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finetuned_bart_for_titlekeygen_custom

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metadata
base_model: beogradjanka/bart_multitask_finetuned_for_title_and_keyphrase_generation
tags:
  - generated_from_trainer
metrics:
  - rouge
model-index:
  - name: finetuned_bart_for_titlekeygen_custom
    results: []

finetuned_bart_for_titlekeygen_custom

This model is a fine-tuned version of beogradjanka/bart_multitask_finetuned_for_title_and_keyphrase_generation on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 4.9518
  • Rouge1: 27.1931
  • Rouge2: 17.9025
  • Rougel: 27.0914
  • Rougelsum: 27.5983
  • Gen Len: 10.4444

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: 2e-05
  • train_batch_size: 4
  • eval_batch_size: 4
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 8
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 30

Training results

Training Loss Epoch Step Validation Loss Rouge1 Rouge2 Rougel Rougelsum Gen Len
No log 0.91 5 4.4910 24.613 15.7516 23.7302 24.6083 11.0
No log 2.0 11 4.5644 23.1235 14.2857 22.4578 23.0522 9.0
No log 2.91 16 4.6286 24.7078 15.2652 24.7869 25.2108 10.0556
No log 4.0 22 4.6774 24.5206 17.2336 24.3437 25.1579 10.5
No log 4.91 27 4.7187 26.304 18.2158 26.3307 26.814 10.2778
No log 6.0 33 4.7962 26.7873 17.4056 26.8439 27.3369 10.1667
No log 6.91 38 4.8356 26.9721 17.3677 26.9926 27.5469 10.0556
No log 8.0 44 4.7903 28.0369 17.3677 28.1825 28.5073 10.0
No log 8.91 49 4.7801 27.1885 17.6215 26.6686 27.0771 10.1111
No log 10.0 55 4.8049 27.1885 17.6215 26.6686 27.0771 10.1667
No log 10.91 60 4.9275 26.8226 17.9025 26.8217 27.2015 10.0
No log 12.0 66 4.9744 26.8226 17.9025 26.8217 27.2015 10.1111
No log 12.91 71 4.9863 26.0231 17.9025 26.0376 26.3104 10.0
No log 14.0 77 4.9497 24.9154 17.2851 25.0693 25.3933 9.8333
No log 14.91 82 4.9272 24.5615 17.037 24.4986 25.0688 10.0556
No log 16.0 88 4.9417 25.307 17.037 25.3656 25.9459 10.0
No log 16.91 93 4.9712 26.7457 17.9025 26.7271 27.1185 9.9444
No log 18.0 99 4.9649 25.9581 17.9025 25.9478 26.2429 10.0556
No log 18.91 104 4.9305 27.0746 17.9025 26.9536 27.4569 10.6111
No log 20.0 110 4.9212 27.9706 18.5997 27.2022 27.7873 10.8889
No log 20.91 115 4.9196 27.7549 18.8225 27.5716 28.1145 10.6667
No log 22.0 121 4.9316 27.1931 17.9025 27.0914 27.5983 10.4444
No log 22.91 126 4.9525 27.1931 17.9025 27.0914 27.5983 10.2222
No log 24.0 132 4.9590 27.1931 17.9025 27.0914 27.5983 10.2222
No log 24.91 137 4.9545 27.1931 17.9025 27.0914 27.5983 10.2222
No log 26.0 143 4.9514 27.1931 17.9025 27.0914 27.5983 10.4444
No log 26.91 148 4.9515 27.1931 17.9025 27.0914 27.5983 10.4444
No log 27.27 150 4.9518 27.1931 17.9025 27.0914 27.5983 10.4444

Framework versions

  • Transformers 4.38.2
  • Pytorch 2.1.2
  • Datasets 2.1.0
  • Tokenizers 0.15.1