language: en
license: mit
GPT-Neo 2.7B - Picard
Model Description
GPT-Neo 2.7B-Picard is a finetune created using EleutherAI's GPT-Neo 2.7B model.
Training data
The training data contains around 1800 ebooks, mostly in the sci-fi and fantasy genres.
How to use
You can use this model directly with a pipeline for text generation. This example generates a different sequence each time it's run:
>>> from transformers import pipeline
>>> generator = pipeline('text-generation', model='mrseeker87/GPT-Neo-2.7B-Picard')
>>> generator("Jean-Luc Picard", do_sample=True, min_length=50)
[{'generated_text': 'Jean-Luc Picard, the captain of a Federation starship in command of one of Starfleet's few fulltime scientists.'}]
Limitations and Biases
GPT-Neo was trained as an autoregressive language model. This means that its core functionality 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. GPT-Neo was trained on the Pile, a dataset known to contain profanity, lewd, and otherwise abrasive language. Depending on your usecase GPT-Neo 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-Neo 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 is made using the following software:
@software{gpt-neo,
author = {Black, Sid and
Leo, Gao and
Wang, Phil and
Leahy, Connor and
Biderman, Stella},
title = {{GPT-Neo: Large Scale Autoregressive Language
Modeling with Mesh-Tensorflow}},
month = mar,
year = 2021,
note = {{If you use this software, please cite it using
these metadata.}},
publisher = {Zenodo},
version = {1.0},
doi = {10.5281/zenodo.5297715},
url = {https://doi.org/10.5281/zenodo.5297715}
}