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:
Citing & Authors
@misc{led_large_16384_billsum_summarization,
title={led_large_16384_billsum_summarization},
author={Matthew Kenney},
year={2023}
}
- Downloads last month
- 41
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.