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bart-abs-1509-0313-lr-0.0003-bs-2-maxep-10

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metadata
license: apache-2.0
base_model: sshleifer/distilbart-xsum-12-6
tags:
  - generated_from_trainer
model-index:
  - name: bart-abs-1509-0313-lr-0.0003-bs-2-maxep-10
    results: []

bart-abs-1509-0313-lr-0.0003-bs-2-maxep-10

This model is a fine-tuned version of sshleifer/distilbart-xsum-12-6 on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 7.6999
  • Rouge/rouge1: 0.2439
  • Rouge/rouge2: 0.0504
  • Rouge/rougel: 0.2065
  • Rouge/rougelsum: 0.2067
  • Bertscore/bertscore-precision: 0.8544
  • Bertscore/bertscore-recall: 0.8581
  • Bertscore/bertscore-f1: 0.8562
  • Meteor: 0.229
  • Gen Len: 44.0

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: 0.0003
  • train_batch_size: 2
  • eval_batch_size: 2
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 10
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Rouge/rouge1 Rouge/rouge2 Rouge/rougel Rouge/rougelsum Bertscore/bertscore-precision Bertscore/bertscore-recall Bertscore/bertscore-f1 Meteor Gen Len
2.1693 1.0 434 4.5206 0.2693 0.0703 0.2315 0.232 0.8864 0.8585 0.8722 0.2188 29.0
1.3403 2.0 868 5.0395 0.3061 0.0778 0.251 0.2513 0.8875 0.864 0.8755 0.239 32.0
1.1783 3.0 1302 5.1339 0.2426 0.0523 0.1835 0.1835 0.8501 0.8566 0.8533 0.248 52.0
0.8203 4.0 1736 5.6678 0.3347 0.0996 0.2675 0.2678 0.8793 0.8662 0.8727 0.2663 27.0
0.623 5.0 2170 6.1732 0.2961 0.0668 0.2313 0.2314 0.8628 0.8608 0.8617 0.2421 52.0
0.5051 6.0 2604 6.1011 0.2953 0.0542 0.2213 0.2211 0.8685 0.8588 0.8636 0.2403 34.0
0.4004 7.0 3038 6.8848 0.2613 0.0803 0.214 0.2142 0.8711 0.8469 0.8588 0.2102 26.0
0.3371 8.0 3472 7.2987 0.2132 0.0353 0.1717 0.1717 0.8605 0.8522 0.8563 0.1839 27.0
0.2954 9.0 3906 7.4692 0.244 0.063 0.1986 0.1986 0.8544 0.8608 0.8575 0.211 52.0
0.2576 10.0 4340 7.6999 0.2439 0.0504 0.2065 0.2067 0.8544 0.8581 0.8562 0.229 44.0

Framework versions

  • Transformers 4.44.0
  • Pytorch 2.4.0
  • Datasets 2.21.0
  • Tokenizers 0.19.1