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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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model-index: |
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- name: cantonese-chinese-parallel-corpus-bart-compare-alpha |
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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-parallel-corpus-bart-compare-alpha |
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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: 1.2307 |
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- Bleu: 28.1911 |
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- Chrf: 27.3934 |
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- Gen Len: 13.1593 |
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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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| 1.8245 | 0.14 | 1000 | 1.5392 | 23.4094 | 22.9586 | 12.9471 | |
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| 1.6283 | 0.29 | 2000 | 1.4433 | 24.6312 | 24.1038 | 12.9882 | |
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| 1.5527 | 0.43 | 3000 | 1.4074 | 25.4368 | 24.7944 | 13.0385 | |
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| 1.5125 | 0.58 | 4000 | 1.3743 | 25.6532 | 25.1073 | 13.0069 | |
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| 1.4572 | 0.72 | 5000 | 1.3468 | 26.2054 | 25.6527 | 13.0221 | |
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| 1.451 | 0.87 | 6000 | 1.3249 | 26.3433 | 25.7717 | 13.0345 | |
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| 1.4087 | 1.01 | 7000 | 1.3162 | 26.7569 | 26.0931 | 13.1037 | |
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| 1.296 | 1.16 | 8000 | 1.2961 | 26.7816 | 26.1834 | 13.0488 | |
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| 1.285 | 1.3 | 9000 | 1.2881 | 27.1895 | 26.4474 | 13.1257 | |
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| 1.281 | 1.45 | 10000 | 1.2778 | 27.248 | 26.5723 | 13.072 | |
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| 1.2809 | 1.59 | 11000 | 1.2772 | 27.3645 | 26.7016 | 13.0937 | |
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| 1.2741 | 1.74 | 12000 | 1.2568 | 27.3857 | 26.7455 | 13.0646 | |
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| 1.2658 | 1.88 | 13000 | 1.2552 | 27.4927 | 26.8279 | 13.0988 | |
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| 1.2412 | 2.03 | 14000 | 1.2632 | 27.5154 | 26.9238 | 13.0482 | |
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| 1.1303 | 2.17 | 15000 | 1.2627 | 27.7288 | 27.0753 | 13.0828 | |
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| 1.1449 | 2.32 | 16000 | 1.2596 | 27.7628 | 27.1038 | 13.0667 | |
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| 1.1352 | 2.46 | 17000 | 1.2465 | 27.9487 | 27.1672 | 13.1585 | |
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| 1.151 | 2.61 | 18000 | 1.2426 | 27.9699 | 27.2496 | 13.1294 | |
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| 1.1361 | 2.75 | 19000 | 1.2348 | 27.9343 | 27.218 | 13.0994 | |
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| 1.1368 | 2.9 | 20000 | 1.2307 | 28.1911 | 27.3934 | 13.1593 | |
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| 1.1012 | 3.04 | 21000 | 1.2487 | 28.1384 | 27.4055 | 13.1253 | |
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| 1.0201 | 3.19 | 22000 | 1.2482 | 28.0577 | 27.3169 | 13.1299 | |
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| 1.0274 | 3.33 | 23000 | 1.2479 | 28.149 | 27.4087 | 13.1401 | |
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### Framework versions |
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- Transformers 4.28.1 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.13.1 |
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- Tokenizers 0.13.3 |
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