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--- |
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license: apache-2.0 |
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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: opus-mt-ja-en-finetuned-ja-to-en_xml |
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results: |
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- task: |
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name: Sequence-to-sequence Language Modeling |
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type: text2text-generation |
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metric: |
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name: Bleu |
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type: bleu |
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value: 73.8646 |
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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-mt-ja-en-finetuned-ja-to-en_xml |
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This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ja-en](https://huggingface.co/Helsinki-NLP/opus-mt-ja-en) on an unkown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7520 |
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- Bleu: 73.8646 |
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- Gen Len: 27.0884 |
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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: 0.0002 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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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: 10 |
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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 | Bleu | Gen Len | |
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|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:| |
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| 1.0512 | 1.0 | 748 | 0.8333 | 59.8234 | 27.905 | |
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| 0.6076 | 2.0 | 1496 | 0.7817 | 62.5606 | 26.1834 | |
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| 0.4174 | 3.0 | 2244 | 0.7817 | 64.8346 | 28.2918 | |
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| 0.2971 | 4.0 | 2992 | 0.7653 | 67.6013 | 27.2222 | |
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| 0.2172 | 5.0 | 3740 | 0.7295 | 69.4017 | 27.0174 | |
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| 0.1447 | 6.0 | 4488 | 0.7522 | 68.8355 | 28.2865 | |
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| 0.0953 | 7.0 | 5236 | 0.7596 | 71.4743 | 27.1861 | |
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| 0.0577 | 8.0 | 5984 | 0.7469 | 72.0684 | 26.921 | |
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| 0.04 | 9.0 | 6732 | 0.7526 | 73.2821 | 27.1365 | |
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| 0.0213 | 10.0 | 7480 | 0.7520 | 73.8646 | 27.0884 | |
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### Framework versions |
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- Transformers 4.9.1 |
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- Pytorch 1.10.0+cu111 |
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- Datasets 1.10.2 |
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- Tokenizers 0.10.3 |
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