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-2-maxep-6
results: []
bart-abs-1509-0313-lr-0.0003-bs-2-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: 6.8567
- Rouge/rouge1: 0.3035
- Rouge/rouge2: 0.072
- Rouge/rougel: 0.2428
- Rouge/rougelsum: 0.2429
- Bertscore/bertscore-precision: 0.8724
- Bertscore/bertscore-recall: 0.8571
- Bertscore/bertscore-f1: 0.8646
- Meteor: 0.2108
- Gen Len: 29.0
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: 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: 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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2.4741 | 1.0 | 434 | 4.0269 | 0.2771 | 0.0691 | 0.2057 | 0.2053 | 0.8702 | 0.8596 | 0.8648 | 0.233 | 39.0 |
| 3.0848 | 2.0 | 868 | 3.9978 | 0.2554 | 0.0651 | 0.2183 | 0.2183 | 0.8646 | 0.8589 | 0.8617 | 0.2022 | 29.1364 |
| 1.9491 | 3.0 | 1302 | 4.4524 | 0.2722 | 0.0714 | 0.2029 | 0.2031 | 0.8612 | 0.8618 | 0.8615 | 0.2582 | 47.0 |
| 1.0603 | 4.0 | 1736 | 5.4022 | 0.2465 | 0.0593 | 0.2071 | 0.2071 | 0.8464 | 0.858 | 0.8521 | 0.2294 | 42.0 |
| 0.5921 | 5.0 | 2170 | 6.1146 | 0.3035 | 0.072 | 0.2428 | 0.2429 | 0.8724 | 0.8571 | 0.8646 | 0.2108 | 29.0 |
| 0.3762 | 6.0 | 2604 | 6.8567 | 0.3035 | 0.072 | 0.2428 | 0.2429 | 0.8724 | 0.8571 | 0.8646 | 0.2108 | 29.0 |
Framework versions
- Transformers 4.44.0
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1