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jeju-ko-nmt-v8
leadawon/jeju-ko-nmt-v8
jeju-ko-nmt-v8 is a machine learning model from leadawon. 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.
leadawon/jeju-ko-nmt-v6 is better than leadawon/jeju-ko-nmt-v8
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At a glance
- Library
- transformers
- Model type
- encoder-decoder
- Access
- Public
- Created
- Jan 27, 2023
- Updated
- Jan 30, 2023
- SHA
- 62ccf0cd
Model card
From the Hugging Face model README
ALERT!!!
leadawon/jeju-ko-nmt-v6 is better than leadawon/jeju-ko-nmt-v8
6버전 성능이 더 좋습니다!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
tag
tags:
- generated_from_trainer model-index:
- name: jeju-ko-nmt-v8 results: []
<!-- 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. -->
jeju-ko-nmt-v8
This model is a fine-tuned version of leadawon/jeju-ko-nmt-v7 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2448
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-06
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 96
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.2684 | 0.04 | 500 | 0.2568 |
| 0.2468 | 0.08 | 1000 | 0.2547 |
| 0.2167 | 0.12 | 1500 | 0.2540 |
| 0.1966 | 0.16 | 2000 | 0.2535 |
| 0.1846 | 0.2 | 2500 | 0.2533 |
| 0.1727 | 0.24 | 3000 | 0.2535 |
| 0.1746 | 0.28 | 3500 | 0.2522 |
| 0.1726 | 0.32 | 4000 | 0.2521 |
| 0.1722 | 0.36 | 4500 | 0.2519 |
| 0.1731 | 0.4 | 5000 | 0.2515 |
| 0.1701 | 0.44 | 5500 | 0.2518 |
| 0.168 | 0.48 | 6000 | 0.2515 |
| 0.1706 | 0.52 | 6500 | 0.2509 |
| 0.1659 | 0.56 | 7000 | 0.2514 |
| 0.1702 | 0.6 | 7500 | 0.2509 |
| 0.1667 | 0.64 | 8000 | 0.2510 |
| 0.1661 | 0.68 | 8500 | 0.2508 |
| 0.1647 | 0.72 | 9000 | 0.2510 |
| 0.1632 | 0.76 | 9500 | 0.2510 |
| 0.1655 | 0.8 | 10000 | 0.2506 |
| 0.1645 | 0.84 | 10500 | 0.2508 |
| 0.1617 | 0.88 | 11000 | 0.2508 |
| 0.1627 | 0.91 | 11500 | 0.2511 |
| 0.2764 | 0.95 | 12000 | 0.2478 |
| 0.2755 | 0.99 | 12500 | 0.2462 |
| 0.2275 | 1.03 | 13000 | 0.2464 |
| 0.2201 | 1.07 | 13500 | 0.2463 |
| 0.2207 | 1.11 | 14000 | 0.2463 |
| 0.2202 | 1.15 | 14500 | 0.2462 |
| 0.2194 | 1.19 | 15000 | 0.2460 |
| 0.2177 | 1.23 | 15500 | 0.2461 |
| 0.2187 | 1.27 | 16000 | 0.2460 |
| 0.2184 | 1.31 | 16500 | 0.2459 |
| 0.2182 | 1.35 | 17000 | 0.2457 |
| 0.219 | 1.39 | 17500 | 0.2458 |
| 0.2206 | 1.43 | 18000 | 0.2455 |
| 0.2211 | 1.47 | 18500 | 0.2455 |
| 0.2164 | 1.51 | 19000 | 0.2455 |
| 0.2202 | 1.55 | 19500 | 0.2454 |
| 0.2208 | 1.59 | 20000 | 0.2452 |
| 0.2208 | 1.63 | 20500 | 0.2450 |
| 0.2204 | 1.67 | 21000 | 0.2450 |
| 0.2193 | 1.71 | 21500 | 0.2450 |
| 0.221 | 1.75 | 22000 | 0.2451 |
| 0.2168 | 1.79 | 22500 | 0.2450 |
| 0.2169 | 1.83 | 23000 | 0.2449 |
| 0.218 | 1.87 | 23500 | 0.2449 |
| 0.2196 | 1.91 | 24000 | 0.2449 |
| 0.2218 | 1.95 | 24500 | 0.2448 |
| 0.2199 | 1.99 | 25000 | 0.2448 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Tokenizers 0.13.2
- Library
- transformers
- Type
- encoder-decoder
- Created
- Jan 27, 2023
- Updated
- Jan 30, 2023