metadata
license: apache-2.0
base_model: sshleifer/distilbart-xsum-12-6
tags:
- generated_from_trainer
model-index:
- name: bart-abs-1509-0313-lr-0.0003-bs-8-maxep-10
results: []
bart-abs-1509-0313-lr-0.0003-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: 7.5826
- Rouge/rouge1: 0.2532
- Rouge/rouge2: 0.0528
- Rouge/rougel: 0.2067
- Rouge/rougelsum: 0.2071
- Bertscore/bertscore-precision: 0.8514
- Bertscore/bertscore-recall: 0.8621
- Bertscore/bertscore-f1: 0.8567
- Meteor: 0.2303
- Gen Len: 46.4909
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: 0.0003
- 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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.6961 | 1.0 | 109 | 5.5084 | 0.2572 | 0.0691 | 0.1962 | 0.1962 | 0.8672 | 0.8617 | 0.8644 | 0.2158 | 34.0 |
| 0.6838 | 2.0 | 218 | 5.7494 | 0.2975 | 0.0945 | 0.2493 | 0.2495 | 0.8739 | 0.8626 | 0.8681 | 0.2433 | 27.0 |
| 0.5113 | 3.0 | 327 | 6.0212 | 0.2722 | 0.0714 | 0.2029 | 0.2031 | 0.8612 | 0.8618 | 0.8615 | 0.2582 | 44.0 |
| 0.4108 | 4.0 | 436 | 6.5957 | 0.2916 | 0.064 | 0.2118 | 0.2121 | 0.8678 | 0.8659 | 0.8668 | 0.2243 | 47.0 |
| 0.3585 | 5.0 | 545 | 6.7542 | 0.2554 | 0.0561 | 0.198 | 0.1977 | 0.8531 | 0.8633 | 0.8581 | 0.2483 | 42.0 |
| 0.3094 | 6.0 | 654 | 6.9956 | 0.3041 | 0.0711 | 0.2307 | 0.2305 | 0.8646 | 0.8658 | 0.8652 | 0.2861 | 42.0 |
| 0.281 | 7.0 | 763 | 7.1181 | 0.2582 | 0.0781 | 0.2156 | 0.2154 | 0.8771 | 0.8626 | 0.8697 | 0.1855 | 29.0 |
| 0.261 | 8.0 | 872 | 7.2717 | 0.3097 | 0.0856 | 0.2463 | 0.2464 | 0.8589 | 0.8656 | 0.8622 | 0.2246 | 36.0 |
| 0.2415 | 9.0 | 981 | 7.4446 | 0.2906 | 0.0847 | 0.2272 | 0.2274 | 0.8671 | 0.8567 | 0.8618 | 0.1991 | 27.0 |
| 0.2228 | 10.0 | 1090 | 7.5826 | 0.2532 | 0.0528 | 0.2067 | 0.2071 | 0.8514 | 0.8621 | 0.8567 | 0.2303 | 46.4909 |
Framework versions
- Transformers 4.44.0
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1