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--- |
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license: mit |
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base_model: VietAI/vit5-base |
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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: pretrain_Law_model_vit5_version1 |
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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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# pretrain_Law_model_vit5_version1 |
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This model is a fine-tuned version of [VietAI/vit5-base](https://huggingface.co/VietAI/vit5-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2779 |
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- Rouge1: 0.4859 |
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- Rouge2: 0.3617 |
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- Rougel: 0.4218 |
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- Rougelsum: 0.4417 |
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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: 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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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| |
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| No log | 1.0 | 245 | 0.3566 | 0.4739 | 0.3369 | 0.4053 | 0.4273 | |
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| No log | 2.0 | 490 | 0.3240 | 0.4752 | 0.3453 | 0.4095 | 0.4300 | |
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| 0.7518 | 3.0 | 735 | 0.3059 | 0.4760 | 0.3510 | 0.4112 | 0.4311 | |
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| 0.7518 | 4.0 | 980 | 0.2951 | 0.4838 | 0.3584 | 0.4164 | 0.4387 | |
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| 0.2808 | 5.0 | 1225 | 0.2858 | 0.4799 | 0.3582 | 0.4166 | 0.4368 | |
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| 0.2808 | 6.0 | 1470 | 0.2831 | 0.4839 | 0.3611 | 0.4194 | 0.4403 | |
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| 0.2351 | 7.0 | 1715 | 0.2814 | 0.4858 | 0.3644 | 0.4218 | 0.4423 | |
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| 0.2351 | 8.0 | 1960 | 0.2779 | 0.4850 | 0.3612 | 0.4206 | 0.4416 | |
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| 0.2074 | 9.0 | 2205 | 0.2775 | 0.4836 | 0.3590 | 0.4199 | 0.4398 | |
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| 0.2074 | 10.0 | 2450 | 0.2779 | 0.4859 | 0.3617 | 0.4218 | 0.4417 | |
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### Framework versions |
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- Transformers 4.32.1 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.4 |
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- Tokenizers 0.13.3 |
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