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NASES-clara-med
joheras/NASES-clara-med
NASES-clara-med is a machine learning model from joheras. 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.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- transformers
- Model type
- bart
- Access
- Public
- Created
- Jan 12, 2023
- Updated
- Jan 30, 2023
- SHA
- 4adb0d60
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. -->
NASES-clara-med
This model is a fine-tuned version of ELiRF/NASES on the None dataset. It achieves the following results on the evaluation set:
- Loss: 3.2666
- Rouge1: 44.0787
- Rouge2: 26.1429
- Rougel: 38.4286
- Rougelsum: 38.5202
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: 5.6e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 190 | 2.1442 | 43.6265 | 25.4681 | 37.6224 | 37.8012 |
| No log | 2.0 | 380 | 2.0839 | 44.0795 | 25.8075 | 37.9463 | 38.0445 |
| 1.8145 | 3.0 | 570 | 2.1689 | 43.3863 | 25.7517 | 37.4822 | 37.7461 |
| 1.8145 | 4.0 | 760 | 2.2569 | 43.9293 | 25.7951 | 37.9177 | 38.0658 |
| 0.6803 | 5.0 | 950 | 2.3760 | 43.9972 | 26.1618 | 38.4315 | 38.5305 |
| 0.6803 | 6.0 | 1140 | 2.4979 | 44.7986 | 27.0088 | 39.0031 | 39.1731 |
| 0.6803 | 7.0 | 1330 | 2.5881 | 43.8723 | 25.9782 | 38.1705 | 38.3225 |
| 0.2323 | 8.0 | 1520 | 2.6624 | 43.851 | 25.9263 | 38.2445 | 38.3659 |
| 0.2323 | 9.0 | 1710 | 2.7113 | 43.5292 | 25.4795 | 37.6883 | 37.8992 |
| 0.1464 | 10.0 | 1900 | 2.7451 | 44.6014 | 27.0125 | 38.9456 | 39.1796 |
| 0.1464 | 11.0 | 2090 | 2.7932 | 43.9568 | 26.0931 | 38.3672 | 38.5118 |
| 0.1464 | 12.0 | 2280 | 2.8651 | 43.8429 | 25.9007 | 38.0691 | 38.191 |
| 0.0863 | 13.0 | 2470 | 2.8978 | 44.192 | 26.1818 | 38.4167 | 38.579 |
| 0.0863 | 14.0 | 2660 | 2.9279 | 43.6745 | 25.6503 | 37.8948 | 38.0051 |
| 0.0657 | 15.0 | 2850 | 2.9942 | 44.1633 | 25.7856 | 38.0295 | 38.1905 |
| 0.0657 | 16.0 | 3040 | 2.9843 | 44.0347 | 25.9893 | 38.3486 | 38.5219 |
| 0.0657 | 17.0 | 3230 | 3.0189 | 44.3013 | 26.1884 | 38.5594 | 38.7396 |
| 0.0473 | 18.0 | 3420 | 3.0837 | 43.5877 | 25.6931 | 38.1147 | 38.2258 |
| 0.0473 | 19.0 | 3610 | 3.1025 | 44.1191 | 25.9657 | 38.338 | 38.5039 |
| 0.0302 | 20.0 | 3800 | 3.1395 | 44.393 | 26.3189 | 38.7891 | 38.8664 |
| 0.0302 | 21.0 | 3990 | 3.1808 | 44.4783 | 26.3023 | 38.4714 | 38.6428 |
| 0.0302 | 22.0 | 4180 | 3.1388 | 44.6364 | 26.7442 | 38.9591 | 39.1097 |
| 0.0194 | 23.0 | 4370 | 3.1859 | 44.919 | 26.9807 | 39.2653 | 39.3442 |
| 0.0194 | 24.0 | 4560 | 3.2126 | 44.4693 | 26.6534 | 38.8354 | 38.9278 |
| 0.0159 | 25.0 | 4750 | 3.1988 | 44.5436 | 26.63 | 38.9413 | 39.0007 |
| 0.0159 | 26.0 | 4940 | 3.2539 | 44.0378 | 26.0958 | 38.4445 | 38.5443 |
| 0.0159 | 27.0 | 5130 | 3.2844 | 44.6057 | 26.476 | 38.6502 | 38.7949 |
| 0.0117 | 28.0 | 5320 | 3.2755 | 44.1804 | 26.3747 | 38.6084 | 38.7027 |
| 0.0117 | 29.0 | 5510 | 3.2731 | 44.0453 | 26.0298 | 38.3911 | 38.4826 |
| 0.0102 | 30.0 | 5700 | 3.2666 | 44.0787 | 26.1429 | 38.4286 | 38.5202 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0
- Datasets 2.8.0
- Tokenizers 0.12.1
- Library
- transformers
- Type
- bart
- Created
- Jan 12, 2023
- Updated
- Jan 30, 2023