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Evaluating some metric results before merging with the "main" wip version

This model is a fine-tuned version of pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP12 on the kmfoda/booksum.

The "base" checkpoint that I update when a training session is productive is here

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: 0.0006
  • train_batch_size: 2
  • eval_batch_size: 1
  • seed: 42
  • distributed_type: multi-GPU
  • gradient_accumulation_steps: 64
  • total_train_batch_size: 128
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_ratio: 0.01
  • num_epochs: 1.1

Framework versions

  • Transformers 4.21.2
  • Pytorch 1.10.0+cu113
  • Datasets 2.4.0
  • Tokenizers 0.12.1
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Dataset used to train pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP13

Evaluation results