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plbart-finetuned-unitTest-1000
yassmine/plbart-finetuned-unitTest-1000
plbart-finetuned-unitTest-1000 is a machine learning model from yassmine. 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
- plbart
- Access
- Public
- Created
- Jul 13, 2023
- Updated
- Jul 13, 2023
- SHA
- 375c3a74
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. -->
plbart-finetuned-unitTest-1000
This model is a fine-tuned version of uclanlp/plbart-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.0000
- Bleu: 0.0000
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: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu |
|---|---|---|---|---|
| No log | 1.0 | 92 | 0.9023 | 0.0000 |
| No log | 2.0 | 184 | 0.8401 | 0.0000 |
| No log | 3.0 | 276 | 0.8096 | 0.0000 |
| No log | 4.0 | 368 | 0.7942 | 0.0000 |
| No log | 5.0 | 460 | 0.7848 | 0.0000 |
| 0.943 | 6.0 | 552 | 0.7818 | 0.0000 |
| 0.943 | 7.0 | 644 | 0.7911 | 0.0000 |
| 0.943 | 8.0 | 736 | 0.7874 | 0.0000 |
| 0.943 | 9.0 | 828 | 0.7970 | 0.0000 |
| 0.943 | 10.0 | 920 | 0.8062 | 0.0000 |
| 0.5025 | 11.0 | 1012 | 0.8085 | 0.0000 |
| 0.5025 | 12.0 | 1104 | 0.8179 | 0.0000 |
| 0.5025 | 13.0 | 1196 | 0.8360 | 0.0000 |
| 0.5025 | 14.0 | 1288 | 0.8385 | 0.0000 |
| 0.5025 | 15.0 | 1380 | 0.8470 | 0.0000 |
| 0.5025 | 16.0 | 1472 | 0.8556 | 0.0000 |
| 0.3309 | 17.0 | 1564 | 0.8619 | 0.0000 |
| 0.3309 | 18.0 | 1656 | 0.8701 | 0.0000 |
| 0.3309 | 19.0 | 1748 | 0.8827 | 0.0000 |
| 0.3309 | 20.0 | 1840 | 0.8871 | 0.0000 |
| 0.3309 | 21.0 | 1932 | 0.8970 | 0.0000 |
| 0.2266 | 22.0 | 2024 | 0.8984 | 0.0000 |
| 0.2266 | 23.0 | 2116 | 0.9051 | 0.0000 |
| 0.2266 | 24.0 | 2208 | 0.9188 | 0.0000 |
| 0.2266 | 25.0 | 2300 | 0.9205 | 0.0000 |
| 0.2266 | 26.0 | 2392 | 0.9278 | 0.0000 |
| 0.2266 | 27.0 | 2484 | 0.9333 | 0.0000 |
| 0.1639 | 28.0 | 2576 | 0.9456 | 0.0000 |
| 0.1639 | 29.0 | 2668 | 0.9454 | 0.0000 |
| 0.1639 | 30.0 | 2760 | 0.9522 | 0.0000 |
| 0.1639 | 31.0 | 2852 | 0.9513 | 0.0000 |
| 0.1639 | 32.0 | 2944 | 0.9554 | 0.0000 |
| 0.1251 | 33.0 | 3036 | 0.9661 | 0.0000 |
| 0.1251 | 34.0 | 3128 | 0.9698 | 0.0000 |
| 0.1251 | 35.0 | 3220 | 0.9750 | 0.0000 |
| 0.1251 | 36.0 | 3312 | 0.9722 | 0.0000 |
| 0.1251 | 37.0 | 3404 | 0.9780 | 0.0000 |
| 0.1251 | 38.0 | 3496 | 0.9789 | 0.0000 |
| 0.1019 | 39.0 | 3588 | 0.9825 | 0.0000 |
| 0.1019 | 40.0 | 3680 | 0.9913 | 0.0000 |
| 0.1019 | 41.0 | 3772 | 0.9906 | 0.0000 |
| 0.1019 | 42.0 | 3864 | 0.9922 | 0.0000 |
| 0.1019 | 43.0 | 3956 | 0.9937 | 0.0000 |
| 0.0863 | 44.0 | 4048 | 0.9981 | 0.0000 |
| 0.0863 | 45.0 | 4140 | 0.9979 | 0.0000 |
| 0.0863 | 46.0 | 4232 | 0.9984 | 0.0000 |
| 0.0863 | 47.0 | 4324 | 0.9970 | 0.0000 |
| 0.0863 | 48.0 | 4416 | 1.0003 | 0.0000 |
| 0.0783 | 49.0 | 4508 | 0.9993 | 0.0000 |
| 0.0783 | 50.0 | 4600 | 1.0000 | 0.0000 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
- Library
- transformers
- Type
- plbart
- Created
- Jul 13, 2023
- Updated
- Jul 13, 2023