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"id": "1146a47e" }, "source": [ "# 1. Problem Statement\n", "\n", "This notebook aims to develop a machine learning model for sarcasm detection in Hinglish text, leveraging the power of pre-trained BERT models. The goal is to classify text into 'Sarcastic' or 'Not Sarcastic', addressing the nuances and complexities of mixed-language communication." ] }, { "cell_type": "markdown", "source": [ "# ***Loading the Dataset***" ], "metadata": { "id": "YrAtiuyLkBrS" } }, { "cell_type": "code", "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns" ], "metadata": { "id": "sMB3ZxKykBfa" }, "execution_count": 32, "outputs": [] }, { "cell_type": "code", "source": [ "import pandas as pd\n", "\n", "# loading the dataset\n", "df = pd.read_excel('/content/final_hinglish_sarcasm_dataset.xlsx')" ], "metadata": { "id": "FObsnhAzjsgC" }, "execution_count": 35, "outputs": [] }, { "cell_type": "code", "source": [ "df.head()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 206 }, "id": "HerlmWVYimwh", "outputId": "6a649826-7c45-4f10-aeb1-225d95373741" }, "execution_count": 36, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " text label category\n", "0 Ckt News: S Ajmal Sohail khan k BaaD Ab Umar A... 0 original\n", "1 Online resources se bahut kuch seekh rahe 0 original\n", "2 Aab maaf kar dena chahiye-or azad bhi kar do b... 0 original\n", "3 Bahan k sath-sath biwi ko v nhi mante.Parinam ... 0 original\n", "4 sakta ham talk ke baare mein something else? k... 0 original" ], "text/html": [ "\n", "
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0Ckt News: S Ajmal Sohail khan k BaaD Ab Umar A...0original
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "df", "summary": "{\n \"name\": \"df\",\n \"rows\": 9301,\n \"fields\": [\n {\n \"column\": \"text\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 9301,\n \"samples\": [\n \"it's theek because they're not american\",\n \"Congress isame kuch nahi bolti esaliye Modi pachad Raha hai har jagah ,avasarvadi politics Congress karate hai Modi gorakshko bhi data hai\",\n \"what a pleasant way to die.\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"label\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 1,\n 0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"category\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 11,\n \"samples\": [\n \"Office\",\n \"original\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 36 } ] }, { "cell_type": "markdown", "metadata": { "id": "1de67a21" }, "source": [ "# ***Dataset Analysis***" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 633 }, "id": "9419a8aa", "outputId": "e56dad3a-fef3-4529-fc49-a18c8adab68c" }, "source": [ "# Calculate text length for each sample\n", "df['text_length'] = df['text'].apply(len)\n", "\n", "# Plotting the distribution of text lengths\n", "plt.figure(figsize=(10, 6))\n", "sns.histplot(df['text_length'], bins=50, kde=True)\n", "plt.title('Distribution of Text Lengths')\n", "plt.xlabel('Text Length')\n", "plt.ylabel('Frequency')\n", "plt.show()\n", "\n", "print(f\"Maximum text length: {df['text_length'].max()}\")\n", "print(f\"Minimum text length: {df['text_length'].min()}\")\n", "print(f\"Average text length: {df['text_length'].mean():.2f}\")\n", "print(f\"Median text length: {df['text_length'].median()}\")" ], "execution_count": 37, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
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}, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "Maximum text length: 393\n", "Minimum text length: 2\n", "Average text length: 79.89\n", "Median text length: 74.0\n" ] } ] }, { "cell_type": "code", "source": [ "df.shape" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "qkkqYkYJqE_F", "outputId": "a101526a-941c-42c4-d9b4-7fcdb5fc7e0f" }, "execution_count": 38, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "(9301, 4)" ] }, "metadata": {}, "execution_count": 38 } ] }, { "cell_type": "markdown", "source": [ "# ***Data Preprocessing (minimal)***" ], "metadata": { "id": "xf_GZIbwqzGl" } }, { "cell_type": "code", "source": [ "# cleaning the dataset\n", "\n", "import re\n", "\n", "def clean_text(text):\n", "\n", " #stripping the text\n", " text = text.strip()\n", "\n", " return text" ], "metadata": { "id": "vNTsYhOvimuS" }, "execution_count": 39, "outputs": [] }, { "cell_type": "code", "source": [ "clean_text(text=\"Ckt News: S Ajmal Sohail khan k BaaD Ab Umar A...\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 36 }, "id": "eP83bE0Nimrx", "outputId": "587ef4c3-a860-4c00-f31d-928b13ae6e9e" }, "execution_count": 40, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "'Ckt News: S Ajmal Sohail khan k BaaD Ab Umar A...'" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "metadata": {}, "execution_count": 40 } ] }, { "cell_type": "code", "source": [ "# claening the whole TEXT column\n", "\n", "# Clean column names to remove potential whitespace issues\n", "df.columns = df.columns.str.strip()\n", "\n", "df['text'] = df['text'].apply(clean_text)" ], "metadata": { "id": "jSjskmQ4impi" }, "execution_count": 41, "outputs": [] }, { "cell_type": "code", "source": [ "df.head()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 206 }, "id": "rfOETFKbimnB", "outputId": "1f4b7e06-4fe6-48e6-c2f6-9e38b72c078e" }, "execution_count": 42, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " text label category \\\n", "0 Ckt News: S Ajmal Sohail khan k BaaD Ab Umar A... 0 original \n", "1 Online resources se bahut kuch seekh rahe 0 original \n", "2 Aab maaf kar dena chahiye-or azad bhi kar do b... 0 original \n", "3 Bahan k sath-sath biwi ko v nhi mante.Parinam ... 0 original \n", "4 sakta ham talk ke baare mein something else? k... 0 original \n", "\n", " text_length \n", "0 138 \n", "1 41 \n", "2 137 \n", "3 63 \n", "4 74 " ], "text/html": [ "\n", "
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" ] }, "metadata": {}, "execution_count": 44 } ] }, { "cell_type": "markdown", "source": [ "# ***Train-Test-split***" ], "metadata": { "id": "uqTFjdIkn717" } }, { "cell_type": "code", "source": [ "from sklearn.model_selection import train_test_split\n", "\n", "X = df['text']\n", "y = df['label']" ], "metadata": { "id": "_xKw3Ivbn7uG" }, "execution_count": 45, "outputs": [] }, { "cell_type": "code", "source": [ "# First, split into training (70%) and a temporary set (30%)\n", "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.3, random_state=42)\n", "\n", "# Then, split the temporary set into validation (15%) and test (15%)\n", "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42) # 0.5 of 0.3 is 0.15\n", "\n", "print(f\"X_train shape: {X_train.shape}, y_train shape: {y_train.shape}\")\n", "print(f\"X_val shape: {X_val.shape}, y_val shape: {y_val.shape}\")\n", "print(f\"X_test shape: {X_test.shape}, y_test shape: {y_test.shape}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "zxoDx6czn7rM", "outputId": "88867b3e-3a2c-4462-dfc4-527299e319db" }, "execution_count": 46, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "X_train shape: (6510,), y_train shape: (6510,)\n", "X_val shape: (1395,), y_val shape: (1395,)\n", "X_test shape: (1396,), y_test shape: (1396,)\n" ] } ] }, { "cell_type": "markdown", "source": [ "# ***Downloading Pre-Trained BERT and Tokenizer***" ], "metadata": { "id": "CNA6vU8-qKSk" } }, { "cell_type": "code", "source": [ "# install tranformer\n", "!pip install transformers" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Q2ZLYrkDqUOc", "outputId": "23f9f9cb-6ab4-41c9-fcbe-9ad6522a1d14" }, "execution_count": 47, "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 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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", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "c56c8d08", "outputId": "a7a91c3a-59ca-497c-8755-61d504a3028b" }, "source": [ "# Install PyTorch if not already present\n", "!pip install torch\n" ], "execution_count": 48, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Requirement already satisfied: torch in /usr/local/lib/python3.12/dist-packages (2.10.0+cu128)\n", "Requirement already satisfied: filelock in /usr/local/lib/python3.12/dist-packages (from torch) (3.29.0)\n", "Requirement already satisfied: typing-extensions>=4.10.0 in /usr/local/lib/python3.12/dist-packages (from torch) (4.15.0)\n", "Requirement already satisfied: setuptools in /usr/local/lib/python3.12/dist-packages (from torch) (75.2.0)\n", "Requirement already satisfied: sympy>=1.13.3 in /usr/local/lib/python3.12/dist-packages (from torch) 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(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) (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) (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) (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) (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) (12.5.8.93)\n", "Requirement already satisfied: nvidia-cusparselt-cu12==0.7.1 in /usr/local/lib/python3.12/dist-packages (from torch) (0.7.1)\n", "Requirement already satisfied: nvidia-nccl-cu12==2.27.5 in /usr/local/lib/python3.12/dist-packages (from torch) (2.27.5)\n", "Requirement already satisfied: 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(3.0.3)\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "6dfe8170" }, "source": [ "Now, let's modify the model loading cell (`acwMYR1bn7mT`) to use the PyTorch version of DistilBERT for sequence classification. Note that `from_tf=True` will be removed, as we want the PyTorch version. The `output_attention` parameter is typically for the model itself, not the tokenizer, so we'll remove it from the tokenizer's call for now." ] }, { "cell_type": "code", "source": [ "import torch\n", "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n", "\n", "# Load pre-trained tokenizer\n", "tokenizer = AutoTokenizer.from_pretrained(\"xlm-roberta-base\")\n", "\n", "# Load pre-trained model for sequence classification (PyTorch version)\n", "# Add output_attentions=True to get attention weights\n", "model = AutoModelForSequenceClassification.from_pretrained(\"xlm-roberta-base\", num_labels=2)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 362, "referenced_widgets": [ "ca509ddf22c34bd1b683839f5117f9de", "74fc82e9f77a419393183a2dc7f004e1", "bee5dc89310d479bbfd8c1e3bcebf59f", "46d0ff9e504944eeb6904cce7b0f2d06", "b4261d17bd714ff8918204fa672078d3", "46d38222977d4158b95fb23a0cee2d04", "724edbf8eb494be392cda7726cecfe4d", "8e25454783374a5d9ee2901d37a9bbff", "e64fb350f2504ebb9f96b3cd80e572d0", "a9a8e61363e34b388e37084c5a4c72bd", "b42a89334c8f418d985effe544813540" ] }, "id": "acwMYR1bn7mT", "outputId": "553b6f5d-900b-4635-8b73-22bfb109fb51" }, "execution_count": 49, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Loading weights: 0%| | 0/197 [00:00" ], "text/html": [ "\n", "
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12000.114562

" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "Writing model shards: 0%| | 0/1 [00:00" ], "image/png": 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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "# ***Confusion Matrix***" ], "metadata": { "id": "mJti2XyYLc7r" } }, { "cell_type": "code", "source": [ "from sklearn.metrics import confusion_matrix, classification_report\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "\n", "# Make predictions on the test set\n", "predictions = trainer.predict(test_dataset)\n", "y_pred_labels = np.argmax(predictions.predictions, axis=1)\n", "\n", "# Compute confusion matrix\n", "cm = confusion_matrix(y_test, y_pred_labels)\n", "\n", "# Plot confusion matrix\n", "plt.figure(figsize=(8, 6))\n", "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', cbar=False,\n", " xticklabels=['Not Sarcastic (0)', 'Sarcastic (1)'],\n", " yticklabels=['Not Sarcastic (0)', 'Sarcastic (1)'])\n", "plt.title('Confusion Matrix')\n", "plt.xlabel('Predicted Label')\n", "plt.ylabel('True Label')\n", "plt.show()" ], "metadata": { "id": "wQiJ9pN1n7j8", "colab": { "base_uri": "https://localhost:8080/", "height": 564 }, "outputId": "3174d960-42ab-4778-c49c-424d3059ea49" }, "execution_count": 57, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "

" ], "image/png": 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}, "metadata": {} } ] }, { "cell_type": "code", "source": [ "# classification Report\n", "\n", "print(classification_report(y_test, y_pred_labels))" ], "metadata": { "id": "WHZN7pbPimiS", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "24a0e224-a7d4-4d5d-ea1e-c0d2c2414262" }, "execution_count": 58, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " precision recall f1-score support\n", "\n", " 0 0.94 0.94 0.94 597\n", " 1 0.96 0.95 0.95 799\n", "\n", " accuracy 0.95 1396\n", " macro avg 0.95 0.95 0.95 1396\n", "weighted avg 0.95 0.95 0.95 1396\n", "\n" ] } ] }, { "cell_type": "markdown", "source": [ "# ***Error Analysis***" ], "metadata": { "id": "I59pn9hXemQJ" } }, { "cell_type": "code", "source": [ "# Identify indices where predicted labels do not match true labels in the test set\n", "mismatched_indices = y_test.index[y_pred_labels != y_test.values]\n", "\n", "# Use these indices to select the corresponding rows from the original DataFrame\n", "wrong_preds = df.loc[mismatched_indices]\n", "wrong_preds" ], "metadata": { "id": "2IQIfJuUi9Xi", "colab": { "base_uri": "https://localhost:8080/", "height": 424 }, "outputId": "3b65fe46-c159-4593-dee8-d5a2bd2c9859" }, "execution_count": 59, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " text label category \\\n", "805 tha original title tha \"a rederivation of maxw... 0 original \n", "1385 Aaj ki special thali #Snap #Sarcasm. Langar ha... 1 original \n", "3590 i'm not saying woh you're not fun. you're tha ... 0 original \n", "2943 Hahahaha ab wahi ek dwaar bacha b hua h #Presi... 1 original \n", "5467 thanks for proving your point. 1 original \n", "... ... ... ... \n", "6627 no way... really? 1 original \n", "4019 he's right, even if it's ko say something comp... 0 original \n", "1919 lois lane hai falling, accelerating par an ini... 0 original \n", "6964 भाग 2 की शुरूआत 1 original \n", "37 when ho tum coming home? 0 original \n", "\n", " text_length \n", "805 201 \n", "1385 85 \n", "3590 72 \n", "2943 90 \n", "5467 30 \n", "... ... \n", "6627 17 \n", "4019 56 \n", "1919 288 \n", "6964 15 \n", "37 24 \n", "\n", "[73 rows x 4 columns]" ], "text/html": [ "\n", "
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805tha original title tha \"a rederivation of maxw...0original201
1385Aaj ki special thali #Snap #Sarcasm. Langar ha...1original85
3590i'm not saying woh you're not fun. you're tha ...0original72
2943Hahahaha ab wahi ek dwaar bacha b hua h #Presi...1original90
5467thanks for proving your point.1original30
...............
6627no way... really?1original17
4019he's right, even if it's ko say something comp...0original56
1919lois lane hai falling, accelerating par an ini...0original288
6964भाग 2 की शुरूआत1original15
37when ho tum coming home?0original24
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "wrong_preds", "summary": "{\n \"name\": \"wrong_preds\",\n \"rows\": 73,\n \"fields\": [\n {\n \"column\": \"text\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 73,\n \"samples\": [\n \"thanks for proving your point.\",\n \"@d0t5 Arz kiya hai, \\\"Dil me kisi ka tanz mere yun utar gaya.Shisha sa koi toot ke under bikhar \\\". #sarcasm\",\n \"wow, there's a denny's mein vegas tum sakta actually get married mein.\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"label\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 1,\n 0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"category\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"Relationships\",\n \"original\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"text_length\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 49,\n \"min\": 4,\n \"max\": 288,\n \"num_unique_values\": 58,\n \"samples\": [\n 201,\n 76\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 59 } ] }, { "cell_type": "markdown", "source": [ "# ***Prediction System***" ], "metadata": { "id": "LbJGBhHWN9-K" } }, { "cell_type": "markdown", "metadata": { "id": "3c1476a5" }, "source": [ "### Real-World Testing" ] }, { "cell_type": "code", "metadata": { "id": "20c6fac8" }, "source": [ "def predict_sarcasm(text):\n", " # Tokenize the input text\n", " inputs = tokenizer(text, padding=True, truncation=True, max_length=max_len, return_tensors=\"pt\")\n", "\n", " # Move input tensors to the same device as the model\n", " input_ids = inputs['input_ids'].to(device)\n", " attention_mask = inputs['attention_mask'].to(device)\n", "\n", " # Make predictions\n", " model.eval() # Set model to evaluation mode\n", " with torch.no_grad():\n", " outputs = model(input_ids=input_ids, attention_mask=attention_mask)\n", "\n", " # Get logits and probabilities\n", " logits = outputs.logits\n", " probabilities = torch.softmax(logits, dim=1)\n", "\n", " # Get predicted class (0 for Not Sarcastic, 1 for Sarcastic)\n", " predicted_class = torch.argmax(probabilities, dim=1).item()\n", "\n", " # Get confidence for the predicted class\n", " confidence = probabilities[0][predicted_class].item()\n", "\n", " label_map = {0: \"Not Sarcastic\", 1: \"Sarcastic\"}\n", " predicted_label = label_map[predicted_class]\n", "\n", " return predicted_label, confidence" ], "execution_count": 60, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "725fc585", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "cef76eb1-ec16-4599-879b-fdcc622e806f" }, "source": [ "# Test the prediction system with a few examples\n", "example_texts = [\n", " \"Kya baat hai, itni achi salary pe bhi khush nahi ho?\" , # Example of sarcasm\n", " \"Yeh movie toh bahut achi thi, bilkul boring nahi lagi.\", # Example of sarcasm\n", " \"Aaj mausam bahut accha hai.\", # Example of non-sarcasm\n", " \"Ye kaam to koi bhi kar lega, isme kya mushkil hai?\", # Example of sarcasm\n", " \"Kya hua aaj breakfast ki photo nai daali kya?\",\n", " \"Wow son you passed, Congrats\",\n", " \"Bahut badiya, sab barbaad kar diya\",\n", " \"Wah kya baat hai, fail ho gaya\",\n", " \"Oh, you're leaving at 6 PM? Half-day le liya kya aaj\",\n", " \"Bhai tera ML model itna fast hai ki output agle janam mein aayega!\", #--> FAILED\n", " \"Wow, you used GenAI for a simple 'Hello World' code? Einstein ho kya\",\n", " \"Nice logic! Iska patent karwa le, dimaag kharch hone se bach jayega.\",\n", " \"Please, thoda aur slow chalao car. Cycle wale bhi humein overtake karke jaa raha hai\", #--> FAILED\n", " \"Meri MLOps knowledge aur mera bank balance—dono hi zero hain\",\n", " \"It's okay, you're only 2 hours late, no wories....\",\n", " \"Bhai kya speed hai... 10 min mein 1 epoch\",\n", " \"Oh you came on time today, miracle hai kya?\",\n", " \"Model itna fast hai, result agle janam mein\",\n", " \"Wah beta fail hoke bhi proud moment\",\n", " \"Sharma ji ka beta to NASA pahunch gaya, aur tum abhi tak 'logic' hi dhund rahe ho.\",\n", " \"Hello, how are you?\",\n", " \"Haan bhai, tu hi toh sabse bada expert hai. Hum toh kal hi paida huye hain.\",\n", " \"Aur kitna 'timepass' karoge life mein?\",\n", " \"Beta, tumse na ho payega. Tum jaake so jao.\",\n", " \"Aapka fashion sense toh bilkul unique hai... itna unique ki koi aur pehan hi nahi sakta.\",\n", " \"Itna dimaag agar padhai mein lagaya hota, toh aaj humein ye din nahi dekhna padta.\"\n", " ]\n", "\n", "for text in example_texts:\n", " label, confidence = predict_sarcasm(text)\n", " print(f\"Text: \\\"{text}\\\"\")\n", " print(f\"Predicted Label: {label} (Confidence: {confidence:.4f})\")\n", " print(\"\\n\")" ], "execution_count": 61, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Text: \"Kya baat hai, itni achi salary pe bhi khush nahi ho?\"\n", "Predicted Label: Sarcastic (Confidence: 0.9990)\n", "\n", "\n", "Text: \"Yeh movie toh bahut achi thi, bilkul boring nahi lagi.\"\n", "Predicted Label: Not Sarcastic (Confidence: 0.9975)\n", "\n", "\n", "Text: \"Aaj mausam bahut accha hai.\"\n", "Predicted Label: Not Sarcastic (Confidence: 0.9996)\n", "\n", "\n", "Text: \"Ye kaam to koi bhi kar lega, isme kya mushkil hai?\"\n", "Predicted Label: Sarcastic (Confidence: 0.9912)\n", "\n", "\n", "Text: \"Kya hua aaj breakfast ki photo nai daali kya?\"\n", "Predicted Label: Sarcastic (Confidence: 0.9230)\n", "\n", "\n", "Text: \"Wow son you passed, Congrats\"\n", "Predicted Label: Sarcastic (Confidence: 0.9995)\n", "\n", "\n", "Text: \"Bahut badiya, sab barbaad kar diya\"\n", "Predicted Label: Sarcastic (Confidence: 0.9983)\n", "\n", "\n", "Text: \"Wah kya baat hai, fail ho gaya\"\n", "Predicted Label: Sarcastic (Confidence: 0.9990)\n", "\n", "\n", "Text: \"Oh, you're leaving at 6 PM? Half-day le liya kya aaj\"\n", "Predicted Label: Sarcastic (Confidence: 0.7785)\n", "\n", "\n", "Text: \"Bhai tera ML model itna fast hai ki output agle janam mein aayega!\"\n", "Predicted Label: Sarcastic (Confidence: 0.9983)\n", "\n", "\n", "Text: \"Wow, you used GenAI for a simple 'Hello World' code? Einstein ho kya\"\n", "Predicted Label: Sarcastic (Confidence: 0.9996)\n", "\n", "\n", "Text: \"Nice logic! Iska patent karwa le, dimaag kharch hone se bach jayega.\"\n", "Predicted Label: Sarcastic (Confidence: 0.9994)\n", "\n", "\n", "Text: \"Please, thoda aur slow chalao car. Cycle wale bhi humein overtake karke jaa raha hai\"\n", "Predicted Label: Sarcastic (Confidence: 0.5588)\n", "\n", "\n", "Text: \"Meri MLOps knowledge aur mera bank balance—dono hi zero hain\"\n", "Predicted Label: Sarcastic (Confidence: 0.6370)\n", "\n", "\n", "Text: \"It's okay, you're only 2 hours late, no wories....\"\n", "Predicted Label: Sarcastic (Confidence: 0.9271)\n", "\n", "\n", "Text: \"Bhai kya speed hai... 10 min mein 1 epoch\"\n", "Predicted Label: Sarcastic (Confidence: 0.9991)\n", "\n", "\n", "Text: \"Oh you came on time today, miracle hai kya?\"\n", "Predicted Label: Sarcastic (Confidence: 0.9994)\n", "\n", "\n", "Text: \"Model itna fast hai, result agle janam mein\"\n", "Predicted Label: Sarcastic (Confidence: 0.9993)\n", "\n", "\n", "Text: \"Wah beta fail hoke bhi proud moment\"\n", "Predicted Label: Sarcastic (Confidence: 0.9995)\n", "\n", "\n", "Text: \"Sharma ji ka beta to NASA pahunch gaya, aur tum abhi tak 'logic' hi dhund rahe ho.\"\n", "Predicted Label: Sarcastic (Confidence: 0.9737)\n", "\n", "\n", "Text: \"Hello, how are you?\"\n", "Predicted Label: Not Sarcastic (Confidence: 0.9079)\n", "\n", "\n", "Text: \"Haan bhai, tu hi toh sabse bada expert hai. Hum toh kal hi paida huye hain.\"\n", "Predicted Label: Sarcastic (Confidence: 0.9990)\n", "\n", "\n", "Text: \"Aur kitna 'timepass' karoge life mein?\"\n", "Predicted Label: Sarcastic (Confidence: 0.9948)\n", "\n", "\n", "Text: \"Beta, tumse na ho payega. Tum jaake so jao.\"\n", "Predicted Label: Sarcastic (Confidence: 0.7475)\n", "\n", "\n", "Text: \"Aapka fashion sense toh bilkul unique hai... itna unique ki koi aur pehan hi nahi sakta.\"\n", "Predicted Label: Not Sarcastic (Confidence: 0.5949)\n", "\n", "\n", "Text: \"Itna dimaag agar padhai mein lagaya hota, toh aaj humein ye din nahi dekhna padta.\"\n", "Predicted Label: Sarcastic (Confidence: 0.9886)\n", "\n", "\n" ] } ] }, { "cell_type": "markdown", "source": [ "# ***Save and Load the Model***" ], "metadata": { "id": "vqViOBpXNxD7" } }, { "cell_type": "code", "source": [ "# saving the Fine-tuned Roberta model\n", "model.resize_token_embeddings(len(tokenizer))\n", "# Create the directory if it doesn't exist\n", "import os\n", "save_path = \"./fine_tuned_roberta_model7/\"\n", "\n", "# SAVE BOTH TOGETHER\n", "model.save_pretrained(save_path)\n", "tokenizer.save_pretrained(save_path)\n", "\n", "print(f\"Model and Tokenizer synced and saved to {save_path}\")\n" ], "metadata": { "id": "urzzi7F3KUQ7", "colab": { "base_uri": "https://localhost:8080/", "height": 66, "referenced_widgets": [ "85e33e93430e4846b6245388bd57d9ec", "864ea5561f014592a72a00e8ee7ae143", "b86d262368f943df948151c46489e91d", "0065f89c5fef408283a2413cb9d65bd6", "4278a98cdb4c4a0a9e44031b6fbd0ee2", "abeb5e7f4dd34a18922245e2a4b63950", "fe4a8bbbe45c4f9b9803eae418b8ce6c", "893cb61dd38049d091aac2dc2ac55021", "9a4dfeaf0e4c4e2b95e9ffbb19e58a15", "12380f0becbd48abbc76561536fda6d1", "2db8edc829ab450380ccca5553a2350d" ] }, "outputId": "484b8f8b-06ff-4ed6-c3ed-25765cec6532" }, "execution_count": 63, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Writing model shards: 0%| | 0/1 [00:00