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{ "cell_type": "code", "execution_count": 2, "metadata": { "id": "PRZmmQuI9Y6r", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "1c306095-5bfd-468b-f3a3-47515875839b" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "[nltk_data] Downloading package punkt to /root/nltk_data...\n", "[nltk_data] Unzipping tokenizers/punkt.zip.\n", "[nltk_data] Downloading package punkt_tab to /root/nltk_data...\n", "[nltk_data] Unzipping tokenizers/punkt_tab.zip.\n", "[nltk_data] Downloading package stopwords to /root/nltk_data...\n", "[nltk_data] Unzipping corpora/stopwords.zip.\n", "[nltk_data] Downloading package wordnet to /root/nltk_data...\n" ] } ], "source": [ "import os\n", "import tarfile\n", "import re\n", "import numpy as np\n", "import pandas as pd\n", "import torch\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "\n", "import nltk\n", "\n", "# Downloading required NLTK resources\n", "nltk.download('punkt')\n", "nltk.download('punkt_tab')\n", "nltk.download('stopwords')\n", "nltk.download('wordnet')\n", "\n", "from nltk.tokenize import word_tokenize\n", "from nltk.corpus import stopwords\n", "from nltk.stem import WordNetLemmatizer\n", "\n", "# Sklearn\n", "from sklearn.feature_extraction.text import TfidfVectorizer\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.metrics import (\n", " accuracy_score,\n", " precision_score,\n", " recall_score,\n", " f1_score,\n", " confusion_matrix\n", ")\n", "\n", "# HuggingFace\n", "from datasets import Dataset\n", "from transformers import (\n", " DistilBertTokenizerFast,\n", " DistilBertForSequenceClassification,\n", " TrainingArguments,\n", " Trainer\n", ")\n", "\n", "from collections import Counter\n", "\n", "import warnings\n", "warnings.filterwarnings(\"ignore\")" ] }, { "cell_type": "markdown", "source": [ "# **Extracting Dataset**" ], "metadata": { "id": "XPi6ypoQzm45" } }, { "cell_type": "code", "source": [ "dataset_path = \"aclImdb_v1.tar.gz\"\n", "\n", "# Extracting the tar.gz file\n", "with tarfile.open(dataset_path, 'r:gz') as tar:\n", " tar.extractall(path=\"/content/\")\n", "\n", "print(\"Dataset extracted successfully!\")\n", "\n", "# Checking extracted folder\n", "os.listdir(\"aclImdb\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "vv9zHVgAuxsl", "outputId": "51e84a3d-575b-4b76-d5a0-8fa4debcebbf" }, "execution_count": 3, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Dataset extracted successfully!\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "['test', 'README', 'imdb.vocab', 'train', 'imdbEr.txt']" ] }, "metadata": {}, "execution_count": 3 } ] }, { "cell_type": "markdown", "source": [ "# **Loading IMDB Data**" ], "metadata": { "id": "5TBsE5IvzseB" } }, { "cell_type": "code", "source": [ "# Function to read reviews from a folder\n", "def load_reviews(folder_path, label):\n", " texts = []\n", " labels = []\n", "\n", " for file in os.listdir(folder_path):\n", " with open(os.path.join(folder_path, file), 'r', encoding='utf-8') as f:\n", " texts.append(f.read())\n", " labels.append(label)\n", "\n", " return texts, labels\n", "\n", "# Loading training data\n", "train_pos, train_pos_labels = load_reviews(\"aclImdb/train/pos\", 1)\n", "train_neg, train_neg_labels = load_reviews(\"aclImdb/train/neg\", 0)\n", "\n", "# Loading test data\n", "test_pos, test_pos_labels = load_reviews(\"aclImdb/test/pos\", 1)\n", "test_neg, test_neg_labels = load_reviews(\"aclImdb/test/neg\", 0)\n", "\n", "# Combining data\n", "X_train = train_pos + train_neg\n", "y_train = train_pos_labels + train_neg_labels\n", "\n", "X_test = test_pos + test_neg\n", "y_test = test_pos_labels + test_neg_labels\n", "\n", "print(\"Training samples:\", len(X_train))\n", "print(\"Testing samples:\", len(X_test))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "INu3u5bBuxqE", "outputId": "b3e11884-581c-455b-dc55-7b073fe69625" }, "execution_count": 4, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Training samples: 25000\n", "Testing samples: 25000\n" ] } ] }, { "cell_type": "markdown", "source": [ "**5 sample training reviews**" ], "metadata": { "id": "tgKnB7TJUsEk" } }, { "cell_type": "code", "source": [ "print(\"\\nShowing 5 sample training reviews:\\n\")\n", "\n", "for i in range(5):\n", " print(f\"Sample {i+1}\")\n", " print(\"Label:\", y_train[i], \"(Positive)\" if y_train[i] == 1 else \"(Negative)\")\n", " print(\"Review Text:\")\n", " print(X_train[i][:500]) # Showing first 500 characters\n", " print(\"-\" * 80)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "VTxKvGnZUfLM", "outputId": "cdbc9466-4425-4b04-f9b6-bd92e81f6f1e" }, "execution_count": 5, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "Showing 5 sample training reviews:\n", "\n", "Sample 1\n", "Label: 1 (Positive)\n", "Review Text:\n", "An hilariously accurate caricature of trying to sell a script. Documentary hits all the beats, plot points, character arcs, seductions, moments of elation and disappointments and the allure but insane prospect of selling a script or getting an agent in Hollywood;and all the fleeting, fantasy-realizing but ultimately empty rites of passage attendant to being socialized into \"the system.\" Hotz and Rice capture the moment of thinking you're finally a player, only to find that what goes up comes dow\n", "--------------------------------------------------------------------------------\n", "Sample 2\n", "Label: 1 (Positive)\n", "Review Text:\n", "I saw a preview of Freebird at the Isle of Man TT as i had heard about it in a couple of motorcycle mags. Although i was over mainly for the racing, the lure of seeing Phil Daniels in a motorcycle movie (yes i love Quadrophenia like everyone else) proved enough to get me away from the beer and partying. At last! we've done it! us British have actually made a great motorcycle film (and no it's not like Torque) this is up there with the best of British comedy. Mark my words, this is Phil Daniels b\n", "--------------------------------------------------------------------------------\n", "Sample 3\n", "Label: 1 (Positive)\n", "Review Text:\n", "This movie is wonderful. It always has been always will be. I'm mean really what is better then a house maid falling in love. Walt really out did him self here. And the music! A dream is a wish your heart makes is beautiful! What else can you say besides terrific! I can't wait until the special edition comes out it will be so awesome! I can't wait to hear all of the deleted songs! This movie is done so beautiful and smart! It is lovely. What about the cute mice too! I know i keep saying the same\n", "--------------------------------------------------------------------------------\n", "Sample 4\n", "Label: 1 (Positive)\n", "Review Text:\n", "I was very moved by the story and because I am going through something similar with my own parents, I really connected. It is so easy to forget that someone whose body is failing was once vibrant and passionate. And then there's the mistakes they made and have to live with. I loved Ellen Burstyn's performance and who is Christine Horne? She's fantastic! A real find. There is probably the most erotic scene I've ever seen in a film, yet nothing was shown - it was just so beautifully done. Overall \n", "--------------------------------------------------------------------------------\n", "Sample 5\n", "Label: 1 (Positive)\n", "Review Text:\n", "Police Story is one of Jackie Chan's classic films that helped shape the Hong Kong cinema. It is a masterpiece that should not be missed by any action movie fan. From the beginning it is obvious that Jackie Chan's stunt team literally risked their live to make this film. Both the action and the stunts are extremely realistic and innovative. Even today, no movie has outdone police story in dangerous stunts. Many people were hospitalized in Police Story including Jackie Chan. The fighting is not a\n", "--------------------------------------------------------------------------------\n" ] } ] }, { "cell_type": "markdown", "source": [ "# **Text Cleaning & Preprocessing**" ], "metadata": { "id": "oyNdlD8q8986" } }, { "cell_type": "code", "source": [ "stop_words = set(stopwords.words('english'))\n", "lemmatizer = WordNetLemmatizer()\n", "\n", "def clean_text(text):\n", "\n", " # Converting to lowercase\n", " text = text.lower()\n", "\n", " # Removing HTML tags\n", " text = re.sub(r'<.*?>', '', text)\n", "\n", " # Removing punctuation and numbers\n", " text = re.sub(r'[^a-zA-Z\\s]', '', text)\n", "\n", " # Tokenize\n", " tokens = word_tokenize(text)\n", "\n", " # Remove stopwords\n", " tokens = [word for word in tokens if word not in stop_words]\n", "\n", " # Lemmatization\n", " tokens = [lemmatizer.lemmatize(word) for word in tokens]\n", "\n", " return \" \".join(tokens)\n", "\n", "# Applying cleaning\n", "X_train_clean = [clean_text(text) for text in X_train]\n", "X_test_clean = [clean_text(text) for text in X_test]" ], "metadata": { "id": "znGrHQZz89x5" }, "execution_count": 6, "outputs": [] }, { "cell_type": "markdown", "source": [ "**5 cleaned training samples**" ], "metadata": { "id": "YFdtO_cCU9ET" } }, { "cell_type": "code", "source": [ " print(\"\\nShowing 5 cleaned training reviews:\\n\")\n", "\n", "for i in range(5):\n", " print(f\"Sample {i+1}\")\n", " print(\"Label:\", y_train[i], \"(Positive)\" if y_train[i] == 1 else \"(Negative)\")\n", " print(\"Cleaned Review Text:\")\n", " print(X_train_clean[i][:300]) # Showing first 300 characters\n", " print(\"-\" * 80)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "IZLmiwVVU60k", "outputId": "79041fcf-11a7-450c-cbe9-f13779308ec9" }, "execution_count": 7, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "Showing 5 cleaned training reviews:\n", "\n", "Sample 1\n", "Label: 1 (Positive)\n", "Cleaned Review Text:\n", "hilariously accurate caricature trying sell script documentary hit beat plot point character arc seduction moment elation disappointment allure insane prospect selling script getting agent hollywoodand fleeting fantasyrealizing ultimately empty rite passage attendant socialized system hotz rice capt\n", "--------------------------------------------------------------------------------\n", "Sample 2\n", "Label: 1 (Positive)\n", "Cleaned Review Text:\n", "saw preview freebird isle man tt heard couple motorcycle mag although mainly racing lure seeing phil daniel motorcycle movie yes love quadrophenia like everyone else proved enough get away beer partying last weve done u british actually made great motorcycle film like torque best british comedy mark\n", "--------------------------------------------------------------------------------\n", "Sample 3\n", "Label: 1 (Positive)\n", "Cleaned Review Text:\n", "movie wonderful always always im mean really better house maid falling love walt really self music dream wish heart make beautiful else say besides terrific cant wait special edition come awesome cant wait hear deleted song movie done beautiful smart lovely cute mouse know keep saying thing hey grea\n", "--------------------------------------------------------------------------------\n", "Sample 4\n", "Label: 1 (Positive)\n", "Cleaned Review Text:\n", "moved story going something similar parent really connected easy forget someone whose body failing vibrant passionate there mistake made live loved ellen burstyns performance christine horne shes fantastic real find probably erotic scene ive ever seen film yet nothing shown beautifully done overall \n", "--------------------------------------------------------------------------------\n", "Sample 5\n", "Label: 1 (Positive)\n", "Cleaned Review Text:\n", "police story one jackie chans classic film helped shape hong kong cinema masterpiece missed action movie fan beginning obvious jackie chans stunt team literally risked live make film action stunt extremely realistic innovative even today movie outdone police story dangerous stunt many people hospita\n", "--------------------------------------------------------------------------------\n" ] } ] }, { "cell_type": "markdown", "source": [ "# **Exploratory Data Analysis**" ], "metadata": { "id": "rZkaamph9M6h" } }, { "cell_type": "markdown", "source": [ "## **Class Balance**" ], "metadata": { "id": "2sWaYc7J9Qsx" } }, { "cell_type": "code", "source": [ "# Creating DataFrame\n", "df = pd.DataFrame({\"text\": X_train_clean, \"label\": y_train})\n", "plt.figure()\n", "\n", "# Plotting countplot\n", "ax = sns.countplot(x=df[\"label\"])\n", "\n", "# Adding data labels on top of bars\n", "for p in ax.patches:\n", " ax.annotate(\n", " f'{int(p.get_height())}', # Count value\n", " (p.get_x() + p.get_width() / 2., p.get_height()), # Position\n", " ha='center', va='bottom', fontsize=12\n", " )\n", "\n", "# Titles and labels\n", "plt.title(\"Class Distribution\")\n", "plt.xlabel(\"Label (0 = Negative, 1 = Positive)\")\n", "plt.ylabel(\"Count\")\n", "\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 472 }, "id": "OZeKP0rkAVC6", "outputId": "88b6bd56-a9c2-4128-d0d6-29812ad23c6b" }, "execution_count": 8, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "**Word Frequency**" ], "metadata": { "id": "9dWFy8_A9W4C" } }, { "cell_type": "code", "source": [ "# Combining all words\n", "all_words = \" \".join(df[\"text\"]).split()\n", "\n", "# Count frequency\n", "word_freq = Counter(all_words)\n", "\n", "# Getting top 20 words\n", "common_words = word_freq.most_common(20)\n", "\n", "words = [w[0] for w in common_words]\n", "freqs = [w[1] for w in common_words]\n", "\n", "plt.figure(figsize=(12,6))\n", "\n", "# Creating bar plot\n", "bars = plt.bar(words, freqs)\n", "\n", "# Adding frequency labels on top of bars\n", "for bar in bars:\n", " height = bar.get_height()\n", " plt.text(\n", " bar.get_x() + bar.get_width()/2,\n", " height,\n", " str(height),\n", " ha='center',\n", " va='bottom',\n", " fontsize=10\n", " )\n", "\n", "plt.xticks(rotation=45)\n", "plt.title(\"Top 20 Most Frequent Words\")\n", "plt.xlabel(\"Words\")\n", "plt.ylabel(\"Frequency\")\n", "\n", "plt.tight_layout()\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 489 }, "id": "lw7GHkJ8Apmi", "outputId": "d06f2db5-2756-416a-c0bc-74f68780c47f" }, "execution_count": 9, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "# **Baseline Model (TF-IDF + Logistic Regression)**" ], "metadata": { "id": "m06LpQeZz_Hp" } }, { "cell_type": "code", "source": [ "# Converting text to numerical features\n", "vectorizer = TfidfVectorizer(max_features=10000, stop_words='english')\n", "\n", "X_train_tfidf = vectorizer.fit_transform(X_train)\n", "X_test_tfidf = vectorizer.transform(X_test)\n", "\n", "# Training Logistic Regression model\n", "lr_model = LogisticRegression(max_iter=1000)\n", "lr_model.fit(X_train_tfidf, y_train)\n", "\n", "# Making predictions\n", "y_pred_lr = lr_model.predict(X_test_tfidf)\n", "\n", "print(\"Logistic Regression Accuracy:\", accuracy_score(y_test, y_pred_lr))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "8nv1LeMnuxnc", "outputId": "5ed49cc8-67ef-4499-eb97-a4182e70a1ef" }, "execution_count": 10, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Logistic Regression Accuracy: 0.88036\n" ] } ] }, { "cell_type": "markdown", "source": [ "# **Evaluating Logistic Regression**" ], "metadata": { "id": "O1Gkh26w0Ct4" } }, { "cell_type": "code", "source": [ "lr_accuracy = accuracy_score(y_test, y_pred_lr)\n", "lr_precision = precision_score(y_test, y_pred_lr)\n", "lr_recall = recall_score(y_test, y_pred_lr)\n", "lr_f1 = f1_score(y_test, y_pred_lr)\n", "\n", "# classification report\n", "print(\"\\nTF-IDF + Logistic Regression Results\")\n", "print(\"--------------------------------------\")\n", "print(\"Accuracy :\", lr_accuracy)\n", "print(\"Precision:\", lr_precision)\n", "print(\"Recall :\", lr_recall)\n", "print(\"F1-score :\", lr_f1)\n", "\n", "# confusion matrix\n", "lr_cm = confusion_matrix(y_test, y_pred_lr)\n", "\n", "plt.figure()\n", "sns.heatmap(lr_cm, annot=True, fmt=\"d\")\n", "plt.title(\"Confusion Matrix - Logistic Regression\")\n", "plt.xlabel(\"Predicted\")\n", "plt.ylabel(\"Actual\")\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 594 }, "id": "99pnrFrB0Ivh", "outputId": "b51b498e-f280-4ef3-c8ab-71ad39984900" }, "execution_count": 11, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "TF-IDF + Logistic Regression Results\n", "--------------------------------------\n", "Accuracy : 0.88036\n", "Precision: 0.879297965696051\n", "Recall : 0.88176\n", "F1-score : 0.8805272618334332\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "# **Preparing Data for DistilBERT**" ], "metadata": { "id": "5PXj2Bg30el5" } }, { "cell_type": "code", "source": [ "# Converting lists into pandas DataFrame\n", "train_df = pd.DataFrame({\"text\": X_train, \"label\": y_train})\n", "test_df = pd.DataFrame({\"text\": X_test, \"label\": y_test})\n", "\n", "# Converting to HuggingFace Dataset format\n", "train_dataset = Dataset.from_pandas(train_df)\n", "test_dataset = Dataset.from_pandas(test_df)" ], "metadata": { "id": "2EmSrBsIuxk0" }, "execution_count": 12, "outputs": [] }, { "cell_type": "markdown", "source": [ "# **Loading DistilBERT Tokenizer**" ], "metadata": { "id": "iR_LzBr_0jIx" } }, { "cell_type": "code", "source": [ "tokenizer = DistilBertTokenizerFast.from_pretrained(\"distilbert-base-uncased\")" ], "metadata": { "id": "7dIjnExRuxiM", "colab": { "base_uri": "https://localhost:8080/", "height": 168, "referenced_widgets": [ "e51c6455fda44545b474cb17e7c802f0", "a58d1abea061455398ae83fba126a9ec", "588f88348fe449fe85d1657878a911f7", "931ce81962124fa59c9257254810b168", "ef198b3b8d27411ab8004ff4a62fb558", "ad01a1636c264fd4bbaff77b4262a585", "b3c5255613dd4ceda6ae949f529eb050", "d8e4aed612b748acba5fc297b3314af5", "b442af57c61048a9b37e292cfe914962", "56a385938e2645178b9f836b3ba9dcf9", "cd468a2ca18c4d07bca2fed3cee79350", "14757cefd42f4646bb3e7c2c9342043f", "d9d88423d9794b76bccc79675ca04155", "8ee8a27edeb14504851d41f2bdbecd1f", "8c09af1503984556a448afae383a538e", "6ef24bbda53e49b584e2d61f228f8282", "d98c4991082d4c1c88d258982d7048a7", "46ceb0c983374bc98980620b019d2555", "be3d4fda2f604409b2ddc0444ccc9430", "afdf710650cc4dd997ceb0359c1e1450", "e95bc64f29d340b8b1e8498116cecdc8", "eb69c660ae094ca79a62c2a496efe354", "b3fa254d12624bd1a9ecd9d2a556715e", "132708bc1ffa44f6985d715dfb2c4675", "c56587870d104968be6d0268e33600cf", "263914d1800a4a56a5a3df8845110045", "472c1ba51d61416e8eef7193553db99b", "a0501123bb164959b66a8ac4601bf69e", "0d78e4f1f6fa4964b1be74b77f07ddc3", "532762af60134d8e985da68802429935", "648a65bc88ec4560a381bec2818b7766", "c902d4a3a85940979dbbe113a26243f6", "26080d5fac2b4874a6342e31524add0c" ] }, "outputId": "4b18e92b-0897-4e2b-fde0-ac149da061b9" }, "execution_count": 13, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n", "WARNING:huggingface_hub.utils._http:Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "tokenizer_config.json: 0%| | 0.00/48.0 [00:00" ], "text/html": [ "\n", "
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EpochTraining LossValidation LossAccuracy
10.2779550.2342740.908560
20.1618510.3236260.908440
30.0596370.4121500.910520

" ] }, "metadata": {} }, { "output_type": "execute_result", "data": { "text/plain": [ "TrainOutput(global_step=4689, training_loss=0.18028965960384866, metrics={'train_runtime': 2370.9366, 'train_samples_per_second': 31.633, 'train_steps_per_second': 1.978, 'total_flos': 4967527449600000.0, 'train_loss': 0.18028965960384866, 'epoch': 3.0})" ] }, "metadata": {}, "execution_count": 17 } ] }, { "cell_type": "markdown", "source": [ "# **Evaluating DistilBERT**" ], "metadata": { "id": "K2SrAEup06dJ" } }, { "cell_type": "code", "source": [ "pred_output = trainer.predict(test_dataset)\n", "\n", "bert_logits = pred_output.predictions\n", "bert_labels = pred_output.label_ids\n", "bert_preds = np.argmax(bert_logits, axis=-1)\n", "\n", "bert_accuracy = accuracy_score(bert_labels, bert_preds)\n", "bert_precision = precision_score(bert_labels, bert_preds)\n", "bert_recall = recall_score(bert_labels, bert_preds)\n", "bert_f1 = f1_score(bert_labels, bert_preds)\n", "\n", "# classification report\n", "print(\"\\nDistilBERT Results\")\n", "print(\"------------------\")\n", "print(\"Accuracy :\", bert_accuracy)\n", "print(\"Precision:\", bert_precision)\n", "print(\"Recall :\", bert_recall)\n", "print(\"F1-score :\", bert_f1)\n", "\n", "# confusion matrix\n", "bert_cm = confusion_matrix(bert_labels, bert_preds)\n", "\n", "plt.figure()\n", "sns.heatmap(bert_cm, annot=True, fmt=\"d\")\n", "plt.title(\"Confusion Matrix - DistilBERT\")\n", "plt.xlabel(\"Predicted\")\n", "plt.ylabel(\"Actual\")\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 594 }, "id": "Ih967FJGuw81", "outputId": "f2021d10-30ea-4901-d23d-1c1e229e26d0" }, "execution_count": 18, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "\n", "DistilBERT Results\n", "------------------\n", "Accuracy : 0.91052\n", "Precision: 0.9120032115616218\n", "Recall : 0.90872\n", "F1-score : 0.9103586455620116\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "

" ], "image/png": 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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "# **Comparison Table**" ], "metadata": { "id": "lKusjDeD1Kgp" } }, { "cell_type": "code", "source": [ "comparison = pd.DataFrame({\n", " \"Model\": [\"TF-IDF + Logistic Regression\", \"DistilBERT\"],\n", " \"Accuracy\": [lr_accuracy, bert_accuracy],\n", " \"Precision\": [lr_precision, bert_precision],\n", " \"Recall\": [lr_recall, bert_recall],\n", " \"F1-Score\": [lr_f1, bert_f1]\n", "})\n", "\n", "comparison" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 112 }, "id": "PD8Eox9yzVxq", "outputId": "42f4fb9a-5d98-40e5-c080-49cc129b4cfa" }, "execution_count": 19, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " Model Accuracy Precision Recall F1-Score\n", "0 TF-IDF + Logistic Regression 0.88036 0.879298 0.88176 0.880527\n", "1 DistilBERT 0.91052 0.912003 0.90872 0.910359" ], "text/html": [ "\n", "
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ModelAccuracyPrecisionRecallF1-Score
0TF-IDF + Logistic Regression0.880360.8792980.881760.880527
1DistilBERT0.910520.9120030.908720.910359
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