This study provides Urdu poetry generated using different deep-learning techniques and algorithms. The data was collected through the Rekhta website, containing 1341 text files with several couplets. The data on poetry was not from any specific genre or poet. Instead, it was a collection of mixed Urdu poems and Ghazals. Different deep learning techniques, such as the model applied Long Short-term Memory Networks (LSTM) and Gated Recurrent Unit (GRU), have been used. Natural Language Processing (NLP) may be used in machine learning to understand, analyze, and generate a language humans may use and understand. Much work has been done on generating poetry for different languages using different techniques. The collection and use of data were also different for different researchers. The primary purpose of this project is to provide a model that generates Urdu poems by using data completely, not by sampling data. Also, this may generate poems in pure Urdu, not Roman Urdu, as in the base paper. The results have shown good accuracy in the poems generated by the model.

翻译：本研究采用不同的深度学习技术和算法生成乌尔都语诗歌。数据通过Rekhta网站收集，包含1341个文本文件，内含若干对联。诗歌数据并非来自特定流派或诗人，而是混合乌尔都语诗歌和加扎勒的集合。研究使用了多种深度学习技术，如长短期记忆网络（LSTM）和门控循环单元（GRU）模型。自然语言处理（NLP）可应用于机器学习中，以理解、分析和生成人类可能使用和理解的语言。目前已有大量工作利用不同技术生成不同语言的诗歌，不同研究者对数据的收集和使用方式也各不相同。本项目的主要目标是提供一个模型，该模型能够完全基于数据生成乌尔都语诗歌，而非通过采样数据。此外，该模型可生成纯正乌尔都语诗歌，而非如基础论文中使用的罗马乌尔都语。实验结果表明，该模型生成的诗歌具有较高准确性。