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bert_large_xsum_samsum3
alexdg19/bert_large_xsum_samsum3
bert_large_xsum_samsum3 is a machine learning model from alexdg19. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- transformers
- License
- mit
- Model type
- bart
- Access
- Public
- Created
- Nov 7, 2023
- Updated
- Nov 7, 2023
- SHA
- b56ab037
Base models
Datasets
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
bert_large_xsum_samsum3
This model is a fine-tuned version of alexdg19/bert_large_xsum_samsum on the samsum dataset. It achieves the following results on the evaluation set:
- Loss: 2.2354
- Rouge1: 0.5313
- Rouge2: 0.2827
- Rougel: 0.4367
- Rougelsum: 0.4357
- Gen Len: 30.939
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: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- 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 | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| No log | 1.0 | 164 | 1.1370 | 0.5599 | 0.3246 | 0.4748 | 0.4743 | 29.0122 |
| No log | 2.0 | 328 | 1.2659 | 0.5494 | 0.3033 | 0.4623 | 0.4612 | 27.0671 |
| No log | 3.0 | 492 | 1.4188 | 0.5198 | 0.2726 | 0.436 | 0.4346 | 28.6768 |
| 0.6603 | 4.0 | 656 | 1.5628 | 0.5391 | 0.2905 | 0.4555 | 0.4553 | 28.6159 |
| 0.6603 | 5.0 | 820 | 1.9045 | 0.5237 | 0.2774 | 0.4326 | 0.4321 | 31.5854 |
| 0.6603 | 6.0 | 984 | 2.0670 | 0.5199 | 0.2689 | 0.4251 | 0.4243 | 31.8049 |
| 0.1722 | 7.0 | 1148 | 1.9653 | 0.5269 | 0.2703 | 0.4342 | 0.4333 | 28.5122 |
| 0.1722 | 8.0 | 1312 | 2.1921 | 0.5296 | 0.2765 | 0.4393 | 0.4387 | 31.8354 |
| 0.1722 | 9.0 | 1476 | 2.4336 | 0.5299 | 0.2825 | 0.4399 | 0.4388 | 31.7988 |
| 0.052 | 10.0 | 1640 | 2.2354 | 0.5313 | 0.2827 | 0.4367 | 0.4357 | 30.939 |
Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
- Library
- transformers
- Type
- bart
- License
- mit
- Created
- Nov 7, 2023
- Updated
- Nov 7, 2023