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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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model-index: |
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- name: ER_new_context |
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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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# ER_new_context |
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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: 0.4057 |
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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: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 3.2979 | 0.1 | 100 | 1.2437 | |
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| 1.1026 | 0.19 | 200 | 0.7365 | |
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| 0.7482 | 0.29 | 300 | 0.5781 | |
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| 0.6258 | 0.38 | 400 | 0.5159 | |
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| 0.5153 | 0.48 | 500 | 0.4504 | |
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| 0.4802 | 0.57 | 600 | 0.4455 | |
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| 0.4905 | 0.67 | 700 | 0.4059 | |
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| 0.382 | 0.76 | 800 | 0.4778 | |
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| 0.3728 | 0.86 | 900 | 0.3985 | |
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| 0.3274 | 0.96 | 1000 | 0.3982 | |
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| 0.3639 | 1.05 | 1100 | 0.4184 | |
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| 0.2881 | 1.15 | 1200 | 0.4454 | |
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| 0.3194 | 1.24 | 1300 | 0.3778 | |
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| 0.2695 | 1.34 | 1400 | 0.3957 | |
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| 0.2894 | 1.43 | 1500 | 0.4000 | |
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| 0.276 | 1.53 | 1600 | 0.3984 | |
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| 0.2325 | 1.62 | 1700 | 0.3627 | |
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| 0.2192 | 1.72 | 1800 | 0.3782 | |
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| 0.279 | 1.81 | 1900 | 0.4161 | |
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| 0.2636 | 1.91 | 2000 | 0.4026 | |
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| 0.2932 | 2.01 | 2100 | 0.3232 | |
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| 0.206 | 2.1 | 2200 | 0.3633 | |
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| 0.1865 | 2.2 | 2300 | 0.4019 | |
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| 0.1651 | 2.29 | 2400 | 0.4385 | |
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| 0.167 | 2.39 | 2500 | 0.4277 | |
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| 0.1705 | 2.48 | 2600 | 0.4083 | |
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| 0.2321 | 2.58 | 2700 | 0.3667 | |
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| 0.1912 | 2.67 | 2800 | 0.3772 | |
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| 0.192 | 2.77 | 2900 | 0.4032 | |
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| 0.1881 | 2.87 | 3000 | 0.4059 | |
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| 0.152 | 2.96 | 3100 | 0.4057 | |
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### Framework versions |
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- Transformers 4.39.3 |
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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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