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pszemraj
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long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP15

Summarization
Transformers
PyTorch
longt5
text2text-generation
summary
booksum
long-document
long-form
Eval Results
Model card Files Community
6
long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP15
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---
tags:
- summarization
- summary
- booksum
- long-document
- long-form
license:
- apache-2.0
- bsd-3-clause
datasets:
- kmfoda/booksum
metrics:
- rouge
inference: false
model-index:
- name: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP15
  results:
  - task:
      type: summarization
      name: Summarization
    dataset:
      name: samsum
      type: samsum
      config: samsum
      split: test
    metrics:
    - name: ROUGE-1
      type: rouge
      value: 24.5482
      verified: true
    - name: ROUGE-2
      type: rouge
      value: 4.811
      verified: true
    - name: ROUGE-L
      type: rouge
      value: 17.2505
      verified: true
    - name: ROUGE-LSUM
      type: rouge
      value: 20.906
      verified: true
    - name: loss
      type: loss
      value: 3.3002164363861084
      verified: true
    - name: gen_len
      type: gen_len
      value: 52.0
      verified: true
---

# long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP15

This model is a fine-tuned version of [pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP13](https://huggingface.co/pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP13.5) on the `kmfoda/booksum` dataset.

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0004
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 64
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 1.4

### Framework versions

- Transformers 4.23.0.dev0
- Pytorch 1.10.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1