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--- |
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language: en |
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license: mit |
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--- |
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# GPT-Neo 2.7B - Shinen |
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## Model Description |
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GPT-Neo 2.7B-Shinen is a finetune created using EleutherAI's GPT-Neo 2.7B model. Compared to GPT-Neo-2.7-Horni, this model is much heavier on the sexual content. |
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**Warning: THIS model is NOT suitable for use by minors. The model will output X-rated content.** |
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## Training data |
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The training data contains user-generated stories from sexstories.com. All stories are tagged using the following way: |
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``` |
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[Theme: <theme1>, <theme2> ,<theme3>] |
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<Story goes here> |
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``` |
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### How to use |
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You can use this model directly with a pipeline for text generation. This example generates a different sequence each time it's run: |
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```py |
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>>> from transformers import pipeline |
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>>> generator = pipeline('text-generation', model='KoboldAI/GPT-Neo-2.7B-Shinen') |
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>>> generator("She was staring at me", do_sample=True, min_length=50) |
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[{'generated_text': 'She was staring at me with a look that said it all. She wanted me so badly tonight that I wanted'}] |
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``` |
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### Limitations and Biases |
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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. |
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GPT-Neo-Shinen was trained on a dataset known to contain profanity, lewd, and otherwise abrasive language. GPT-Neo-Shinen *WILL* produce socially unacceptable text without warning. |
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GPT-Neo-Shinen 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. |
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### BibTeX entry and citation info |
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The model is made using the following software: |
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```bibtex |
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@software{gpt-neo, |
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author = {Black, Sid and |
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Leo, Gao and |
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Wang, Phil and |
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Leahy, Connor and |
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Biderman, Stella}, |
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title = {{GPT-Neo: Large Scale Autoregressive Language |
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Modeling with Mesh-Tensorflow}}, |
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month = mar, |
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year = 2021, |
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note = {{If you use this software, please cite it using |
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these metadata.}}, |
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publisher = {Zenodo}, |
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version = {1.0}, |
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doi = {10.5281/zenodo.5297715}, |
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url = {https://doi.org/10.5281/zenodo.5297715} |
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} |
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``` |
