title: ÐÐµÐ½ÐµÑÐ°ÑÐ¸Ñ ÐºÐ°ÑÑ Ð´Ð»Ñ ÐºÐ¾Ð»Ð»ÐµÐºÑÐ¸Ð¾Ð½Ð½Ð¾-ÐºÐ°ÑÑÐ¾ÑÐ½ÑÑ Ð¸Ð³Ñ Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Collectible Card Games cards generation with neural network
Creators: name: Ð§Ð¸Ð¶Ð¾Ð², ÐÐ¸ÐºÐ¸ÑÐ°
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2022
Subjects: Ð³ÐµÐ½ÐµÑÐ°ÑÐ¸Ð²Ð½Ð¾-ÑÐ¾ÑÑÑÐ·Ð°ÑÐµÐ»ÑÐ½ÑÐµ ÑÐµÑÐ¸
GPT-2
Ð³ÐµÐ½ÐµÑÐ°ÑÐ¸Ñ ÑÐµÐºÑÑÐ¾Ð²
Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
generative adversarial networks
text generation
neural network
machine learning

description: ÐÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸ Ð¸ ÑÐµÑÐ¸ Ð´Ð»Ñ Ð³ÐµÐ½ÐµÑÐ°ÑÐ¸Ð¸ ÑÐµÐºÑÑÐ°, ÑÐ°ÐºÐ¸Ðµ ÐºÐ°Ðº GPT-2, Ð·Ð°ÑÐ°Ð±Ð¾ÑÐ°Ð»Ð¸ Ð±Ð¾Ð»ÑÑÑÑ Ð¿Ð¾Ð¿ÑÐ»ÑÑÐ½Ð¾ÑÑÑ Ð² ÑÐ°Ð·Ð½ÑÑ Ð¾Ð±Ð»Ð°ÑÑÑÑ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ñ. ÐÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸ ÑÐ°ÐºÐ¾Ð³Ð¾ ÑÐ¸Ð¿Ð° ÑÐ°ÑÑÐ¾ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·ÑÑÑÑÑ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°ÑÐµÐ»ÑÐ¼Ð¸ Ð¸ ÑÐ½ÑÑÐ·Ð¸Ð°ÑÑÐ°Ð¼Ð¸ Ð´Ð»Ñ Ð¿Ð¾Ð»ÑÑÐµÐ½Ð¸Ñ Ð¾ÑÐ²ÐµÑÐ¾Ð² Ð½Ð° Ð·Ð°Ð´Ð°Ð½Ð½ÑÐµ Ð²Ð¾Ð¿ÑÐ¾ÑÑ Ð¸Ð»Ð¸ Ð´Ð»Ñ ÑÐ¾Ð·Ð´Ð°Ð½Ð¸Ñ ÑÐµÐºÑÑÐ¾Ð² Ð¿Ð¾ Ð¾Ð¿ÑÐµÐ´ÐµÐ»ÑÐ½Ð½Ð¾Ð¼Ñ ÑÐ¾ÑÐ¼Ð°ÑÑ. Ð Ð¿ÑÐ¸Ð¼ÐµÑÑ, ÑÐ°ÐºÐ¸Ðµ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¼Ð¾Ð³ÑÑ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°ÑÑÑÑ Ð¿ÑÐ¸ ÑÐ¾Ð·Ð´Ð°Ð½Ð¸Ð¸ Ð¸Ð³Ñ. Ð ÑÐ°Ð¼ÐºÐ°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÑ ÑÐµÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°Ð½ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼ Ð´Ð¾Ð¿Ð¾Ð»Ð½Ð¸ÑÐµÐ»ÑÐ½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð³ÐµÐ½ÐµÑÐ°ÑÐ¸Ð¸ ÑÐµÐºÑÑÐ° Ð¿ÑÐ¸ Ð¿Ð¾Ð¼Ð¾ÑÐ¸ Ð³ÐµÐ½ÐµÑÐ°ÑÐ¸Ð²Ð½Ð¾-ÑÐ¾ÑÑÑÐ·Ð°ÑÐµÐ»ÑÐ½Ð¾Ð³Ð¾ Ð¼ÐµÑÐ¾Ð´Ð°. ÐÐ´Ð½Ð° Ð¸Ð· ÑÐµÐ»ÐµÐ¹ ÑÐ°Ð±Ð¾ÑÑ â Ð¾Ð¿ÑÐµÐ´ÐµÐ»Ð¸ÑÑ, ÑÐ¿Ð¾ÑÐ¾Ð±Ð½Ñ Ð»Ð¸ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð´Ð¾Ð¿Ð¾Ð»Ð½Ð¸ÑÐµÐ»ÑÐ½Ð¾ Ð¾Ð±ÑÑÐ¸ÑÑÑÑ Ð¾ÑÐ¾Ð±ÐµÐ½Ð½Ð¾ÑÑÑÐ¼ Ð´Ð°Ð½Ð½ÑÑ Ð¿ÑÐ¸ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ð¸ Ð´Ð°Ð½Ð½Ð¾Ð³Ð¾ Ð¼ÐµÑÐ¾Ð´Ð°. Ð Ð±ÑÐ´ÑÑÐµÐ¼ Ð¼ÐµÑÐ¾Ð´ Ð¼Ð¾Ð¶ÐµÑ Ð±ÑÑÑ ÑÐ»ÑÑÑÐµÐ½ Ð¸ Ð¼Ð¾Ð´Ð¸ÑÐ¸ÑÐ¸ÑÐ¾Ð²Ð°Ð½. ÐÐ½Ð°Ð»Ð¸Ð· ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ÑÑÐ¸ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð±ÑÐ´ÐµÑ Ð¿ÑÐ¾Ð²Ð¾Ð´Ð¸ÑÑÑÑ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ð°Ð½Ð¸Ð¸ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð¾Ð¿ÑÐ¾ÑÐ° Ð³ÑÑÐ¿Ð¿Ñ Ð¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°ÑÐµÐ»ÐµÐ¹, ÐºÐ¾ÑÐ¾ÑÑÐ¼ Ð±ÑÐ»Ð° Ð¿Ð¾ÑÑÐ°Ð²Ð»ÐµÐ½Ð° Ð·Ð°Ð´Ð°ÑÐ° Ð½Ð° Ð²ÑÐ´ÐµÐ»ÐµÐ½Ð¸Ðµ Ð»ÑÑÑÐµÐ³Ð¾ Ð²ÑÐ²Ð¾Ð´Ð° ÑÐµÑÐ¸. Ð ÐºÐ°ÑÐµÑÑÐ²Ðµ Ð¾ÑÐ½Ð¾Ð²Ð½Ð¾Ð¹ Ð¼Ð¾Ð´ÐµÐ»Ð¸, Ð² ÑÐ°Ð±Ð¾ÑÐµ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·ÑÐµÑÑÑ GPT-2. ÐÐ°Ð½Ð½Ð°Ñ Ð¼Ð¾Ð´ÐµÐ»Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·ÑÐµÑÑÑ ÐºÐ°Ðº Ð² ÐºÐ°ÑÐµÑÑÐ²Ðµ Ð³ÐµÐ½ÐµÑÐ°ÑÐ¾ÑÐ°, ÑÐ°Ðº Ð¸ Ð² Ð²Ð¸Ð´Ðµ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¾ÑÐ° Ð² ÑÐ¾Ð»Ð¸ Ð´Ð¸ÑÐºÑÐ¸Ð¼Ð¸Ð½Ð°ÑÐ¾ÑÐ° Ð² Ð³ÐµÐ½ÐµÑÐ°ÑÐ¸Ð²Ð½Ð¾-ÑÐ¾ÑÑÑÐ·Ð°ÑÐµÐ»ÑÐ½Ð¾Ð¹ Ð¼Ð¾Ð´ÐµÐ»Ð¸. ÐÑÐµ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð±ÑÐ»Ð¸ Ð¿Ð¾ÑÑÑÐ¾ÐµÐ½Ñ Ð¸ Ð¾Ð±ÑÑÐµÐ½Ñ Ð¿ÑÐ¸ Ð¿Ð¾Ð¼Ð¾ÑÐ¸ pyTorch. Ð¢ÐµÑÑÐ¾Ð²Ð°Ñ ÑÑÑÐ°Ð½Ð¸ÑÐ° Ð² ÑÐµÑÐ¸ ÐÐ½ÑÐµÑÐ½ÐµÑ Ð±ÑÐ»Ð° ÑÐ¾Ð·Ð´Ð°Ð½Ð° Ð¿ÑÐ¸ Ð¿Ð¾Ð¼Ð¾ÑÐ¸ Flask.
description: Neural networks, and text generation models like GPT-2, gained popularity in various applications. Text generation networks usually help researchers and enthusiasts to answer on given questions or to generate text like some style or format. For example, they can be used to generate ideas for games. In this paper will be covered complex algorithm of model training using standard fine-tuning and Generative Adversarial method. One objective is to evaluate, is this method can be used for text generation tuning and identify potential problems. In the future this algorithm can be modified further, to fit problems better. An analysis of algorithm effectivity will be evaluated with group of users that will be tasked to select most fitting text for given problem. In this paper GPT-2 was used as main model. GPT-2 language model was used for text generation and GPT-2 for sequence classification was used as discriminator in GAN method. All models were trained with pyTorch. Testing website was made with Flask.