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long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP

Summarization
Transformers
PyTorch
longt5
text2text-generation
summary
booksum
long-document
long-form
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long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP / README.md
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metadata
license: apache-2.0
tags:
  - summarization
  - summary
  - booksum
  - long-document
  - long-form
datasets:
  - kmfoda/booksum
metrics:
  - rouge
inference: false
model-index:
  - name: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP
    results:
      - task:
          type: summarization
          name: Summarization
        dataset:
          name: kmfoda/booksum
          type: kmfoda/booksum
          config: kmfoda--booksum
          split: test
        metrics:
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            value: 35.9969
            name: ROUGE-1
            verified: true
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      - task:
          type: summarization
          name: Summarization
        dataset:
          name: samsum
          type: samsum
          config: samsum
          split: test
        metrics:
          - type: rouge
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      - task:
          type: summarization
          name: Summarization
        dataset:
          name: xsum
          type: xsum
          config: default
          split: test
        metrics:
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            name: ROUGE-1
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      - task:
          type: summarization
          name: Summarization
        dataset:
          name: billsum
          type: billsum
          config: default
          split: test
        metrics:
          - type: rouge
            value: 36.5688
            name: ROUGE-1
            verified: true
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      - task:
          type: summarization
          name: Summarization
        dataset:
          name: launch/gov_report
          type: launch/gov_report
          config: plain_text
          split: test
        metrics:
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long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP

NOTE: this is still a work-in-progress (WIP) and not completed/converged by any means, but sharing to maybe save some time for others :)

Updates

As I update this WIP checkpoint, I will post a note here.

  • July 26, 2022: add two more epochs of training, metrics starting to be almost as good as the more-tuned base variant
  • July 8, 2022: add checkpoint with ~4 epochs of training on A100, equating to approx 350 steps of functional batch size 128
  • July 4, 2022: add checkpoint with six additional epochs of training with the dataset summary outputs filtered to 1024 tokens, resolving the prior issue of short summaries.

About

  • a checkpoint of Stancld/longt5-tglobal-large-16384-pubmed-3k_steps trained on kmfoda/booksum for about 26 epochs
  • max input lengths during training vary between 8192 and 16384 tokens depending on GPU availability. This checkpoint was trained with 16384 tokens as the max input length for the final 10+ epochs

Comparisons