en-vi-model_v3_opus
This model is a fine-tuned version of t5-small on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.8572
- Bleu: 8.2434
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: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Bleu | Validation Loss |
|---|---|---|---|---|
| 1.4493 | 0.13 | 500 | 3.9998 | 1.3541 |
| 1.2915 | 0.26 | 1000 | 5.3936 | 1.2113 |
| 1.2059 | 0.38 | 1500 | 5.8381 | 1.1367 |
| 1.1573 | 0.51 | 2000 | 6.2422 | 1.0901 |
| 1.1121 | 0.64 | 2500 | 6.6271 | 1.0542 |
| 1.0867 | 0.77 | 3000 | 6.8796 | 1.0252 |
| 1.0623 | 0.9 | 3500 | 7.0393 | 1.0068 |
| 1.0408 | 1.02 | 4000 | 7.2660 | 0.9882 |
| 1.0203 | 1.15 | 4500 | 7.0553 | 0.9723 |
| 1.0054 | 1.28 | 5000 | 7.4555 | 0.9624 |
| 0.9977 | 1.41 | 5500 | 7.4260 | 0.9526 |
| 0.9931 | 1.54 | 6000 | 7.5231 | 0.9396 |
| 0.9804 | 1.66 | 6500 | 7.4376 | 0.9324 |
| 0.9691 | 1.79 | 7000 | 7.5227 | 0.9264 |
| 0.9645 | 1.92 | 7500 | 7.6859 | 0.9193 |
| 0.9509 | 2.05 | 8000 | 7.6473 | 0.9144 |
| 0.9485 | 2.18 | 8500 | 7.6548 | 0.9118 |
| 0.9437 | 2.3 | 9000 | 7.6066 | 0.9073 |
| 0.9393 | 2.43 | 9500 | 7.7140 | 0.9019 |
| 0.9336 | 2.56 | 10000 | 7.8095 | 0.8970 |
| 0.9368 | 2.69 | 10500 | 7.9377 | 0.8937 |
| 0.925 | 2.82 | 11000 | 7.8425 | 0.8898 |
| 0.921 | 2.94 | 11500 | 7.9008 | 0.8864 |
| 0.9177 | 3.07 | 12000 | 7.9134 | 0.8836 |
| 0.9151 | 3.2 | 12500 | 0.8821 | 7.8647 |
| 0.9104 | 3.33 | 13000 | 0.8790 | 8.0830 |
| 0.9035 | 3.46 | 13500 | 0.8766 | 8.0959 |
| 0.8992 | 3.58 | 14000 | 0.8741 | 8.0178 |
| 0.8986 | 3.71 | 14500 | 0.8720 | 8.0384 |
| 0.894 | 3.84 | 15000 | 0.8683 | 8.0913 |
| 0.8932 | 3.97 | 15500 | 0.8663 | 8.0997 |
| 0.8889 | 4.1 | 16000 | 0.8641 | 8.1088 |
| 0.8888 | 4.22 | 16500 | 0.8629 | 8.0665 |
| 0.8856 | 4.35 | 17000 | 0.8607 | 8.2836 |
| 0.8826 | 4.48 | 17500 | 0.8613 | 8.2354 |
| 0.8862 | 4.61 | 18000 | 0.8578 | 8.1166 |
| 0.8811 | 4.74 | 18500 | 0.8583 | 8.1473 |
| 0.8799 | 4.86 | 19000 | 0.8579 | 8.1836 |
| 0.8827 | 4.99 | 19500 | 0.8572 | 8.2434 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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