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@@ -20,7 +20,27 @@ Fine-tuned on a synthetic dataset of curated long-context text and `GPT-3.5-turb
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  Try it in [gradio demo](https://huggingface.co/spaces/pszemraj/document-summarization) | [.md with example outputs](evals-outputs/GAUNTLET.md) (gauntlet)
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- ## Model description
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  This model is a fine-tuned version of [pszemraj/pegasus-x-large-book-summary](https://huggingface.co/pszemraj/pegasus-x-large-book-summary) on the None dataset.
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  It achieves the following results on the evaluation set:
 
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  Try it in [gradio demo](https://huggingface.co/spaces/pszemraj/document-summarization) | [.md with example outputs](evals-outputs/GAUNTLET.md) (gauntlet)
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+ ## Usage
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+ It's recommended to use this model with [beam search decoding](https://huggingface.co/docs/transformers/generation_strategies#beamsearch-decoding). If interested, you can also use the `textsum` [util repo](https://github.com/pszemraj/textsum) to have most of this abstracted out for you:
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+ ```bash
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+ pip install -U textsum
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+ ```
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+ ```python
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+ from textsum.summarize import Summarizer
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+ model_name = "pszemraj/pegasus-x-large-book_synthsumm"
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+ summarizer = Summarizer(model_name) # GPU auto-detected
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+ text = "put the text you don't want to read here"
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+ summary = summarizer.summarize_string(text)
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+ print(summary)
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+ ```
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+ ## Details
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  This model is a fine-tuned version of [pszemraj/pegasus-x-large-book-summary](https://huggingface.co/pszemraj/pegasus-x-large-book-summary) on the None dataset.
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  It achieves the following results on the evaluation set: