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---
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library_name: transformers
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tags:
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- synthetic
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- '16384'
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license: apache-2.0
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datasets:
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- BEE-spoke-data/synthsumm-open-v1.0
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language:
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- en
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base_model:
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- google/pegasus-x-base
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pipeline_tag: summarization
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---
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# pegasus-x-base-synthsumm_open-16k |
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<a href="https://colab.research.google.com/gist/pszemraj/230db7f3fef91ebe5e2957465198ea26/pegasus-x-base-synthsumm_open-16k-example.ipynb"> |
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<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> |
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</a> |
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This is a text-to-text summarization model fine-tuned from [pegasus-x-base](https://hf.co/google/pegasus-x-base) on a dataset of long documents from various sources/domains and their synthetic summaries. |
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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_). |
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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](https://github.com/pszemraj/textsum) util package 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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then: |
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```python |
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from textsum.summarize import Summarizer |
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model_name = "BEE-spoke-data/pegasus-x-base-synthsumm_open-16k" |
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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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``` |
