metadata

library_name: transformers
tags:
  - synthetic
  - '16384'
license: apache-2.0
datasets:
  - BEE-spoke-data/synthsumm-open-v1.0
language:
  - en
base_model:
  - google/pegasus-x-base
pipeline_tag: summarization

pegasus-x-base-synthsumm_open-16k

This is a text-to-text summarization model fine-tuned from pegasus-x-base on a dataset of long documents from various sources/domains and their synthetic summaries.

It performs surprisingly well as a general summarization model for its size. More details, a larger model, and the dataset will be released (as time permits).

Usage

It's recommended to use this model with beam search decoding. If interested, you can also use the textsum util package to have most of this abstracted out for you:

pip install -U textsum

then:

from textsum.summarize import Summarizer

model_name = "BEE-spoke-data/pegasus-x-base-synthsumm_open-16k"
summarizer = Summarizer(model_name) # GPU auto-detected
text = "put the text you don't want to read here"
summary = summarizer.summarize_string(text)
print(summary)