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--- |
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language: |
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- vie |
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- lao |
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license: apache-2.0 |
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base_model: google/mt5-xl |
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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: mt5-full-v6.1 |
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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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# mt5-full-v6.1 |
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This model is a fine-tuned version of [google/mt5-xl](https://huggingface.co/google/mt5-xl) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.8711 |
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- Bleu: 19.5743 |
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- Gen Len: 41.77 |
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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: 5e-06 |
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- train_batch_size: 2 |
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- eval_batch_size: 2 |
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- seed: 3435 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 4 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Bleu | Gen Len | Validation Loss | |
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|:-------------:|:-----:|:------:|:------:|:-------:|:---------------:| |
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| 1.5671 | 0.14 | 5000 | 6.5196 | 18.9699 | 1.1691 | |
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| 1.2277 | 0.28 | 10000 | 7.082 | 18.9724 | 1.0592 | |
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| 1.1316 | 0.42 | 15000 | 7.3283 | 18.9825 | 1.0112 | |
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| 1.0833 | 0.56 | 20000 | 7.4462 | 18.977 | 0.9728 | |
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| 1.0339 | 0.7 | 25000 | 8.0126 | 18.982 | 0.9546 | |
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| 1.025 | 0.83 | 30000 | 7.7648 | 18.9805 | 0.9337 | |
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| 0.9733 | 0.97 | 35000 | 7.9496 | 18.9815 | 0.9228 | |
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| 0.9035 | 1.11 | 40000 | 7.689 | 18.9795 | 0.9162 | |
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| 0.9386 | 1.25 | 45000 | 7.6781 | 18.9825 | 0.9039 | |
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| 0.9073 | 1.39 | 50000 | 7.8607 | 18.9805 | 0.8986 | |
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| 0.8928 | 1.53 | 55000 | 8.0666 | 18.981 | 0.8942 | |
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| 0.884 | 1.67 | 60000 | 8.1679 | 18.9785 | 0.8874 | |
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| 0.8786 | 1.81 | 65000 | 7.8516 | 18.9805 | 0.8831 | |
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| 0.8899 | 1.95 | 70000 | 0.8789 | 7.9392 | 18.9785 | |
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| 0.8638 | 2.09 | 75000 | 0.8781 | 8.1623 | 18.979 | |
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| 0.8293 | 2.22 | 80000 | 0.8752 | 8.0989 | 18.98 | |
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| 0.8625 | 2.36 | 85000 | 0.8743 | 8.176 | 18.979 | |
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| 0.8605 | 2.5 | 90000 | 0.8721 | 8.0117 | 18.9805 | |
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| 0.8479 | 2.64 | 95000 | 0.8711 | 8.1008 | 18.978 | |
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| 0.8391 | 2.78 | 100000 | 0.8708 | 8.2041 | 18.9795 | |
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| 0.8649 | 2.92 | 105000 | 0.8710 | 8.1488 | 18.9785 | |
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### Framework versions |
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- Transformers 4.37.1 |
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- Pytorch 2.1.2+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.1 |
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