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text_shortening_model_v49

This model is a fine-tuned version of facebook/bart-large-xsum on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 1.7760
  • Rouge1: 0.5119
  • Rouge2: 0.2768
  • Rougel: 0.4448
  • Rougelsum: 0.4444
  • Bert precision: 0.8755
  • Bert recall: 0.8801
  • Average word count: 8.8492
  • Max word count: 20
  • Min word count: 5
  • Average token count: 16.4709
  • % shortened texts with length > 12: 8.7302

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: 0.0001
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 5

Training results

Training Loss Epoch Step Validation Loss Rouge1 Rouge2 Rougel Rougelsum Bert precision Bert recall Average word count Max word count Min word count Average token count % shortened texts with length > 12
1.8542 1.0 83 1.6189 0.5121 0.2699 0.4302 0.4304 0.863 0.8909 11.3386 21 5 19.4312 31.746
0.9651 2.0 166 1.4837 0.4957 0.2664 0.4347 0.4362 0.8687 0.8758 8.8598 19 4 16.9815 9.2593
0.608 3.0 249 1.4074 0.5012 0.2693 0.4346 0.4342 0.8725 0.8781 8.836 20 4 15.5265 5.5556
0.3788 4.0 332 1.5646 0.5202 0.2836 0.4535 0.4537 0.876 0.881 8.9312 18 5 16.4365 10.3175
0.2296 5.0 415 1.7760 0.5119 0.2768 0.4448 0.4444 0.8755 0.8801 8.8492 20 5 16.4709 8.7302

Framework versions

  • Transformers 4.33.1
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.5
  • Tokenizers 0.13.3
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