IPHS 300: Artificial Intelligence for the Humanities: Text, Image, and Sound
Document Type
Poster
Publication Date
Fall 2021
Abstract
GPT-2 has a number of hyper parameters that can be tuned to adjust the generated text, including temperature, number of epochs, size of the training text corpus, batch size, and number of samples generated. Since GPT-2 is a new AI model, the effects of fine-tuning are still relatively unknown and unexplored. This paper summits experiments adjusting these GPT-2 parameters and how they affect the text output that the model generates.
Recommended Citation
Lawson, Rebecca, "Fine-tuning Daria: Exploring the Implications of Temperature, Epochs, & Corpus Size on GPT-2 Screenplay Generation" (2021). IPHS 300: Artificial Intelligence for the Humanities: Text, Image, and Sound. Paper 29.
https://digital.kenyon.edu/dh_iphs_ai/29
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.