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/usr/local/lib/python3.12/dist-packages (from typer->huggingface-hub<2.0,>=1.3.0->transformers) (13.9.4)\n", "Requirement already satisfied: annotated-doc>=0.0.2 in /usr/local/lib/python3.12/dist-packages (from typer->huggingface-hub<2.0,>=1.3.0->transformers) (0.0.4)\n", "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.12/dist-packages (from rich>=12.3.0->typer->huggingface-hub<2.0,>=1.3.0->transformers) (4.0.0)\n", "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.12/dist-packages (from rich>=12.3.0->typer->huggingface-hub<2.0,>=1.3.0->transformers) (2.19.2)\n", "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.12/dist-packages (from markdown-it-py>=2.2.0->rich>=12.3.0->typer->huggingface-hub<2.0,>=1.3.0->transformers) (0.1.2)\n" ] } ], "source": [ "!pip install transformers datasets nltk scikit-learn shap seaborn" ] }, { "cell_type": "markdown", "source": [ "## Import Libraries" ], "metadata": { "id": "q5csi3zwhzBH" } }, { "cell_type": "code", "source": [ "import os\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "import nltk\n", "from nltk.corpus import stopwords\n", "from nltk.stem import WordNetLemmatizer\n", "from nltk.tokenize import word_tokenize\n", "\n", "from sklearn.feature_extraction.text import TfidfVectorizer\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n", "\n", "import shap\n", "import torch\n", "from transformers import BertTokenizer, BertForSequenceClassification, Trainer, TrainingArguments" ], "metadata": { "id": "PvBm5fgrhw5Z" }, "execution_count": 1, "outputs": [] }, { "cell_type": "code", "source": [ "nltk.download('punkt')\n", "nltk.download('stopwords')\n", "nltk.download('wordnet')" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ZFGooAQYh9x6", "outputId": "4c60ef39-74d7-4fa7-ca8d-ba46f0801172" }, "execution_count": 2, "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 stopwords to /root/nltk_data...\n", "[nltk_data] Unzipping corpora/stopwords.zip.\n", "[nltk_data] Downloading package wordnet to /root/nltk_data...\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "True" ] }, "metadata": {}, "execution_count": 2 } ] }, { "cell_type": "markdown", "source": [ "## Upload or Extract Dataset in Colab" ], "metadata": { "id": "g4CB5wBLiBmK" } }, { "cell_type": "code", "source": [ "# import zipfile\n", "# zip_dir=\"/content/drive/MyDrive/aclImdb.zip\"\n", "# extract_dir=\"/content/drive/MyDrive/aclImdb\"\n", "# with zipfile.ZipFile(zip_dir,'r') as zip_ref:\n", "# zip_ref.extractall(extract_dir)\n" ], "metadata": { "id": "TUAaXNs4k6ai" }, "execution_count": 22, "outputs": [] }, { "cell_type": "markdown", "source": [ "## Load Training Data" ], "metadata": { "id": "LQqCfp2unAoB" } }, { "cell_type": "code", "source": [ "import os\n", "import pandas as pd\n", "\n", "def load_imdb_data(data_dir, samples_per_class=100):\n", "\n", " texts = []\n", " labels = []\n", "\n", " for label in ['pos', 'neg']:\n", " folder = os.path.join(data_dir, label)\n", "\n", " files = os.listdir(folder)[:samples_per_class]\n", "\n", " for file in files:\n", " file_path = os.path.join(folder, file)\n", "\n", " with open(file_path, 'r', encoding='utf8') as f:\n", " texts.append(f.read())\n", " labels.append(1 if label == 'pos' else 0)\n", "\n", " return pd.DataFrame({\n", " 'review': texts,\n", " 'sentiment': labels\n", " })\n", "\n", "\n", "train_path = \"/content/drive/MyDrive/aclImdb/train\"\n", "test_path = \"/content/drive/MyDrive/aclImdb/test\"\n", "\n", "\n", "train_df = load_imdb_data(train_path, 100)\n", "\n", "test_df = load_imdb_data(test_path, 100)\n", "\n", "\n", "print(\"Train dataset size:\", len(train_df))\n", "print(train_df['sentiment'].value_counts())\n", "\n", "print(\"\\nTest dataset size:\", len(test_df))\n", "print(test_df['sentiment'].value_counts())\n", "\n", "train_df.head()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 397 }, "id": "aeszro5giDHC", "outputId": "30bc98ca-6036-461e-caf5-711b227c4e2e" }, "execution_count": 4, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Train dataset size: 200\n", "sentiment\n", "1 100\n", "0 100\n", "Name: count, dtype: int64\n", "\n", "Test dataset size: 200\n", "sentiment\n", "1 100\n", "0 100\n", "Name: count, dtype: int64\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ " review sentiment\n", "0 This review comes nearly 30 years late. Nevert... 1\n", "1 This movie is of almost generation-defining im... 1\n", "2 This movie will kick your ass! Powerful acting... 1\n", "3 Let's keep it simple: My two kids were glued t... 1\n", "4 If a more masterful adaptation than this one e... 1" ], "text/html": [ "\n", "
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reviewsentiment
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "train_df", "summary": "{\n \"name\": \"train_df\",\n \"rows\": 200,\n \"fields\": [\n {\n \"column\": \"review\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 200,\n \"samples\": [\n \"A true story about a true revolution, 25 of April ; a revolution against a repressive regime of 41 years, that was imposing a colonial war on it's military's, for maintaining an empire (Angola, Mozambique, Guine-Bissau, Cabo Verde, S. Tom\\u00e9 e Principe; the first and the last of the great colonial empire's of Europe) of 600 years, since it's beginning in the conquest of Ceuta in 1415; a revolution by the army for the people, and for a democratic Portugal; the most's surprising fact in this revolution is that it were no people killed in it (except those that died in the hand's of PIDE, the political police of the State, during a brutal gunfire against an unarmed crowd protesting in front of it's headquarters in the day of the revolution, in 25 of April 1974, has it show's on the film).And has all revolutions it has it's heroes, one them of was Captain Salgueiro Maia, a returned soldier from the war, whose convictions along with the rest of the army, was that they were fighting (since 1961) a hopeless war, and that sometimes a soldier has to disobey it's country.\",\n \"Interesting way of looking at how we as humans so often behave we are sometimes blinded by our desire to achieve perfection that we some times destroy the foundation of what we are trying to achieve. It also addresses the issue how we tend to ignore those among us who are not as outspoken and by doing this may miss out on a great opportunity. The injection of comedy also makes watching the film an enjoyable experience..A must see for anyone who is interested in a reflective yet comical look at life. I am eagerly looking forward to your next product.Hope that you will continue to provide us with quality entertainment. Excellent work ......Joanne\",\n \"This movie had lots of great actors and actresses in it and it addressed some very noble issues. It's full of emotion and the direction is done well. The storyline progresses very quickly, but I guess that's better than having to watch a 3 hour movie. This is an easy movie to watch again and again and enjoy.\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"sentiment\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 0,\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 4 } ] }, { "cell_type": "markdown", "source": [ "## Text Preprocessing" ], "metadata": { "id": "baTmoiNzyk7m" } }, { "cell_type": "code", "source": [ "nltk.download('punkt_tab')\n", "\n", "stop_words = set(stopwords.words('english'))\n", "lemmatizer = WordNetLemmatizer()\n", "\n", "def preprocess_text(text):\n", "\n", " tokens = word_tokenize(text.lower())\n", "\n", " tokens = [word for word in tokens if word.isalpha()]\n", "\n", " tokens = [word for word in tokens if word not in stop_words]\n", "\n", " tokens = [lemmatizer.lemmatize(word) for word in tokens]\n", "\n", " return \" \".join(tokens)\n", "\n", "\n", "train_df['clean_text'] = train_df['review'].apply(preprocess_text)\n", "test_df['clean_text'] = test_df['review'].apply(preprocess_text)\n", "\n", "train_df.head()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 241 }, "id": "NcSBlQz_ymTa", "outputId": "4e9dfc04-0fd5-4ee9-f685-a043d273ec76" }, "execution_count": 14, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "[nltk_data] Downloading package punkt_tab to /root/nltk_data...\n", "[nltk_data] Unzipping tokenizers/punkt_tab.zip.\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ " review sentiment length \\\n", "0 This review comes nearly 30 years late. Nevert... 1 1416 \n", "1 This movie is of almost generation-defining im... 1 1663 \n", "2 This movie will kick your ass! Powerful acting... 1 261 \n", "3 Let's keep it simple: My two kids were glued t... 1 530 \n", "4 If a more masterful adaptation than this one e... 1 1336 \n", "\n", " clean_text \n", "0 review come nearly year late nevertheless ment... \n", "1 movie almost importance u born early year espe... \n", "2 movie kick as powerful acting story push u liv... \n", "3 let keep simple two kid glued movie flaw adult... \n", "4 masterful adaptation one even existed need loo... " ], "text/html": [ "\n", "
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "train_df", "summary": "{\n \"name\": \"train_df\",\n \"rows\": 200,\n \"fields\": [\n {\n \"column\": \"review\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 200,\n \"samples\": [\n \"A true story about a true revolution, 25 of April ; a revolution against a repressive regime of 41 years, that was imposing a colonial war on it's military's, for maintaining an empire (Angola, Mozambique, Guine-Bissau, Cabo Verde, S. Tom\\u00e9 e Principe; the first and the last of the great colonial empire's of Europe) of 600 years, since it's beginning in the conquest of Ceuta in 1415; a revolution by the army for the people, and for a democratic Portugal; the most's surprising fact in this revolution is that it were no people killed in it (except those that died in the hand's of PIDE, the political police of the State, during a brutal gunfire against an unarmed crowd protesting in front of it's headquarters in the day of the revolution, in 25 of April 1974, has it show's on the film).And has all revolutions it has it's heroes, one them of was Captain Salgueiro Maia, a returned soldier from the war, whose convictions along with the rest of the army, was that they were fighting (since 1961) a hopeless war, and that sometimes a soldier has to disobey it's country.\",\n \"Interesting way of looking at how we as humans so often behave we are sometimes blinded by our desire to achieve perfection that we some times destroy the foundation of what we are trying to achieve. It also addresses the issue how we tend to ignore those among us who are not as outspoken and by doing this may miss out on a great opportunity. The injection of comedy also makes watching the film an enjoyable experience..A must see for anyone who is interested in a reflective yet comical look at life. I am eagerly looking forward to your next product.Hope that you will continue to provide us with quality entertainment. Excellent work ......Joanne\",\n \"This movie had lots of great actors and actresses in it and it addressed some very noble issues. It's full of emotion and the direction is done well. The storyline progresses very quickly, but I guess that's better than having to watch a 3 hour movie. This is an easy movie to watch again and again and enjoy.\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"sentiment\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 0,\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"length\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 886,\n \"min\": 121,\n \"max\": 4612,\n \"num_unique_values\": 192,\n \"samples\": [\n 1659,\n 1556\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"clean_text\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 200,\n \"samples\": [\n \"true story true revolution april revolution repressive regime year imposing colonial war military maintaining empire angola mozambique cabo verde tom\\u00e9 e principe first last great colonial empire europe year since beginning conquest ceuta revolution army people democratic portugal surprising fact revolution people killed except died hand pide political police state brutal gunfire unarmed crowd protesting front headquarters day revolution april show film revolution hero one captain salgueiro maia returned soldier war whose conviction along rest army fighting since hopeless war sometimes soldier disobey country\",\n \"interesting way looking human often behave sometimes blinded desire achieve perfection time destroy foundation trying achieve also address issue tend ignore among u outspoken may miss great opportunity injection comedy also make watching film enjoyable experience must see anyone interested reflective yet comical look life eagerly looking forward next continue provide u quality entertainment excellent work joanne\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 14 } ] }, { "cell_type": "markdown", "source": [ "## Exploratory Data Analysis (EDA)" ], "metadata": { "id": "dE_P76EuzCGP" } }, { "cell_type": "markdown", "source": [ "### Class Balance" ], "metadata": { "id": "pv1I5F1nzEkO" } }, { "cell_type": "code", "source": [ "sns.countplot(x=train_df['sentiment'])\n", "plt.title(\"Train Sentiment Distribution\")\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 472 }, "id": "3r0-UXTqzDAW", "outputId": "6c49e32f-72c3-450b-e608-937b200c7131" }, "execution_count": 15, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "### Word Frequency" ], "metadata": { "id": "lABfRQlrzJJG" } }, { "cell_type": "code", "source": [ "from collections import Counter\n", "\n", "all_words = \" \".join(train_df['clean_text']).split()\n", "\n", "word_freq = Counter(all_words).most_common(20)\n", "\n", "words = [w[0] for w in word_freq]\n", "counts = [w[1] for w in word_freq]\n", "\n", "plt.figure(figsize=(10,5))\n", "plt.bar(words, counts)\n", "plt.xticks(rotation=90)\n", "plt.title(\"Top 20 Frequent Words\")\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 520 }, "id": "oIcabV39zKYo", "outputId": "ed1ab151-0fca-4517-b520-385e04aa03f2" }, "execution_count": 16, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "## TF-IDF Feature Extraction" ], "metadata": { "id": "P2zjZyd4zOq8" } }, { "cell_type": "code", "source": [ "tfidf = TfidfVectorizer(max_features=3000)\n", "\n", "X_train = tfidf.fit_transform(train_df['clean_text'])\n", "X_test = tfidf.transform(test_df['clean_text'])\n", "\n", "y_train = train_df['sentiment']\n", "y_test = test_df['sentiment']" ], "metadata": { "id": "dGnigWt6zOH-" }, "execution_count": 17, "outputs": [] }, { "cell_type": "markdown", "source": [ "## Model 1: Support Vector Machine (SVM)" ], "metadata": { "id": "K7s1SXSszX1u" } }, { "cell_type": "code", "source": [ "from sklearn.svm import LinearSVC\n", "\n", "svm_model = LinearSVC()\n", "\n", "svm_model.fit(X_train, y_train)\n", "\n", "pred_svm = svm_model.predict(X_test)" ], "metadata": { "id": "XXn08-O0zYt2" }, "execution_count": 20, "outputs": [] }, { "cell_type": "markdown", "source": [ "### Evaluate SVM Model" ], "metadata": { "id": "ta_HxK__zc0Y" } }, { "cell_type": "code", "source": [ "from sklearn.metrics import accuracy_score, classification_report\n", "\n", "print(\"Accuracy:\", accuracy_score(y_test, pred_svm))\n", "\n", "print(\"\\nClassification Report\\n\")\n", "print(classification_report(y_test, pred_svm))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "6zWfGjuGzeMm", "outputId": "aeebd5cc-59f6-49da-bbef-add9498e9794" }, "execution_count": 21, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Accuracy: 0.675\n", "\n", "Classification Report\n", "\n", " precision recall f1-score support\n", "\n", " 0 0.84 0.43 0.57 100\n", " 1 0.62 0.92 0.74 100\n", "\n", " accuracy 0.68 200\n", " macro avg 0.73 0.68 0.65 200\n", "weighted avg 0.73 0.68 0.65 200\n", "\n" ] } ] }, { "cell_type": "markdown", "source": [ "### Confusion Matrix" ], "metadata": { "id": "nG2ZDpRQzhsk" } }, { "cell_type": "code", "source": [ "from sklearn.metrics import confusion_matrix\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "\n", "cm = confusion_matrix(y_test, pred_svm)\n", "\n", "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n", "\n", "plt.title(\"Confusion Matrix - SVM\")\n", "plt.xlabel(\"Predicted\")\n", "plt.ylabel(\"Actual\")\n", "\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 472 }, "id": "bEQycpbJzkn2", "outputId": "84e02df3-0592-43a6-a716-4e2f9f9961ce" }, "execution_count": 22, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "### Hyperparameter Tuning for SVM" ], "metadata": { "id": "wpIeoPF-z9k4" } }, { "cell_type": "code", "source": [ "from sklearn.model_selection import GridSearchCV\n", "from sklearn.svm import SVC\n", "\n", "param_grid = {\n", " 'C':[0.1,1,10],\n", " 'kernel':['linear']\n", "}\n", "\n", "grid = GridSearchCV(SVC(), param_grid, cv=3)\n", "\n", "grid.fit(X_train, y_train)\n", "\n", "print(\"Best Parameters:\", grid.best_params_)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "11xP73Glz_BH", "outputId": "ba9a2500-dff7-40b1-b780-9f604a85707d" }, "execution_count": 23, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Best Parameters: {'C': 1, 'kernel': 'linear'}\n" ] } ] }, { "cell_type": "markdown", "source": [ "## Transformer Model (BERT)" ], "metadata": { "id": "tIEf4vW30Ezm" } }, { "cell_type": "code", "source": [ "!pip install transformers accelerate" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ve1VLx0y0Gf1", "outputId": "b63b58e9-0fd6-45e3-ad77-6b684a7bc3cf" }, "execution_count": 24, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Requirement already satisfied: transformers in /usr/local/lib/python3.12/dist-packages (5.0.0)\n", "Requirement already satisfied: accelerate in /usr/local/lib/python3.12/dist-packages (1.13.0)\n", "Requirement already satisfied: filelock in /usr/local/lib/python3.12/dist-packages (from transformers) (3.25.1)\n", "Requirement already satisfied: huggingface-hub<2.0,>=1.3.0 in /usr/local/lib/python3.12/dist-packages (from transformers) (1.6.0)\n", "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.12/dist-packages (from transformers) (2.0.2)\n", "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.12/dist-packages (from transformers) (26.0)\n", "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.12/dist-packages (from transformers) (6.0.3)\n", "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.12/dist-packages (from transformers) (2025.11.3)\n", "Requirement already satisfied: tokenizers<=0.23.0,>=0.22.0 in /usr/local/lib/python3.12/dist-packages (from transformers) (0.22.2)\n", "Requirement already satisfied: typer-slim in /usr/local/lib/python3.12/dist-packages (from transformers) (0.24.0)\n", "Requirement already satisfied: safetensors>=0.4.3 in /usr/local/lib/python3.12/dist-packages (from transformers) (0.7.0)\n", "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.12/dist-packages (from transformers) (4.67.3)\n", "Requirement already satisfied: psutil in /usr/local/lib/python3.12/dist-packages (from accelerate) (5.9.5)\n", "Requirement already satisfied: torch>=2.0.0 in /usr/local/lib/python3.12/dist-packages (from accelerate) (2.10.0+cu128)\n", "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<2.0,>=1.3.0->transformers) (2025.3.0)\n", "Requirement already satisfied: hf-xet<2.0.0,>=1.3.2 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<2.0,>=1.3.0->transformers) (1.3.2)\n", "Requirement already satisfied: httpx<1,>=0.23.0 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<2.0,>=1.3.0->transformers) (0.28.1)\n", "Requirement already satisfied: typer in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<2.0,>=1.3.0->transformers) (0.24.1)\n", "Requirement already satisfied: typing-extensions>=4.1.0 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<2.0,>=1.3.0->transformers) (4.15.0)\n", "Requirement already satisfied: setuptools in /usr/local/lib/python3.12/dist-packages (from torch>=2.0.0->accelerate) (75.2.0)\n", "Requirement already satisfied: sympy>=1.13.3 in /usr/local/lib/python3.12/dist-packages (from torch>=2.0.0->accelerate) (1.14.0)\n", "Requirement already satisfied: networkx>=2.5.1 in /usr/local/lib/python3.12/dist-packages (from torch>=2.0.0->accelerate) (3.6.1)\n", "Requirement already satisfied: jinja2 in /usr/local/lib/python3.12/dist-packages (from torch>=2.0.0->accelerate) (3.1.6)\n", "Requirement already satisfied: cuda-bindings==12.9.4 in /usr/local/lib/python3.12/dist-packages (from torch>=2.0.0->accelerate) (12.9.4)\n", "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.8.93 in /usr/local/lib/python3.12/dist-packages (from torch>=2.0.0->accelerate) (12.8.93)\n", "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.8.90 in /usr/local/lib/python3.12/dist-packages (from torch>=2.0.0->accelerate) (12.8.90)\n", "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.8.90 in /usr/local/lib/python3.12/dist-packages (from torch>=2.0.0->accelerate) (12.8.90)\n", "Requirement already satisfied: nvidia-cudnn-cu12==9.10.2.21 in /usr/local/lib/python3.12/dist-packages (from torch>=2.0.0->accelerate) (9.10.2.21)\n", "Requirement already satisfied: nvidia-cublas-cu12==12.8.4.1 in /usr/local/lib/python3.12/dist-packages (from torch>=2.0.0->accelerate) (12.8.4.1)\n", "Requirement already satisfied: nvidia-cufft-cu12==11.3.3.83 in /usr/local/lib/python3.12/dist-packages (from torch>=2.0.0->accelerate) (11.3.3.83)\n", "Requirement already satisfied: nvidia-curand-cu12==10.3.9.90 in /usr/local/lib/python3.12/dist-packages (from torch>=2.0.0->accelerate) (10.3.9.90)\n", "Requirement already satisfied: nvidia-cusolver-cu12==11.7.3.90 in /usr/local/lib/python3.12/dist-packages (from torch>=2.0.0->accelerate) (11.7.3.90)\n", "Requirement already satisfied: nvidia-cusparse-cu12==12.5.8.93 in /usr/local/lib/python3.12/dist-packages (from torch>=2.0.0->accelerate) (12.5.8.93)\n", "Requirement already satisfied: nvidia-cusparselt-cu12==0.7.1 in /usr/local/lib/python3.12/dist-packages (from torch>=2.0.0->accelerate) (0.7.1)\n", "Requirement already satisfied: nvidia-nccl-cu12==2.27.5 in /usr/local/lib/python3.12/dist-packages (from torch>=2.0.0->accelerate) (2.27.5)\n", "Requirement already satisfied: nvidia-nvshmem-cu12==3.4.5 in /usr/local/lib/python3.12/dist-packages (from torch>=2.0.0->accelerate) (3.4.5)\n", "Requirement already satisfied: nvidia-nvtx-cu12==12.8.90 in /usr/local/lib/python3.12/dist-packages (from torch>=2.0.0->accelerate) (12.8.90)\n", "Requirement already satisfied: nvidia-nvjitlink-cu12==12.8.93 in /usr/local/lib/python3.12/dist-packages (from torch>=2.0.0->accelerate) (12.8.93)\n", "Requirement already satisfied: nvidia-cufile-cu12==1.13.1.3 in /usr/local/lib/python3.12/dist-packages (from torch>=2.0.0->accelerate) (1.13.1.3)\n", "Requirement already satisfied: triton==3.6.0 in /usr/local/lib/python3.12/dist-packages (from torch>=2.0.0->accelerate) (3.6.0)\n", "Requirement already satisfied: cuda-pathfinder~=1.1 in /usr/local/lib/python3.12/dist-packages (from cuda-bindings==12.9.4->torch>=2.0.0->accelerate) (1.4.1)\n", "Requirement already satisfied: anyio in /usr/local/lib/python3.12/dist-packages (from httpx<1,>=0.23.0->huggingface-hub<2.0,>=1.3.0->transformers) (4.12.1)\n", "Requirement already satisfied: certifi in /usr/local/lib/python3.12/dist-packages (from httpx<1,>=0.23.0->huggingface-hub<2.0,>=1.3.0->transformers) (2026.2.25)\n", "Requirement already satisfied: httpcore==1.* in /usr/local/lib/python3.12/dist-packages (from httpx<1,>=0.23.0->huggingface-hub<2.0,>=1.3.0->transformers) (1.0.9)\n", "Requirement already satisfied: idna in /usr/local/lib/python3.12/dist-packages (from httpx<1,>=0.23.0->huggingface-hub<2.0,>=1.3.0->transformers) (3.11)\n", "Requirement already satisfied: h11>=0.16 in /usr/local/lib/python3.12/dist-packages (from httpcore==1.*->httpx<1,>=0.23.0->huggingface-hub<2.0,>=1.3.0->transformers) (0.16.0)\n", "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.12/dist-packages (from sympy>=1.13.3->torch>=2.0.0->accelerate) (1.3.0)\n", "Requirement already satisfied: click>=8.2.1 in /usr/local/lib/python3.12/dist-packages (from typer->huggingface-hub<2.0,>=1.3.0->transformers) (8.3.1)\n", "Requirement already satisfied: shellingham>=1.3.0 in /usr/local/lib/python3.12/dist-packages (from typer->huggingface-hub<2.0,>=1.3.0->transformers) (1.5.4)\n", "Requirement already satisfied: rich>=12.3.0 in /usr/local/lib/python3.12/dist-packages (from typer->huggingface-hub<2.0,>=1.3.0->transformers) (13.9.4)\n", "Requirement already satisfied: annotated-doc>=0.0.2 in /usr/local/lib/python3.12/dist-packages (from typer->huggingface-hub<2.0,>=1.3.0->transformers) (0.0.4)\n", "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.12/dist-packages (from jinja2->torch>=2.0.0->accelerate) (3.0.3)\n", "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.12/dist-packages (from rich>=12.3.0->typer->huggingface-hub<2.0,>=1.3.0->transformers) (4.0.0)\n", "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.12/dist-packages (from rich>=12.3.0->typer->huggingface-hub<2.0,>=1.3.0->transformers) (2.19.2)\n", "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.12/dist-packages (from markdown-it-py>=2.2.0->rich>=12.3.0->typer->huggingface-hub<2.0,>=1.3.0->transformers) (0.1.2)\n" ] } ] }, { "cell_type": "code", "source": [ "import torch\n", "from transformers import BertTokenizer, BertForSequenceClassification\n", "from transformers import Trainer, TrainingArguments" ], "metadata": { "id": "iPvZYr5e0K69" }, "execution_count": 25, "outputs": [] }, { "cell_type": "markdown", "source": [ "## Tokenization" ], "metadata": { "id": "AXGXGdm00VeN" } }, { "cell_type": "code", "source": [ "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n", "\n", "train_encodings = tokenizer(\n", " train_df['review'].tolist(),\n", " truncation=True,\n", " padding=True,\n", " max_length=256\n", ")\n", "\n", "test_encodings = tokenizer(\n", " test_df['review'].tolist(),\n", " truncation=True,\n", " padding=True,\n", " max_length=256\n", ")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 272, "referenced_widgets": [ "b7bb359bc0be4dac884b55d106521d5e", "31d9d2ee6020491389f31d6c597e64c3", "4e2dc0be9458449eaa4cf3ea3dd72954", "7cc4ee8af1dc4747a70cecf38401adc0", "9557fbe70c314d5283427cec6c0a0c3a", "bc01d3d054734ded8acf7ced320228c5", "1356f0ec580f4329b69c1b7c0b6f840e", "030698e82a8845199a70c8aa8b8c8d49", "bfa93ac3f5fe4f3aad97e148248a3866", "dac84c8b1f0545fd9f9e0563b1405aa7", "eef704ef2d70413d8cf697f661d4568a", "e9e923f54534422b8488877224b2a1a3", "a32fe04a8e854ee3abe05d372ba52b6f", "cb65e800abf040f1ac7bf1bc5acfd0b1", "5d111090a80a46369e3280ad1bed433b", "0194a701783442f9bdecca7558289137", "bde7417183f2437fb696bb88e91957ee", "03740b8e59164e288c623706ac218eee", "1c996d863605471da66a940da40e6844", "ff9c32ff17e94c2e9ebd91ce78cb384e", "8bea14ea09f245f5b0bd3a9eee66b791", "5b9223bc51aa4467b3ce5beedc043df2", "1bc40403c20d40a29d3dc87165018b3b", "5b12880cc8f04f2f9c7d4ff94f353479", "7adbd41881df4e499a5fcae43acfe65e", "f31ed4b37900401aba4cca7640ee8ce6", "489010cc182b4a91aaddd88d84ed9ada", "0963d5b2f2b040338951a24556ad1aca", "c23213912abb4972880bed89e237bb7e", "78bec50c05fe452482888a2da072261e", "9095b92a2bb74182ae3f38ada10a370d", "c76e6db1cc1a4a64b8451d0a0d5418e6", "30b7806af8d14fd69ef3ad5498cc0d49" ] }, "id": "7Wlb17kS0WqO", "outputId": "c2cc2747-5737-4444-fa09-5def4d43cacc" }, "execution_count": 26, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n", "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", "You will be able to reuse this secret in all of your notebooks.\n", "Please note that authentication is recommended but still optional to access public models or datasets.\n", " warnings.warn(\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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StepTraining Loss
100.722831
200.721181
300.678714
400.654541
500.622898

" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "Writing model shards: 0%| | 0/1 [00:00" ], "text/html": [] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ " precision recall f1-score support\n", "\n", " 0 0.79 0.61 0.69 100\n", " 1 0.68 0.84 0.75 100\n", "\n", " accuracy 0.72 200\n", " macro avg 0.74 0.72 0.72 200\n", "weighted avg 0.74 0.72 0.72 200\n", "\n" ] } ] }, { "cell_type": "markdown", "source": [ "### Confusion Matrix (BERT)" ], "metadata": { "id": "nui0oRcR08d9" } }, { "cell_type": "code", "source": [ "cm = confusion_matrix(y_test, pred_bert)\n", "\n", "sns.heatmap(cm, annot=True, fmt='d', cmap=\"Greens\")\n", "\n", "plt.title(\"Confusion Matrix - BERT\")\n", "\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 452 }, "id": "vJ0PcSc409tG", "outputId": "4b50a6d8-0ac1-4a94-b03b-316b0539f368" }, "execution_count": 32, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "

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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "## Insight Generation" ], "metadata": { "id": "x94EtURM1CIe" } }, { "cell_type": "code", "source": [ "import shap\n", "\n", "explainer = shap.Explainer(model_lr, X_train)\n", "\n", "shap_values = explainer(X_train[:50])\n", "\n", "shap.plots.bar(shap_values)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 585 }, "id": "5jC8PvNB1EIN", "outputId": "df818caf-19c1-425d-aea0-d0fc5adb5463" }, "execution_count": 33, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": {} } ] } ] }