metadata
library_name: transformers
license: apache-2.0
base_model: sshleifer/distilbart-xsum-12-6
tags:
- generated_from_trainer
model-index:
- name: bart-abs-2409-1947-lr-3e-05-bs-8-maxep-10
results: []
bart-abs-2409-1947-lr-3e-05-bs-8-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: 4.2084
- Rouge/rouge1: 0.4731
- Rouge/rouge2: 0.2204
- Rouge/rougel: 0.4091
- Rouge/rougelsum: 0.4105
- Bertscore/bertscore-precision: 0.8963
- Bertscore/bertscore-recall: 0.8954
- Bertscore/bertscore-f1: 0.8957
- Meteor: 0.4281
- Gen Len: 38.3182
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: 8
- eval_batch_size: 8
- 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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.0785 | 1.0 | 109 | 3.9838 | 0.4581 | 0.1987 | 0.3887 | 0.3892 | 0.8957 | 0.8906 | 0.893 | 0.4019 | 35.6455 |
| 0.0862 | 2.0 | 218 | 3.9075 | 0.4574 | 0.2028 | 0.3856 | 0.3863 | 0.8923 | 0.8918 | 0.8919 | 0.4091 | 39.1545 |
| 0.0792 | 3.0 | 327 | 3.9794 | 0.4593 | 0.1972 | 0.3807 | 0.3814 | 0.889 | 0.8926 | 0.8906 | 0.4145 | 41.9182 |
| 0.0673 | 4.0 | 436 | 4.0419 | 0.4634 | 0.2047 | 0.3928 | 0.3937 | 0.8948 | 0.8918 | 0.8931 | 0.4133 | 36.6909 |
| 0.0604 | 5.0 | 545 | 4.1048 | 0.4629 | 0.2112 | 0.396 | 0.3971 | 0.8956 | 0.8926 | 0.8939 | 0.4118 | 36.9727 |
| 0.0548 | 6.0 | 654 | 4.1331 | 0.4556 | 0.2042 | 0.3904 | 0.391 | 0.8938 | 0.8917 | 0.8926 | 0.4079 | 38.1545 |
| 0.0508 | 7.0 | 763 | 4.1740 | 0.4546 | 0.1949 | 0.383 | 0.3842 | 0.8925 | 0.8903 | 0.8913 | 0.4028 | 37.5273 |
| 0.0473 | 8.0 | 872 | 4.1643 | 0.4653 | 0.212 | 0.401 | 0.4026 | 0.8949 | 0.8939 | 0.8942 | 0.4212 | 38.4818 |
| 0.0438 | 9.0 | 981 | 4.1913 | 0.472 | 0.2155 | 0.4063 | 0.4071 | 0.8969 | 0.8947 | 0.8956 | 0.4223 | 37.9091 |
| 0.0401 | 10.0 | 1090 | 4.2084 | 0.4731 | 0.2204 | 0.4091 | 0.4105 | 0.8963 | 0.8954 | 0.8957 | 0.4281 | 38.3182 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0
- Datasets 3.0.0
- Tokenizers 0.19.1