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-6
results: []
bart-abs-1509-0313-lr-3e-05-bs-8-maxep-6
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: 3.3957
- Rouge/rouge1: 0.4646
- Rouge/rouge2: 0.2089
- Rouge/rougel: 0.3939
- Rouge/rougelsum: 0.3945
- Bertscore/bertscore-precision: 0.8956
- Bertscore/bertscore-recall: 0.8935
- Bertscore/bertscore-f1: 0.8944
- Meteor: 0.4132
- Gen Len: 37.5
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: 6
- 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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.6107 | 1.0 | 109 | 2.4784 | 0.449 | 0.1974 | 0.3774 | 0.3776 | 0.8943 | 0.8904 | 0.8922 | 0.3981 | 36.9182 |
| 0.3993 | 2.0 | 218 | 2.7984 | 0.4656 | 0.2145 | 0.3954 | 0.3965 | 0.8975 | 0.8914 | 0.8943 | 0.408 | 35.1364 |
| 0.2779 | 3.0 | 327 | 3.0563 | 0.4669 | 0.2112 | 0.3981 | 0.3995 | 0.8961 | 0.8905 | 0.8931 | 0.4088 | 36.0545 |
| 0.2038 | 4.0 | 436 | 3.2410 | 0.4639 | 0.2052 | 0.3895 | 0.3904 | 0.896 | 0.8949 | 0.8953 | 0.4109 | 37.9 |
| 0.1606 | 5.0 | 545 | 3.3263 | 0.4582 | 0.2063 | 0.391 | 0.392 | 0.8961 | 0.893 | 0.8944 | 0.4033 | 36.5545 |
| 0.1282 | 6.0 | 654 | 3.3957 | 0.4646 | 0.2089 | 0.3939 | 0.3945 | 0.8956 | 0.8935 | 0.8944 | 0.4132 | 37.5 |
Framework versions
- Transformers 4.44.0
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1