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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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metrics: |
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- rouge |
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model-index: |
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- name: finetune-newwiki-summarization-ver-augmented |
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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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# finetune-newwiki-summarization-ver-augmented |
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This model is a fine-tuned version of [VietAI/vit5-base](https://huggingface.co/VietAI/vit5-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4282 |
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- Rouge1: 48.7749 |
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- Rouge2: 26.3665 |
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- Rougel: 35.7765 |
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- Rougelsum: 38.0111 |
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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: 1e-05 |
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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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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 7 |
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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 | Rouge1 | Rouge2 | Rougel | Rougelsum | |
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|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:| |
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| 0.6784 | 1.0 | 2312 | 0.5136 | 46.7374 | 23.3000 | 33.5379 | 35.8923 | |
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| 0.6015 | 2.0 | 4624 | 0.4759 | 47.7112 | 24.5817 | 34.4939 | 36.9831 | |
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| 0.5587 | 3.0 | 6936 | 0.4543 | 48.4891 | 25.6592 | 35.2310 | 37.5477 | |
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| 0.5128 | 4.0 | 9248 | 0.4405 | 48.7777 | 26.0690 | 35.5187 | 37.7896 | |
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| 0.4899 | 5.0 | 11560 | 0.4338 | 48.6758 | 26.0670 | 35.5783 | 37.8850 | |
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| 0.4796 | 6.0 | 13872 | 0.4295 | 48.8914 | 26.5018 | 35.8671 | 38.1289 | |
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| 0.4671 | 7.0 | 16184 | 0.4282 | 48.7749 | 26.3665 | 35.7765 | 38.0111 | |
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### Framework versions |
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- Transformers 4.17.0 |
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- Pytorch 2.1.2 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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