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--- |
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license: mit |
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base_model: KingLTD/pretrain_Law_model_vit5_version1 |
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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_version2 |
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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_version2 |
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This model is a fine-tuned version of [KingLTD/pretrain_Law_model_vit5_version1](https://huggingface.co/KingLTD/pretrain_Law_model_vit5_version1) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1644 |
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- Rouge1: 0.4902 |
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- Rouge2: 0.3828 |
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- Rougel: 0.4363 |
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- Rougelsum: 0.4521 |
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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 | 0.1692 | 0.4844 | 0.3722 | 0.4279 | 0.4439 | |
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| No log | 2.0 | 490 | 0.1664 | 0.4846 | 0.3719 | 0.4261 | 0.4442 | |
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| 0.2198 | 3.0 | 735 | 0.1636 | 0.4840 | 0.3752 | 0.4292 | 0.4443 | |
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| 0.2198 | 4.0 | 980 | 0.1628 | 0.4878 | 0.3754 | 0.4300 | 0.4468 | |
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| 0.178 | 5.0 | 1225 | 0.1621 | 0.4871 | 0.3791 | 0.4349 | 0.4494 | |
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| 0.178 | 6.0 | 1470 | 0.1625 | 0.4881 | 0.3782 | 0.4328 | 0.4495 | |
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| 0.1466 | 7.0 | 1715 | 0.1636 | 0.4910 | 0.3837 | 0.4383 | 0.4538 | |
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| 0.1466 | 8.0 | 1960 | 0.1633 | 0.4890 | 0.3802 | 0.4343 | 0.4506 | |
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| 0.1307 | 9.0 | 2205 | 0.1642 | 0.4904 | 0.3836 | 0.4364 | 0.4531 | |
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| 0.1307 | 10.0 | 2450 | 0.1644 | 0.4902 | 0.3828 | 0.4363 | 0.4521 | |
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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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