ArtifactAI
Add evaluation results on the section config and test split of ccdv/arxiv-summarization (#2)
5f55d8a
metadata
language: en
license: apache-2.0
tags:
  - summarization
datasets: arxiv-summarization
model-index:
  - name: ArtifactAI/led_large_16384_arxiv_summarization
    results:
      - task:
          type: summarization
          name: Summarization
        dataset:
          name: ccdv/arxiv-summarization
          type: ccdv/arxiv-summarization
          config: section
          split: test
        metrics:
          - type: rouge
            value: 37.9472
            name: ROUGE-1
            verified: true
            verifyToken: >-
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          - type: rouge
            value: 11.3138
            name: ROUGE-2
            verified: true
            verifyToken: >-
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          - type: rouge
            value: 20.5557
            name: ROUGE-L
            verified: true
            verifyToken: >-
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          - type: rouge
            value: 33.8336
            name: ROUGE-LSUM
            verified: true
            verifyToken: >-
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          - type: loss
            value: 2.8064792156219482
            name: loss
            verified: true
            verifyToken: >-
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          - type: gen_len
            value: 157.4174
            name: gen_len
            verified: true
            verifyToken: >-
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Introduction

A led-large-16384 model to summarize ArXiv papers. Inputs are the abstracts of papers and full documents, and outputs are the summaries of the papers.

Allenai's Longformer Encoder-Decoder (LED).

As described in Longformer: The Long-Document Transformer by Iz Beltagy, Matthew E. Peters, Arman Cohan, led-base-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.