notebook-train-bert-sentiment-analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "080c2c09-037b-4aff-b83a-f5ef5e81f0e1",
   "metadata": {},
   "source": [
    "# TUTORIAL: Fine-Tune BERT model for sentiment analysis with AI notebooks\n",
    "\n",
    "The aim is to fine-tune a **BERT** model for sentiment analysis on e-commerce site reviews.\n",
    "\n",
    "![](./assets/sentiment-analysis-bert.png)\n",
    "\n",
    "- **What is a BERT model?**\n",
    "\n",
    "`BERT` (Bidirectional Encoder Representations from Transformers) is a model that represents an encoder block of a transformer. It is pre-trained with a huge corpus of unlabeled text. The power of BERT is that the same pre-trained model can be used, simply by adding an additional output layer, in a variety of tasks like sentiment analysis\n",
    "\n",
    "### Code:\n",
    "- Install dependencies\n",
    "- Import Python librairies\n",
    "- Check GPU availability\n",
    "- Load dataset from Hugging Face hub\n",
    "- EDA\n",
    "- Data Cleaning\n",
    "- Select relevant features\n",
    "- Feature Engineering\n",
    "- Fine-Tune BERT model for sentiment analysis\n",
    "- Test the trained model\n",
    "\n",
    "## Step 1 - Install dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "54481140-83fc-4b94-9452-2126ae6ab64a",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install datasets scikit-learn nltk transformers matplotlib torch"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4422ad54-dd4c-463c-9ee0-2009cdba00fc",
   "metadata": {},
   "source": [
    "## Step 2 - Import Python librairies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "89ac76a5-c90e-4067-b329-f47ad01efdcb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# basic python librairies\n",
    "import re\n",
    "import os\n",
    "import json\n",
    "import string\n",
    "import time\n",
    "import random\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# data loading\n",
    "from datasets import load_dataset\n",
    "\n",
    "# eda and data processing\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem.snowball import SnowballStemmer\n",
    "from collections import Counter\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "# bert fine-tuning\n",
    "from transformers import BertTokenizer, BertForSequenceClassification, AdamW, get_linear_schedule_with_warmup\n",
    "\n",
    "# metrics evaluation\n",
    "from sklearn.metrics import f1_score\n",
    "\n",
    "# device setting and data loaders \n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.utils.data import DataLoader, TensorDataset, RandomSampler, SequentialSampler"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b55ffd48-b165-47d9-8583-1a13267660ff",
   "metadata": {},
   "source": [
    "## Step 3 - Check GPU availability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "43784834-5446-4330-b200-576e8da18540",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using device: cuda\n"
     ]
    }
   ],
   "source": [
    "# check device\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(f\"Using device: {device}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8b1aca4-223c-4a60-bdd2-b6ec6afcb951",
   "metadata": {},
   "source": [
    "## Step 4 - Load dataset from Hugging Face hub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "38073e3c-cdc4-45df-a2fb-9a623f90b7f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load the dataset of womens clothing e-commerce reviews \n",
    "dataset_reviews = load_dataset(\"saattrupdan/womens-clothing-ecommerce-reviews\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "80fd606a-3026-49a1-bf48-16d974d0ff13",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define training, validation and test sets\n",
    "df_train = pd.DataFrame(dataset_reviews['train'])\n",
    "df_val = pd.DataFrame(dataset_reviews['val'])\n",
    "df_test = pd.DataFrame(dataset_reviews['test'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "387a0ae0-d3c4-4753-89d4-b432304e83d8",
   "metadata": {},
   "source": [
    "## Step 5 - EDA\n",
    "\n",
    "- **Check dataset balance**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3aa667c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Classes distribution (from 1 to 5 stars)')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# display the rating repartition\n",
    "labels_occurence = Counter(dataset_reviews['train']['rating'])\n",
    "plt.pie(labels_occurence.values(), labels=labels_occurence)\n",
    "plt.title(\"Classes distribution (from 1 to 5 stars)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "387430a3-41ba-480e-a880-0479f6daabae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Product reviews distribution')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# display the products repartition\n",
    "product_occurence = Counter(dataset_reviews['train']['class_name'])\n",
    "plt.pie(product_occurence.values(), labels=product_occurence)\n",
    "plt.title(\"Product reviews distribution\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6683ce0e-da5d-458f-91e2-a295e2a5ed2b",
   "metadata": {},
   "source": [
    "- **Estimate review length**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f836dadd-cb3c-44a4-8e4c-75065c65f388",
   "metadata": {},
   "outputs": [],
   "source": [
    "# review length\n",
    "lens = [len(i.split()) for i in dataset_reviews['train']['review_text']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cb7c5607-452b-49b5-8162-57300a90cbc2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# display histogramm\n",
    "plt.hist(lens, bins=30, range=[0, 150])\n",
    "plt.title(\"Breakdown of review lengths\")\n",
    "plt.xlabel(\"Number of words\")\n",
    "plt.ylabel(\"Count\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46ba0710-34f7-45f9-8e22-d90ae00d1e93",
   "metadata": {},
   "source": [
    "- **Calculate word frequency**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ff0f92b6-ffe3-4738-94ad-1abd5b099b84",
   "metadata": {},
   "outputs": [],
   "source": [
    "# CountVectorizer object\n",
    "vectorizer = CountVectorizer()\n",
    "\n",
    "# fit_transform() method\n",
    "Xtrain = vectorizer.fit_transform(dataset_reviews['train']['review_text'])\n",
    "\n",
    "# list of terms \n",
    "words = vectorizer.get_feature_names_out()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1051f232-49e2-4e65-b21e-988d888d8fc3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of different terms in dictionary: 13594\n"
     ]
    }
   ],
   "source": [
    "# number of terms \n",
    "print(\"Number of different terms in dictionary:\", len(vectorizer.get_feature_names_out()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ff5cbcf-7cc0-420f-9e7f-26dc4e64a54c",
   "metadata": {},
   "source": [
    "With the resulting matrix, calculate the frequency of the terms by summing each row of the matrix.\n",
    "\n",
    "`frequency of term 1 = sum(row 1)`\n",
    "\n",
    "...\n",
    "\n",
    "`frequency of term n = sum(row n)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d8021f84-c5e9-4ea8-b909-f41402f0245f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# display terms frequence\n",
    "Xtrain = Xtrain.toarray()\n",
    "freq = np.sum(Xtrain, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3236bf03-20ae-46b6-9c81-0a56c350cdb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# transform words into a dataframe\n",
    "df_words = pd.DataFrame(words)\n",
    "\n",
    "# transform frequences into a dataframe\n",
    "df_freq = pd.DataFrame(freq)\n",
    "\n",
    "# create the term-frequence dataframe\n",
    "df_term_freq = pd.concat([df_words, df_freq], axis=1)\n",
    "df_term_freq.columns = ['term', 'frequence']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7d05f129-dc2b-402b-857f-089745d1b079",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create list for frequences > 1\n",
    "freq_sup_one = []\n",
    "term = []\n",
    "\n",
    "# create a filter to save only frequences > 1\n",
    "for i in range(len(df_term_freq)): \n",
    "    if df_term_freq['frequence'][i]>1:\n",
    "        freq_sup_one.append(df_term_freq['frequence'][i])\n",
    "        term.append(df_term_freq['term'][i])\n",
    "\n",
    "# transform lists into a dataframe\n",
    "df_freq_sup_one = pd.DataFrame(freq_sup_one)\n",
    "df_term = pd.DataFrame(term)\n",
    "\n",
    "df_term_frequent = pd.concat([df_term, df_freq_sup_one], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b81049b2-185a-4489-a28d-326f14538a5e",
   "metadata": {},
   "source": [
    "## Step 6 - Data Cleaning\n",
    "\n",
    "Remove stop words, url, emojis, low term frequencies...\n",
    "\n",
    "- **Clean the reviews**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d2d9a479-7b0d-4516-8aa8-757c6fcd4bc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define data cleaning function\n",
    "def clean_text(text):\n",
    "    \n",
    "    # Remove special characters and numbers\n",
    "    text = re.sub(r'[^A-Za-zÀ-ú ]+', '', text)\n",
    "    # Analyzing the most used words below, i chose to exclude these because there are too many and are unnecessary\n",
    "    text = re.sub('book|one', '', text)\n",
    "    # Convert to lower case\n",
    "    text = text.lower()\n",
    "    # remove scores\n",
    "    text = text.translate(str.maketrans('', '', string.punctuation))\n",
    "    # Remove extra whitespace\n",
    "    text = re.sub(r'\\s+', ' ', text).strip()\n",
    "    return text\n",
    "\n",
    "# apply the function\n",
    "df_train['review_text'] = df_train['review_text'].apply(clean_text)\n",
    "df_val['review_text'] = df_val['review_text'].apply(clean_text)\n",
    "df_test['review_text'] = df_test['review_text'].apply(clean_text)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14bce964-fcd4-49a5-a54a-a223eee9b507",
   "metadata": {},
   "source": [
    "- **Remove stop words**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "69578350-4160-4d42-a1d9-b9b52d4adb53",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to /workspace/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n",
      "[nltk_data] Downloading package punkt to /workspace/nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# download stopwords list\n",
    "nltk.download('stopwords')\n",
    "nltk.download('punkt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "16047032-a053-48d3-918d-ca1807a5654f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define remove stopwords function\n",
    "def remove_stopwords(texto):\n",
    "    stop_words = set(stopwords.words('english'))\n",
    "    tokens = nltk.word_tokenize(texto.lower())\n",
    "    return \" \".join([token for token in tokens if token not in stop_words])\n",
    "\n",
    "# apply the function\n",
    "df_train['review_text'] = df_train['review_text'].apply(remove_stopwords)\n",
    "df_val['review_text'] = df_val['review_text'].apply(remove_stopwords)\n",
    "df_test['review_text'] = df_test['review_text'].apply(remove_stopwords)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97b72121-099c-44cd-a762-8a18814386e8",
   "metadata": {},
   "source": [
    "- **Normalize text**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "924fec8f-ba3c-4252-b232-a77426296c8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define text normalization function\n",
    "def normalize_text(text):\n",
    "    stemmer = SnowballStemmer(\"english\")\n",
    "    normalized_text = []\n",
    "    for word in text.split():\n",
    "        stemmed_word = stemmer.stem(word)\n",
    "        normalized_text.append(stemmed_word)\n",
    "    return ' '.join(normalized_text)\n",
    "    \n",
    "# apply the function\n",
    "df_train['review_text'] = df_train['review_text'].apply(normalize_text)\n",
    "df_val['review_text'] = df_val['review_text'].apply(normalize_text)\n",
    "df_test['review_text'] = df_test['review_text'].apply(normalize_text)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f80d932d-9e70-4ca0-8711-a62567956933",
   "metadata": {},
   "source": [
    "## Step 7 - Select relevant features\n",
    "\n",
    "- **Remove the correlated and non pertinent data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8a1eddfa-a71f-4970-83b1-792d2b7381e1",
   "metadata": {},
   "outputs": [
    {
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       "      <th>20640</th>\n",
       "      <td>pilcro pant alway posit purchas qualiti fabric...</td>\n",
       "      <td>5</td>\n",
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       "<p>20641 rows × 2 columns</p>\n",
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       "                                             review_text  rating\n",
       "0      love shirt first time wash shrunk much becam u...       1\n",
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       "2      fell love bottom first site littl worri would ...       5\n",
       "3      love dress purchas dress wear hymoon pari made...       5\n",
       "4      fell love dress saw onlin got soon went sale c...       5\n",
       "...                                                  ...     ...\n",
       "20636  realli like jean initi end day waist realli ba...       3\n",
       "20637  top soo pretti cool edgeit look feel like real...       5\n",
       "20638  absolut love top im alway either xsmall mauv t...       5\n",
       "20639  tts bought usual pilcro size enjoy fit better ...       4\n",
       "20640  pilcro pant alway posit purchas qualiti fabric...       5\n",
       "\n",
       "[20641 rows x 2 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# training data\n",
    "df_train = df_train.drop(['age', 'positive_feedback_count', 'division_name', 'department_name', 'class_name', 'recommended_ind'], axis=1)\n",
    "df_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "12162163-4d40-41bd-b575-776e8f7342d0",
   "metadata": {},
   "outputs": [
    {
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       "      <td>2</td>\n",
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       "      <td>5</td>\n",
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       "<p>1000 rows × 2 columns</p>\n",
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       "                                           review_text  rating\n",
       "0    shed cloth someth fierc much dont want return ...       4\n",
       "1    bought dress wear lowkey backyard rehears dinn...       3\n",
       "2    soft fit without tight flatter necklin nice em...       5\n",
       "3    came across store immedi fell love onlin photo...       5\n",
       "4    saw store got green differ typic xs dress some...       4\n",
       "..                                                 ...     ...\n",
       "995  dress beauti uniqu cutout lace realli stand ho...       2\n",
       "996  great jacket love hoodi insert remov wear oute...       5\n",
       "997  bought green motif color beauti look like pict...       4\n",
       "998  fell love dress soon came across onlin order d...       4\n",
       "999  like review said lot bulki fabric wear pictur ...       5\n",
       "\n",
       "[1000 rows x 2 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# validation data\n",
    "df_val = df_val.drop(['age', 'positive_feedback_count', 'division_name', 'department_name', 'class_name', 'recommended_ind'], axis=1)\n",
    "df_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7c24f6eb-75f2-49bb-a6f8-28e5a04a9644",
   "metadata": {},
   "outputs": [
    {
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       "      <th>1</th>\n",
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       "      <th>2</th>\n",
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       "      <td>immedi drawn dress saw store im normal size fe...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>dress beauti pretti look pictur prettier great...</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>im pleas purchas dress perfect danc springsumm...</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>hourglass figur dress accentu curv need wear t...</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
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       "      <td>cute waffl tshirt materi thin seethrough cute ...</td>\n",
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       "    <tr>\n",
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       "      <td>top pictur im feet pound small petit great fit...</td>\n",
       "      <td>5</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 2 columns</p>\n",
       "</div>"
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      "text/plain": [
       "                                           review_text  rating\n",
       "0    im usual size l retail tri button bottom excit...       3\n",
       "1       order blue top great hot summer southeast year       4\n",
       "2    seem pretti shapeless surpris design perhap bi...       1\n",
       "3    saw store immedi love even tri style classic f...       5\n",
       "4    immedi drawn dress saw store im normal size fe...       4\n",
       "..                                                 ...     ...\n",
       "995  dress beauti pretti look pictur prettier great...       5\n",
       "996  im pleas purchas dress perfect danc springsumm...       5\n",
       "997  hourglass figur dress accentu curv need wear t...       5\n",
       "998  cute waffl tshirt materi thin seethrough cute ...       4\n",
       "999  top pictur im feet pound small petit great fit...       5\n",
       "\n",
       "[1000 rows x 2 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# test data\n",
    "df_test = df_test.drop(['age', 'positive_feedback_count', 'division_name', 'department_name', 'class_name', 'recommended_ind'], axis=1)\n",
    "df_test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "258ab29f-2aeb-4f31-9dcc-f793548f87f6",
   "metadata": {},
   "source": [
    "## Step 8 - Feature Engineering\n",
    "\n",
    "- **Encode labels**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "70b4196f-2c9b-48f5-9248-b1813cb392b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 3, 5, ..., 5, 4, 5])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# y_train before encoding\n",
    "df_train['rating'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2893bdb5-30d4-459e-bdcf-cf46fc858158",
   "metadata": {},
   "outputs": [],
   "source": [
    "# encode 1,2,3,4,5 -> 0,1,2,3,4\n",
    "encoder = LabelEncoder()\n",
    "\n",
    "# training data\n",
    "X_train = df_train['review_text']\n",
    "y_train = encoder.fit_transform(df_train['rating'])\n",
    " \n",
    "# validation data\n",
    "X_val = df_val['review_text']\n",
    "y_val = encoder.transform(df_val['rating'])\n",
    "    \n",
    "# test data\n",
    "X_test = df_test['review_text']\n",
    "y_test = encoder.transform(df_test['rating'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1f5f4069-5c23-4695-ba04-6824be399fd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 4, ..., 4, 3, 4])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# y_train encoded\n",
    "y_train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51b1fe9e-e866-4db9-b09b-3b4bd4163fc1",
   "metadata": {},
   "source": [
    "## Step 9 - Fine-Tune BERT model for sentiment analysis\n",
    "\n",
    "- **Define the max length**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4d12c0f7-f7b3-41b1-9ffa-4931fe779c30",
   "metadata": {},
   "outputs": [],
   "source": [
    "# set it to 120\n",
    "pad_len=120"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de2985f7-9414-4348-a7dd-75d1f78b7eb5",
   "metadata": {},
   "source": [
    "- **Load tokenizer and encode data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f0679fce-1f6d-436a-8b4e-b707f278d60b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define bert tokenizer\n",
    "tokenizer = BertTokenizer.from_pretrained(\n",
    "    'bert-base-uncased',\n",
    "    do_lower_case=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "50b3b7fd-6695-4ccf-96a3-b802273e3030",
   "metadata": {},
   "outputs": [],
   "source": [
    "# tokenize and encode the sentences\n",
    "X_train_encoded = tokenizer.batch_encode_plus(X_train.tolist(),\n",
    "                                              padding=True,\n",
    "                                              truncation=True,\n",
    "                                              max_length = pad_len,\n",
    "                                              return_tensors='pt')\n",
    " \n",
    "X_val_encoded = tokenizer.batch_encode_plus(X_val.tolist(),\n",
    "                                              padding=True,\n",
    "                                              truncation=True,\n",
    "                                              max_length = pad_len,\n",
    "                                              return_tensors='pt')\n",
    "\n",
    "X_test_encoded = tokenizer.batch_encode_plus(X_test.tolist(),\n",
    "                                              padding=True,\n",
    "                                              truncation=True,\n",
    "                                              max_length = pad_len,\n",
    "                                              return_tensors='pt')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0515e02-e688-4a3f-b1c7-4a4de41f56da",
   "metadata": {},
   "source": [
    "- **Transform data to Tensor**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "bf973886-1a03-44c6-bf61-996f91600725",
   "metadata": {},
   "outputs": [],
   "source": [
    "# train dataset\n",
    "dataset_train = TensorDataset(X_train_encoded['input_ids'], \n",
    "                              X_train_encoded['attention_mask'],\n",
    "                              torch.tensor(y_train))\n",
    "\n",
    "# val dataset\n",
    "dataset_val = TensorDataset(X_val_encoded['input_ids'], \n",
    "                            X_val_encoded['attention_mask'],\n",
    "                           torch.tensor(y_val))\n",
    "\n",
    "# test dataset\n",
    "dataset_test = TensorDataset(X_test_encoded['input_ids'], \n",
    "                            X_test_encoded['attention_mask'],\n",
    "                           torch.tensor(y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e944b10a-02b9-4f86-aacc-a064eaadeab0",
   "metadata": {},
   "source": [
    "- **Load Bert model for sequence classification**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d7bac99d-8dd6-4212-9f5f-b7ab25a7a3cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.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": [
    "# load model\n",
    "model = BertForSequenceClassification.from_pretrained(\"bert-base-uncased\",\n",
    "                                                      num_labels=len(np.unique(y_train)),\n",
    "                                                      output_attentions=False,\n",
    "                                                      output_hidden_states=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f443436d-13b7-4b16-87a4-a240d3aa00af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda\n"
     ]
    }
   ],
   "source": [
    "# train model with GPUs\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "model.to(device)\n",
    "print(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fede56f8-9c66-411b-b908-5c9e2e71cad7",
   "metadata": {},
   "source": [
    "- **Create dataloaders**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d105b09b-bc54-4801-94c5-18058f5b483e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# batch size\n",
    "batch_size = 32\n",
    "\n",
    "# training set\n",
    "dataloader_train = DataLoader(\n",
    "    dataset_train,\n",
    "    sampler=RandomSampler(dataset_train),\n",
    "    batch_size=batch_size\n",
    ")\n",
    "\n",
    "# validation set\n",
    "dataloader_val = DataLoader(\n",
    "    dataset_val,\n",
    "    sampler=SequentialSampler(dataset_val),\n",
    "    batch_size=batch_size\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "caaf96d3-b0a3-49f3-88d4-de03ab80aa69",
   "metadata": {},
   "source": [
    "- **Create a dict for the labels**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a538cbd0-93d7-4d3b-a31c-e61fffbfb4ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_items([('1', 0), ('2', 1), ('3', 2), ('4', 3), ('5', 4)])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# key: basic rating (from 1 to 5 stars)\n",
    "# value: encoded label\n",
    "label_dict = {'1':0, '2':1, '3':2, '4':3, '5':4}\n",
    "label_dict.items()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97862a5d-e34b-4868-b678-e97ad68fd7f7",
   "metadata": {},
   "source": [
    "- **Set up model optimizer and scheduler**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f6135cf9-2301-4230-b0f1-ac12d452bf75",
   "metadata": {},
   "outputs": [],
   "source": [
    "# optimizer\n",
    "optimizer = AdamW(model.parameters(), lr = 2e-5, eps = 1e-8)\n",
    "\n",
    "# number of steps\n",
    "epochs = 5\n",
    "total_steps = len(dataloader_train) * epochs\n",
    "\n",
    "# scheduler\n",
    "scheduler = get_linear_schedule_with_warmup(\n",
    "    optimizer,\n",
    "    num_warmup_steps = 0,\n",
    "    num_training_steps = total_steps\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19c6457c-1492-46e8-9a50-0c808502d974",
   "metadata": {},
   "source": [
    "- **Create accuracy function**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "101c8c63-124b-45d2-9b4f-a43761490964",
   "metadata": {},
   "outputs": [],
   "source": [
    "def flat_accuracy(preds, labels):\n",
    "    pred_flat = np.argmax(preds, axis=1).flatten()\n",
    "    labels_flat = labels.flatten()\n",
    "    return np.sum(pred_flat == labels_flat) / len(labels_flat)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54194948-745a-425f-84a9-8e9fcb11728c",
   "metadata": {},
   "source": [
    "- **Define model evaluation function**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e7752cfd-03ff-43d1-9ffc-b6652d64c52d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate(dataloader):\n",
    "\n",
    "    model.eval()\n",
    "\n",
    "    total_loss = 0\n",
    "    total_accuracy = 0\n",
    "\n",
    "    for batch in dataloader:\n",
    "\n",
    "        batch = tuple(b.to(device) for b in batch)\n",
    "\n",
    "        inputs = {'input_ids':      batch[0],\n",
    "                  'attention_mask': batch[1],\n",
    "                  'labels':         batch[2],\n",
    "                 }\n",
    "\n",
    "        with torch.no_grad():        \n",
    "\n",
    "            outputs = model(**inputs) \n",
    "\n",
    "            loss = outputs[0]\n",
    "            logits = outputs[1]\n",
    "\n",
    "            total_loss += loss.item()\n",
    "\n",
    "            logits = logits.detach().cpu().numpy()\n",
    "            label_ids = inputs['labels'].cpu().numpy()\n",
    "            total_accuracy += flat_accuracy(logits, label_ids)\n",
    "\n",
    "    avg_loss = total_loss / len(dataloader)       \n",
    "    avg_accuracy = total_accuracy / len(dataloader)\n",
    "\n",
    "    return avg_loss, avg_accuracy\n",
    "\n",
    "seed_val = 42\n",
    "\n",
    "random.seed(seed_val)\n",
    "np.random.seed(seed_val)\n",
    "torch.manual_seed(seed_val)\n",
    "torch.cuda.manual_seed_all(seed_val)\n",
    "\n",
    "training_stats = []"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "923cac1b-ad38-4d89-9a6d-3850306d2216",
   "metadata": {},
   "source": [
    "- **Launch model fine-tuning on 5 epochs**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "73289459-f7c9-4e7b-abbc-b90ee065fdc2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fine Tuning of BERT model is starting...\n",
      "\n",
      "Epoch: 1/5\n",
      "Training Loss: 0.9787735664069468\n",
      "Training Accuracy: 0.6750193498452013\n",
      "Validation Loss: 0.8087834380567074\n",
      "Validation Accuracy: 0.6484375\n",
      "\n",
      "\n",
      "Epoch: 2/5\n",
      "Training Loss: 0.8105400662188744\n",
      "Training Accuracy: 0.7261029411764706\n",
      "Validation Loss: 0.8035906106233597\n",
      "Validation Accuracy: 0.6572265625\n",
      "\n",
      "\n",
      "Epoch: 3/5\n",
      "Training Loss: 0.7036994074704847\n",
      "Training Accuracy: 0.7864260835913313\n",
      "Validation Loss: 0.8122159857302904\n",
      "Validation Accuracy: 0.658203125\n",
      "\n",
      "\n",
      "Epoch: 4/5\n",
      "Training Loss: 0.6027057669111081\n",
      "Training Accuracy: 0.834703947368421\n",
      "Validation Loss: 0.860997449606657\n",
      "Validation Accuracy: 0.6416015625\n",
      "\n",
      "\n",
      "Epoch: 5/5\n",
      "Training Loss: 0.5268889718772641\n",
      "Training Accuracy: 0.8518769349845201\n",
      "Validation Loss: 0.9054699223488569\n",
      "Validation Accuracy: 0.64453125\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Fine Tuning of BERT model is starting...\")\n",
    "\n",
    "for epoch_i in range(0, epochs):\n",
    "    \n",
    "    total_train_loss = 0\n",
    "\n",
    "    model.train()\n",
    "\n",
    "    for step, batch in enumerate(dataloader_train):\n",
    "\n",
    "        batch = tuple(b.to(device) for b in batch)\n",
    "\n",
    "        inputs = {'input_ids':      batch[0],\n",
    "                  'attention_mask': batch[1],\n",
    "                  'labels':         batch[2],\n",
    "                 }       \n",
    "\n",
    "        outputs = model(**inputs)\n",
    "\n",
    "        loss = outputs[0]\n",
    "\n",
    "        total_train_loss += loss.item()\n",
    "\n",
    "        loss.backward()\n",
    "\n",
    "        torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n",
    "\n",
    "        optimizer.step()\n",
    "\n",
    "        scheduler.step()\n",
    "\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "    avg_train_loss = total_train_loss / len(dataloader_train)            \n",
    "\n",
    "    training_loss, training_accuracy = evaluate(dataloader_train)\n",
    "    validation_loss, validation_accuracy = evaluate(dataloader_val)\n",
    "\n",
    "    print(\"\")\n",
    "    print(\"Epoch: {}/{}\".format(epoch_i + 1, epochs))\n",
    "    print(\"Training Loss: {}\".format(avg_train_loss))\n",
    "    print(\"Training Accuracy: {}\".format(training_accuracy))\n",
    "    print(\"Validation Loss: {}\".format(validation_loss))\n",
    "    print(\"Validation Accuracy: {}\".format(validation_accuracy))\n",
    "    print(\"\")\n",
    "\n",
    "    training_stats.append(\n",
    "        {\n",
    "            'epoch': epoch_i + 1,\n",
    "            'Training Loss': avg_train_loss,\n",
    "            'Training Accuracy': training_accuracy,\n",
    "            'Validation Loss': validation_loss,\n",
    "            'Validation Accuracy': validation_accuracy\n",
    "        }\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82f4cac3-1c16-445e-a9c3-feeae5d6f06b",
   "metadata": {},
   "source": [
    "## Step 10 - Test the trained model\n",
    "\n",
    "Now that the model is trained, test it on the **test dataset**: `X_test`,`y_test`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b10c8cb3-7813-49c3-a905-1e3645d7b98a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# sentiment analysis function for BERT model on test dataset\n",
    "def bert_sentiment(dataset_test, y_test):\n",
    "    \n",
    "    # encode dataset\n",
    "    test_encoded = tokenizer.batch_encode_plus(dataset_test.tolist(),\n",
    "                                              padding=True,\n",
    "                                              truncation=True,\n",
    "                                              max_length = pad_len,\n",
    "                                              return_tensors='pt')\n",
    "    \n",
    "    # inputs and label\n",
    "    test_seq = test_encoded['input_ids'].to(device)\n",
    "    attention_mask = test_encoded['attention_mask'].to(device)\n",
    "    test_y = torch.tensor(y_test)\n",
    "\n",
    "    # get predictions for test data\n",
    "    with torch.no_grad():\n",
    "        preds = model(test_seq.to(device), attention_mask.to(device))\n",
    "        preds = preds[0].detach().cpu().numpy()\n",
    "    \n",
    "    # get the labels\n",
    "    pred = np.argmax(preds, axis = 1)\n",
    "    \n",
    "    # save it as a dataframe\n",
    "    result_model_bert = pd.DataFrame(pred, columns = ['rate_bert'])\n",
    "    \n",
    "    return result_model_bert"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "199e6eb1-7ac0-4acd-ac1f-133a1b824297",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The BERT model starts analysis...\n",
      "BERT model process time: 1.8481903076171875 seconds\n"
     ]
    }
   ],
   "source": [
    "# get sentiment analysis result\n",
    "print(\"The BERT model starts analysis...\")\n",
    "\n",
    "# time the inference\n",
    "start = time.time()\n",
    "\n",
    "# get sentiment analysis result\n",
    "bert_output = bert_sentiment(X_test, y_test)\n",
    "\n",
    "# end of the process\n",
    "end = time.time()\n",
    "print(f\"BERT model process time: {end - start} seconds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "03b80632-3d29-4533-8a02-06b97c39b0c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# save results as a json file\n",
    "result_bert_json = '/workspace/results/result_model_bert.json'\n",
    "bert_output.to_json(result_bert_json, orient=\"records\")"
   ]
  }
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