metadata
license: apache-2.0
base_model: sshleifer/distilbart-xsum-12-6
tags:
- generated_from_trainer
model-index:
- name: bart-abs-2409-0144-lr-3e-05-bs-4-maxep-10
results: []
bart-abs-2409-0144-lr-3e-05-bs-4-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: 3.7398
- Rouge/rouge1: 0.4519
- Rouge/rouge2: 0.1957
- Rouge/rougel: 0.3795
- Rouge/rougelsum: 0.3809
- Bertscore/bertscore-precision: 0.8939
- Bertscore/bertscore-recall: 0.8913
- Bertscore/bertscore-f1: 0.8925
- Meteor: 0.4012
- Gen Len: 37.1455
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: 3e-05
- 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: 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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.7649 | 1.0 | 217 | 2.3871 | 0.4493 | 0.2047 | 0.3867 | 0.3879 | 0.8969 | 0.89 | 0.8933 | 0.3916 | 35.6364 |
| 0.5599 | 2.0 | 434 | 2.5775 | 0.4553 | 0.2005 | 0.3849 | 0.3861 | 0.8951 | 0.8914 | 0.8931 | 0.3951 | 36.1636 |
| 0.4078 | 3.0 | 651 | 2.9176 | 0.4622 | 0.2118 | 0.3904 | 0.3927 | 0.8942 | 0.8925 | 0.8932 | 0.4137 | 36.9818 |
| 0.2969 | 4.0 | 868 | 3.1512 | 0.4589 | 0.2038 | 0.3877 | 0.3892 | 0.8957 | 0.8886 | 0.892 | 0.3961 | 34.6364 |
| 0.2291 | 5.0 | 1085 | 3.3475 | 0.4594 | 0.2035 | 0.3899 | 0.3915 | 0.8964 | 0.8925 | 0.8943 | 0.4099 | 36.9364 |
| 0.2006 | 6.0 | 1302 | 3.3661 | 0.466 | 0.209 | 0.3934 | 0.3959 | 0.896 | 0.8933 | 0.8945 | 0.4136 | 37.2818 |
| 0.1485 | 7.0 | 1519 | 3.5165 | 0.4639 | 0.2054 | 0.3846 | 0.3862 | 0.8939 | 0.8931 | 0.8934 | 0.4137 | 38.7636 |
| 0.1131 | 8.0 | 1736 | 3.6478 | 0.4595 | 0.202 | 0.3882 | 0.3908 | 0.8958 | 0.8903 | 0.8929 | 0.402 | 35.2727 |
| 0.0945 | 9.0 | 1953 | 3.7024 | 0.4614 | 0.2048 | 0.39 | 0.391 | 0.8933 | 0.894 | 0.8935 | 0.4163 | 40.1545 |
| 0.0794 | 10.0 | 2170 | 3.7398 | 0.4519 | 0.1957 | 0.3795 | 0.3809 | 0.8939 | 0.8913 | 0.8925 | 0.4012 | 37.1455 |
Framework versions
- Transformers 4.44.0
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1