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--- |
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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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base_model: fnlp/bart-base-chinese |
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model-index: |
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- name: cantonese-chinese-translation |
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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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# cantonese-chinese-translation |
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This model is a fine-tuned version of [fnlp/bart-base-chinese](https://huggingface.co/fnlp/bart-base-chinese) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2258 |
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- Bleu: 62.1085 |
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- Chrf: 60.1854 |
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- Gen Len: 12.8755 |
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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: 16 |
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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: 30 |
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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 | Chrf | Gen Len | |
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|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:| |
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| 0.3606 | 0.48 | 1000 | 0.2592 | 60.9844 | 58.8851 | 12.8446 | |
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| 0.3059 | 0.96 | 2000 | 0.2291 | 61.9606 | 60.1201 | 12.8621 | |
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| 0.2296 | 1.44 | 3000 | 0.2254 | 61.9458 | 60.0434 | 12.8578 | |
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| 0.2231 | 1.92 | 4000 | 0.2176 | 61.9617 | 59.9299 | 12.8827 | |
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| 0.174 | 2.39 | 5000 | 0.2290 | 61.9661 | 59.8844 | 12.9068 | |
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| 0.171 | 2.87 | 6000 | 0.2258 | 62.1085 | 60.1854 | 12.8755 | |
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| 0.1346 | 3.35 | 7000 | 0.2334 | 61.4554 | 59.5055 | 12.8175 | |
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| 0.1285 | 3.83 | 8000 | 0.2408 | 61.3332 | 59.3276 | 12.8412 | |
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| 0.1061 | 4.31 | 9000 | 0.2530 | 61.6505 | 59.614 | 12.8566 | |
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### Framework versions |
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- Transformers 4.28.1 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.14.6 |
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- Tokenizers 0.13.3 |
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