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ko-en_mbartLarge_exp20p_linear_lr_3gram
yesj1234/ko-en_mbartLarge_exp20p_linear_lr_3gram
ko-en_mbartLarge_exp20p_linear_lr_3gram is a machine learning model from yesj1234. 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
- mbart
- Access
- Public
- Created
- Oct 27, 2023
- Updated
- Oct 27, 2023
- SHA
- 50d86e60
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. -->
ko-en_mbartLarge_exp20p_linear_alpha
This model is a fine-tuned version of facebook/mbart-large-50-many-to-many-mmt on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.1682
- Bleu: 29.1144
- Gen Len: 18.5459
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: 5.5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 40
Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|---|---|---|---|---|---|
| 1.404 | 0.46 | 4000 | 1.3738 | 22.5375 | 18.6852 |
| 1.2629 | 0.93 | 8000 | 1.2458 | 25.3741 | 18.7797 |
| 1.1951 | 1.39 | 12000 | 1.2067 | 26.1281 | 18.6597 |
| 1.1317 | 1.86 | 16000 | 1.1768 | 26.5384 | 19.2055 |
| 0.9906 | 2.32 | 20000 | 1.1363 | 28.2459 | 18.7269 |
| 0.9894 | 2.78 | 24000 | 1.1239 | 28.5124 | 18.6882 |
| 0.8965 | 3.25 | 28000 | 1.1278 | 28.5335 | 18.4917 |
| 0.9138 | 3.71 | 32000 | 1.1216 | 28.8189 | 18.7873 |
| 0.8272 | 4.18 | 36000 | 1.1468 | 28.332 | 18.6516 |
| 0.8753 | 4.64 | 40000 | 1.1345 | 28.2695 | 18.4919 |
| 0.6855 | 5.11 | 44000 | 1.1542 | 28.7913 | 18.7596 |
| 0.7088 | 5.57 | 48000 | 1.1531 | 29.0865 | 18.6626 |
| 0.6738 | 6.03 | 52000 | 1.1906 | 28.0235 | 18.4243 |
| 0.6763 | 6.5 | 56000 | 1.1941 | 28.1501 | 18.6932 |
| 0.6594 | 6.96 | 60000 | 1.1682 | 29.1144 | 18.5459 |
| 0.5971 | 7.43 | 64000 | 1.2449 | 27.9464 | 18.4482 |
| 0.5935 | 7.89 | 68000 | 1.2156 | 28.6034 | 18.5967 |
| 0.5383 | 8.35 | 72000 | 1.2927 | 27.891 | 18.6539 |
| 0.6022 | 8.82 | 76000 | 1.2831 | 27.7624 | 18.5558 |
Framework versions
- Transformers 4.34.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.5
- Tokenizers 0.14.1
- Library
- transformers
- Type
- mbart
- Languages
- ko, en
- Created
- Oct 27, 2023
- Updated
- Oct 27, 2023