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Update README.md

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@@ -38,7 +38,7 @@ inference:
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  - summarizing text via arXiv models will typically make the summary sound so needlessly complicated you might as well have read the original text in that time anyway.
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  - this model is one attempt to help with that
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  - this model has been trained for 7 epochs total and is closer to finished.
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- - Will continue to improve based on any result findings/feedback.
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  - the starting checkpoint was `google/bigbird-pegasus-large-bigpatent`
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  # example usage
@@ -52,12 +52,12 @@ from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
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  from transformers import pipeline
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  _model = AutoModelForSeq2SeqLM.from_pretrained(
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- "pszemraj/bigbird-pegasus-large-booksum-40k-K",
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  low_cpu_mem_usage=True,
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  )
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  _tokenizer = AutoTokenizer.from_pretrained(
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- "pszemraj/bigbird-pegasus-large-booksum-40k-K"
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  )
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  - summarizing text via arXiv models will typically make the summary sound so needlessly complicated you might as well have read the original text in that time anyway.
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  - this model is one attempt to help with that
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  - this model has been trained for 7 epochs total and is closer to finished.
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+ - Will continue to improve (slowly, now that it has been trained on the dataset for 70k steps) based on any result findings/feedback.
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  - the starting checkpoint was `google/bigbird-pegasus-large-bigpatent`
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  # example usage
 
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  from transformers import pipeline
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  _model = AutoModelForSeq2SeqLM.from_pretrained(
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+ "pszemraj/bigbird-pegasus-large-K-booksum",
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  low_cpu_mem_usage=True,
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  )
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  _tokenizer = AutoTokenizer.from_pretrained(
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+ "pszemraj/bigbird-pegasus-large-K-booksum",
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  )
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