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ko-en_mbartLarge_exp5p
yesj1234/ko-en_mbartLarge_exp5p
ko-en_mbartLarge_exp5p 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 19, 2023
- Updated
- Oct 19, 2023
- SHA
- 980154b0
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_exp5p
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: 1.2328
- Bleu: 26.5495
- Gen Len: 18.4213
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: cosine
- lr_scheduler_warmup_steps: 500
- num_epochs: 40
Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|---|---|---|---|---|---|
| 1.6447 | 0.46 | 1000 | 1.5338 | 20.0927 | 18.3986 |
| 1.4737 | 0.93 | 2000 | 1.4057 | 22.6168 | 18.5462 |
| 1.3708 | 1.39 | 3000 | 1.3645 | 23.158 | 18.5132 |
| 1.3357 | 1.86 | 4000 | 1.3166 | 24.2178 | 18.4343 |
| 1.2274 | 2.32 | 5000 | 1.2854 | 24.8105 | 18.4761 |
| 1.2113 | 2.78 | 6000 | 1.2622 | 25.4518 | 18.2672 |
| 1.1392 | 3.25 | 7000 | 1.2540 | 25.6184 | 18.4032 |
| 1.125 | 3.71 | 8000 | 1.2401 | 25.3848 | 18.3781 |
| 1.0423 | 4.18 | 9000 | 1.2354 | 25.9776 | 18.3387 |
| 1.011 | 4.64 | 10000 | 1.2418 | 26.1619 | 18.4858 |
| 0.9493 | 5.1 | 11000 | 1.2616 | 25.6398 | 18.2273 |
| 0.888 | 5.57 | 12000 | 1.2328 | 26.5446 | 18.438 |
| 0.8648 | 6.03 | 13000 | 1.2618 | 26.0371 | 18.4074 |
| 0.776 | 6.5 | 14000 | 1.2669 | 26.0043 | 18.4629 |
| 0.7856 | 6.96 | 15000 | 1.2592 | 26.2716 | 18.403 |
| 0.6997 | 7.42 | 16000 | 1.3154 | 25.7842 | 18.3693 |
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 19, 2023
- Updated
- Oct 19, 2023