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README.md CHANGED
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  ---
 
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  license: mit
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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 1.3B - Adventure
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+ ## Model Description
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+ GPT-Neo 1.3B-Adventure is a finetune created using EleutherAI's GPT-Neo 1.3B model.
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+ ## Training data
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+ The training data is a direct copy of the "cys" dataset by VE, a CYOA-based dataset.
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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-1.3B-Adventure')
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+ >>> generator("> You wake up.", do_sample=True, min_length=50)
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+ [{'generated_text': '> You wake up"\nYou get out of bed, don your armor and get out of the door in search for new adventures.'}]
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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 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.
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+ 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.
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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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+ ```
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+ "bos_token_id": 50256,
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