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-4-maxep-6
results: []
bart-abs-1509-0313-lr-0.0003-bs-4-maxep-6
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: 3.8541
- Rouge/rouge1: 0.4315
- Rouge/rouge2: 0.1861
- Rouge/rougel: 0.3638
- Rouge/rougelsum: 0.3654
- Bertscore/bertscore-precision: 0.8936
- Bertscore/bertscore-recall: 0.8875
- Bertscore/bertscore-f1: 0.8904
- Meteor: 0.3814
- Gen Len: 35.4273
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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
- 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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2.1071 | 1.0 | 217 | 2.5496 | 0.4238 | 0.1753 | 0.3521 | 0.3541 | 0.8943 | 0.8836 | 0.8888 | 0.3359 | 32.1 |
| 1.5392 | 2.0 | 434 | 2.5867 | 0.4296 | 0.1807 | 0.3556 | 0.3551 | 0.8921 | 0.886 | 0.8889 | 0.3598 | 35.0909 |
| 1.0328 | 3.0 | 651 | 2.6952 | 0.4096 | 0.1667 | 0.3444 | 0.3453 | 0.8919 | 0.8826 | 0.8871 | 0.3519 | 33.8818 |
| 0.62 | 4.0 | 868 | 2.9126 | 0.4104 | 0.16 | 0.3478 | 0.3487 | 0.8904 | 0.8815 | 0.8858 | 0.3524 | 33.4273 |
| 0.3251 | 5.0 | 1085 | 3.3250 | 0.43 | 0.1771 | 0.3591 | 0.3598 | 0.8935 | 0.8861 | 0.8896 | 0.3744 | 34.9636 |
| 0.1503 | 6.0 | 1302 | 3.8541 | 0.4315 | 0.1861 | 0.3638 | 0.3654 | 0.8936 | 0.8875 | 0.8904 | 0.3814 | 35.4273 |
Framework versions
- Transformers 4.44.0
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1