{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-modern-turkish-poet-fine-tuned-gpt-2","title":"A Modern Turkish Poet: Fine-Tuned GPT-2","arxiv_id":null,"date":"2023-09-15","proceeding":"8th International Conference on Computer Science and Engineering (UBMK) 2023 9","authors":["Uygar Kurt","Aykut Çayır"],"abstract":"Generative tasks are getting more realistic thanks to the improvements in deep learning. Text generation is getting increasingly important as LLMs (large language models) get more advanced. Even though ChatGPT gave rise to many computer-generated literary works, it has several limitations, such as not being open-sourced and can't be fine-tuned. Because other LLMs are understudied in Turkish, they usually can't be used efficiently to generate literary work. In this paper, we fine-tuned GPT-2 [1] models on sixty Turkish poem categories and trained five models that convey different emotions and write on different topics. The results are gathered in a 70-page Turkish poem book named “Gerçekligin İçinde” that contains 50 poems in total, 10 poems for each chapter. The book got published by Amazon [2], Goodreads [3] and Google Books [4].","url_abs":"https://ieeexplore.ieee.org/document/10286720","url_pdf":"https://ieeexplore.ieee.org/document/10286720","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-modern-turkish-poet-fine-tuned-gpt-2","repo_url":"https://github.com/uygarkurt/A-Modern-Turkish-Poet-Fine-Tuned-GPT-2","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"text-generation","task_name":"Text Generation"}],"methods":[{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"attention-dropout","method_name":"Attention Dropout"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"cosine-annealing","method_name":"Cosine Annealing"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"discriminative-fine-tuning","method_name":"Discriminative Fine-Tuning"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"gpt-2","method_name":"GPT-2"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"linear-warmup-with-cosine-annealing","method_name":"Linear Warmup With Cosine Annealing"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"weight-decay","method_name":"Weight Decay"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}