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ko-en_mbartLarge_exp15
yesj1234/ko-en_mbartLarge_exp15
ko-en_mbartLarge_exp15 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 20, 2023
- Updated
- Oct 20, 2023
- SHA
- f12647c3
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_exp10p
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: 1.1283
- Bleu: 28.8237
- Gen Len: 18.5382
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_with_restarts
- lr_scheduler_warmup_steps: 1000
- num_epochs: 40
Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|---|---|---|---|---|---|
| 1.4782 | 0.31 | 2000 | 1.4360 | 21.538 | 18.6032 |
| 1.3618 | 0.62 | 4000 | 1.3226 | 23.8354 | 18.5594 |
| 1.2983 | 0.93 | 6000 | 1.2637 | 25.0795 | 18.7894 |
| 1.2065 | 1.24 | 8000 | 1.2371 | 25.7409 | 18.5615 |
| 1.1926 | 1.55 | 10000 | 1.2116 | 26.0527 | 18.4019 |
| 1.1734 | 1.86 | 12000 | 1.1907 | 26.9802 | 18.6141 |
| 1.0677 | 2.17 | 14000 | 1.1802 | 27.1925 | 18.4547 |
| 1.0773 | 2.48 | 16000 | 1.1655 | 27.5641 | 18.6726 |
| 1.0688 | 2.78 | 18000 | 1.1521 | 27.6261 | 18.6127 |
| 0.9542 | 3.09 | 20000 | 1.1709 | 27.16 | 18.3782 |
| 0.9531 | 3.4 | 22000 | 1.1435 | 28.0684 | 18.436 |
| 0.9756 | 3.71 | 24000 | 1.1565 | 27.6025 | 18.7284 |
| 0.9964 | 4.02 | 26000 | 1.2285 | 25.6999 | 18.3255 |
| 0.9721 | 4.33 | 28000 | 1.1881 | 27.3499 | 18.5409 |
| 0.9237 | 4.64 | 30000 | 1.1497 | 28.2692 | 18.6614 |
| 0.9041 | 4.95 | 32000 | 1.1283 | 28.8215 | 18.5493 |
| 0.6842 | 5.26 | 34000 | 1.1741 | 28.6873 | 18.515 |
| 0.7101 | 5.57 | 36000 | 1.1876 | 28.0778 | 18.3422 |
| 0.7697 | 5.88 | 38000 | 1.1898 | 27.6338 | 18.6766 |
| 0.6028 | 6.19 | 40000 | 1.2393 | 28.0713 | 18.5903 |
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 20, 2023
- Updated
- Oct 20, 2023