metadata
license: apache-2.0
base_model: sshleifer/distilbart-xsum-12-6
tags:
- generated_from_trainer
model-index:
- name: bart-abs-1509-0313-lr-3e-05-bs-2-maxep-10
results: []
bart-abs-1509-0313-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: 4.0180
- Rouge/rouge1: 0.4724
- Rouge/rouge2: 0.2094
- Rouge/rougel: 0.3964
- Rouge/rougelsum: 0.3976
- Bertscore/bertscore-precision: 0.8964
- Bertscore/bertscore-recall: 0.8932
- Bertscore/bertscore-f1: 0.8947
- Meteor: 0.4217
- Gen Len: 36.8818
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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.6813 | 1.0 | 434 | 2.4588 | 0.4636 | 0.2101 | 0.3907 | 0.3921 | 0.8975 | 0.8904 | 0.8937 | 0.4099 | 35.0727 |
| 0.6702 | 2.0 | 868 | 2.5377 | 0.4448 | 0.1862 | 0.3725 | 0.3735 | 0.8942 | 0.8887 | 0.8913 | 0.3825 | 35.7273 |
| 0.4591 | 3.0 | 1302 | 2.8762 | 0.4533 | 0.1916 | 0.3767 | 0.3778 | 0.8961 | 0.8897 | 0.8928 | 0.3911 | 35.2091 |
| 0.3165 | 4.0 | 1736 | 3.2129 | 0.4519 | 0.1976 | 0.3803 | 0.3806 | 0.8936 | 0.891 | 0.8922 | 0.4023 | 37.6364 |
| 0.2222 | 5.0 | 2170 | 3.4971 | 0.47 | 0.2049 | 0.392 | 0.3924 | 0.8959 | 0.8926 | 0.8941 | 0.4107 | 36.5545 |
| 0.1596 | 6.0 | 2604 | 3.6405 | 0.4607 | 0.2101 | 0.3853 | 0.3879 | 0.8943 | 0.8908 | 0.8924 | 0.4021 | 37.2273 |
| 0.1166 | 7.0 | 3038 | 3.7827 | 0.4759 | 0.2191 | 0.4086 | 0.4106 | 0.8988 | 0.8928 | 0.8956 | 0.4173 | 35.5727 |
| 0.0891 | 8.0 | 3472 | 3.9388 | 0.4677 | 0.2047 | 0.3905 | 0.3925 | 0.8933 | 0.8927 | 0.8929 | 0.417 | 38.7 |
| 0.0695 | 9.0 | 3906 | 3.9583 | 0.4775 | 0.2116 | 0.4032 | 0.4051 | 0.8981 | 0.8931 | 0.8955 | 0.4228 | 36.3182 |
| 0.0592 | 10.0 | 4340 | 4.0180 | 0.4724 | 0.2094 | 0.3964 | 0.3976 | 0.8964 | 0.8932 | 0.8947 | 0.4217 | 36.8818 |
Framework versions
- Transformers 4.44.0
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1