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@@ -179,4 +179,31 @@ from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
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  tokenizer = AutoTokenizer.from_pretrained("Artifact-AI/led_large_16384_billsum_summarization")
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  model = AutoModelForSeq2SeqLM.from_pretrained("Artifact-AI/led_large_16384_billsum_summarization")
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- ```
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  tokenizer = AutoTokenizer.from_pretrained("Artifact-AI/led_large_16384_billsum_summarization")
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  model = AutoModelForSeq2SeqLM.from_pretrained("Artifact-AI/led_large_16384_billsum_summarization")
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+ ```
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+
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+ ### Results
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+ | Model | Rouge-1 | Rouge-2 | Rouge-L | Rouge-Lsum |
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+ | --- | --- | --- | --- | --- |
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+ | LED Large | 47.843 | 26.342 | 34.230 | 41.689 |
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+ | LED Base | 47.672 | 26.737 | 34.568 | 41.529 |
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+ [![LED Large](figure/results.png)
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+ The model is trained on the BillSum summarization dataset found [here](https://huggingface.co/datasets/billsum)
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+ ### Test The Model
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+ Please find a notebook to test the model below:
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+ [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1TX4IVyvwduDqsok6XuS6i9VIOs9pTXra?usp=sharing)
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+ ## Citing & Authors
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+ @misc{led_large_16384_billsum_summarization,
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+ title={led_large_16384_billsum_summarization},
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+ author={Matthew Kenney},
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+ year={2023}
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+ }