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language: |
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- zh |
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- ko |
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base_model: facebook/mbart-large-50-many-to-many-mmt |
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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: zhko_mbartLarge_50p_tokenize_run1 |
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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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# zhko_mbartLarge_50p_tokenize_run1 |
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This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.8482 |
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- Bleu: 30.6847 |
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- Gen Len: 14.6895 |
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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-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 4 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 32 |
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- total_eval_batch_size: 16 |
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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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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 8 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |
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|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:| |
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| 1.6487 | 1.0 | 2786 | 1.5519 | 26.5985 | 15.0925 | |
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| 1.1763 | 2.0 | 5572 | 1.4910 | 29.1024 | 14.8538 | |
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| 0.8697 | 3.0 | 8358 | 1.5510 | 29.5842 | 14.7611 | |
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| 0.6221 | 4.0 | 11144 | 1.6445 | 29.6959 | 14.7091 | |
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| 0.4444 | 5.0 | 13930 | 1.7176 | 29.6231 | 14.6204 | |
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| 0.3137 | 6.0 | 16716 | 1.7916 | 29.6666 | 14.524 | |
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| 0.2303 | 7.0 | 19502 | 1.8368 | 30.4697 | 14.5571 | |
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| 0.1888 | 8.0 | 22288 | 1.8482 | 30.6847 | 14.6895 | |
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### Framework versions |
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- Transformers 4.34.1 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.14.6 |
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- Tokenizers 0.14.1 |
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