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End of training
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metadata
license: mit
base_model: facebook/bart-large-xsum
tags:
  - generated_from_trainer
metrics:
  - rouge
model-index:
  - name: text_shortening_model_v47
    results: []

text_shortening_model_v47

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: 6.3912
  • Rouge1: 0.0
  • Rouge2: 0.0
  • Rougel: 0.0
  • Rougelsum: 0.0
  • Bert precision: 0.6047
  • Bert recall: 0.5681
  • Average word count: 1.0
  • Max word count: 1
  • Min word count: 1
  • Average token count: 12.0
  • % shortened texts with length > 12: 0.0

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: 0.005
  • 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: 7

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
7.822 1.0 83 7.4737 0.0776 0.0 0.0775 0.0776 0.6348 0.6223 2.0 2 2 13.0 0.0
3.2859 2.0 166 6.6585 0.1063 0.0 0.1063 0.1063 0.6469 0.608 5.0026 6 5 12.0 0.0
3.0284 3.0 249 6.4761 0.116 0.0 0.116 0.1161 0.6479 0.6388 3.9974 4 3 14.0 0.0
2.9681 4.0 332 6.4592 0.0 0.0 0.0 0.0 0.6071 0.5723 1.0 1 1 12.0 0.0
2.9377 5.0 415 6.4142 0.0 0.0 0.0 0.0 0.6047 0.5681 1.0 1 1 12.0 0.0
2.9168 6.0 498 6.4049 0.0 0.0 0.0 0.0 0.6049 0.5685 1.0 1 1 12.0 0.0
2.8964 7.0 581 6.3912 0.0 0.0 0.0 0.0 0.6047 0.5681 1.0 1 1 12.0 0.0

Framework versions

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