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t5_8_3e-5_datav2_min30_lp2_sample
nlp04/t5_8_3e-5_datav2_min30_lp2_sample
t5_8_3e-5_datav2_min30_lp2_sample is a machine learning model from nlp04. 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
- t5
- Access
- Public
- Created
- Jan 25, 2023
- Updated
- Jan 25, 2023
- SHA
- b2acf545
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. -->
t5_8_3e-5_datav2_min30_lp2_sample
This model is a fine-tuned version of KETI-AIR/ke-t5-large-ko on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 6.2375
- Rouge1: 24.1102
- Rouge2: 5.3137
- Rougel: 16.1086
- Bleu1: 18.6424
- Bleu2: 8.0483
- Bleu3: 2.7046
- Bleu4: 0.7308
- Gen Len: 36.4012
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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Bleu1 | Bleu2 | Bleu3 | Bleu4 | Gen Len |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 4.1641 | 1.04 | 5000 | 6.8094 | 21.6187 | 4.959 | 14.8344 | 16.9553 | 7.4791 | 2.8017 | 1.1852 | 38.0426 |
| 3.1804 | 2.08 | 10000 | 5.6664 | 22.2631 | 5.127 | 15.5533 | 16.881 | 7.515 | 2.8628 | 1.0614 | 33.7325 |
| 2.779 | 3.12 | 15000 | 5.3350 | 22.5781 | 5.1137 | 15.7717 | 16.8632 | 7.3067 | 2.7117 | 0.9906 | 31.459 |
| 2.4111 | 4.15 | 20000 | 5.2687 | 24.4915 | 6.003 | 16.8096 | 18.5998 | 8.54 | 3.4084 | 1.1511 | 32.7477 |
| 2.2192 | 5.19 | 25000 | 5.3300 | 24.9661 | 6.0773 | 16.8486 | 19.0105 | 8.6794 | 3.4052 | 1.3281 | 32.9696 |
| 1.9306 | 6.23 | 30000 | 5.4806 | 24.8662 | 5.9711 | 16.235 | 19.2093 | 8.7044 | 3.2412 | 1.0675 | 35.0973 |
| 1.6696 | 7.27 | 35000 | 5.6865 | 24.3913 | 5.6936 | 16.4663 | 18.5884 | 8.3035 | 2.9593 | 1.0997 | 34.617 |
| 1.4566 | 8.31 | 40000 | 5.8677 | 24.9166 | 5.8251 | 16.647 | 19.0703 | 8.5159 | 3.3477 | 1.1257 | 35.1763 |
| 1.2808 | 9.35 | 45000 | 6.2375 | 24.1102 | 5.3137 | 16.1086 | 18.6424 | 8.0483 | 2.7046 | 0.7308 | 36.4012 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.13.2
- Library
- transformers
- Type
- t5
- License
- apache-2.0
- Created
- Jan 25, 2023
- Updated
- Jan 25, 2023