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bart-abs-2409-1947-lr-0.0003-bs-2-maxep-10
roequitz/bart-abs-2409-1947-lr-0.0003-bs-2-maxep-10
bart-abs-2409-1947-lr-0.0003-bs-2-maxep-10 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
- 750f3fe5
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-2-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: 7.4055
- Rouge/rouge1: 0.2434
- Rouge/rouge2: 0.0406
- Rouge/rougel: 0.2027
- Rouge/rougelsum: 0.203
- Bertscore/bertscore-precision: 0.8533
- Bertscore/bertscore-recall: 0.8523
- Bertscore/bertscore-f1: 0.8528
- Meteor: 0.194
- Gen Len: 34.0
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: 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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.6036 | 1.0 | 434 | 6.4324 | 0.2828 | 0.0721 | 0.2287 | 0.229 | 0.8776 | 0.863 | 0.8702 | 0.1865 | 29.0 |
| 0.6828 | 2.0 | 868 | 5.8330 | 0.2817 | 0.0594 | 0.2119 | 0.2118 | 0.8553 | 0.8592 | 0.8572 | 0.2216 | 38.0 |
| 0.5861 | 3.0 | 1302 | 6.4604 | 0.2456 | 0.0556 | 0.1909 | 0.1914 | 0.8622 | 0.8598 | 0.861 | 0.2363 | 41.0 |
| 0.4888 | 4.0 | 1736 | 6.3098 | 0.2591 | 0.0557 | 0.2009 | 0.2012 | 0.859 | 0.8605 | 0.8597 | 0.2511 | 47.0 |
| 0.4314 | 5.0 | 2170 | 6.7076 | 0.3042 | 0.0843 | 0.2354 | 0.2357 | 0.8695 | 0.8605 | 0.8649 | 0.22 | 32.0 |
| 0.379 | 6.0 | 2604 | 6.7173 | 0.2844 | 0.0777 | 0.2203 | 0.22 | 0.8641 | 0.8631 | 0.8635 | 0.2167 | 34.0 |
| 0.3245 | 7.0 | 3038 | 6.8382 | 0.2613 | 0.0803 | 0.214 | 0.2142 | 0.8711 | 0.8469 | 0.8588 | 0.2102 | 25.0 |
| 0.289 | 8.0 | 3472 | 7.0253 | 0.2396 | 0.0481 | 0.1978 | 0.198 | 0.8526 | 0.8544 | 0.8534 | 0.2079 | 34.0 |
| 0.2575 | 9.0 | 3906 | 7.1264 | 0.3089 | 0.0693 | 0.2341 | 0.2343 | 0.8686 | 0.8645 | 0.8665 | 0.2205 | 36.0 |
| 0.2312 | 10.0 | 4340 | 7.4055 | 0.2434 | 0.0406 | 0.2027 | 0.203 | 0.8533 | 0.8523 | 0.8528 | 0.194 | 34.0 |
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