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AlgorithmicResearchGroup/
led_large_16384_billsum_summarization

Name: led_large_16384_billsum_summarization
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Longformer Encoder-Decoder (LED) fine-tuned on Billsum

This model is a fine-tuned version of led-large-16384 on the billsum dataset.

As described in Longformer: The Long-Document Transformer by Iz Beltagy, Matthew E. Peters, Arman Cohan, led-large-16384 was initialized from bart-base since both models share the exact same architecture. To be able to process 16K tokens, bart-base's position embedding matrix was simply copied 16 times.

Use In Transformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("Artifact-AI/led_large_16384_billsum_summarization")

model = AutoModelForSeq2SeqLM.from_pretrained("Artifact-AI/led_large_16384_billsum_summarization")

Results

Model	Rouge-1	Rouge-2	Rouge-L	Rouge-Lsum
LED Large	47.843	26.342	34.230	41.689
LED Base	47.672	26.737	34.568	41.529

The model is trained on the BillSum summarization dataset found here

Test The Model

Please find a notebook to test the model below:

Open In Colab

Citing & Authors

@misc{led_large_16384_billsum_summarization,
    title={led_large_16384_billsum_summarization},
    author={Matthew Kenney},
    year={2023}
}

AlgorithmicResearchGroup/led_large_16384_billsum_summarization