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ko-en_mbartLarge_exp20p_linear_3gram
yesj1234/ko-en_mbartLarge_exp20p_linear_3gram
ko-en_mbartLarge_exp20p_linear_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 26, 2023
- Updated
- Oct 26, 2023
- SHA
- 13695b04
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
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: 1.1514
- Bleu: 29.2703
- Gen Len: 18.512
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: 2000
- num_epochs: 40
Training results
| Training Loss | Epoch | Step | Bleu | Gen Len | Validation Loss |
|---|---|---|---|---|---|
| 1.3977 | 0.46 | 4000 | 22.7153 | 18.7135 | 1.3720 |
| 1.2824 | 0.93 | 8000 | 24.8579 | 18.7821 | 1.2633 |
| 1.1989 | 1.39 | 12000 | 26.2533 | 18.7975 | 1.2069 |
| 1.1534 | 1.86 | 16000 | 26.1503 | 19.2075 | 1.1907 |
| 1.0245 | 2.32 | 20000 | 27.8764 | 18.6046 | 1.1464 |
| 1.0186 | 2.78 | 24000 | 28.4585 | 18.6731 | 1.1286 |
| 0.9245 | 3.25 | 28000 | 1.1264 | 28.4834 | 18.5428 |
| 0.9343 | 3.71 | 32000 | 1.1182 | 28.8235 | 18.7833 |
| 0.8215 | 4.18 | 36000 | 1.1331 | 28.6134 | 18.5656 |
| 0.8456 | 4.64 | 40000 | 1.1203 | 28.7324 | 18.459 |
| 0.7437 | 5.11 | 44000 | 1.1458 | 28.7297 | 18.7835 |
| 0.7829 | 5.57 | 48000 | 1.1367 | 28.8328 | 18.6052 |
| 0.7434 | 6.03 | 52000 | 1.1697 | 28.2106 | 18.4871 |
| 0.7153 | 6.5 | 56000 | 1.1771 | 28.1455 | 18.7413 |
| 0.6996 | 6.96 | 60000 | 1.1514 | 29.2694 | 18.5162 |
| 0.6336 | 7.43 | 64000 | 1.2213 | 28.1465 | 18.5439 |
| 0.7218 | 7.89 | 68000 | 1.1835 | 28.2245 | 18.5246 |
| 0.5934 | 8.35 | 72000 | 1.2387 | 28.3836 | 18.6717 |
| 0.5723 | 8.82 | 76000 | 1.2323 | 28.5925 | 18.5566 |
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 26, 2023
- Updated
- Oct 26, 2023