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ko-en_mbartLarge_exp20p_linear_decay
yesj1234/ko-en_mbartLarge_exp20p_linear_decay
ko-en_mbartLarge_exp20p_linear_decay 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 28, 2023
- Updated
- Oct 28, 2023
- SHA
- ee13c066
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_decay
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: 1.2291
- Bleu: 26.9332
- Gen Len: 18.743
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: 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|---|---|---|---|---|---|
| 1.4396 | 0.23 | 1000 | 1.3815 | 21.7052 | 18.6047 |
| 1.338 | 0.46 | 2000 | 1.3044 | 23.7087 | 18.9939 |
| 1.2938 | 0.7 | 3000 | 1.2556 | 24.6339 | 18.8866 |
| 1.251 | 0.93 | 4000 | 1.2229 | 25.2975 | 19.0918 |
| 0.9843 | 1.16 | 5000 | 1.2309 | 25.609 | 18.7589 |
| 0.9874 | 1.39 | 6000 | 1.2101 | 26.1792 | 18.8287 |
| 0.9838 | 1.62 | 7000 | 1.2053 | 26.024 | 18.4025 |
| 0.9927 | 1.86 | 8000 | 1.1907 | 26.3148 | 19.09 |
| 0.7835 | 2.09 | 9000 | 1.2300 | 26.5613 | 18.7196 |
| 0.7437 | 2.32 | 10000 | 1.2358 | 26.8232 | 18.6513 |
| 0.7585 | 2.55 | 11000 | 1.2291 | 26.9203 | 18.7513 |
| 0.7631 | 2.78 | 12000 | 1.2170 | 26.8668 | 18.5441 |
| 0.7428 | 3.02 | 13000 | 1.3272 | 26.2506 | 18.6959 |
| 0.5502 | 3.25 | 14000 | 1.3392 | 26.419 | 18.6722 |
| 0.5577 | 3.48 | 15000 | 1.3204 | 26.1621 | 18.7036 |
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 28, 2023
- Updated
- Oct 28, 2023