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LICENSE

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model_weights.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:97172d5f7c44fa2488ade1fa4cc373ad800f7d2a779d732a361b3688a78769b4
+size 243207232

poetry_generation.ipynb

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+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "gpuType": "T4"
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    },
+    "accelerator": "GPU"
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "I9Z5guQ6CDt8"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "PGeicEbzCDw9"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "pip install sentencepiece torch torchvision torchaudio pandas scikit-learn\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "zQFdKxIICD0H",
+        "outputId": "5d35d6a1-a876-4c7f-fee8-4f04888f3854"
+      },
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: sentencepiece in /usr/local/lib/python3.11/dist-packages (0.2.0)\n",
+            "Requirement already satisfied: torch in /usr/local/lib/python3.11/dist-packages (2.5.1+cu124)\n",
+            "Requirement already satisfied: torchvision in /usr/local/lib/python3.11/dist-packages (0.20.1+cu124)\n",
+            "Requirement already satisfied: torchaudio in /usr/local/lib/python3.11/dist-packages (2.5.1+cu124)\n",
+            "Requirement already satisfied: pandas in /usr/local/lib/python3.11/dist-packages (2.2.2)\n",
+            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.11/dist-packages (1.6.1)\n",
+            "Requirement already satisfied: filelock in /usr/local/lib/python3.11/dist-packages (from torch) (3.17.0)\n",
+            "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.11/dist-packages (from torch) (4.12.2)\n",
+            "Requirement already satisfied: networkx in /usr/local/lib/python3.11/dist-packages (from torch) (3.4.2)\n",
+            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.11/dist-packages (from torch) (3.1.5)\n",
+            "Requirement already satisfied: fsspec in /usr/local/lib/python3.11/dist-packages (from torch) (2024.10.0)\n",
+            "Collecting nvidia-cuda-nvrtc-cu12==12.4.127 (from torch)\n",
+            "  Downloading nvidia_cuda_nvrtc_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
+            "Collecting nvidia-cuda-runtime-cu12==12.4.127 (from torch)\n",
+            "  Downloading nvidia_cuda_runtime_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
+            "Collecting nvidia-cuda-cupti-cu12==12.4.127 (from torch)\n",
+            "  Downloading nvidia_cuda_cupti_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\n",
+            "Collecting nvidia-cudnn-cu12==9.1.0.70 (from torch)\n",
+            "  Downloading nvidia_cudnn_cu12-9.1.0.70-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\n",
+            "Collecting nvidia-cublas-cu12==12.4.5.8 (from torch)\n",
+            "  Downloading nvidia_cublas_cu12-12.4.5.8-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
+            "Collecting nvidia-cufft-cu12==11.2.1.3 (from torch)\n",
+            "  Downloading nvidia_cufft_cu12-11.2.1.3-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
+            "Collecting nvidia-curand-cu12==10.3.5.147 (from torch)\n",
+            "  Downloading nvidia_curand_cu12-10.3.5.147-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
+            "Collecting nvidia-cusolver-cu12==11.6.1.9 (from torch)\n",
+            "  Downloading nvidia_cusolver_cu12-11.6.1.9-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\n",
+            "Collecting nvidia-cusparse-cu12==12.3.1.170 (from torch)\n",
+            "  Downloading nvidia_cusparse_cu12-12.3.1.170-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\n",
+            "Requirement already satisfied: nvidia-nccl-cu12==2.21.5 in /usr/local/lib/python3.11/dist-packages (from torch) (2.21.5)\n",
+            "Requirement already satisfied: nvidia-nvtx-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch) (12.4.127)\n",
+            "Collecting nvidia-nvjitlink-cu12==12.4.127 (from torch)\n",
+            "  Downloading nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
+            "Requirement already satisfied: triton==3.1.0 in /usr/local/lib/python3.11/dist-packages (from torch) (3.1.0)\n",
+            "Requirement already satisfied: sympy==1.13.1 in /usr/local/lib/python3.11/dist-packages (from torch) (1.13.1)\n",
+            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.11/dist-packages (from sympy==1.13.1->torch) (1.3.0)\n",
+            "Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (from torchvision) (1.26.4)\n",
+            "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.11/dist-packages (from torchvision) (11.1.0)\n",
+            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas) (2.8.2)\n",
+            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas) (2025.1)\n",
+            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas) (2025.1)\n",
+            "Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.13.1)\n",
+            "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.4.2)\n",
+            "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (3.5.0)\n",
+            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.11/dist-packages (from python-dateutil>=2.8.2->pandas) (1.17.0)\n",
+            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.11/dist-packages (from jinja2->torch) (3.0.2)\n",
+            "Downloading nvidia_cublas_cu12-12.4.5.8-py3-none-manylinux2014_x86_64.whl (363.4 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m363.4/363.4 MB\u001b[0m \u001b[31m4.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading nvidia_cuda_cupti_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (13.8 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.8/13.8 MB\u001b[0m \u001b[31m110.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading nvidia_cuda_nvrtc_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (24.6 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m24.6/24.6 MB\u001b[0m \u001b[31m89.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading nvidia_cuda_runtime_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (883 kB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m883.7/883.7 kB\u001b[0m \u001b[31m58.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading nvidia_cudnn_cu12-9.1.0.70-py3-none-manylinux2014_x86_64.whl (664.8 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m664.8/664.8 MB\u001b[0m \u001b[31m1.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading nvidia_cufft_cu12-11.2.1.3-py3-none-manylinux2014_x86_64.whl (211.5 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m211.5/211.5 MB\u001b[0m \u001b[31m5.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading nvidia_curand_cu12-10.3.5.147-py3-none-manylinux2014_x86_64.whl (56.3 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m56.3/56.3 MB\u001b[0m \u001b[31m12.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading nvidia_cusolver_cu12-11.6.1.9-py3-none-manylinux2014_x86_64.whl (127.9 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m127.9/127.9 MB\u001b[0m \u001b[31m6.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading nvidia_cusparse_cu12-12.3.1.170-py3-none-manylinux2014_x86_64.whl (207.5 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m207.5/207.5 MB\u001b[0m \u001b[31m5.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (21.1 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.1/21.1 MB\u001b[0m \u001b[31m85.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hInstalling collected packages: nvidia-nvjitlink-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12\n",
+            "  Attempting uninstall: nvidia-nvjitlink-cu12\n",
+            "    Found existing installation: nvidia-nvjitlink-cu12 12.5.82\n",
+            "    Uninstalling nvidia-nvjitlink-cu12-12.5.82:\n",
+            "      Successfully uninstalled nvidia-nvjitlink-cu12-12.5.82\n",
+            "  Attempting uninstall: nvidia-curand-cu12\n",
+            "    Found existing installation: nvidia-curand-cu12 10.3.6.82\n",
+            "    Uninstalling nvidia-curand-cu12-10.3.6.82:\n",
+            "      Successfully uninstalled nvidia-curand-cu12-10.3.6.82\n",
+            "  Attempting uninstall: nvidia-cufft-cu12\n",
+            "    Found existing installation: nvidia-cufft-cu12 11.2.3.61\n",
+            "    Uninstalling nvidia-cufft-cu12-11.2.3.61:\n",
+            "      Successfully uninstalled nvidia-cufft-cu12-11.2.3.61\n",
+            "  Attempting uninstall: nvidia-cuda-runtime-cu12\n",
+            "    Found existing installation: nvidia-cuda-runtime-cu12 12.5.82\n",
+            "    Uninstalling nvidia-cuda-runtime-cu12-12.5.82:\n",
+            "      Successfully uninstalled nvidia-cuda-runtime-cu12-12.5.82\n",
+            "  Attempting uninstall: nvidia-cuda-nvrtc-cu12\n",
+            "    Found existing installation: nvidia-cuda-nvrtc-cu12 12.5.82\n",
+            "    Uninstalling nvidia-cuda-nvrtc-cu12-12.5.82:\n",
+            "      Successfully uninstalled nvidia-cuda-nvrtc-cu12-12.5.82\n",
+            "  Attempting uninstall: nvidia-cuda-cupti-cu12\n",
+            "    Found existing installation: nvidia-cuda-cupti-cu12 12.5.82\n",
+            "    Uninstalling nvidia-cuda-cupti-cu12-12.5.82:\n",
+            "      Successfully uninstalled nvidia-cuda-cupti-cu12-12.5.82\n",
+            "  Attempting uninstall: nvidia-cublas-cu12\n",
+            "    Found existing installation: nvidia-cublas-cu12 12.5.3.2\n",
+            "    Uninstalling nvidia-cublas-cu12-12.5.3.2:\n",
+            "      Successfully uninstalled nvidia-cublas-cu12-12.5.3.2\n",
+            "  Attempting uninstall: nvidia-cusparse-cu12\n",
+            "    Found existing installation: nvidia-cusparse-cu12 12.5.1.3\n",
+            "    Uninstalling nvidia-cusparse-cu12-12.5.1.3:\n",
+            "      Successfully uninstalled nvidia-cusparse-cu12-12.5.1.3\n",
+            "  Attempting uninstall: nvidia-cudnn-cu12\n",
+            "    Found existing installation: nvidia-cudnn-cu12 9.3.0.75\n",
+            "    Uninstalling nvidia-cudnn-cu12-9.3.0.75:\n",
+            "      Successfully uninstalled nvidia-cudnn-cu12-9.3.0.75\n",
+            "  Attempting uninstall: nvidia-cusolver-cu12\n",
+            "    Found existing installation: nvidia-cusolver-cu12 11.6.3.83\n",
+            "    Uninstalling nvidia-cusolver-cu12-11.6.3.83:\n",
+            "      Successfully uninstalled nvidia-cusolver-cu12-11.6.3.83\n",
+            "Successfully installed nvidia-cublas-cu12-12.4.5.8 nvidia-cuda-cupti-cu12-12.4.127 nvidia-cuda-nvrtc-cu12-12.4.127 nvidia-cuda-runtime-cu12-12.4.127 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.2.1.3 nvidia-curand-cu12-10.3.5.147 nvidia-cusolver-cu12-11.6.1.9 nvidia-cusparse-cu12-12.3.1.170 nvidia-nvjitlink-cu12-12.4.127\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "\n",
+        "!pip install sentencepiece --quiet"
+      ],
+      "metadata": {
+        "id": "6DbIAMlqNDRK"
+      },
+      "execution_count": 2,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "\"\"\"\n",
+        "Model File for Roman Urdu Poetry Generation\n",
+        "\n",
+        "This file contains the complete code for:\n",
+        "  - Data loading, cleaning, and tokenization using SentencePiece\n",
+        "  - Train/Test/Validation split creation\n",
+        "  - Dataset and DataLoader creation\n",
+        "  - Definition of a BiLSTM Language Model (with 3 layers, dropout, etc.)\n",
+        "  - Training, validation, and testing routines\n",
+        "  - Saving the trained model weights\n",
+        "  - A poetry generation function using nucleus (top-p) sampling with formatted output\n",
+        "\n",
+        "Run this file to train and test the model. The trained weights will be saved to a file and loaded on subsequent runs.\n",
+        "\"\"\""
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 157
+        },
+        "id": "DjB6rAwz-D3Q",
+        "outputId": "817edbf7-6063-4c8c-fb49-30b18dd386b5"
+      },
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "'\\nModel File for Roman Urdu Poetry Generation\\n\\nThis file contains the complete code for:\\n  - Data loading, cleaning, and tokenization using SentencePiece\\n  - Train/Test/Validation split creation\\n  - Dataset and DataLoader creation\\n  - Definition of a BiLSTM Language Model (with 3 layers, dropout, etc.)\\n  - Training, validation, and testing routines\\n  - Saving the trained model weights\\n  - A poetry generation function using nucleus (top-p) sampling with formatted output\\n\\nRun this file to train and test the model. The trained weights will be saved to a file and loaded on subsequent runs.\\n'"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            }
+          },
+          "metadata": {},
+          "execution_count": 3
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# -------------------------\n",
+        "# 1. Import Libraries\n",
+        "# -------------------------\n",
+        "import os\n",
+        "import random\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import sentencepiece as spm\n",
+        "import re\n",
+        "import torch\n",
+        "import torch.nn as nn\n",
+        "from torch.utils.data import Dataset, DataLoader\n",
+        "import torch.nn.functional as F\n",
+        "import unicodedata\n",
+        "from sklearn.model_selection import train_test_split"
+      ],
+      "metadata": {
+        "id": "HoqaPLEq-Ega"
+      },
+      "execution_count": 4,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "# -------------------------\n",
+        "# 2. Set Random Seeds and Device\n",
+        "# -------------------------\n",
+        "SEED = 42\n",
+        "random.seed(SEED)\n",
+        "np.random.seed(SEED)\n",
+        "torch.manual_seed(SEED)\n",
+        "\n",
+        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+        "print(\"Using device:\", device)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "u4Xf1Ck6-H-M",
+        "outputId": "f171c4a8-4e30-4873-ebf9-2782aa3e9bdc"
+      },
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Using device: cuda\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# -------------------------\n",
+        "# 3. Load and Clean Dataset\n",
+        "# -------------------------\n",
+        "DATA_PATH = \"Roman-Urdu-Poetry.csv\"  # Make sure this file exists in your working directory\n",
+        "df = pd.read_csv(DATA_PATH)\n",
+        "\n",
+        "def remove_diacritics(text: str) -> str:\n",
+        "    \"\"\"\n",
+        "    Removes Unicode diacritical marks from the text.\n",
+        "    \"\"\"\n",
+        "    return ''.join(ch for ch in unicodedata.normalize('NFD', text)\n",
+        "                   if not unicodedata.combining(ch))\n",
+        "\n",
+        "def clean_text(text):\n",
+        "    \"\"\"\n",
+        "    Cleans the input text by removing diacritics, extra spaces, and unwanted punctuation.\n",
+        "    \"\"\"\n",
+        "    text = remove_diacritics(text)\n",
+        "    text = re.sub(r\"\\s+\", \" \", text)\n",
+        "    text = re.sub(r\"[^\\w\\s\\.\\,\\;\\:\\'\\?\\!\\-]+\", \"\", text)\n",
+        "    return text.strip()\n",
+        "\n",
+        "df[\"Poetry\"] = df[\"Poetry\"].astype(str).apply(clean_text)\n",
+        "texts = df[\"Poetry\"].tolist()\n",
+        "print(f\"Total number of poetry lines: {len(texts)}\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "MYJTunkz-LDb",
+        "outputId": "82609d66-3e91-4795-eac5-251bf9bf8dd1"
+      },
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Total number of poetry lines: 1314\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# -------------------------\n",
+        "# 4. Train/Test/Validation Split (80/10/10)\n",
+        "# -------------------------\n",
+        "train_texts, test_texts = train_test_split(texts, test_size=0.1, random_state=SEED)\n",
+        "train_texts, val_texts = train_test_split(train_texts, test_size=0.1111, random_state=SEED)\n",
+        "print(f\"Train samples: {len(train_texts)}\")\n",
+        "print(f\"Validation samples: {len(val_texts)}\")\n",
+        "print(f\"Test samples: {len(test_texts)}\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "_VvgUa3L-MAR",
+        "outputId": "d045fd71-3f09-4d6c-eea9-34c3e444db59"
+      },
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Train samples: 1050\n",
+            "Validation samples: 132\n",
+            "Test samples: 132\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# -------------------------\n",
+        "# 5. Train a SentencePiece BPE Tokenizer\n",
+        "# -------------------------\n",
+        "all_texts_file = \"all_texts.txt\"\n",
+        "if not os.path.exists(all_texts_file):\n",
+        "    with open(all_texts_file, \"w\", encoding=\"utf-8\") as f:\n",
+        "        for line in texts:\n",
+        "            f.write(line.strip() + \"\\n\")\n",
+        "else:\n",
+        "    print(f\"{all_texts_file} already exists; skipping file creation.\")\n",
+        "\n",
+        "\n",
+        "sp_model_prefix = \"urdu_sp\"\n",
+        "model_file = f\"{sp_model_prefix}.model\"\n",
+        "vocab_file = f\"{sp_model_prefix}.vocab\"\n",
+        "\n",
+        "vocab_size = 12000  # Adjust as needed\n",
+        "model_type = \"bpe\"\n",
+        "\n",
+        "if not (os.path.exists(model_file) and os.path.exists(vocab_file)):\n",
+        "    print(\"SentencePiece model or vocab not found. Training...\")\n",
+        "    spm.SentencePieceTrainer.Train(\n",
+        "        f\"--input={all_texts_file} \"\n",
+        "        f\"--model_prefix={sp_model_prefix} \"\n",
+        "        f\"--vocab_size={vocab_size} \"\n",
+        "        f\"--model_type={model_type} \"\n",
+        "        \"--character_coverage=1.0 \"\n",
+        "        \"--pad_id=0 --unk_id=1 --bos_id=2 --eos_id=3\"\n",
+        "    )\n",
+        "else:\n",
+        "    print(\"SentencePiece model & vocab found; skipping training.\")\n",
+        "\n",
+        "# Load the SentencePiece model\n",
+        "sp = spm.SentencePieceProcessor()\n",
+        "sp.load(model_file)\n",
+        "print(\"Loaded SentencePiece model with vocab size:\", sp.get_piece_size())\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "2L1JgC02-OBW",
+        "outputId": "d6ea06cf-8f54-47d8-fada-a016ca1df4c9"
+      },
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Loaded SentencePiece model with vocab size: 12000\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# -------------------------\n",
+        "# 6. Tokenize Data\n",
+        "# -------------------------\n",
+        "train_ids = [sp.encode_as_ids(t) for t in train_texts]\n",
+        "val_ids   = [sp.encode_as_ids(t) for t in val_texts]\n",
+        "test_ids  = [sp.encode_as_ids(t) for t in test_texts]"
+      ],
+      "metadata": {
+        "id": "lq7lbUcu-RDU"
+      },
+      "execution_count": 9,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# -------------------------\n",
+        "# 7. Create Dataset and DataLoader\n",
+        "# -------------------------\n",
+        "class PoetryDataset(Dataset):\n",
+        "    def __init__(self, token_ids_list, max_length=250):\n",
+        "        self.data = token_ids_list\n",
+        "        self.max_length = max_length\n",
+        "\n",
+        "    def __len__(self):\n",
+        "        return len(self.data)\n",
+        "\n",
+        "    def __getitem__(self, idx):\n",
+        "        # Truncate tokens to max_length\n",
+        "        token_ids = self.data[idx][:self.max_length]\n",
+        "        # Create input by adding BOS token (2) at the beginning\n",
+        "        input_ids = [2] + token_ids\n",
+        "        # Create target by appending EOS token (3) at the end\n",
+        "        target_ids = token_ids + [3]\n",
+        "        return torch.tensor(input_ids, dtype=torch.long), torch.tensor(target_ids, dtype=torch.long)\n",
+        "\n",
+        "def collate_fn(batch):\n",
+        "    inputs, targets = zip(*batch)\n",
+        "    max_len = max(len(x) for x in inputs)\n",
+        "    padded_inputs = [torch.cat([x, torch.zeros(max_len - len(x), dtype=torch.long)]) for x in inputs]\n",
+        "    padded_targets = [torch.cat([t, torch.zeros(max_len - len(t), dtype=torch.long)]) for t in targets]\n",
+        "    return torch.stack(padded_inputs), torch.stack(padded_targets)"
+      ],
+      "metadata": {
+        "id": "OZ9_kG0M-TOF"
+      },
+      "execution_count": 10,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "train_dataset = PoetryDataset(train_ids, max_length=250)\n",
+        "val_dataset   = PoetryDataset(val_ids, max_length=250)\n",
+        "test_dataset  = PoetryDataset(test_ids, max_length=250)\n",
+        "\n",
+        "batch_size = 64\n",
+        "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, collate_fn=collate_fn, drop_last=True)\n",
+        "val_loader   = DataLoader(val_dataset, batch_size=batch_size, shuffle=False, collate_fn=collate_fn, drop_last=True)\n",
+        "test_loader  = DataLoader(test_dataset, batch_size=batch_size, shuffle=False, collate_fn=collate_fn, drop_last=True)"
+      ],
+      "metadata": {
+        "id": "z1aGUj-w-Xh9"
+      },
+      "execution_count": 11,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# -------------------------\n",
+        "# 8. Define the BiLSTM Language Model\n",
+        "# -------------------------\n",
+        "class BiLSTMLanguageModel(nn.Module):\n",
+        "    def __init__(self, vocab_size, embed_dim=512, hidden_dim=768, num_layers=3, dropout=0.2):\n",
+        "        super(BiLSTMLanguageModel, self).__init__()\n",
+        "        self.embed = nn.Embedding(vocab_size, embed_dim, padding_idx=0)\n",
+        "        # Stacked Bi-LSTM layers\n",
+        "        self.lstm = nn.LSTM(\n",
+        "            input_size=embed_dim,\n",
+        "            hidden_size=hidden_dim,\n",
+        "            num_layers=num_layers,\n",
+        "            batch_first=True,\n",
+        "            bidirectional=True,\n",
+        "            dropout=dropout\n",
+        "        )\n",
+        "        # Linear layer to project LSTM outputs to vocabulary size\n",
+        "        self.fc = nn.Linear(hidden_dim * 2, vocab_size)\n",
+        "\n",
+        "    def forward(self, x, hidden=None):\n",
+        "        emb = self.embed(x)\n",
+        "        out, hidden = self.lstm(emb, hidden)\n",
+        "        logits = self.fc(out)\n",
+        "        return logits, hidden"
+      ],
+      "metadata": {
+        "id": "YD8F_0WM-apV"
+      },
+      "execution_count": 12,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "vocab_size = sp.get_piece_size()\n",
+        "model = BiLSTMLanguageModel(vocab_size, embed_dim=512, hidden_dim=768, num_layers=3, dropout=0.2)\n",
+        "model = model.to(device)"
+      ],
+      "metadata": {
+        "id": "aKWTogmN-gaq"
+      },
+      "execution_count": 13,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# -------------------------\n",
+        "# 9. Training Setup (Loss, Optimizer, Scheduler)\n",
+        "# -------------------------\n",
+        "criterion = nn.CrossEntropyLoss(ignore_index=0)\n",
+        "optimizer = torch.optim.Adam(model.parameters(), lr=1e-3, weight_decay=1e-5)\n",
+        "scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=2, gamma=0.5)\n",
+        "\n",
+        "def evaluate(model, data_loader):\n",
+        "    model.eval()\n",
+        "    total_loss, total_tokens = 0, 0\n",
+        "    with torch.no_grad():\n",
+        "        for inputs, targets in data_loader:\n",
+        "            inputs = inputs.to(device)\n",
+        "            targets = targets.to(device)\n",
+        "            logits, _ = model(inputs)\n",
+        "            logits = logits.view(-1, vocab_size)\n",
+        "            targets = targets.view(-1)\n",
+        "            loss = criterion(logits, targets)\n",
+        "            total_loss += loss.item() * (targets != 0).sum().item()\n",
+        "            total_tokens += (targets != 0).sum().item()\n",
+        "    return total_loss / total_tokens"
+      ],
+      "metadata": {
+        "id": "9W5USllq-i83"
+      },
+      "execution_count": 14,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# -------------------------\n",
+        "# 10. Training Loop with Testing Code and Weight Saving\n",
+        "# -------------------------\n",
+        "num_epochs = 10\n",
+        "weights_path = \"model_weights.pth\"\n",
+        "\n",
+        "if not os.path.exists(weights_path):\n",
+        "    for epoch in range(num_epochs):\n",
+        "        model.train()\n",
+        "        total_loss, total_tokens = 0, 0\n",
+        "        for inputs, targets in train_loader:\n",
+        "            inputs = inputs.to(device)\n",
+        "            targets = targets.to(device)\n",
+        "            optimizer.zero_grad()\n",
+        "            logits, _ = model(inputs)\n",
+        "            logits = logits.view(-1, vocab_size)\n",
+        "            targets = targets.view(-1)\n",
+        "            loss = criterion(logits, targets)\n",
+        "            loss.backward()\n",
+        "            torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=5.0)\n",
+        "            optimizer.step()\n",
+        "            total_loss += loss.item() * (targets != 0).sum().item()\n",
+        "            total_tokens += (targets != 0).sum().item()\n",
+        "        train_loss = total_loss / total_tokens\n",
+        "        val_loss = evaluate(model, val_loader)\n",
+        "        scheduler.step()\n",
+        "        print(f\"Epoch [{epoch+1}/{num_epochs}], Train Loss: {train_loss:.4f}, Val Loss: {val_loss:.4f}\")\n",
+        "    test_loss = evaluate(model, test_loader)\n",
+        "    print(f\"Test Loss: {test_loss:.4f}\")\n",
+        "    torch.save(model.state_dict(), weights_path)\n",
+        "else:\n",
+        "    print(\"Loading pre-trained model weights...\")\n",
+        "    model.load_state_dict(torch.load(weights_path, map_location=device))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "B0nDauKT-nQC",
+        "outputId": "c082b8a8-70fb-4375-8b89-6deb72b31f6f"
+      },
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch [1/10], Train Loss: 7.1034, Val Loss: 6.2269\n",
+            "Epoch [2/10], Train Loss: 5.7528, Val Loss: 5.4652\n",
+            "Epoch [3/10], Train Loss: 5.0948, Val Loss: 4.9459\n",
+            "Epoch [4/10], Train Loss: 4.4997, Val Loss: 4.2981\n",
+            "Epoch [5/10], Train Loss: 3.9654, Val Loss: 3.9398\n",
+            "Epoch [6/10], Train Loss: 3.6264, Val Loss: 3.6214\n",
+            "Epoch [7/10], Train Loss: 3.3671, Val Loss: 3.4665\n",
+            "Epoch [8/10], Train Loss: 3.2082, Val Loss: 3.3188\n",
+            "Epoch [9/10], Train Loss: 3.0880, Val Loss: 3.2478\n",
+            "Epoch [10/10], Train Loss: 3.0126, Val Loss: 3.1772\n",
+            "Test Loss: 3.1696\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "\n",
+        "def generate_poetry_nucleus(model, sp, start_word, num_words=12, temperature=1.2, top_p=0.85):\n",
+        "    \"\"\"\n",
+        "    Generate a poetry sequence using nucleus (top-p) sampling.\n",
+        "    The output is formatted so that every 6 words appear on a new line.\n",
+        "    If num_words is specified, it means 1 starting word + (num_words - 1) generated tokens.\n",
+        "    \"\"\"\n",
+        "    model.eval()\n",
+        "    start_ids = sp.encode_as_ids(start_word)\n",
+        "    input_ids = [2] + start_ids  # Insert BOS (token 2)\n",
+        "    input_tensor = torch.tensor([input_ids], dtype=torch.long, device=device)\n",
+        "    hidden = None\n",
+        "\n",
+        "    with torch.no_grad():\n",
+        "        logits, hidden = model(input_tensor, hidden)\n",
+        "\n",
+        "    generated_ids = input_ids[:]  # Copy initial tokens\n",
+        "\n",
+        "    for _ in range(num_words - 1):  # Generate one less token\n",
+        "        # Get the logits of the last generated token\n",
+        "        last_logits = logits[:, -1, :]  # Shape: (1, vocab_size)\n",
+        "        scaled_logits = last_logits / temperature\n",
+        "\n",
+        "        # Sort the logits in descending order\n",
+        "        sorted_logits, sorted_indices = torch.sort(scaled_logits, descending=True)\n",
+        "        cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)\n",
+        "\n",
+        "        # Filter out tokens with cumulative probability above top_p\n",
+        "        filtered_indices = cumulative_probs > top_p\n",
+        "        if torch.all(filtered_indices):\n",
+        "            filtered_indices[-1] = False  # Ensure at least one token remains\n",
+        "        sorted_indices = sorted_indices[~filtered_indices]\n",
+        "        sorted_logits = sorted_logits[~filtered_indices]\n",
+        "\n",
+        "        # Sample the next token from the filtered distribution\n",
+        "        if len(sorted_indices) > 0:\n",
+        "            next_token_id = sorted_indices[torch.multinomial(F.softmax(sorted_logits, dim=-1), 1).item()].item()\n",
+        "        else:\n",
+        "            next_token_id = torch.argmax(last_logits).item()\n",
+        "        generated_ids.append(next_token_id)\n",
+        "\n",
+        "        # Prepare next input and update hidden state\n",
+        "        next_input = torch.tensor([[next_token_id]], dtype=torch.long, device=device)\n",
+        "        logits, hidden = model(next_input, hidden)\n",
+        "\n",
+        "    # Decode generated tokens (skip BOS) and format output: 6 words per line\n",
+        "    generated_text = sp.decode_ids(generated_ids[1:])\n",
+        "    words = generated_text.split()\n",
+        "    formatted_text = \"\\n\".join([\" \".join(words[i:i+6]) for i in range(0, len(words), 6)])\n",
+        "    return formatted_text\n"
+      ],
+      "metadata": {
+        "id": "kmsILzIh_0um"
+      },
+      "execution_count": 16,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "\n",
+        "# -------------------------\n",
+        "# 12. Example Usage for Testing (Optional)\n",
+        "# -------------------------\n",
+        "if __name__ == \"__main__\":\n",
+        "    # Test the generation function in the notebook/script\n",
+        "    start_word = \"ishq\"\n",
+        "    print(\"Generated Poetry:\\n\", generate_poetry_nucleus(model, sp, start_word, num_words=12, temperature=1.2, top_p=0.85))\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "a3WKAKtJ_8YU",
+        "outputId": "9571d2a7-97a4-4b1d-d106-3b7ccd0da43f"
+      },
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Generated Poetry:\n",
+            " ishq nishan tum phir kar phir\n",
+            "ik baat aur phir ye phir\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "hK3-OgKI98Ia"
+      },
+      "execution_count": 17,
+      "outputs": []
+    }
+  ]
+}

requirements.txt

@@ -0,0 +1,2 @@
+torch==2.6.0
+sentencepiece==0.2.0

urdu_sp.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:81ccdc84bc97783bd3b3ae632ec37ebd85124be7dd75650f5512824df6a413e2
+size 429486