GPT-J 6B-Shinen is a finetune created using EleutherAI's GPT-J 6B model. Compared to GPT-Neo-2.7-Horni, this model is much heavier on the sexual content.
Warning: THIS model is NOT suitable for use by minors. The model will output X-rated content.
Training data
The training data contains user-generated stories from sexstories.com. All stories are tagged using the following way:
You can use this model directly with a pipeline for text generation. This example generates a different sequence each time it's run:
py
1>>>from transformers import pipeline
2>>> generator = pipeline('text-generation', model='KoboldAI/GPT-J-6B-Shinen')3>>> generator("She was staring at me", do_sample=True, min_length=50)4[{'generated_text':'She was staring at me with a look that said it all. She wanted me so badly tonight that I wanted'}]
Limitations and Biases
The core functionality of GPT-J is taking a string of text and predicting the next token. While language models are widely used for tasks other than this, there are a lot of unknowns with this work. When prompting GPT-J it is important to remember that the statistically most likely next token is often not the token that produces the most "accurate" text. Never depend upon GPT-J to produce factually accurate output.
GPT-J was trained on the Pile, a dataset known to contain profanity, lewd, and otherwise abrasive language. Depending upon use case GPT-J may produce socially unacceptable text. See Sections 5 and 6 of the Pile paper for a more detailed analysis of the biases in the Pile.
As with all language models, it is hard to predict in advance how GPT-J will respond to particular prompts and offensive content may occur without warning. We recommend having a human curate or filter the outputs before releasing them, both to censor undesirable content and to improve the quality of the results.
BibTeX entry and citation info
The model uses the following model as base:
bibtex
1@misc{gpt-j,
2 author = {Wang, Ben and Komatsuzaki, Aran},
3 title = {{GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model}},
4 howpublished = {\url{https://github.com/kingoflolz/mesh-transformer-jax}},
5 year = 2021,
6 month = May
7}
Acknowledgements
This project would not have been possible without compute generously provided by Google through the
TPU Research Cloud, as well as the Cloud TPU team for providing early access to the Cloud TPU VM Alpha.
