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-2-maxep-10
results: []
bart-abs-2409-1947-lr-3e-05-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: 3.6542
- Rouge/rouge1: 0.4733
- Rouge/rouge2: 0.2228
- Rouge/rougel: 0.409
- Rouge/rougelsum: 0.4103
- Bertscore/bertscore-precision: 0.8957
- Bertscore/bertscore-recall: 0.8945
- Bertscore/bertscore-f1: 0.8949
- Meteor: 0.4257
- Gen Len: 38.0727
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: 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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2.3299 | 1.0 | 434 | 2.1997 | 0.4484 | 0.2127 | 0.3842 | 0.3856 | 0.8939 | 0.8904 | 0.892 | 0.4107 | 38.4273 |
| 1.6174 | 2.0 | 868 | 2.0581 | 0.4617 | 0.2158 | 0.3919 | 0.393 | 0.8972 | 0.8907 | 0.8938 | 0.4049 | 36.0545 |
| 1.1854 | 3.0 | 1302 | 2.1888 | 0.4631 | 0.2153 | 0.3893 | 0.3914 | 0.9 | 0.891 | 0.8953 | 0.4044 | 35.8273 |
| 0.8568 | 4.0 | 1736 | 2.3776 | 0.4518 | 0.201 | 0.3822 | 0.3839 | 0.8973 | 0.8895 | 0.8932 | 0.3907 | 34.7636 |
| 0.6056 | 5.0 | 2170 | 2.6468 | 0.4731 | 0.2207 | 0.4031 | 0.4036 | 0.8974 | 0.8946 | 0.8959 | 0.4173 | 37.5818 |
| 0.4235 | 6.0 | 2604 | 2.9226 | 0.4816 | 0.2258 | 0.4055 | 0.4064 | 0.8989 | 0.8955 | 0.897 | 0.4317 | 36.8909 |
| 0.2995 | 7.0 | 3038 | 3.1938 | 0.4541 | 0.1989 | 0.3839 | 0.3843 | 0.8941 | 0.8916 | 0.8927 | 0.4071 | 37.0091 |
| 0.2079 | 8.0 | 3472 | 3.4178 | 0.4684 | 0.2094 | 0.3926 | 0.3931 | 0.8941 | 0.8942 | 0.894 | 0.4177 | 39.4545 |
| 0.1527 | 9.0 | 3906 | 3.5347 | 0.4716 | 0.2151 | 0.3989 | 0.4009 | 0.8937 | 0.8942 | 0.8938 | 0.4277 | 39.5909 |
| 0.1203 | 10.0 | 4340 | 3.6542 | 0.4733 | 0.2228 | 0.409 | 0.4103 | 0.8957 | 0.8945 | 0.8949 | 0.4257 | 38.0727 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0
- Datasets 3.0.0
- Tokenizers 0.19.1