xlm_roberta_multilabel.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kLB3I4FKZ5Lr"
   },
   "source": [
    "# Fine-tuning BERT (and friends) for multi-label text classification\n",
    "\n",
    "In this notebook, we are going to fine-tune BERT to predict one or more labels for a given piece of text. Note that this notebook illustrates how to fine-tune a bert-base-uncased model, but you can also fine-tune a RoBERTa, DeBERTa, DistilBERT, CANINE, ... checkpoint in the same way.\n",
    "\n",
    "All of those work in the same way: they add a linear layer on top of the base model, which is used to produce a tensor of shape (batch_size, num_labels), indicating the unnormalized scores for a number of labels for every example in the batch.\n",
    "\n",
    "\n",
    "\n",
    "## Set-up environment\n",
    "\n",
    "First, we install the libraries which we'll use: HuggingFace Transformers and Datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4wxY3x-ZZz8h",
    "outputId": "5a1089c4-efe8-484c-ead5-33b5096a9f64"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
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     ]
    }
   ],
   "source": [
    "! pip install datasets\n",
    "! pip install -U accelerate\n",
    "! pip install -U transformers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "aFlgId2tiQtc",
    "outputId": "471fcef1-2723-4fa9-e6fb-25c60710630e",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: tqdm in /sw/pkgs/arc/python3.9-anaconda/2021.11/lib/python3.9/site-packages (4.62.3)\n",
      "Collecting tqdm\n",
      "  Downloading tqdm-4.66.2-py3-none-any.whl (78 kB)\n",
      "\u001b[K     |████████████████████████████████| 78 kB 1.7 MB/s eta 0:00:01\n",
      "\u001b[?25hInstalling collected packages: tqdm\n",
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "ru-core-news-lg 3.7.0 requires spacy<3.8.0,>=3.7.0, but you have spacy 3.4.4 which is incompatible.\n",
      "ro-core-news-lg 3.7.0 requires spacy<3.8.0,>=3.7.0, but you have spacy 3.4.4 which is incompatible.\n",
      "ko-core-news-lg 3.7.0 requires spacy<3.8.0,>=3.7.0, but you have spacy 3.4.4 which is incompatible.\n",
      "it-core-news-lg 3.7.0 requires spacy<3.8.0,>=3.7.0, but you have spacy 3.4.4 which is incompatible.\n",
      "fr-core-news-lg 3.7.0 requires spacy<3.8.0,>=3.7.0, but you have spacy 3.4.4 which is incompatible.\n",
      "es-core-news-lg 3.7.0 requires spacy<3.8.0,>=3.7.0, but you have spacy 3.4.4 which is incompatible.\n",
      "de-core-news-lg 3.7.0 requires spacy<3.8.0,>=3.7.0, but you have spacy 3.4.4 which is incompatible.\u001b[0m\n",
      "Successfully installed tqdm-4.66.2\n"
     ]
    }
   ],
   "source": [
    "! pip install --upgrade tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8EDz6L-tuLTa",
    "outputId": "e6d12a6e-e335-41a8-e804-a635bb985d60"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
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      "Requirement already satisfied: frozenlist>=1.1.1 in /home/oignat/.local/lib/python3.9/site-packages (from aiohttp->datasets>=2.0.0->evaluate) (1.4.0)\n",
      "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /home/oignat/.local/lib/python3.9/site-packages (from aiohttp->datasets>=2.0.0->evaluate) (4.0.2)\n",
      "Requirement already satisfied: aiosignal>=1.1.2 in /home/oignat/.local/lib/python3.9/site-packages (from aiohttp->datasets>=2.0.0->evaluate) (1.3.1)\n",
      "Requirement already satisfied: attrs>=17.3.0 in /sw/pkgs/arc/python3.9-anaconda/2021.11/lib/python3.9/site-packages (from aiohttp->datasets>=2.0.0->evaluate) (21.2.0)\n",
      "Requirement already satisfied: yarl<2.0,>=1.0 in /home/oignat/.local/lib/python3.9/site-packages (from aiohttp->datasets>=2.0.0->evaluate) (1.9.2)\n",
      "Requirement already satisfied: typing-extensions>=3.7.4.3 in /home/oignat/.local/lib/python3.9/site-packages (from huggingface-hub>=0.7.0->evaluate) (4.10.0)\n",
      "Requirement already satisfied: filelock in /sw/pkgs/arc/python3.9-anaconda/2021.11/lib/python3.9/site-packages (from huggingface-hub>=0.7.0->evaluate) (3.3.1)\n",
      "Requirement already satisfied: pyparsing>=2.0.2 in /sw/pkgs/arc/python3.9-anaconda/2021.11/lib/python3.9/site-packages (from packaging->evaluate) (3.0.4)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /sw/pkgs/arc/python3.9-anaconda/2021.11/lib/python3.9/site-packages (from requests>=2.19.0->evaluate) (1.26.7)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /sw/pkgs/arc/python3.9-anaconda/2021.11/lib/python3.9/site-packages (from requests>=2.19.0->evaluate) (2021.10.8)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /sw/pkgs/arc/python3.9-anaconda/2021.11/lib/python3.9/site-packages (from requests>=2.19.0->evaluate) (3.2)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in /sw/pkgs/arc/python3.9-anaconda/2021.11/lib/python3.9/site-packages (from pandas->evaluate) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2017.3 in /sw/pkgs/arc/python3.9-anaconda/2021.11/lib/python3.9/site-packages (from pandas->evaluate) (2021.3)\n",
      "Requirement already satisfied: six>=1.5 in /sw/pkgs/arc/python3.9-anaconda/2021.11/lib/python3.9/site-packages (from python-dateutil>=2.7.3->pandas->evaluate) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install evaluate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "niBMCLOALHku"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from datasets import Dataset, DatasetDict\n",
    "from transformers import AutoTokenizer\n",
    "import numpy as np\n",
    "import torch\n",
    "from transformers import (\n",
    "    RobertaTokenizerFast,\n",
    "    RobertaForSequenceClassification,\n",
    "    TrainingArguments,\n",
    "    Trainer,\n",
    "    AutoConfig,\n",
    ")\n",
    "from transformers import (\n",
    "  AutoTokenizer,\n",
    "  AutoModelForSequenceClassification,\n",
    "  Trainer,\n",
    "  TrainingArguments,\n",
    "  DataCollatorWithPadding\n",
    ")\n",
    "from huggingface_hub import HfFolder, notebook_login\n",
    "from datasets import Dataset\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "import evaluate\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bIH9NP0MZ6-O"
   },
   "source": [
    "## Load dataset\n",
    "\n",
    "Next, let's download a multi-label text classification dataset from the [hub](https://huggingface.co/).\n",
    "\n",
    "At the time of writing, I picked a random one as follows:   \n",
    "\n",
    "* first, go to the \"datasets\" tab on huggingface.co\n",
    "* next, select the \"multi-label-classification\" tag on the left as well as the the \"1k<10k\" tag (fo find a relatively small dataset).\n",
    "\n",
    "Note that you can also easily load your local data (i.e. csv files, txt files, Parquet files, JSON, ...) as explained [here](https://huggingface.co/docs/datasets/loading.html#local-and-remote-files).\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "TjAIldFRHJgS"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>Text</th>\n",
       "      <th>Country</th>\n",
       "      <th>Source</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Palaeontologist.</td>\n",
       "      <td>uk</td>\n",
       "      <td>real</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>\\n&gt; We have the capability of being better.  B...</td>\n",
       "      <td>uk</td>\n",
       "      <td>real</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Only just noticed it myself, ha! Or Ha, accord...</td>\n",
       "      <td>uk</td>\n",
       "      <td>real</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mate all he does is play football and misses p...</td>\n",
       "      <td>uk</td>\n",
       "      <td>real</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Me neither. But then, I'm not sure Cleese woul...</td>\n",
       "      <td>uk</td>\n",
       "      <td>real</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5995</th>\n",
       "      <td>Often, I find my inspiration in the diverse an...</td>\n",
       "      <td>india</td>\n",
       "      <td>generated</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5996</th>\n",
       "      <td>I often find inspiration in people overcoming ...</td>\n",
       "      <td>india</td>\n",
       "      <td>generated</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5997</th>\n",
       "      <td>What inspires me most is the sheer determinati...</td>\n",
       "      <td>india</td>\n",
       "      <td>generated</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5998</th>\n",
       "      <td>Just the other day, I watched a fascinating do...</td>\n",
       "      <td>india</td>\n",
       "      <td>generated</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5999</th>\n",
       "      <td>The resilience of our local farmers truly insp...</td>\n",
       "      <td>india</td>\n",
       "      <td>generated</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6000 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   Text Country     Source  \\\n",
       "0                                      Palaeontologist.      uk       real   \n",
       "1     \\n> We have the capability of being better.  B...      uk       real   \n",
       "2     Only just noticed it myself, ha! Or Ha, accord...      uk       real   \n",
       "3     Mate all he does is play football and misses p...      uk       real   \n",
       "4     Me neither. But then, I'm not sure Cleese woul...      uk       real   \n",
       "...                                                 ...     ...        ...   \n",
       "5995  Often, I find my inspiration in the diverse an...   india  generated   \n",
       "5996  I often find inspiration in people overcoming ...   india  generated   \n",
       "5997  What inspires me most is the sheer determinati...   india  generated   \n",
       "5998  Just the other day, I watched a fascinating do...   india  generated   \n",
       "5999  The resilience of our local farmers truly insp...   india  generated   \n",
       "\n",
       "      Label  \n",
       "0         0  \n",
       "1         1  \n",
       "2         0  \n",
       "3         1  \n",
       "4         0  \n",
       "...     ...  \n",
       "5995      1  \n",
       "5996      1  \n",
       "5997      1  \n",
       "5998      1  \n",
       "5999      1  \n",
       "\n",
       "[6000 rows x 4 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/all_data.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "Etg5-MG9LXFw"
   },
   "outputs": [],
   "source": [
    "df['UK'] = df['Country'] == 'uk'\n",
    "df['India'] = df['Country'] == 'india'\n",
    "df['Real'] = df['Source'] == 'real'\n",
    "df['Generated'] = df['Source'] == 'generated'\n",
    "df['Inspirational'] = df['Label'] == 1\n",
    "df['Non-Inspirational'] = df['Label'] == 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "Dj4YndpgHOOm"
   },
   "outputs": [],
   "source": [
    "df = df[['Text', 'UK', 'India', 'Real', 'Generated', 'Inspirational', 'Non-Inspirational']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "f35k-56yMB1M",
    "outputId": "b1cd6694-7d20-4756-ae73-cecb21136a4d"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>Text</th>\n",
       "      <th>UK</th>\n",
       "      <th>India</th>\n",
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       "      <th>Generated</th>\n",
       "      <th>Inspirational</th>\n",
       "      <th>Non-Inspirational</th>\n",
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       "      <th>0</th>\n",
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       "      <td>Only just noticed it myself, ha! Or Ha, accord...</td>\n",
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       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "      <td>True</td>\n",
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       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Me neither. But then, I'm not sure Cleese woul...</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5995</th>\n",
       "      <td>Often, I find my inspiration in the diverse an...</td>\n",
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       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>5996</th>\n",
       "      <td>I often find inspiration in people overcoming ...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5997</th>\n",
       "      <td>What inspires me most is the sheer determinati...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5998</th>\n",
       "      <td>Just the other day, I watched a fascinating do...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5999</th>\n",
       "      <td>The resilience of our local farmers truly insp...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6000 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   Text     UK  India   Real  \\\n",
       "0                                      Palaeontologist.   True  False   True   \n",
       "1     \\n> We have the capability of being better.  B...   True  False   True   \n",
       "2     Only just noticed it myself, ha! Or Ha, accord...   True  False   True   \n",
       "3     Mate all he does is play football and misses p...   True  False   True   \n",
       "4     Me neither. But then, I'm not sure Cleese woul...   True  False   True   \n",
       "...                                                 ...    ...    ...    ...   \n",
       "5995  Often, I find my inspiration in the diverse an...  False   True  False   \n",
       "5996  I often find inspiration in people overcoming ...  False   True  False   \n",
       "5997  What inspires me most is the sheer determinati...  False   True  False   \n",
       "5998  Just the other day, I watched a fascinating do...  False   True  False   \n",
       "5999  The resilience of our local farmers truly insp...  False   True  False   \n",
       "\n",
       "      Generated  Inspirational  Non-Inspirational  \n",
       "0         False          False               True  \n",
       "1         False           True              False  \n",
       "2         False          False               True  \n",
       "3         False           True              False  \n",
       "4         False          False               True  \n",
       "...         ...            ...                ...  \n",
       "5995       True           True              False  \n",
       "5996       True           True              False  \n",
       "5997       True           True              False  \n",
       "5998       True           True              False  \n",
       "5999       True           True              False  \n",
       "\n",
       "[6000 rows x 7 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4SmdcfgeAIfp",
    "outputId": "178ea372-ce2e-4647-977f-679229ea998d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Null values:  True\n",
      "Null values:  False\n"
     ]
    }
   ],
   "source": [
    "print('Null values: ', df.isnull().values.any())\n",
    "df = df.dropna()\n",
    "print('Null values: ', df.isnull().values.any())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hm5Xl7kYJNrR",
    "outputId": "cf2a86da-804b-4538-997d-e093a1499852"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_165751/3500421930.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['Text'] = df['Text'].astype(str)\n"
     ]
    }
   ],
   "source": [
    "df['Text'] = df['Text'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "i4QOsLitL1dR"
   },
   "outputs": [],
   "source": [
    "df = df[df['Text'].str.strip() != '']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "PxVfkTdlLLeE"
   },
   "outputs": [],
   "source": [
    "# Split the DataFrame into train, validation, and test sets\n",
    "# Defaul split\n",
    "train_df, temp_df = train_test_split(df, test_size=0.2, random_state=42, shuffle=True)\n",
    "val_df, test_df = train_test_split(temp_df, test_size=0.2, random_state=42, shuffle=True)\n",
    "\n",
    "# # Few shot split\n",
    "# train_df, temp_df = train_test_split(df, test_size=0.9, random_state=42, shuffle=True)\n",
    "# val_df, test_df = train_test_split(temp_df, test_size=0.9, random_state=42, shuffle=True)\n",
    "\n",
    "# Reset index\n",
    "train_df.reset_index(drop=True, inplace=True)\n",
    "val_df.reset_index(drop=True, inplace=True)\n",
    "test_df.reset_index(drop=True, inplace=True)\n",
    "\n",
    "# Create Dataset objects\n",
    "train_dataset = Dataset.from_pandas(train_df)\n",
    "val_dataset = Dataset.from_pandas(val_df)\n",
    "test_dataset = Dataset.from_pandas(test_df)\n",
    "\n",
    "# Create DatasetDict\n",
    "dataset = DatasetDict({\n",
    "    \"train\": train_dataset,\n",
    "    \"validation\": val_dataset,\n",
    "    \"test\": test_dataset\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "SFR5GGkkMT03",
    "outputId": "0df32656-9f58-4ea4-c74c-08c2dfd9b2e0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['Text', 'UK', 'India', 'Real', 'Generated', 'Inspirational', 'Non-Inspirational'],\n",
       "        num_rows: 4799\n",
       "    })\n",
       "    validation: Dataset({\n",
       "        features: ['Text', 'UK', 'India', 'Real', 'Generated', 'Inspirational', 'Non-Inspirational'],\n",
       "        num_rows: 960\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['Text', 'UK', 'India', 'Real', 'Generated', 'Inspirational', 'Non-Inspirational'],\n",
       "        num_rows: 240\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "e5vZhQpvkE8s",
    "outputId": "5badf756-140a-483d-9bed-4f955df9d826"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['UK', 'India', 'Real', 'Generated', 'Inspirational', 'Non-Inspirational']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels = [label for label in dataset['train'].features.keys() if label not in ['Text']]\n",
    "id2label = {idx:label for idx, label in enumerate(labels)}\n",
    "label2id = {label:idx for idx, label in enumerate(labels)}\n",
    "labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nJ3Teyjmank2"
   },
   "source": [
    "## Preprocess data\n",
    "\n",
    "As models like BERT don't expect text as direct input, but rather `input_ids`, etc., we tokenize the text using the tokenizer. Here I'm using the `AutoTokenizer` API, which will automatically load the appropriate tokenizer based on the checkpoint on the hub.\n",
    "\n",
    "What's a bit tricky is that we also need to provide labels to the model. For multi-label text classification, this is a matrix of shape (batch_size, num_labels). Also important: this should be a tensor of floats rather than integers, otherwise PyTorch' `BCEWithLogitsLoss` (which the model will use) will complain, as explained [here](https://discuss.pytorch.org/t/multi-label-binary-classification-result-type-float-cant-be-cast-to-the-desired-output-type-long/117915/3)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "8G_0hebiNFGc"
   },
   "outputs": [],
   "source": [
    "model_id = \"FacebookAI/xlm-roberta-base\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 249,
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      "50e7e4f154124818b51ac36be99f7efb"
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    },
    "id": "AFWlSsbZaRLc",
    "outputId": "9f88a68d-dfe4-41a0-89e5-80b3b687aaae"
   },
   "outputs": [],
   "source": [
    "tokenizer = AutoTokenizer.from_pretrained(model_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "a6bMRAz8NM-z"
   },
   "outputs": [],
   "source": [
    "def preprocess_data(examples):\n",
    "  # take a batch of texts\n",
    "  text = examples[\"Text\"]\n",
    "  # encode them\n",
    "  encoding = tokenizer(text, padding=True, truncation=True, max_length=256)\n",
    "  # add labels\n",
    "  labels_batch = {k: examples[k] for k in examples.keys() if k in labels}\n",
    "  # create numpy array of shape (batch_size, num_labels)\n",
    "  labels_matrix = np.zeros((len(text), len(labels)))\n",
    "  # fill numpy array\n",
    "  for idx, label in enumerate(labels):\n",
    "    labels_matrix[:, idx] = labels_batch[label]\n",
    "  encoding[\"labels\"] = labels_matrix.tolist()\n",
    "  return encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 113,
     "referenced_widgets": [
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      "0fc9797762964d29b2cd9336b64a6d11",
      "d9750e8883a9410faeb795ad84cfbbbb",
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      "1fd96dbd8c394d339d00528b1916c222",
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     ]
    },
    "id": "i4ENBTdulBEI",
    "outputId": "f77b2b93-1197-4423-f1d1-757137b72d48"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4f25834ecd93445e95f3c8d48566dec9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/4799 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c945c7febb9041f9928088ba2b333545",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/960 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "70a4b65f48304028b07c3f0d3fc13bf1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/240 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "encoded_dataset = dataset.map(preprocess_data, batched=True, remove_columns=dataset['train'].column_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0enAb0W9o25W",
    "outputId": "dbf68131-7118-45ed-f49d-d53a91424bb3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['input_ids', 'attention_mask', 'labels'])\n"
     ]
    }
   ],
   "source": [
    "example = encoded_dataset['train'][0]\n",
    "print(example.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 105
    },
    "id": "D0McCtJ8HRJY",
    "outputId": "c88a12e4-461a-4bfa-f3dd-737693aa1ce5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"<s> In this challenging world, it's the strength and resilience of everyday individuals in India that truly inspire me. Seeing people embrace the daily struggles, yet maintaining their smiles, fighting all odds and emerging triumphantly; these are the stories that keep me motivated. Cheers to the human spirit! Never underestimate the power of hope and determination.</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>\""
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer.decode(example['input_ids'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "VdIvj6WjHeZQ",
    "outputId": "ebb4ed1f-6aa6-4cb1-8d8e-23aae9d33a7f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.0, 1.0, 0.0, 1.0, 1.0, 0.0]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example['labels']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "q4Dx95t2o6N9",
    "outputId": "5a7bc959-a999-4437-f1f5-876a3e422eca"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['India', 'Generated', 'Inspirational']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[id2label[idx] for idx, label in enumerate(example['labels']) if label == 1.0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HgpKXDfvKBxn"
   },
   "source": [
    "Finally, we set the format of our data to PyTorch tensors. This will turn the training, validation and test sets into standard PyTorch [datasets](https://pytorch.org/docs/stable/data.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "Lk6Cq9duKBkA"
   },
   "outputs": [],
   "source": [
    "encoded_dataset.set_format(\"torch\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "w5qSmCgWefWs"
   },
   "source": [
    "## Define model\n",
    "\n",
    "Here we define a model that includes a pre-trained base (i.e. the weights from bert-base-uncased) are loaded, with a random initialized classification head (linear layer) on top. One should fine-tune this head, together with the pre-trained base on a labeled dataset.\n",
    "\n",
    "This is also printed by the warning.\n",
    "\n",
    "We set the `problem_type` to be \"multi_label_classification\", as this will make sure the appropriate loss function is used (namely [`BCEWithLogitsLoss`](https://pytorch.org/docs/stable/generated/torch.nn.BCEWithLogitsLoss.html)). We also make sure the output layer has `len(labels)` output neurons, and we set the id2label and label2id mappings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6XPL1Z_RegBF",
    "outputId": "1bbfa22e-9a79-44e1-e89b-fac7143d7a1d"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of XLMRobertaForSequenceClassification were not initialized from the model checkpoint at FacebookAI/xlm-roberta-base and are newly initialized: ['classifier.dense.bias', 'classifier.dense.weight', 'classifier.out_proj.bias', 'classifier.out_proj.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    }
   ],
   "source": [
    "from transformers import AutoModelForSequenceClassification\n",
    "\n",
    "model = AutoModelForSequenceClassification.from_pretrained(model_id,\n",
    "                                                           problem_type=\"multi_label_classification\",\n",
    "                                                           num_labels=len(labels),\n",
    "                                                           id2label=id2label,\n",
    "                                                           label2id=label2id)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mjJGEXShp7te"
   },
   "source": [
    "## Train the model!\n",
    "\n",
    "We are going to train the model using HuggingFace's Trainer API. This requires us to define 2 things:\n",
    "\n",
    "* `TrainingArguments`, which specify training hyperparameters. All options can be found in the [docs](https://huggingface.co/transformers/main_classes/trainer.html#trainingarguments). Below, we for example specify that we want to evaluate after every epoch of training, we would like to save the model every epoch, we set the learning rate, the batch size to use for training/evaluation, how many epochs to train for, and so on.\n",
    "* a `Trainer` object (docs can be found [here](https://huggingface.co/transformers/main_classes/trainer.html#id1))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "K5a8_vIKqr7P"
   },
   "outputs": [],
   "source": [
    "batch_size = 8\n",
    "num_train_epochs = 5\n",
    "metric_name = \"f1\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "dR2GmpvDqbuZ"
   },
   "outputs": [],
   "source": [
    "from transformers import TrainingArguments, Trainer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "CxIa-VyAQtlZ"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
      "To disable this warning, you can either:\n",
      "\t- Avoid using `tokenizers` before the fork if possible\n",
      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n",
      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
      "To disable this warning, you can either:\n",
      "\t- Avoid using `tokenizers` before the fork if possible\n",
      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n",
      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
      "To disable this warning, you can either:\n",
      "\t- Avoid using `tokenizers` before the fork if possible\n",
      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
     ]
    }
   ],
   "source": [
    "args = TrainingArguments(\n",
    "    f\"reecursion/roberta-finetuned-inspirational\",\n",
    "    evaluation_strategy = \"epoch\",\n",
    "    save_strategy = \"epoch\",\n",
    "    learning_rate=2e-5,\n",
    "    per_device_train_batch_size=batch_size,\n",
    "    per_device_eval_batch_size=batch_size,\n",
    "    num_train_epochs=num_train_epochs,\n",
    "    weight_decay=0.01,\n",
    "    load_best_model_at_end=True,\n",
    "    metric_for_best_model=metric_name,\n",
    "    push_to_hub=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1_v2fPFFJ3-v"
   },
   "source": [
    "We are also going to compute metrics while training. For this, we need to define a `compute_metrics` function, that returns a dictionary with the desired metric values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "label_names = ['UK', 'India', 'Real', 'Generated', 'Inspirational', 'Non-Inspirational']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "797b2WHJqUgZ"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import f1_score, roc_auc_score, accuracy_score\n",
    "from transformers import EvalPrediction\n",
    "import torch\n",
    "\n",
    "# source: https://jesusleal.io/2021/04/21/Longformer-multilabel-classification/\n",
    "def multi_label_metrics(predictions, labels, threshold=0.5):\n",
    "    # first, apply sigmoid on predictions which are of shape (batch_size, num_labels)\n",
    "    sigmoid = torch.nn.Sigmoid()\n",
    "    probs = sigmoid(torch.Tensor(predictions))\n",
    "    # next, use threshold to turn them into integer predictions\n",
    "    y_pred = np.zeros(probs.shape)\n",
    "    y_pred[np.where(probs >= threshold)] = 1\n",
    "    # finally, compute metrics\n",
    "    y_true = labels\n",
    "    f1_micro_average = f1_score(y_true=y_true, y_pred=y_pred, average='micro')\n",
    "    roc_auc = roc_auc_score(y_true, y_pred, average = 'micro')\n",
    "    accuracy = accuracy_score(y_true, y_pred)\n",
    "    \n",
    "    clf_report = classification_report(y_true, y_pred, target_names=label_names, digits=3, output_dict=True)\n",
    "    print(clf_report)\n",
    "    # return as dictionary\n",
    "    metrics = {'f1': f1_micro_average,\n",
    "               'roc_auc': roc_auc,\n",
    "               'accuracy': accuracy}\n",
    "    return metrics\n",
    "\n",
    "def compute_metrics(p: EvalPrediction):\n",
    "    preds = p.predictions[0] if isinstance(p.predictions,\n",
    "            tuple) else p.predictions\n",
    "    result = multi_label_metrics(\n",
    "        predictions=preds,\n",
    "        labels=p.label_ids)\n",
    "    return result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fxNo4_TsvzDm"
   },
   "source": [
    "Let's verify a batch as well as a forward pass:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "id": "IlOgGiojuWwG",
    "outputId": "ad565931-3f63-47f0-a61b-34bdcf99f10a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'torch.FloatTensor'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoded_dataset['train'][0]['labels'].type()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Y41Kre_jvD7x",
    "outputId": "f7c53d51-f59a-4ebb-96e1-9bf7651e24bd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([     0,    360,    903, 223920,   8999,      4,    442,     25,      7,\n",
       "            70,  90254,    136,   3332,   2104,   6620,    111, 190704,  79165,\n",
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       "             1,      1,      1,      1,      1,      1,      1,      1,      1,\n",
       "             1,      1,      1,      1])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoded_dataset['train']['input_ids'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "sxWcnZ8ku12V",
    "outputId": "992b7f99-64dc-4866-edd0-4210e97c0400"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "We strongly recommend passing in an `attention_mask` since your input_ids may be padded. See https://huggingface.co/docs/transformers/troubleshooting#incorrect-output-when-padding-tokens-arent-masked.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SequenceClassifierOutput(loss=tensor(0.6421, grad_fn=<BinaryCrossEntropyWithLogitsBackward0>), logits=tensor([[-0.2605,  0.0786,  0.2128, -0.1368,  0.2710, -0.4558]],\n",
       "       grad_fn=<AddmmBackward0>), hidden_states=None, attentions=None)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#forward pass\n",
    "outputs = model(input_ids=encoded_dataset['train']['input_ids'][0].unsqueeze(0), labels=encoded_dataset['train'][0]['labels'].unsqueeze(0))\n",
    "outputs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "f-X2brZcv0X6"
   },
   "source": [
    "Let's start training!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "chq_3nUz73ib",
    "outputId": "373a7d3c-69cc-466f-e0cb-2d49c4528c6e"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/oignat/.local/lib/python3.9/site-packages/accelerate/accelerator.py:436: FutureWarning: Passing the following arguments to `Accelerator` is deprecated and will be removed in version 1.0 of Accelerate: dict_keys(['dispatch_batches', 'split_batches', 'even_batches', 'use_seedable_sampler']). Please pass an `accelerate.DataLoaderConfiguration` instead: \n",
      "dataloader_config = DataLoaderConfiguration(dispatch_batches=None, split_batches=False, even_batches=True, use_seedable_sampler=True)\n",
      "  warnings.warn(\n",
      "Detected kernel version 4.18.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.\n"
     ]
    }
   ],
   "source": [
    "trainer = Trainer(\n",
    "    model,\n",
    "    args,\n",
    "    train_dataset=encoded_dataset[\"train\"],\n",
    "    eval_dataset=encoded_dataset[\"validation\"],\n",
    "    tokenizer=tokenizer,\n",
    "    compute_metrics=compute_metrics\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 345
    },
    "id": "KXmFds8js6P8",
    "outputId": "a1568f12-aca8-4773-dcb6-6c4ecdbadd3c"
   },
   "outputs": [
    {
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       "      \n",
       "      <progress value='3000' max='3000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [3000/3000 09:12, Epoch 5/5]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Epoch</th>\n",
       "      <th>Training Loss</th>\n",
       "      <th>Validation Loss</th>\n",
       "      <th>F1</th>\n",
       "      <th>Roc Auc</th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.305000</td>\n",
       "      <td>0.201673</td>\n",
       "      <td>0.913164</td>\n",
       "      <td>0.913194</td>\n",
       "      <td>0.748958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.146500</td>\n",
       "      <td>0.147387</td>\n",
       "      <td>0.947405</td>\n",
       "      <td>0.947396</td>\n",
       "      <td>0.847917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.088000</td>\n",
       "      <td>0.140641</td>\n",
       "      <td>0.957292</td>\n",
       "      <td>0.957292</td>\n",
       "      <td>0.882292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.058500</td>\n",
       "      <td>0.126463</td>\n",
       "      <td>0.965793</td>\n",
       "      <td>0.965799</td>\n",
       "      <td>0.904167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.024900</td>\n",
       "      <td>0.134681</td>\n",
       "      <td>0.967350</td>\n",
       "      <td>0.967361</td>\n",
       "      <td>0.907292</td>\n",
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      "{'UK': {'precision': 0.9457142857142857, 'recall': 0.6741344195519349, 'f1-score': 0.7871581450653983, 'support': 491}, 'India': {'precision': 0.7368421052631579, 'recall': 0.9552238805970149, 'f1-score': 0.8319405756731663, 'support': 469}, 'Real': {'precision': 1.0, 'recall': 0.9798136645962733, 'f1-score': 0.9898039215686275, 'support': 644}, 'Generated': {'precision': 0.9604863221884499, 'recall': 1.0, 'f1-score': 0.9798449612403101, 'support': 316}, 'Inspirational': {'precision': 0.9385307346326837, 'recall': 0.9750778816199377, 'f1-score': 0.9564553093964858, 'support': 642}, 'Non-Inspirational': {'precision': 0.9453924914675768, 'recall': 0.8710691823899371, 'f1-score': 0.90671031096563, 'support': 318}, 'micro avg': {'precision': 0.9134815844336345, 'recall': 0.9128472222222223, 'f1-score': 0.9131642931573463, 'support': 2880}, 'macro avg': {'precision': 0.9211609898776923, 'recall': 0.9092198381258497, 'f1-score': 0.9086522039849362, 'support': 2880}, 'weighted avg': {'precision': 0.9238228761758882, 'recall': 0.9128472222222223, 'f1-score': 0.9118464236040189, 'support': 2880}, 'samples avg': {'precision': 0.9138020833333333, 'recall': 0.9128472222222221, 'f1-score': 0.9131547619047619, 'support': 2880}}\n",
      "{'UK': {'precision': 0.82842287694974, 'recall': 0.9735234215885947, 'f1-score': 0.8951310861423221, 'support': 491}, 'India': {'precision': 0.9686684073107049, 'recall': 0.7910447761194029, 'f1-score': 0.8708920187793426, 'support': 469}, 'Real': {'precision': 0.9984375, 'recall': 0.9922360248447205, 'f1-score': 0.9953271028037384, 'support': 644}, 'Generated': {'precision': 0.984375, 'recall': 0.9968354430379747, 'f1-score': 0.9905660377358491, 'support': 316}, 'Inspirational': {'precision': 0.9676425269645609, 'recall': 0.9781931464174455, 'f1-score': 0.9728892331525949, 'support': 642}, 'Non-Inspirational': {'precision': 0.9551282051282052, 'recall': 0.9371069182389937, 'f1-score': 0.946031746031746, 'support': 318}, 'micro avg': {'precision': 0.9472405414786532, 'recall': 0.9475694444444445, 'f1-score': 0.9474049644158999, 'support': 2880}, 'macro avg': {'precision': 0.9504457527255351, 'recall': 0.944823288374522, 'f1-score': 0.9451395374409323, 'support': 2880}, 'weighted avg': {'precision': 0.9514148045670348, 'recall': 0.9475694444444445, 'f1-score': 0.9470139670679129, 'support': 2880}, 'samples avg': {'precision': 0.9474826388888888, 'recall': 0.9475694444444444, 'f1-score': 0.9474603174603174, 'support': 2880}}\n",
      "{'UK': {'precision': 0.8974854932301741, 'recall': 0.945010183299389, 'f1-score': 0.9206349206349207, 'support': 491}, 'India': {'precision': 0.9389140271493213, 'recall': 0.8848614072494669, 'f1-score': 0.9110867178924259, 'support': 469}, 'Real': {'precision': 0.9984423676012462, 'recall': 0.9953416149068323, 'f1-score': 0.9968895800933125, 'support': 644}, 'Generated': {'precision': 0.9905660377358491, 'recall': 0.9968354430379747, 'f1-score': 0.9936908517350158, 'support': 316}, 'Inspirational': {'precision': 0.9617151607963247, 'recall': 0.9781931464174455, 'f1-score': 0.9698841698841699, 'support': 642}, 'Non-Inspirational': {'precision': 0.9545454545454546, 'recall': 0.9245283018867925, 'f1-score': 0.939297124600639, 'support': 318}, 'micro avg': {'precision': 0.9572916666666667, 'recall': 0.9572916666666667, 'f1-score': 0.9572916666666667, 'support': 2880}, 'macro avg': {'precision': 0.9569447568430617, 'recall': 0.9541283494663167, 'f1-score': 0.955247227473414, 'support': 2880}, 'weighted avg': {'precision': 0.9576383320644003, 'recall': 0.9572916666666667, 'f1-score': 0.9571863674098238, 'support': 2880}, 'samples avg': {'precision': 0.9573784722222222, 'recall': 0.9572916666666667, 'f1-score': 0.9572817460317461, 'support': 2880}}\n",
      "{'UK': {'precision': 0.9364754098360656, 'recall': 0.9307535641547862, 'f1-score': 0.933605720122574, 'support': 491}, 'India': {'precision': 0.9279661016949152, 'recall': 0.9339019189765458, 'f1-score': 0.9309245483528161, 'support': 469}, 'Real': {'precision': 1.0, 'recall': 0.9937888198757764, 'f1-score': 0.9968847352024921, 'support': 644}, 'Generated': {'precision': 0.987460815047022, 'recall': 0.9968354430379747, 'f1-score': 0.9921259842519685, 'support': 316}, 'Inspirational': {'precision': 0.9766718506998445, 'recall': 0.9781931464174455, 'f1-score': 0.977431906614786, 'support': 642}, 'Non-Inspirational': {'precision': 0.9558359621451105, 'recall': 0.9528301886792453, 'f1-score': 0.9543307086614173, 'support': 318}, 'micro avg': {'precision': 0.9659604029176797, 'recall': 0.965625, 'f1-score': 0.9657926723389476, 'support': 2880}, 'macro avg': {'precision': 0.964068356570493, 'recall': 0.9643838468569622, 'f1-score': 0.964217267201009, 'support': 2880}, 'weighted avg': {'precision': 0.965986913052336, 'recall': 0.965625, 'f1-score': 0.9657979346016338, 'support': 2880}, 'samples avg': {'precision': 0.9659722222222222, 'recall': 0.965625, 'f1-score': 0.9657638888888889, 'support': 2880}}\n",
      "{'UK': {'precision': 0.9369918699186992, 'recall': 0.9389002036659878, 'f1-score': 0.9379450661241098, 'support': 491}, 'India': {'precision': 0.9357601713062098, 'recall': 0.9317697228144989, 'f1-score': 0.9337606837606836, 'support': 469}, 'Real': {'precision': 1.0, 'recall': 0.9922360248447205, 'f1-score': 0.9961028838659391, 'support': 644}, 'Generated': {'precision': 0.9875, 'recall': 1.0, 'f1-score': 0.9937106918238994, 'support': 316}, 'Inspirational': {'precision': 0.9811912225705329, 'recall': 0.9750778816199377, 'f1-score': 0.978125, 'support': 642}, 'Non-Inspirational': {'precision': 0.9503105590062112, 'recall': 0.9622641509433962, 'f1-score': 0.95625, 'support': 318}, 'micro avg': {'precision': 0.967685892981237, 'recall': 0.9670138888888888, 'f1-score': 0.9673497742271622, 'support': 2880}, 'macro avg': {'precision': 0.9652923038002754, 'recall': 0.9667079973147569, 'f1-score': 0.9659823875957719, 'support': 2880}, 'weighted avg': {'precision': 0.9677458510857468, 'recall': 0.9670138888888888, 'f1-score': 0.9673650604085818, 'support': 2880}, 'samples avg': {'precision': 0.9677083333333333, 'recall': 0.967013888888889, 'f1-score': 0.9672916666666667, 'support': 2880}}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "TrainOutput(global_step=3000, training_loss=0.11016682942708333, metrics={'train_runtime': 553.3883, 'train_samples_per_second': 43.36, 'train_steps_per_second': 5.421, 'total_flos': 3156788256721920.0, 'train_loss': 0.11016682942708333, 'epoch': 5.0})"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hiloh9eMK91o"
   },
   "source": [
    "## Evaluate\n",
    "\n",
    "After training, we evaluate our model on the validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 173
    },
    "id": "cMlebJ83LRYG",
    "outputId": "7d8c2090-52e6-450e-dd75-e832bf088f61"
   },
   "outputs": [
    {
     "data": {
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       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='30' max='30' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [30/30 00:01]\n",
       "    </div>\n",
       "    "
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     "metadata": {},
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    {
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     "text": [
      "{'UK': {'precision': 0.9576271186440678, 'recall': 0.9338842975206612, 'f1-score': 0.9456066945606695, 'support': 121}, 'India': {'precision': 0.9344262295081968, 'recall': 0.957983193277311, 'f1-score': 0.9460580912863071, 'support': 119}, 'Real': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 162}, 'Generated': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 78}, 'Inspirational': {'precision': 0.9625, 'recall': 0.9565217391304348, 'f1-score': 0.9595015576323987, 'support': 161}, 'Non-Inspirational': {'precision': 0.9125, 'recall': 0.9240506329113924, 'f1-score': 0.9182389937106918, 'support': 79}, 'micro avg': {'precision': 0.9638888888888889, 'recall': 0.9638888888888889, 'f1-score': 0.9638888888888889, 'support': 720}, 'macro avg': {'precision': 0.9611755580253774, 'recall': 0.9620733104733, 'f1-score': 0.9615675561983444, 'support': 720}, 'weighted avg': {'precision': 0.9640550037047327, 'recall': 0.9638888888888889, 'f1-score': 0.9639166030373227, 'support': 720}, 'samples avg': {'precision': 0.9638888888888888, 'recall': 0.9638888888888888, 'f1-score': 0.9638888888888888, 'support': 720}}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'eval_loss': 0.14738111197948456,\n",
       " 'eval_f1': 0.9638888888888889,\n",
       " 'eval_roc_auc': 0.963888888888889,\n",
       " 'eval_accuracy': 0.8916666666666667,\n",
       " 'eval_runtime': 1.2042,\n",
       " 'eval_samples_per_second': 199.301,\n",
       " 'eval_steps_per_second': 24.913,\n",
       " 'epoch': 5.0}"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# trainer.evaluate()\n",
    "trainer.evaluate(eval_dataset=encoded_dataset[\"test\"]) # evaluate on test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'reecursion/roberta-finetuned-inspirational'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_136319/3488449488.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mshutil\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mempty_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mshutil\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrmtree\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'reecursion/roberta-finetuned-inspirational'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# CHECKPOINTS might mess when trying to re-train\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/sw/pkgs/arc/python3.9-anaconda/2021.11/lib/python3.9/shutil.py\u001b[0m in \u001b[0;36mrmtree\u001b[0;34m(path, ignore_errors, onerror)\u001b[0m\n\u001b[1;32m    715\u001b[0m             \u001b[0morig_st\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlstat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    716\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 717\u001b[0;31m             \u001b[0monerror\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlstat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    718\u001b[0m             \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    719\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/sw/pkgs/arc/python3.9-anaconda/2021.11/lib/python3.9/shutil.py\u001b[0m in \u001b[0;36mrmtree\u001b[0;34m(path, ignore_errors, onerror)\u001b[0m\n\u001b[1;32m    713\u001b[0m         \u001b[0;31m# lstat()/open()/fstat() trick.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    714\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 715\u001b[0;31m             \u001b[0morig_st\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlstat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    716\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    717\u001b[0m             \u001b[0monerror\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlstat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'reecursion/roberta-finetuned-inspirational'"
     ]
    }
   ],
   "source": [
    "import shutil\n",
    "torch.cuda.empty_cache()\n",
    "shutil.rmtree('reecursion/roberta-finetuned-inspirational') # CHECKPOINTS might mess when trying to re-train"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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    {
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       "model_id": "29a82093b7dd4a1c94d160fef47e25dc",
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      },
      "text/plain": [
       "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "notebook_login()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "8Pm3hi8IohlY"
   },
   "outputs": [],
   "source": [
    "repository_id = 'reecursion/roberta-finetuned-inspirational'"
   ]
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    "id": "QYxlKKiIizKC",
    "outputId": "c547360e-8610-4d96-b351-4c4fbcf3bcf2"
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    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b09d41c8281c4343b848497d110bed40",
       "version_major": 2,
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      "text/plain": [
       "model.safetensors:   0%|          | 0.00/1.11G [00:00<?, ?B/s]"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
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     },
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     "output_type": "display_data"
    },
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     },
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    },
    {
     "data": {
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      "text/plain": [
       "Upload 4 LFS files:   0%|          | 0/4 [00:00<?, ?it/s]"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "060f25aefac4465ea6641a04c457ca80",
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      "text/plain": [
       "training_args.bin:   0%|          | 0.00/4.98k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "CommitInfo(commit_url='https://huggingface.co/reecursion/roberta-finetuned-inspirational/commit/feb25227ae1a1196280450c6064aa0289a7d3488', commit_message='End of training', commit_description='', oid='feb25227ae1a1196280450c6064aa0289a7d3488', pr_url=None, pr_revision=None, pr_num=None)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Save our tokenizer and create a model card\n",
    "model.save_pretrained(repository_id)\n",
    "tokenizer.save_pretrained(repository_id)\n",
    "trainer.create_model_card()\n",
    "# Push the results to the hub\n",
    "trainer.push_to_hub()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3nmvJp0pLq-3"
   },
   "source": [
    "## Inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "3fxjfr8PLD42"
   },
   "outputs": [],
   "source": [
    "text = \"sample text\"\n",
    "\n",
    "encoding = tokenizer(text, return_tensors=\"pt\")\n",
    "encoding = {k: v.to(trainer.model.device) for k,v in encoding.items()}\n",
    "\n",
    "outputs = trainer.model(**encoding)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8THm5-XgNHPm"
   },
   "source": [
    "The logits that come out of the model are of shape (batch_size, num_labels). As we are only forwarding a single sentence through the model, the `batch_size` equals 1. The logits is a tensor that contains the (unnormalized) scores for every individual label."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "KOBosj4UL2tU",
    "outputId": "04ac89e9-c7ee-49fd-9309-930ba95b2ff3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 4])"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logits = outputs.logits\n",
    "logits.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "REXtwdiipYCv",
    "outputId": "71b0f298-e02b-49ce-fdeb-9af2321f4d62"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[-3.9328,  4.0487,  8.0196, -6.3656]], device='cuda:0',\n",
       "       grad_fn=<AddmmBackward0>)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DC4XdDaHNVcd"
   },
   "source": [
    "To turn them into actual predicted labels, we first apply a sigmoid function independently to every score, such that every score is turned into a number between 0 and 1, that can be interpreted as a \"probability\" for how certain the model is that a given class belongs to the input text.\n",
    "\n",
    "Next, we use a threshold (typically, 0.5) to turn every probability into either a 1 (which means, we predict the label for the given example) or a 0 (which means, we don't predict the label for the given example)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "mEkAQleMMT0k",
    "outputId": "a332c273-5103-45da-b014-79241952924f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['India', 'Real']\n"
     ]
    }
   ],
   "source": [
    "# apply sigmoid + threshold\n",
    "sigmoid = torch.nn.Sigmoid()\n",
    "probs = sigmoid(logits.squeeze().cpu())\n",
    "predictions = np.zeros(probs.shape)\n",
    "predictions[np.where(probs >= 0.5)] = 1\n",
    "# turn predicted id's into actual label names\n",
    "predicted_labels = [id2label[idx] for idx, label in enumerate(predictions) if label == 1.0]\n",
    "print(predicted_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# #default\n",
    "clf_report = {'UK': {'precision': 0.9652173913043478 * 100, 'recall': 0.9173553719008265* 100, 'f1-score': 0.940677966101695* 100, 'support': 121}, \n",
    " 'India': {'precision': 0.92* 100, 'recall': 0.9663865546218487* 100, 'f1-score': 0.9426229508196722* 100, 'support': 119}, \n",
    " 'Real': {'precision': 1.0* 100, 'recall': 1.0* 100, 'f1-score': 1.0* 100, 'support': 162}, \n",
    " 'Generated': {'precision': 1.0* 100, 'recall': 1.0* 100, 'f1-score': 1.0* 100, 'support': 78}, \n",
    " 'Inspirational': {'precision': 0.9746835443037974* 100, 'recall': 0.9565217391304348* 100, 'f1-score': 0.9655172413793103* 100, 'support': 161}, \n",
    " 'Non-Inspirational': {'precision': 0.9146341463414634* 100, 'recall': 0.9493670886075949* 100, 'f1-score': 0.9316770186335404* 100, 'support': 79}, \n",
    " 'micro avg': {'precision': 0.9652777777777778* 100, 'recall': 0.9652777777777778* 100, 'f1-score': 0.9652777777777778* 100, 'support': 720}, \n",
    " 'macro avg': {'precision': 0.9624225136582681* 100, 'recall': 0.9649384590434508* 100, 'f1-score': 0.9634158628223696* 100, 'support': 720}\n",
    "}\n",
    "\n",
    "# #few-shot\n",
    "clf_report = {'UK': {'precision': 0.8424474187380497*100, 'recall': 0.9043513957307061*100, 'f1-score': 0.8723025143535934*100, 'support': 2436},\n",
    "'India': {'precision': 0.8928571428571429*100, 'recall': 0.8353960396039604*100, 'f1-score': 0.8631713554987213*100, 'support': 2424}, \n",
    "'Real': {'precision': 0.9962417788913248*100, 'recall': 0.9778665846910544*100, 'f1-score': 0.9869686627365809*100, 'support': 3253}, \n",
    "'Generated': {'precision': 0.9449052132701422*100, 'recall': 0.9925326695706285*100, 'f1-score': 0.968133535660091*100, 'support': 1607}, \n",
    "'Inspirational': {'precision': 0.9051932367149759*100, 'recall': 0.9244526672833796*100, 'f1-score': 0.9147215865751335*100, 'support': 3243}, \n",
    "'Non-Inspirational': {'precision': 0.8414322250639387*100, 'recall': 0.8138528138528138*100, 'f1-score': 0.8274127632819868*100, 'support': 1617}, \n",
    "'micro avg': {'precision': 0.9096994535519126*100, 'recall': 0.9134430727023319*100, 'f1-score': 0.9115674195756333*100, 'support': 14580}, \n",
    "'macro avg': {'precision': 0.903846169255929*100, 'recall': 0.908075361788757*100, 'f1-score': 0.9054517363510178*100, 'support': 14580}\n",
    "}\n",
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    "                 cmap=sns.cubehelix_palette(as_cmap=True))\n",
    "ax.xaxis.tick_top()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('data/default.png', bbox_inches='tight')\n",
    "# plt.savefig('data/fewshot.png', bbox_inches='tight')"
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