Introduction:
This repository contains a finetuned DistilGPT2 model for generating diverse essays on topics spanning Arts, Science, and Culture.
Dataset:
The training dataset comprises 2000+ essays covering diverse topics in Arts, Science, and Culture. These essays are written by human experts and contain a diverse set of opinions and knowledge, ensuring that the model learns from high-quality and diverse content.
Description:
The model achieved a mean perplexity of 698.603519 on the validation set, indicating its ability to generate diverse and high-quality essays on the given topics.
During Text Generation, the following parameters are used:
max_length: The maximum length of the generated text, set to 400 tokens.
num_beams: The number of beams for beam search, set to 10. A higher value will increase the diversity of the generated text but may also increase the inference time.
early_stopping: If set to True, the generation will stop as soon as the end-of-sequence token is generated.
temperature: The sampling temperature, is set to 0.3.
no_repeat_ngram_size: The size of the n-gram window to avoid repetitions, set to 2.
