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--- |
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license: apache-2.0 |
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base_model: t5-small |
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tags: |
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- generated_from_trainer |
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metrics: |
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- bleu |
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model-index: |
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- name: en-vi-model_v3_opus |
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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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# en-vi-model_v3_opus |
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This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.8572 |
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- Bleu: 8.2434 |
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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: 0.0005 |
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- train_batch_size: 128 |
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- eval_batch_size: 128 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 256 |
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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: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Bleu | Validation Loss | |
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|:-------------:|:-----:|:-----:|:------:|:---------------:| |
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| 1.4493 | 0.13 | 500 | 3.9998 | 1.3541 | |
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| 1.2915 | 0.26 | 1000 | 5.3936 | 1.2113 | |
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| 1.2059 | 0.38 | 1500 | 5.8381 | 1.1367 | |
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| 1.1573 | 0.51 | 2000 | 6.2422 | 1.0901 | |
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| 1.1121 | 0.64 | 2500 | 6.6271 | 1.0542 | |
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| 1.0867 | 0.77 | 3000 | 6.8796 | 1.0252 | |
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| 1.0623 | 0.9 | 3500 | 7.0393 | 1.0068 | |
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| 1.0408 | 1.02 | 4000 | 7.2660 | 0.9882 | |
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| 1.0203 | 1.15 | 4500 | 7.0553 | 0.9723 | |
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| 1.0054 | 1.28 | 5000 | 7.4555 | 0.9624 | |
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| 0.9977 | 1.41 | 5500 | 7.4260 | 0.9526 | |
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| 0.9931 | 1.54 | 6000 | 7.5231 | 0.9396 | |
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| 0.9804 | 1.66 | 6500 | 7.4376 | 0.9324 | |
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| 0.9691 | 1.79 | 7000 | 7.5227 | 0.9264 | |
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| 0.9645 | 1.92 | 7500 | 7.6859 | 0.9193 | |
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| 0.9509 | 2.05 | 8000 | 7.6473 | 0.9144 | |
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| 0.9485 | 2.18 | 8500 | 7.6548 | 0.9118 | |
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| 0.9437 | 2.3 | 9000 | 7.6066 | 0.9073 | |
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| 0.9393 | 2.43 | 9500 | 7.7140 | 0.9019 | |
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| 0.9336 | 2.56 | 10000 | 7.8095 | 0.8970 | |
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| 0.9368 | 2.69 | 10500 | 7.9377 | 0.8937 | |
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| 0.925 | 2.82 | 11000 | 7.8425 | 0.8898 | |
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| 0.921 | 2.94 | 11500 | 7.9008 | 0.8864 | |
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| 0.9177 | 3.07 | 12000 | 7.9134 | 0.8836 | |
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| 0.9151 | 3.2 | 12500 | 0.8821 | 7.8647 | |
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| 0.9104 | 3.33 | 13000 | 0.8790 | 8.0830 | |
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| 0.9035 | 3.46 | 13500 | 0.8766 | 8.0959 | |
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| 0.8992 | 3.58 | 14000 | 0.8741 | 8.0178 | |
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| 0.8986 | 3.71 | 14500 | 0.8720 | 8.0384 | |
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| 0.894 | 3.84 | 15000 | 0.8683 | 8.0913 | |
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| 0.8932 | 3.97 | 15500 | 0.8663 | 8.0997 | |
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| 0.8889 | 4.1 | 16000 | 0.8641 | 8.1088 | |
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| 0.8888 | 4.22 | 16500 | 0.8629 | 8.0665 | |
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| 0.8856 | 4.35 | 17000 | 0.8607 | 8.2836 | |
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| 0.8826 | 4.48 | 17500 | 0.8613 | 8.2354 | |
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| 0.8862 | 4.61 | 18000 | 0.8578 | 8.1166 | |
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| 0.8811 | 4.74 | 18500 | 0.8583 | 8.1473 | |
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| 0.8799 | 4.86 | 19000 | 0.8579 | 8.1836 | |
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| 0.8827 | 4.99 | 19500 | 0.8572 | 8.2434 | |
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### Framework versions |
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- Transformers 4.34.1 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.14.1 |
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