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-8-maxep-10
results: []
bart-abs-1509-0313-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: 3.7689
- Rouge/rouge1: 0.4647
- Rouge/rouge2: 0.2065
- Rouge/rougel: 0.3953
- Rouge/rougelsum: 0.3967
- Bertscore/bertscore-precision: 0.8961
- Bertscore/bertscore-recall: 0.8941
- Bertscore/bertscore-f1: 0.895
- Meteor: 0.4195
- Gen Len: 37.9545
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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.332 | 1.0 | 109 | 2.9323 | 0.4383 | 0.1841 | 0.3691 | 0.3701 | 0.8915 | 0.8887 | 0.8899 | 0.3951 | 37.2182 |
| 0.3384 | 2.0 | 218 | 3.0419 | 0.4611 | 0.2038 | 0.3901 | 0.3918 | 0.8941 | 0.8913 | 0.8925 | 0.4163 | 37.1909 |
| 0.2354 | 3.0 | 327 | 3.2793 | 0.445 | 0.1903 | 0.3776 | 0.3785 | 0.8938 | 0.8895 | 0.8915 | 0.394 | 36.4545 |
| 0.1736 | 4.0 | 436 | 3.4093 | 0.4545 | 0.2 | 0.3877 | 0.3885 | 0.8939 | 0.8921 | 0.8928 | 0.4094 | 38.3818 |
| 0.1406 | 5.0 | 545 | 3.5183 | 0.4634 | 0.2065 | 0.394 | 0.3945 | 0.898 | 0.8925 | 0.8951 | 0.4032 | 35.5182 |
| 0.1108 | 6.0 | 654 | 3.6131 | 0.4667 | 0.2075 | 0.3961 | 0.3964 | 0.8966 | 0.8936 | 0.8949 | 0.4155 | 37.4364 |
| 0.0906 | 7.0 | 763 | 3.6935 | 0.4602 | 0.2002 | 0.3892 | 0.3903 | 0.8922 | 0.8944 | 0.8931 | 0.4116 | 39.6 |
| 0.0788 | 8.0 | 872 | 3.7280 | 0.4704 | 0.212 | 0.4 | 0.4018 | 0.8941 | 0.8955 | 0.8946 | 0.431 | 39.8727 |
| 0.0708 | 9.0 | 981 | 3.7601 | 0.468 | 0.2062 | 0.3979 | 0.3994 | 0.8953 | 0.8948 | 0.8949 | 0.4244 | 38.9727 |
| 0.0637 | 10.0 | 1090 | 3.7689 | 0.4647 | 0.2065 | 0.3953 | 0.3967 | 0.8961 | 0.8941 | 0.895 | 0.4195 | 37.9545 |
Framework versions
- Transformers 4.44.0
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1