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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-J 6B - Janeway
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+ ## Model Description
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+ GPT-J 6B-Janeway is a finetune created using EleutherAI's GPT-J 6B model.
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+ ## Training data
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+ The training data contains around 2210 ebooks, mostly in the sci-fi and fantasy genres. The dataset is based on the same dataset used by GPT-Neo-2.7B-Picard, with 20% more data in various genres.
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+ Some parts of the dataset have been prepended using the following text: `[Genre: <genre1>,<genre2>]`
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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-J-6B-Janeway')
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+ >>> generator("Welcome Captain Janeway, I apologize for the delay.", do_sample=True, min_length=50)
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+ [{'generated_text': 'Welcome Captain Janeway, I apologize for the delay."\nIt's all right," Janeway said. "I'm certain that you're doing your best to keep me informed of what\'s going on."'}]
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+ ```
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+ ### Limitations and Biases
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+
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+ 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.
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+
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+ 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](https://arxiv.org/abs/2101.00027) for a more detailed analysis of the biases in the Pile.
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+
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+ 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.
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+
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+ ### BibTeX entry and citation info
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+ The model uses the following model as base:
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+ ```bibtex
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+ @misc{gpt-j,
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+ author = {Wang, Ben and Komatsuzaki, Aran},
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+ title = {{GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model}},
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+ howpublished = {\url{https://github.com/kingoflolz/mesh-transformer-jax}},
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+ year = 2021,
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+ month = May
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+ }
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+ ```
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+
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+ ## Acknowledgements
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+
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+ This project would not have been possible without compute generously provided by Google through the
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+ [TPU Research Cloud](https://sites.research.google/trc/), as well as the Cloud TPU team for providing early access to the [Cloud TPU VM](https://cloud.google.com/blog/products/compute/introducing-cloud-tpu-vms) Alpha.
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+ "tokenizer_class": "GPT2Tokenizer",
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+ "task_specific_params": {
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+ "text-generation": {
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+ "do_sample": true,
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+ "temperature": 1.0,
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+ "max_length": 50
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+ }
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+ },
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+ "torch_dtype": "float16",
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+ "use_cache": true,
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+ "vocab_size": 50400
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+ }
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