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ko-en_mbartLarge_exp5p_linear_3gram
yesj1234/ko-en_mbartLarge_exp5p_linear_3gram
ko-en_mbartLarge_exp5p_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 24, 2023
- Updated
- Oct 24, 2023
- SHA
- 04d6d73e
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_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: 1.2308
- Bleu: 26.2764
- Gen Len: 18.3888
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: 40
Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|---|---|---|---|---|---|
| 1.7665 | 0.46 | 1000 | 1.6564 | 17.6773 | 18.7196 |
| 1.5688 | 0.93 | 2000 | 1.4939 | 20.8837 | 18.3983 |
| 1.457 | 1.39 | 3000 | 1.4350 | 21.9168 | 18.458 |
| 1.4107 | 1.86 | 4000 | 1.3752 | 22.8881 | 18.4826 |
| 1.3039 | 2.32 | 5000 | 1.3327 | 23.8115 | 18.4348 |
| 1.282 | 2.78 | 6000 | 1.3079 | 24.235 | 18.3561 |
| 1.2133 | 3.25 | 7000 | 1.2820 | 24.8877 | 18.5204 |
| 1.1787 | 3.71 | 8000 | 1.2580 | 25.2719 | 18.415 |
| 1.1154 | 4.18 | 9000 | 1.2543 | 25.5507 | 18.3528 |
| 1.0956 | 4.64 | 10000 | 1.2415 | 25.7284 | 18.5348 |
| 1.023 | 5.1 | 11000 | 1.2410 | 25.7912 | 18.3347 |
| 0.95 | 5.57 | 12000 | 1.2327 | 25.9921 | 18.2593 |
| 0.9476 | 6.03 | 13000 | 1.2631 | 25.829 | 18.3686 |
| 0.9061 | 6.5 | 14000 | 1.2548 | 25.8316 | 18.7481 |
| 0.9037 | 6.96 | 15000 | 1.2308 | 26.2764 | 18.3888 |
| 0.7431 | 7.42 | 16000 | 1.2716 | 25.9268 | 18.256 |
| 0.7526 | 7.89 | 17000 | 1.2655 | 25.9883 | 18.2052 |
| 0.6654 | 8.35 | 18000 | 1.3118 | 25.6866 | 18.2217 |
| 0.6953 | 8.82 | 19000 | 1.3050 | 25.8958 | 18.3387 |
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 24, 2023
- Updated
- Oct 24, 2023