csv\")"
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   ]
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  },
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  {
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   "cell_type": "code",
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   "execution_count": 15,
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   "metadata": {
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    "colab": {
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     "base_uri": "https://localhost:8080/",
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     "height": 332
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    },
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    "id": "u7N6F8IrXPR6",
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    "outputId": "8e1e3e08-5956-43bd-d7f0-95a8b03e7d69"
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   "outputs": [
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    {
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       "  <thead>\n",
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th></th>\n",
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       "      <th>instruction</th>\n",
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       "      <th>chosen_response</th>\n",
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       "      <th>rejected_response</th>\n",
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       "    </tr>\n",
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       "  </thead>\n",
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       "  <tbody>\n",
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       "    <tr>\n",
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       "      <th>0</th>\n",
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       "      <td>Can Instana use OpenTelemetry trace data?​</td>\n",
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       "      <td>Yes, Instana can use OpenTelemetry trace data....</td>\n",
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       "      <td>Answer: Yes, Instana can use OpenTelemetry tr...</td>\n",
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       "    </tr>\n",
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       "    <tr>\n",
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       "      <th>1</th>\n",
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       "      <td>Can Instana use OpenTelemetry trace data?​​​​​</td>\n",
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       "      <td>Yes, Instana can ingest OpenTelemetry trace da...</td>\n",
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       "      <td>Answer: Yes, Instana can use OpenTelemetry tr...</td>\n",
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       "    </tr>\n",
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       "    <tr>\n",
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       "      <th>2</th>\n",
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       "      <td>Can Maximo Visual Inspection run on prem?​​​</td>\n",
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       "      <td>Answer: Yes. Maximo Visual Inspection is a cl...</td>\n",
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       "      <td>Answer: Yes, Maximo Visual Inspection can be ...</td>\n",
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       "    </tr>\n",
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       "    <tr>\n",
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       "      <th>3</th>\n",
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       "      <td>Explain me step by step how can I integrate da...</td>\n",
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       "      <td>ive tried to follow the documentation but I do...</td>\n",
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       "      <td>ive tried to do it but it is not working. Answ...</td>\n",
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       "    </tr>\n",
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       "    <tr>\n",
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       "      <th>4</th>\n",
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       "      <td>What is watson knowledge catalog?</td>\n",
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       "      <td>The IBM Watson Knowledge Catalog is a data cat...</td>\n",
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       "      <td>Answer: The IBM Watson Knowledge Catalog is a...</td>\n",
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       "    </tr>\n",
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       "    <tr>\n",
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       "      <th>5</th>\n",
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       "      <td>what is cloud pak for watson aiops</td>\n",
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       "      <td>? Answer: Cloud Pak for Watson AIOps is an AI-...</td>\n",
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       "      <td>? Answer: Cloud Pak for Watson AIOps is a plat...</td>\n",
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       "    </tr>\n",
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       "    <tr>\n",
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       "      <th>6</th>\n",
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       "      <td>what is ibm?</td>\n",
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       "      <td>Answer: IBM is an American multinational techn...</td>\n",
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       "      <td>Answer: IBM is an American multinational techn...</td>\n",
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       "    </tr>\n",
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       "    <tr>\n",
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       "      <th>7</th>\n",
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       "      <td>which cloudpak provides business process autom...</td>\n",
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       "      <td>? Answer: IBM Cloud Pak for Automation</td>\n",
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       "      <td>? : What is the difference between IBM Cloud P...</td>\n",
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       "    </tr>\n",
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       "    <tr>\n",
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       "      <th>8</th>\n",
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       "      <td>who is the CEO of IBM?</td>\n",
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       "      <td>Answer: Arvind Krishna Answer: Arvind Krishna</td>\n",
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       "      <td>.</td>\n",
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       "    </tr>\n",
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       "  </tbody>\n",
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       "</table>\n",
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       "</div>"
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      ],
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      "text/plain": [
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       "                                         instruction  \\\n",
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       "0         Can Instana use OpenTelemetry trace data?​   \n",
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       "1     Can Instana use OpenTelemetry trace data?​​​​​   \n",
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       "2       Can Maximo Visual Inspection run on prem?​​​   \n",
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       "3  Explain me step by step how can I integrate da...   \n",
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       "4                  What is watson knowledge catalog?   \n",
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       "5                 what is cloud pak for watson aiops   \n",
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       "6                                       what is ibm?   \n",
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       "7  which cloudpak provides business process autom...   \n",
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       "8                             who is the CEO of IBM?   \n",
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       "\n",
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       "                                     chosen_response  \\\n",
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       "0  Yes, Instana can use OpenTelemetry trace data....   \n",
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       "1  Yes, Instana can ingest OpenTelemetry trace da...   \n",
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       "2   Answer: Yes. Maximo Visual Inspection is a cl...   \n",
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       "3  ive tried to follow the documentation but I do...   \n",
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       "4  The IBM Watson Knowledge Catalog is a data cat...   \n",
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       "5  ? Answer: Cloud Pak for Watson AIOps is an AI-...   \n",
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       "6  Answer: IBM is an American multinational techn...   \n",
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       "7            ? Answer: IBM Cloud Pak for Automation    \n",
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       "8     Answer: Arvind Krishna Answer: Arvind Krishna    \n",
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       "\n",
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       "                                   rejected_response  \n",
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       "0   Answer: Yes, Instana can use OpenTelemetry tr...  \n",
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       "1   Answer: Yes, Instana can use OpenTelemetry tr...  \n",
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       "2   Answer: Yes, Maximo Visual Inspection can be ...  \n",
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       "3  ive tried to do it but it is not working. Answ...  \n",
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       "4   Answer: The IBM Watson Knowledge Catalog is a...  \n",
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       "5  ? Answer: Cloud Pak for Watson AIOps is a plat...  \n",
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       "6  Answer: IBM is an American multinational techn...  \n",
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       "7  ? : What is the difference between IBM Cloud P...  \n",
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       "8                                                  .  "
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      ]
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     },
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     "execution_count": 15,
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     "metadata": {},
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     "output_type": "execute_result"
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    }
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   ],
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   "source": [
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    "rows = []\n",
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    "for record in df_g.itertuples(index=True, name='Pandas'):\n",
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    "    if record is None or len(record) == 0:\n",
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    "        continue\n",
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    "    rows.append({\n",
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    "        \"instruction\": record.question,\n",
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    "        \"chosen_response\": record.chosen,\n",
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    "        \"rejected_response\": record.rejected\n",
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    "    })\n",
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    "\n",
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    "prepared_dataset = Dataset.from_list(rows)\n",
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    "prepared_dataset.to_pandas()"
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   ]
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  },
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  {
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   "cell_type": "markdown",
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   "metadata": {
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    "id": "YRS9MzF6gEmu"
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   },
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   "source": [
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    "## Train the reward model with TRL"
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   ]
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  },
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  {
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   "cell_type": "code",
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   "execution_count": 21,
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   "metadata": {
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    "colab": {
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     "base_uri": "https://localhost:8080/",
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     "height": 399,
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     "referenced_widgets": [
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      "71d2dab421d24180b3702f8527774891"
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     ]
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    },
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    "id": "-MtSUam61J_Z",
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    "outputId": "4a21a596-5bba-48b1-949e-c747e20f8d3d"
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   },
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   "outputs": [
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    {
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     "name": "stderr",
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     "output_type": "stream",
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     "text": [
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      "Some weights of the model checkpoint at distilroberta-base were not used when initializing RobertaForSequenceClassification: ['lm_head.bias', 'roberta.pooler.dense.bias', 'lm_head.layer_norm.bias', 'roberta.pooler.dense.weight', 'lm_head.dense.weight', 'lm_head.decoder.weight', 'lm_head.dense.bias', 'lm_head.layer_norm.weight']\n",
391
      "- This IS expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
392
      "- This IS NOT expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
393
      "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at distilroberta-base and are newly initialized: ['classifier.dense.bias', 'classifier.out_proj.bias', 'classifier.out_proj.weight', 'classifier.dense.weight']\n",
394
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
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     ]
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    },
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       "Map:   0%|          | 0/9 [00:00<?, ? examples/s]"
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      ]
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    {
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     "name": "stderr",
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     "output_type": "stream",
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     "text": [
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      "You're using a RobertaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n",
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      "Could not estimate the number of tokens of the input, floating-point operations will not be computed\n"
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     ]
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    },
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    {
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     "data": {
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      "text/html": [
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       "\n",
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       "    <div>\n",
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       "      \n",
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       "      <progress value='10' max='10' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
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       "      [10/10 00:03, Epoch 10/10]\n",
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       "    </div>\n",
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       "    <table border=\"1\" class=\"dataframe\">\n",
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       "  <thead>\n",
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       " <tr style=\"text-align: left;\">\n",
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       "      <th>Step</th>\n",
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       "      <th>Training Loss</th>\n",
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       "      <th>Validation Loss</th>\n",
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       "      <th>Accuracy</th>\n",
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       "    </tr>\n",
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       "  </thead>\n",
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       "  <tbody>\n",
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       "    <tr>\n",
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       "      <td>1</td>\n",
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       "      <td>0.644600</td>\n",
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       "      <td>0.693641</td>\n",
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       "      <td>0.666667</td>\n",
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       "    </tr>\n",
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       "    <tr>\n",
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       "      <td>2</td>\n",
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       "      <td>0.655800</td>\n",
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       "      <td>0.694708</td>\n",
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       "      <td>0.666667</td>\n",
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       "    </tr>\n",
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       "    <tr>\n",
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       "      <td>3</td>\n",
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       "      <td>0.657000</td>\n",
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       "      <td>0.696189</td>\n",
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       "      <td>0.666667</td>\n",
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       "    </tr>\n",
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       "    <tr>\n",
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       "      <td>4</td>\n",
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       "      <td>0.620500</td>\n",
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       "      <td>0.696142</td>\n",
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       "      <td>0.666667</td>\n",
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       "    </tr>\n",
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       "    <tr>\n",
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       "      <td>5</td>\n",
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       "      <td>0.675500</td>\n",
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       "      <td>0.696126</td>\n",
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       "      <td>0.666667</td>\n",
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       "    </tr>\n",
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       "    <tr>\n",
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       "      <td>6</td>\n",
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       "      <td>0.700300</td>\n",
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       "      <td>0.696020</td>\n",
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       "      <td>0.666667</td>\n",
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       "    </tr>\n",
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       "    <tr>\n",
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       "      <td>7</td>\n",
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       "      <td>0.640700</td>\n",
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       "      <td>0.695554</td>\n",
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       "      <td>0.666667</td>\n",
479
       "    </tr>\n",
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       "    <tr>\n",
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       "      <td>8</td>\n",
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       "      <td>0.584700</td>\n",
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       "      <td>0.694999</td>\n",
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       "      <td>0.666667</td>\n",
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       "    </tr>\n",
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       "    <tr>\n",
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       "      <td>9</td>\n",
488
       "      <td>0.650300</td>\n",
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       "      <td>0.666667</td>\n",
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       "    </tr>\n",
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       "    <tr>\n",
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       "      <td>10</td>\n",
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       "      <td>0.663200</td>\n",
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       "      <td>0.666667</td>\n",
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       "    </tr>\n",
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       "  </tbody>\n",
499
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       "TrainOutput(global_step=10, training_loss=0.6492631733417511, metrics={'train_runtime': 3.5165, 'train_samples_per_second': 17.063, 'train_steps_per_second': 2.844, 'total_flos': 0.0, 'train_loss': 0.6492631733417511, 'epoch': 10.0})"
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     "execution_count": 21,
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     "metadata": {},
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     "output_type": "execute_result"
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    }
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   ],
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   "source": [
520
    "#Select a base model whch we need to train for reward modeling.\n",
521
    "model_name = \"distilroberta-base\"\n",
522
    "model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=1)\n",
523
    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
524
    "\n",
525
    "if tokenizer.pad_token is None:\n",
526
    "    tokenizer.pad_token = tokenizer.eos_token\n",
527
    "    model.config.pad_token_id = model.config.eos_token_id\n",
528
    "\n",
529
    "def formatting_func(examples):\n",
530
    "    kwargs = {\"padding\": \"max_length\", \"truncation\": True, \"max_length\": 512, \"return_tensors\": \"pt\"}\n",
531
    "\n",
532
    "    prompt_plus_chosen_response = examples[\"instruction\"] + \"\\n\" + examples[\"chosen_response\"]\n",
533
    "    prompt_plus_rejected_response = examples[\"instruction\"] + \"\\n\" + examples[\"rejected_response\"]\n",
534
    "    tokens_chosen = tokenizer.encode_plus(prompt_plus_chosen_response, **kwargs)\n",
535
    "    tokens_rejected = tokenizer.encode_plus(prompt_plus_rejected_response, **kwargs)\n",
536
    "\n",
537
    "    return {\n",
538
    "        \"input_ids_chosen\": tokens_chosen[\"input_ids\"][0], \"attention_mask_chosen\": tokens_chosen[\"attention_mask\"][0],\n",
539
    "        \"input_ids_rejected\": tokens_rejected[\"input_ids\"][0], \"attention_mask_rejected\": tokens_rejected[\"attention_mask\"][0]\n",
540
    "    }\n",
541
    "\n",
542
    "formatted_dataset = prepared_dataset.map(formatting_func)\n",
543
    "formatted_dataset = formatted_dataset.train_test_split()\n",
544
    "\n",
545
    "training_args = TrainingArguments(\n",
546
    "    output_dir=\"./reward_model\",\n",
547
    "    per_device_train_batch_size=16,\n",
548
    "    evaluation_strategy=\"steps\",\n",
549
    "    logging_steps=1,\n",
550
    "    num_train_epochs = 10,\n",
551
    "    report_to=None,\n",
552
    "\n",
553
    ")\n",
554
    "\n",
555
    "trainer = RewardTrainer(\n",
556
    "    model=model,\n",
557
    "    args=training_args,\n",
558
    "    tokenizer=tokenizer,\n",
559
    "    train_dataset=formatted_dataset[\"train\"],\n",
560
    "    eval_dataset=formatted_dataset[\"test\"],\n",
561
    "\n",
562
    ")\n",
563
    "\n",
564
    "trainer.train()\n"
565
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   "cell_type": "code",
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   "metadata": {
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   "outputs": [],
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   "source": [
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    "trainer.save_model() "
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