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ALERT!!!
leadawon/jeju-ko-nmt-v6 is better than leadawon/jeju-ko-nmt-v8
6๋ฒ์ ์ฑ๋ฅ์ด ๋ ์ข์ต๋๋ค!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
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tags: - generated_from_trainer model-index: - name: jeju-ko-nmt-v8 results: []
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
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