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bigbird pegasus on the booksum dataset

this is the "latest" version of the model that has been trained the longest, currently at 70k steps

  • GOAL: A summarization model that 1) summarizes the source content accurately 2) more important IMO produces summaries that are easy to read and understand (* cough * unlike arXiv * cough *)
    • This model attempts to help with that by using the booksum dataset to provide explanatory summarization
    • Explanatory Summary - A summary that both consolidates information and also explains why said consolidated information is important.
  • This model was trained for seven epochs total (approx 70,000 steps) and is closer to finished.
    • Will continue to improve (slowly, now that it has been trained for a long time) based on any result findings/feedback.
  • starting checkpoint was google/bigbird-pegasus-large-bigpatent

example usage

An extended example, including a demo of batch summarization, is here.

  • create the summarizer object:
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
from transformers import pipeline

model = AutoModelForSeq2SeqLM.from_pretrained(

tokenizer = AutoTokenizer.from_pretrained(

summarizer = pipeline(
  • define text to be summarized, and pass it through the pipeline. Boom done.
wall_of_text = "your text to be summarized goes here."

result = summarizer(


Alternate Checkpoint

  • if experiencing runtime/memory issues, try this earlier checkpoint at 40,000 steps which is almost as good at the explanatory summarization task but runs faster.
  • see similar summarization models fine-tuned on booksum but using different architectures: long-t5 base and LED-Large

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Model size
577M params
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Dataset used to train pszemraj/bigbird-pegasus-large-K-booksum

Evaluation results