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bart-abs-2409-1947-lr-0.0003-bs-4-maxep-6
roequitz/bart-abs-2409-1947-lr-0.0003-bs-4-maxep-6
bart-abs-2409-1947-lr-0.0003-bs-4-maxep-6 is a machine learning model from roequitz. 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 apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- transformers
- License
- apache-2.0
- Model type
- bart
- Access
- Public
- Created
- Sep 24, 2024
- Updated
- Sep 24, 2024
- SHA
- 40ed03b0
Base models
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. -->
bart-abs-2409-1947-lr-0.0003-bs-4-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: 6.3423
- Rouge/rouge1: 0.2439
- Rouge/rouge2: 0.0504
- Rouge/rougel: 0.2065
- Rouge/rougelsum: 0.2067
- Bertscore/bertscore-precision: 0.8544
- Bertscore/bertscore-recall: 0.8581
- Bertscore/bertscore-f1: 0.8562
- Meteor: 0.229
- Gen Len: 46.0
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: 4
- eval_batch_size: 4
- 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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2.1377 | 1.0 | 217 | 3.9220 | 0.3283 | 0.1009 | 0.2612 | 0.2612 | 0.8785 | 0.8638 | 0.871 | 0.2695 | 33.0 |
| 3.3219 | 2.0 | 434 | 3.7523 | 0.2756 | 0.0805 | 0.2368 | 0.237 | 0.8845 | 0.8545 | 0.8692 | 0.2111 | 25.0 |
| 2.115 | 3.0 | 651 | 4.0783 | 0.282 | 0.0747 | 0.2116 | 0.2118 | 0.8663 | 0.8623 | 0.8642 | 0.2582 | 41.0 |
| 1.1461 | 4.0 | 868 | 4.8795 | 0.251 | 0.0501 | 0.21 | 0.2102 | 0.8497 | 0.8506 | 0.8501 | 0.2025 | 37.0 |
| 0.6272 | 5.0 | 1085 | 5.8094 | 0.2811 | 0.0751 | 0.229 | 0.2293 | 0.8628 | 0.8693 | 0.866 | 0.2058 | 44.0 |
| 0.3841 | 6.0 | 1302 | 6.3423 | 0.2439 | 0.0504 | 0.2065 | 0.2067 | 0.8544 | 0.8581 | 0.8562 | 0.229 | 46.0 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0
- Datasets 3.0.0
- Tokenizers 0.19.1
- Library
- transformers
- Type
- bart
- License
- apache-2.0
- Created
- Sep 24, 2024
- Updated
- Sep 24, 2024