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---
license: mit
base_model: VietAI/vit5-base
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: pretrain_Law_model_vit5_version1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pretrain_Law_model_vit5_version1
This model is a fine-tuned version of [VietAI/vit5-base](https://huggingface.co/VietAI/vit5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2779
- Rouge1: 0.4859
- Rouge2: 0.3617
- Rougel: 0.4218
- Rougelsum: 0.4417
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| No log | 1.0 | 245 | 0.3566 | 0.4739 | 0.3369 | 0.4053 | 0.4273 |
| No log | 2.0 | 490 | 0.3240 | 0.4752 | 0.3453 | 0.4095 | 0.4300 |
| 0.7518 | 3.0 | 735 | 0.3059 | 0.4760 | 0.3510 | 0.4112 | 0.4311 |
| 0.7518 | 4.0 | 980 | 0.2951 | 0.4838 | 0.3584 | 0.4164 | 0.4387 |
| 0.2808 | 5.0 | 1225 | 0.2858 | 0.4799 | 0.3582 | 0.4166 | 0.4368 |
| 0.2808 | 6.0 | 1470 | 0.2831 | 0.4839 | 0.3611 | 0.4194 | 0.4403 |
| 0.2351 | 7.0 | 1715 | 0.2814 | 0.4858 | 0.3644 | 0.4218 | 0.4423 |
| 0.2351 | 8.0 | 1960 | 0.2779 | 0.4850 | 0.3612 | 0.4206 | 0.4416 |
| 0.2074 | 9.0 | 2205 | 0.2775 | 0.4836 | 0.3590 | 0.4199 | 0.4398 |
| 0.2074 | 10.0 | 2450 | 0.2779 | 0.4859 | 0.3617 | 0.4218 | 0.4417 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
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