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flan-t5-large-stacked-samsum-1024

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This model is a fine-tuned version of google/flan-t5-large on the stacked-summaries/stacked-samsum-1024 dataset.

It achieves the following results on the evaluation set:

  • Loss: 2.1846
  • Rouge1: 57.9637
  • Rouge2: 28.7446
  • Rougel: 44.3826
  • Rougelsum: 54.0399
  • Gen Len: 122.77

Model description

This model card presents a model trained on a stacked dataset that aims to improve summarization by testing the benefits of "task-oriented pretraining". The model is designed to learn how to effectively condense and distill information from text by stacking summaries and separating them into independent concepts. In this way, the model can learn to identify essential information without simply mimicking the style of the dataset summaries.

The token used to identify a new concept in the summary is [NEXT_CONCEPT]. You can split an output summary based on this token to see how it split the input text information: summary_text.split("[NEXT_CONCEPT]") etc.

Intended uses & limitations

  • max input/output is 1024 tokens
  • this is mostly a test because samsum is not exactly the best dataset for general-purpose summarization

Training and evaluation data

See the dataset card linked on this page for info

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 0.0001
  • train_batch_size: 8
  • eval_batch_size: 4
  • seed: 24915
  • distributed_type: multi-GPU
  • gradient_accumulation_steps: 32
  • total_train_batch_size: 256
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_ratio: 0.02
  • num_epochs: 1.0

Training results

Training Loss Epoch Step Validation Loss Rouge1 Rouge2 Rougel Rougelsum Gen Len
0.1195 0.17 20 2.0635 57.8829 28.7887 44.4256 54.1299 121.8
0.1084 0.35 40 2.1178 58.0416 28.6487 44.3905 54.1557 122.893
0.1019 0.52 60 2.1576 57.816 28.7069 44.4242 53.9598 120.524
0.0975 0.7 80 2.1821 57.9597 28.8178 44.4854 54.068 121.793
0.0947 0.87 100 2.1846 57.9637 28.7446 44.3826 54.0399 122.77

Framework versions

  • Transformers 4.26.0.dev0
  • Pytorch 1.13.0+cu117
  • Datasets 2.6.1
  • Tokenizers 0.13.1
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Evaluation results