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--- |
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license: cc-by-4.0 |
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base_model: Helsinki-NLP/opus-mt-tc-big-en-pt |
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tags: |
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- generated_from_trainer |
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datasets: |
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- kde4 |
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model-index: |
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- name: opus-en-to-pt-translate |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# opus-en-to-pt-translate |
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This model is a fine-tuned version of [Helsinki-NLP/opus-mt-tc-big-en-pt](https://huggingface.co/Helsinki-NLP/opus-mt-tc-big-en-pt) on the kde4 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5618 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 3 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:-----:|:---------------:| |
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| 0.4982 | 0.08 | 500 | 0.6398 | |
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| 0.5475 | 0.15 | 1000 | 0.6370 | |
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| 0.5397 | 0.23 | 1500 | 0.6333 | |
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| 0.5267 | 0.31 | 2000 | 0.6272 | |
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| 0.5212 | 0.39 | 2500 | 0.6240 | |
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| 0.522 | 0.46 | 3000 | 0.6179 | |
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| 0.5213 | 0.54 | 3500 | 0.6124 | |
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| 0.5155 | 0.62 | 4000 | 0.6114 | |
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| 0.5143 | 0.7 | 4500 | 0.6053 | |
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| 0.5037 | 0.77 | 5000 | 0.6058 | |
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| 0.5093 | 0.85 | 5500 | 0.6002 | |
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| 0.5253 | 0.93 | 6000 | 0.5945 | |
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| 0.5138 | 1.01 | 6500 | 0.5892 | |
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| 0.4864 | 1.08 | 7000 | 0.5906 | |
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| 0.491 | 1.16 | 7500 | 0.5889 | |
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| 0.4993 | 1.24 | 8000 | 0.5849 | |
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| 0.4749 | 1.32 | 8500 | 0.5849 | |
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| 0.4911 | 1.39 | 9000 | 0.5812 | |
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| 0.487 | 1.47 | 9500 | 0.5796 | |
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| 0.4846 | 1.55 | 10000 | 0.5758 | |
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| 0.4863 | 1.63 | 10500 | 0.5739 | |
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| 0.4792 | 1.7 | 11000 | 0.5725 | |
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| 0.4816 | 1.78 | 11500 | 0.5704 | |
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| 0.4811 | 1.86 | 12000 | 0.5684 | |
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| 0.4773 | 1.94 | 12500 | 0.5676 | |
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| 0.4657 | 2.01 | 13000 | 0.5691 | |
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| 0.4246 | 2.09 | 13500 | 0.5683 | |
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| 0.4285 | 2.17 | 14000 | 0.5693 | |
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| 0.4241 | 2.25 | 14500 | 0.5676 | |
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| 0.422 | 2.32 | 15000 | 0.5669 | |
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| 0.4199 | 2.4 | 15500 | 0.5656 | |
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| 0.4273 | 2.48 | 16000 | 0.5650 | |
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| 0.4161 | 2.56 | 16500 | 0.5651 | |
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| 0.4243 | 2.63 | 17000 | 0.5635 | |
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| 0.4202 | 2.71 | 17500 | 0.5628 | |
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| 0.4152 | 2.79 | 18000 | 0.5627 | |
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| 0.4179 | 2.87 | 18500 | 0.5619 | |
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| 0.4241 | 2.94 | 19000 | 0.5618 | |
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### Framework versions |
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- Transformers 4.38.1 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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